Pith. sign in

REVIEW 3 major objections 5 minor 126 references

Image Classification with Deep Reinforcement Active Learning

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Deep reinforcement learning learns a sample-selection policy for active learning that outperforms standard handcrafted strategies on three image benchmarks.

desk verdict The acceptance gating in Sec. 3.1 breaks the equal-budget comparison, and the reward accuracy is never pinned down; the method is a sensible tweak on [30] but the evaluation needs a serious redo. read the letter →

arxiv 2412.19877 v1 pith:STERPRQA submitted 2024-12-27 cs.CV

Mingyuan Jiu , Xuguang Song , Hichem Sahbi , Shupan Li , Yan Chen , Wei Guo , Lihua Guo , Mingliang Xu This is my paper · ORCID
classification cs.CV
keywords ActivelearningDeepreinforcementImageclassificationMarkovdecisionprocessDDPGActor-criticSampleselectionUncertaintysampling
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Active learning normally relies on handcrafted rules, such as picking the most uncertain or least confident examples, which can fail as the data or task changes. This paper argues that sample selection can itself be learned: it casts active learning as a Markov decision process and trains an actor-critic agent with deep deterministic policy gradient (DDPG) to decide, for each uncertainty-ranked candidate, whether to spend a label on it. The claimed payoff is a dynamic policy that adapts to the oracle's feedback and the classifier's state, and the experiments report higher test accuracy than random, entropy, least-confidence, and margin sampling on CIFAR-10, SVHN, and Fashion-MNIST at labeling budgets from 1,000 to 7,000 images. A sympathetic reader would care because label budgets are the bottleneck in many applied vision tasks, and a learned policy could remove the need to guess which heuristic works.

What carries the argument

The key mechanism is the MDP formulation of active learning. The state $S_t$ is a matrix of deep features of the $n$ unlabeled images ranked highest by classifier margin uncertainty. An actor network with convolutional and fully connected layers maps that state to per-sample actions in $[-1,1]$, thresholded at zero to select or discard each candidate; the critic estimates the Q-value via Eq. (3); and the reward in Eq. (2), $r(S,a)=\mathrm{Acc}(\phi_t)-\mathrm{Acc}(\phi_{t-1})$, is the accuracy change of the classifier. Training uses DDPG with target actor and critic networks, soft updates via Eq. (7), and a replay buffer to stabilize learning.

What would settle it

Compute the reward in Eq. (2) using a held-out validation split instead of the test set and rerun the reported experiments; if the accuracy gains over margin sampling shrink or reverse, the method's edge depends on test labels that are unavailable in real active learning.

Watch

Extended reading notes

Core claim

The paper's central claim is that a deep reinforcement learning agent, trained by DDPG over an MDP whose state is the top-$n$ margin-uncertain unlabeled images, can learn a greedy binary selection policy that outperforms fixed handcrafted active learning strategies. The agent receives a reward equal to the change in classifier accuracy before and after adding the selected samples, and only samples with positive predicted impact get labeled and added to the training set. Across three image classification benchmarks, the reported DRAL accuracy monotonically exceeds the baselines at every tested budget size, with the largest gaps typically at small budgets, and t-SNE visualizations suggest the selected samples are more evenly spread across the class distribution.

Load-bearing premise

The reward signal is defined as the change in classifier accuracy, but the paper never states whether that accuracy is measured on a held-out validation set or on the test set, so the learned policy's improvements could depend on labels that a real active learner would not have.

Editorial extensions

If this is right

  • If DRAL's gains hold, active learning no longer requires choosing a heuristic in advance; the selection strategy is learned from interaction.
  • The same framework could be applied to other prediction tasks beyond image classification, since the state and reward are defined generically in terms of classifier features and accuracy.
  • The budget scaling behavior (bigger gains at small budgets) suggests learned selection is most valuable when labels are scarce.
  • Because the method preselects by margin uncertainty, it reduces the RL problem size and can scale to larger unlabeled pools without exhaustive per-sample evaluation.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper leaves open whether the reported improvements survive when the reward accuracy is computed on a held-out validation split rather than the test set; that is a natural testable check of the method's validity.
  • The preselection step could be replaced by other cheap criteria (e.g., diversity or density) and the RL policy might compensate, which would decouple the method's gains from margin uncertainty.
  • A stronger comparison would pit DRAL against learning-based active learning baselines (e.g., loss-prediction methods), not only handcrafted heuristics; the current experiments do not show how it fares against other learned policies.
  • Since the policy is learned per dataset, an implicit extension is to test whether a policy trained on one dataset transfers to another, which the paper does not examine.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper proposes DRAL, a deep reinforcement active learning method that casts sample selection as a Markov decision process. Unlabeled images are first ranked by classifier margin uncertainty, and the top n ranked feature vectors form the state. An actor network outputs accept/reject decisions for these candidates; accepted samples are labeled by the oracle, and a reward equal to the change in classifier accuracy is used to train the actor and critic via DDPG. Experiments on CIFAR-10, SVHN, and Fashion-MNIST compare DRAL against random, entropy, least-confidence, and margin-sampling baselines at labeled sizes from 1,000 to 7,000, reporting consistent but modest accuracy gains for DRAL.

Significance. If the comparisons were at equal labeling budgets and the reward were computed from a legitimate validation split rather than the test set, the paper would make a useful contribution: learning a selection policy over uncertainty-ranked candidates with an actor-critic DDPG framework is a reasonable and potentially adaptive alternative to handcrafted strategies. The consistent numerical gains across three benchmarks would then be meaningful, and the t-SNE visualizations provide qualitative support. However, as written, two protocol-level gaps undermine the central claim: the acceptance-gating procedure changes the effective labeling cost for DRAL relative to the baselines, and the definition of the reward accuracy is never tied to a specific data split. These issues must be resolved before the claimed superiority can be accepted.

major comments (3)
  1. [Sec. 3.1, Tables 1-3] The acceptance-gating procedure in Sec. 3.1 invalidates the equal-budget comparison. The text states that selected samples are labeled by the oracle and used in reward estimation, and that 'only when the difference is positive, the selected samples are added to the pool of labeled training data'. Rejected batches therefore consume oracle labels without increasing the training set. The 'Sizes' columns in Tables 1-3 can only count accepted samples for DRAL, whereas all baselines add every selected sample. Reaching a reported size of, say, 7,000 therefore requires DRAL to issue more oracle queries than the baselines, so the claimed 'same number of samples' comparison is not a same-cost comparison. The authors must either remove the gating, compare all methods at equal numbers of oracle queries, or report both accepted-sample counts and total query counts.
  2. [Eq. (2), Sec. 3.2.4] The reward r(S,a) = Acc(phi_t) - Acc(phi_{t-1}) is defined in terms of 'the accuracy of the classifier', but the manuscript never states which dataset is used to compute Acc. If Acc is computed on the test set, then test labels are used to train the selection policy, and the reported test accuracy is partly a fitted quantity; this would be circular and would explain the observed gains. If Acc is computed on a held-out validation set, that fact and the construction of the validation set must be stated explicitly, together with a guarantee that the validation set is not used for early stopping or model selection in a way that leaks into the test results. Because this reward is the sole training signal for the RL policy, the missing specification is a load-bearing omission.
  3. [Tables 1-3, Sec. 4.2] The claim that DRAL 'significantly outperforms' the baselines is not supported by the reported experiments. The tables show single runs with no variance estimates, no multiple seeds, and no significance tests. Several margins are small: on CIFAR-10 at size 7,000 the gap to margin sampling is 0.62 percentage points, and on Fashion-MNIST at size 7,000 it is 0.26 points. Given the modest differences, the authors should report means and standard deviations over at least several random seeds and, where appropriate, paired comparisons, before using the word 'significant'.
minor comments (5)
  1. [Eq. (2)] The left-hand side r(S,a) does not depend on the action a; the paper should clarify whether the reward is defined only for accepted samples or is taken as zero for rejected samples.
  2. [Sec. 3.2.3] The description of the mini-batch budget b is contradictory: it first says 'we do not obtain b samples at once and submit them to the oracle for labeling', then says 'Once b is reached, the b samples are labeled and fed to the classifier for further retraining'; the intended accumulation-and-query process should be restated precisely.
  3. [Sec. 4.1] The network description is inconsistent: Sec. 3.2.2 describes the actor as having two convolutional layers, one pooling layer, and three fully connected layers, while Sec. 4.1 says both actor and critic consist of five fully connected layers; the actual architecture used in the experiments should be given unambiguously.
  4. [Fig. 2 caption] The caption says 'In each iteration, 1000 new images are selected for manual annotation', while Sec. 4.1 sets the small budget b to 100; the relationship between b, the iteration count, and the 1,000-sample increments in the tables should be clarified.
  5. [References] The reference list contains many entries that are never cited in the body (e.g., [2], [4], [7], [16], [20], [26], and a long tail of multi-label classification references [43]-[127]); the list should be trimmed to works actually discussed in the text.

Circularity Check

2 steps flagged · score 6.0 of 10

DRAL's reward is defined as the same accuracy it is evaluated on, and its acceptance-gating discards non-improving labeled samples, so the reported accuracy gains are partly by construction.

  1. fitted input called prediction [Section 3.2.4, Eq. (2); compared against Section 4.1 and Tables 1–3]
    "r(St, a) =Acc(ϕt) − Acc(ϕt−1), (2) here Acc denotes the accuracy of the classifier ϕt when a = 1."

    Tables 1–3 report classifier accuracy at various 'Sizes' as the evidence that DRAL 'significantly outperforms' baselines. Eq. (2) makes exactly that accuracy the reward that trains the actor-critic policy. Section 4.1 describes only train/test splits and never introduces a held-out validation set for Acc. Thus, as written, the DDPG objective is to maximize the very quantity later reported as the result. Even if Acc were intended to be validation accuracy, the paper omits that restriction; under the paper's own definition the final accuracies are values of the training objective and are not independent measurements.

  2. other [Section 3.1 (acceptance gating); Tables 1–3]
    "Only when the difference is positive, the selected samples are added to the pool of labeled training data in order to further retrain the classifier."

    The experiments claim comparison 'when selecting the same number of samples' and report Sizes 1000–7000 as equal budgets. But the quoted sentence means every batch whose reward Acc(phi_t)-Acc(phi_{t-1}) is non-positive is discarded, so DRAL's reported Size counts only accepted samples, not oracle queries. RANDOM, EN, LC, and MS add all selected samples. DRAL's labeled pool is therefore filtered by whether it increased accuracy, so its final accuracy is partly forced by deleting samples that would have hurt it. The equal-budget comparison is not realized; the performance gap is an artifact of the acceptance rule.

full rationale

The central claim of the paper is that DRAL 'significantly outperforms' handcrafted active learning strategies at the same labeling budgets. That claim is not independent of the algorithm's own objective. Eq. (2) defines the RL reward as the change in classifier accuracy, which is the same quantity reported in Tables 1–3, and Section 3.1 filters the labeled pool by requiring a positive accuracy difference. Together these two passages mean the reported accuracy is partly produced by the algorithm's own training signal and acceptance rule, rather than being measured on a fixed, equally-budgeted protocol. The paper does not specify whether Acc in Eq. (2) is computed on a held-out set; Section 4.1 describes only the train/test splits, so the reward may be test accuracy itself. No load-bearing self-citation chain was found: the self-citations to Sahbi and coauthors appear in the related-work section and are not used to justify the central derivation. If the authors had explicitly used a held-out validation accuracy for the reward and had counted oracle queries rather than accepted samples, the comparison could be legitimate; as written, the claimed gains reduce in part to fitting the evaluation metric.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central claim is empirical, so the ledger captures the unspecified and hand-chosen elements that the results depend on. The most consequential is the undefined accuracy set used in the reward function, which determines whether the evaluation is valid. The hyperparameters are numerous but typical for DDPG; the missing value of n and the missing classifier architecture are larger reproducibility gaps.

free parameters (5)
  • n (number of top-ranked samples in the state) = not specified
    The state is formed by the top n unlabeled samples sorted by margin uncertainty; n is never reported in the paper, yet it controls the actor's input size and the scope of selection.
  • b (small labeling budget) = 100
    Set to 100 in the experiments; chosen by hand without sensitivity analysis.
  • gamma (discount factor) = 0.99
    Chosen by hand for the Bellman equation; no ablation is provided.
  • lambda (target network soft update coefficient) = 0.01
    Chosen by hand; affects stability of the DDPG update.
  • replay buffer size = 3000
    Chosen by hand; no sensitivity analysis is reported.
assumptions (4)
  • domain assumption An oracle returns correct labels for all queried samples.
    Active learning typically assumes a perfect oracle; this is standard but unstated in the paper.
  • ad hoc to paper The reward Acc(phi_t) - Acc(phi_{t-1}) is computable from data available to the learner at training time.
    Eq. (2) does not specify whether Acc is measured on test, validation, or labeled training data. If it is measured on test data, the selection policy is trained using test labels, which is not a valid active learning protocol.
  • domain assumption Margin uncertainty ranking from a traditional CNN is a useful prior for sample selection.
    The method preselects top n samples using margin uncertainty, but no analysis is provided that this ranking preserves the most informative samples or that the actor's decisions add value beyond this heuristic.
  • standard math The Bellman equation and DDPG optimization converge to a good policy with the given network capacities and hyperparameters.
    The paper relies on standard RL machinery without providing convergence guarantees or a proof that the trained actor-critic approximates the optimal policy in this setting.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Image Classification with Deep Reinforcement Active Learning." pith.science (2026). https://pith.science/paper/STERPRQA

@misc{pith2026241219877,
  author       = {Pith},
  title        = {Pith review of: Image Classification with Deep Reinforcement Active Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/STERPRQA}},
  note         = {Machine review of arXiv:2412.19877}
}
read the original abstract

Deep learning is currently reaching outstanding performances on different tasks, including image classification, especially when using large neural networks. The success of these models is tributary to the availability of large collections of labeled training data. In many real-world scenarios, labeled data are scarce, and their hand-labeling is time, effort and cost demanding. Active learning is an alternative paradigm that mitigates the effort in hand-labeling data, where only a small fraction is iteratively selected from a large pool of unlabeled data, and annotated by an expert (a.k.a oracle), and eventually used to update the learning models. However, existing active learning solutions are dependent on handcrafted strategies that may fail in highly variable learning environments (datasets, scenarios, etc). In this work, we devise an adaptive active learning method based on Markov Decision Process (MDP). Our framework leverages deep reinforcement learning and active learning together with a Deep Deterministic Policy Gradient (DDPG) in order to dynamically adapt sample selection strategies to the oracle's feedback and the learning environment. Extensive experiments conducted on three different image classification benchmarks show superior performances against several existing active learning strategies.

Figures

Figures reproduced from arXiv: 2412.19877 by the authors.

Figure 1
Figure 1. The proposed Deep Reinforcement Active Learning framework. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Comparison of visualization of the two-dimensional distribution of the selected samples by different methods using t-SNE [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

126 extracted references · 71 canonical work pages

  1. [30]

    Deep reinforced active learning for multi-class image classification

    E. Slade and K. Branson, “Deep reinforced active learning for multi-class image classification,” ArXiv, vol. abs/2206.13391,

    work page Pith review arXiv

  2. [1]

    Review of image classification algorithms based on convolutional neural networks,

    L. Chen, S. Li, Q. Bai, J. Yang, S. Jiang, and Y. Miao, “Review of image classification algorithms based on convolutional neural networks,” Remote. Sens., vol. 13, p. 4712, 2021. [Online]. Available: https://api.semanticscholar.org/CorpusID:244505196

    2021

  3. [2]

    Sahbi and D

    H. Sahbi and D. Geman. ”A Hierarchy of Support Vector Machines for Pattern Detection.” Journal of Machine Learning Research 7.10 (2006)

    2006

  4. [3]

    Attention is all you need,

    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in Proceedings of the 31st International Conference on Neural Information Processing Systems , ser. NIPS’17, Red Hook, NY, USA, 2017, p. 6000–6010

    2017

  5. [4]

    Neurocomputing 474, 154–167 (2022)

    Jiu, M., Sahbi, H.: Context-aware deep kernel networks for image annotation. Neurocomputing 474, 154–167 (2022)

    2022

  6. [5]

    Active learning through density clustering,

    M. Wang, F. Min, Z. H. Zhang, and Y. X. Wu, “Active learning through density clustering,” Expert Systems with Applications , vol. 85, pp. 305–317, Nov. 2017. [Online]. Available: https://doi.org/10.1016/j.eswa.2017.05.046

    work page doi:10.1016/j.eswa.2017.05.046 2017

  7. [6]

    Visual transformer for task-aware active learning,

    R. Caramalau, B. Bhattarai, and T.-K. Kim, “Visual transformer for task-aware active learning,” 2021, active Learning;Classification tasks;Labelings;Learning models;Multi-stages;Nonlocal;Pool-based;Task-aware;Transformer model- ing;Visual concept

    2021

  8. [7]

    H. Sahbi. ”Coarse-to-fine deep kernel networks.” IEEE ICCV-W, 2017. 8

    2017

Show all 126 references
  1. [8]

    Pmal: A proxy model active learning approach for vision based industrial applications,

    A. Khan, I. U. Haq, T. Hussain, K. Muhammad, M. Hijji, M. Sajjad, V . H. C. De Albuquerque, and S. W. Baik, “Pmal: A proxy model active learning approach for vision based industrial applications,” ACM Trans. Multimedia Comput. Commun. Appl., vol. 18, no. 2s, oct 2022. [Online]...

    2022 doi

  2. [9]

    Mazari and H

    A. Mazari and H. Sahbi. ”MLGCN: Multi-Laplacian graph convolutional networks for human action recognition.” The British Machine Vision Conference (BMVC). 2019

    2019

  3. [10]

    Transformer-based multi-task learning for classification and segmentation of gastrointestinal tract endoscopic images,

    S. Tang, X. Yu, C.-F. Cheang, Y. Liang, P . Zhao, H. H. Yu, and I. C. Choi, “Transformer-based multi-task learning for classification and segmentation of gastrointestinal tract endoscopic images,” Computers in biology and medicine , vol. 157, p. 106723, 2023. [Online]. Availab...

    2023

  4. [11]

    Wang and H

    L. Wang and H. Sahbi. ”Directed acyclic graph kernels for action recognition.” Proceedings of the IEEE International Conference on Computer Vision. 2013

    2013

  5. [12]

    Batch-mode active-learning methods for the interactive classification of remote sensing images,

    B. Demir, C. Persello, and L. Bruzzone, “Batch-mode active-learning methods for the interactive classification of remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 3, pp. 1014–1031, 2011

    2011

  6. [14]

    Towards general and efficient active learning,

    Y. Xie, M. Tomizuka, and W. Zhan, “Towards general and efficient active learning,” arXiv preprint arXiv:2112.07963, 2021

    2021 arXiv

  7. [15]

    Ferecatu and H

    M. Ferecatu and H. Sahbi. ”TELECOM ParisTech at ImageClefphoto 2008: Bi-Modal Text and Image Retrieval with Diversity Enhancement.” CLEF (Working Notes). 2008

    2008

  8. [16]

    Bourdis, D

    N. Bourdis, D. Marraud and H. Sahbi. ”Constrained optical flow for aerial image change detection.” in IEEE IGARSS, 2011

    2011

  9. [17]

    Analysis of a greedy active learning strategy,

    S. Dasgupta, “Analysis of a greedy active learning strategy,” in Neural Information Processing Systems , 2004. [Online]. Available: https://api.semanticscholar.org/CorpusID:2861519

    2004

  10. [18]

    Reinforcement-based display selection for frugal learning,

    S. Deschamps and H. Sahbi, “Reinforcement-based display selection for frugal learning,” in 26th International Conference on Pattern Recognition, ICPR 2022, Montreal, QC, Canada, August 21-25, 2022 , 2022, pp. 1186–1193

    2022

  11. [19]

    Continuous control with deep reinforcement learning,

    T. P . Lillicrap, J. J. Hunt, A. Pritzel, N. M. O. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra, “Continuous control with deep reinforcement learning,” CoRR, vol. abs/1509.02971, 2015. [Online]. Available: https://api.semanticscholar.org/CorpusID:16326763

    2015 arXiv

  12. [20]

    H. Sahbi. ”Lightweight Connectivity In Graph Convolutional Networks For Skeleton-Based Recognition.” 2021 IEEE International Conference on Image Processing (ICIP). IEEE, 2021

    2021

  13. [21]

    Discriminative active learning,

    D. Gissin and S. Shalev-Shwartz, “Discriminative active learning,” arXiv preprint arXiv:1907.06347, 2019

    1907 arXiv

  14. [22]

    Multiple-boundary clustering and prioritization to promote neural network retraining,

    W. Shen, Y. Li, L. Chen, Y. Han, Y. Zhou, and B. Xu, “Multiple-boundary clustering and prioritization to promote neural network retraining,” in 2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) , Virtual, Melbourne, VIC, Australia, 2020, pp. 4...

    2020

  15. [23]

    Reinforcement-based frugal learning for interactive satellite image change detection,

    S. Deschamps and H. Sahbi, “Reinforcement-based frugal learning for interactive satellite image change detection,” in IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium , vol. 2022-July, Kuala Lumpur, Malaysia, 2022, pp. 627 – 630. [Online]. Availabl...

    2022

  16. [24]

    Learning how to active learn: A deep reinforcement learning approach,

    M. Fang, Y. Li, and T. Cohn, “Learning how to active learn: A deep reinforcement learning approach,” ArXiv, vol. abs/1708.02383, 2017. [Online]. Available: https://api.semanticscholar.org/CorpusID:22272492

    2017 arXiv

  17. [25]

    Deep active learning with adaptive acquisition,

    M. Haussmann, F. A. Hamprecht, and M. Kandemir, “Deep active learning with adaptive acquisition,” ArXiv, vol. abs/1906.11471, 2019. [Online]. Available: https://api.semanticscholar.org/CorpusID:195699964

    1906 arXiv

  18. [26]

    Sahbi, Jean-Yves Audibert, Jaonary Rabarisoa, and Renaud Keriven

    H. Sahbi, Jean-Yves Audibert, Jaonary Rabarisoa, and Renaud Keriven. ”Context-dependent kernel design for object matching and recognition.” In 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8. IEEE, 2008

    2008

  19. [27]

    Deep reinforcement active learning for human-in-the-loop person re- identification,

    Z. Liu, J. Wang, S. Gong, D. Tao, and H. Lu, “Deep reinforcement active learning for human-in-the-loop person re- identification,” in 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 6121–6130

    2019

  20. [28]

    Active learning for image classification: A deep reinforcement learning approach,

    L. Sun and Y. Gong, “Active learning for image classification: A deep reinforcement learning approach,” in 2019 2nd China Symposium on Cognitive Computing and Hybrid Intelligence (CCHI) , 2019, pp. 71–76

    2019

  21. [29]

    Sahbi, Jean-Yves Audibert, and Renaud Keriven

    H. Sahbi, Jean-Yves Audibert, and Renaud Keriven. ”Graph-cut transducers for relevance feedback in content based image retrieval.” 2007 IEEE 11th International Conference on Computer Vision. IEEE, 2007

    2007

  22. [31]

    Human-level control through deep reinforcement learning,

    V . Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. A. Riedmiller, A. K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis, “Human-level control through d...

    2015

  23. [32]

    Thiemert, H

    S. Thiemert, H. Sahbi, and M. Steinebach. ”Applying interest operators in semi-fragile video watermarking.” Security, Steganography, and Watermarking of Multimedia Contents VII. Vol. 5681. SPIE, 2005

    2005

  24. [33]

    Learning to label with active learning and reinforcement learning,

    X. Tang, S. Wu, G. Chen, K. Chen, and L. Shou, “Learning to label with active learning and reinforcement learning,” in International Conference on Database Systems for Advanced Applications , 2021. [Online]. Available: https://api.semanticscholar.org/CorpusID:233236121

    2021

  25. [34]

    Deep residual learning for image recognition,

    K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, 2015. [Online]. Available: https://api.semanticscholar.org/CorpusID:206594692

    2016

  26. [35]

    Sequential graph convolutional network for active learning,

    R. Caramalau, B. Bhattarai, and T.-K. Kim, “Sequential graph convolutional network for active learning,” 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 9578–9587, 2020. [Online]. Available: https://api.semanticscholar.org/CorpusID:219793059

    2021

  27. [36]

    Wang and H

    L. Wang and H. Sahbi. ”Bags-of-daglets for action recognition.” 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014

    2014

  28. [37]

    Pytorch: An imperative style, high-performance deep learning library,

    A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al., “Pytorch: An imperative style, high-performance deep learning library,” Advances in neural information processing systems, vol. 32, 2019

    2019

  29. [38]

    A mathematical theory of communication,

    C. E. Shannon, “A mathematical theory of communication,” The Bell System Technical Journal, vol. 27, no. 3, pp. 379–423, 1948. 9

    1948

  30. [39]

    Active learning literature survey,

    B. Settles, “Active learning literature survey,” University of Wisconsinmadison , 2009. [Online]. Available: https: //api.semanticscholar.org/CorpusID:324600

    2009

  31. [40]

    Wang and H

    L. Wang and H. Sahbi. ”Nonlinear cross-view sample enrichment for action recognition.” European Conference on Computer Vision. Springer, Cham, 2014

    2014

  32. [41]

    Active hidden markov models for information extraction,

    T. Scheffer, C. Decomain, and S. Wrobel, “Active hidden markov models for information extraction,” in International Symposium on Intelligent Data Analysis, 2001. [Online]. Available: https://api.semanticscholar.org/CorpusID:38833768

    2001

  33. [42]

    Visualizing data using t-sne,

    V . D. M. Laurens and G. Hinton, “Visualizing data using t-sne,” Journal of Machine Learning Research , vol. 9, no. 2605, pp. 2579–2605, 2008

    2008

  34. [43]

    Sahbi and F

    H. Sahbi and F. Fleuret. Scale-invariance of support vector machines based on the triangular kernel. Diss. INRIA, 2002

    2002

  35. [44]

    International Journal of Advanced Computer Science and Applications 7(10), 127–131 (2016)

    Alazaidah, R., Ahmad, F.K.: Trending challenges in multi label classification. International Journal of Advanced Computer Science and Applications 7(10), 127–131 (2016)

    2016

  36. [45]

    IEEE Transactions on Image Processing 26(4), 1820–1832 (2017)

    Jiu, M., Sahbi, H.: Nonlinear deep kernel learning for image annotation. IEEE Transactions on Image Processing 26(4), 1820–1832 (2017)

    2017

  37. [46]

    In: Proceedings of the AAAI conference on artificial intelligence

    Chen, T., Wang, Z., Li, G., Lin, L.: Recurrent attentional reinforcement learning for multi-label image recognition. In: Proceedings of the AAAI conference on artificial intelligence. vol. 32 (2018)

    2018

  38. [47]

    Sahbi and N

    H. Sahbi and N. Boujemaa. ”From coarse to fine skin and face detection.” Proceedings of the eighth ACM international conference on Multimedia. 2000

    2000

  39. [48]

    IEEE Transactions on Image Processing 31, 2570–2583 (2022)

    Chen, Z.M., Cui, Q., Zhao, B., Song, R., Zhang, X., Yoshie, O.: Sst: Spatial and semantic transformers for multi-label image recognition. IEEE Transactions on Image Processing 31, 2570–2583 (2022)

    2022

  40. [49]

    Bourdis, D

    N. Bourdis, D. Marraud, and H. Sahbi, Spatio-temporal interaction for aerial video change detection, in IGARSS, 2012, pp. 2253–2256

    2012

  41. [50]

    In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

    Chen, Z.M., Wei, X.S., Wang, P ., Guo, Y.: Multi-label image recognition with graph convolutional networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 5177–5186 (2019)

    2019

  42. [51]

    In: 2009 IEEE conference on computer vision and pattern recognition

    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. pp. 248–255. Ieee (2009)

    2009

  43. [52]

    Pattern Recognition 88, 447–457 (2019)

    Jiu, M., Sahbi, H.: Deep representation design from deep kernel networks. Pattern Recognition 88, 447–457 (2019)

    2019

  44. [53]

    arXiv preprint arXiv:2010.11929 (2020)

    Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)

    2020 arXiv

  45. [54]

    In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

    Guo, H., Zheng, K., Fan, X., Yu, H., Wang, S.: Visual attention consistency under image transforms for multi-label image classification. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 729–739 (2019)

    2019

  46. [55]

    In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

    Li, X., Sahbi, H.: Superpixel-based object class segmentation using conditional random fields. In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 1101–1104. IEEE (2011)

    2011

  47. [56]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Lanchantin, J., Wang, T., Ordonez, V ., Qi, Y.: General multi-label image classification with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 16478–16488 (2021)

    2021

  48. [57]

    In: 2020 25th International Conference on Pattern Recognition (ICPR)

    Li, Y., Yang, L.: More correlations better performance: Fully associative networks for multi-label image classification. In: 2020 25th International Conference on Pattern Recognition (ICPR). pp. 9437–9444. IEEE (2021)

    2021

  49. [58]

    arXiv preprint arXiv:2107.10834 (2021)

    Liu, S., Zhang, L., Yang, X., Su, H., Zhu, J.: Query2label: A simple transformer way to multi-label classification. arXiv preprint arXiv:2107.10834 (2021)

    2021 arXiv

  50. [59]

    arXiv preprint arXiv:2307.09715 (2023)

    Ma, L., Sun, D., Wang, L., Zhao, H., Luo, B.: Semantic-aware dual contrastive learning for multi-label image classification. arXiv preprint arXiv:2307.09715 (2023)

    2023 arXiv

  51. [60]

    International Journal of Multimedia Information Retrieval 4, 113–128 (2015)

    Sahbi, H.: Imageclef annotation with explicit context-aware kernel maps. International Journal of Multimedia Information Retrieval 4, 113–128 (2015)

    2015

  52. [61]

    In: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

    Murthy, V .N., Maji, S., Manmatha, R.: Automatic image annotation using deep learning representations. In: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval. pp. 603–606 (2015)

    2015

  53. [62]

    Progress in brain research 155, 23–36 (2006)

    Oliva, A., Torralba, A.: Building the gist of a scene: The role of global image features in recognition. Progress in brain research 155, 23–36 (2006)

    2006

  54. [63]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision

    Sahbi, H.: Learning laplacians in chebyshev graph convolutional networks. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 2064–2075 (2021)

    2021

  55. [64]

    In: Proceedings of the IEEE/CVF international conference on computer vision

    Ridnik, T., Ben-Baruch, E., Zamir, N., Noy, A., Friedman, I., Protter, M., Zelnik-Manor, L.: Asymmetric loss for multi-label classification. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 82–91 (2021)

    2021

  56. [65]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Tamura, M., Ohashi, H., Yoshinaga, T.: Qpic: Query-based pairwise human-object interaction detection with image-wide contextual information. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10410–10419 (2021)

    2021

  57. [66]

    Jiu and H

    M. Jiu and H. Sahbi. ”Laplacian deep kernel learning for image annotation.” IEEE ICASSP , 2016

    2016

  58. [67]

    In: Proceedings of the IEEE conference on computer vision and pattern recognition

    Wang, J., Yang, Y., Mao, J., Huang, Z., Huang, C., Xu, W.: Cnn-rnn: A unified framework for multi-label image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2285–2294 (2016)

    2016

  59. [68]

    Bourdis, D

    N. Bourdis, D. Marraud and H. Sahbi. ”Camera pose estimation using visual servoing for aerial video change detection.” IEEE IGARSS 2012

    2012

  60. [69]

    In: Proceedings of the AAAI Conference on Artificial Intelligence

    Wang, Y., He, D., Li, F., Long, X., Zhou, Z., Ma, J., Wen, S.: Multi-label classification with label graph superimposing. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 12265–12272 (2020)

    2020

  61. [70]

    Yuan, G-S Xia, H

    F. Yuan, G-S Xia, H. Sahbi and V . Prinet (2012). Mid-level features and spatio-temporal context for activity recognition. Pattern Recognition, 45(12), 4182-4191

    2012

  62. [71]

    In: Proceedings of the IEEE international conference on computer vision

    Wang, Z., Chen, T., Li, G., Xu, R., Lin, L.: Multi-label image recognition by recurrently discovering attentional regions. In: Proceedings of the IEEE international conference on computer vision. pp. 464–472 (2017) 10

    2017

  63. [72]

    IEEE transactions on pattern analysis and machine intelligence 38(9), 1901–1907 (2015)

    Wei, Y., Xia, W., Lin, M., Huang, J., Ni, B., Dong, J., Zhao, Y., Yan, S.: Hcp: A flexible cnn framework for multi-label image classification. IEEE transactions on pattern analysis and machine intelligence 38(9), 1901–1907 (2015)

    2015

  64. [73]

    Oliveau and H

    Q. Oliveau and H. Sahbi. ”Learning attribute representations for remote sensing ship category classification.” IEEE JSTARS 10.6 (2017): 2830-2840

    2017

  65. [74]

    In: Proceedings of the IEEE/CVF international conference on computer vision

    Wu, H., Xiao, B., Codella, N., Liu, M., Dai, X., Yuan, L., Zhang, L.: Cvt: Introducing convolutions to vision transformers. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 22–31 (2021)

    2021

  66. [75]

    Sahbi and N

    H. Sahbi and N. Boujemaa. ”Robust matching by dynamic space warping for accurate face recognition.” Proceedings 2001 International Conference on Image Processing (Cat. No. 01CH37205). Vol. 1. IEEE, 2001

    2001

  67. [76]

    IEEE Transactions on Circuits and Systems for Video Technology (2023)

    Wu, Y., Feng, S., Wang, Y.: Semantic-aware graph matching mechanism for multi-label image recognition. IEEE Transactions on Circuits and Systems for Video Technology (2023)

    2023

  68. [77]

    H. Sahbi. ”Interactive satellite image change detection with context-aware canonical correlation analysis.” IEEE GRSL, (14)5, 2017

    2017

  69. [78]

    arXiv preprint arXiv:2104.14762 (2021)

    Wu, Y., Liu, H., Feng, S., Jin, Y., Lyu, G., Wu, Z.: Gm-mlic: graph matching based multi-label image classification. arXiv preprint arXiv:2104.14762 (2021)

    2021 arXiv

  70. [79]

    In: Proceedings of the AAAI conference on artificial intelligence

    You, R., Guo, Z., Cui, L., Long, X., Bao, Y., Wen, S.: Cross-modality attention with semantic graph embedding for multi-label classification. In: Proceedings of the AAAI conference on artificial intelligence. vol. 34, pp. 12709–12716 (2020)

    2020

  71. [80]

    Multimedia Tools and Applications 77, 22385– 22406 (2018)

    Zhang, W., Hu, H., Hu, H.: Neural ranking for automatic image annotation. Multimedia Tools and Applications 77, 22385– 22406 (2018)

    2018

  72. [81]

    H. Sahbi. ”Relevance feedback for satellite image change detection.” IEEE ICASSP , 2013

    2013

  73. [82]

    In: Proceedings of the IEEE conference on computer vision and pattern recognition

    Zhu, F., Li, H., Ouyang, W., Yu, N., Wang, X.: Learning spatial regularization with image-level supervisions for multi-label image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 5513–5522 (2017)

    2017

  74. [83]

    H. Sahbi. ”CNRS-TELECOM ParisTech at ImageCLEF 2013 Scalable Concept Image Annotation Task: Winning Annotations with Context Dependent SVMs.” CLEF (Working Notes). 2013

    2013

  75. [84]

    ”Finite rank deep kernel learning.” Third workshop on Bayesian Deep Learning, NeurIPS

    Dasgupta, Sambarta, Kumar Sricharan, and Ashok Srivastava. ”Finite rank deep kernel learning.” Third workshop on Bayesian Deep Learning, NeurIPS. 2018

    2018

  76. [85]

    ”To understand deep learning we need to understand kernel learning.” International Conference on Machine Learning

    Belkin, Mikhail, Siyuan Ma, and Soumik Mandal. ”To understand deep learning we need to understand kernel learning.” International Conference on Machine Learning. PMLR, 2018

    2018

  77. [86]

    ”Stochastic variational deep kernel learning.” Advances in neural information processing systems 29 (2016)

    Wilson, Andrew G., et al. ”Stochastic variational deep kernel learning.” Advances in neural information processing systems 29 (2016)

    2016

  78. [87]

    Boujemaa

    Sahbi, H., and N. Boujemaa. ”Robust face recognition using dynamic space warping.” International Workshop on Biometric Authentication. Springer, Berlin, Heidelberg, 2002

    2002

  79. [88]

    Rasmussen, and Mark van der Wilk

    Ober, Sebastian W., Carl E. Rasmussen, and Mark van der Wilk. ”The promises and pitfalls of deep kernel learning.” Uncertainty in Artificial Intelligence. PMLR, 2021

    2021

  80. [90]

    Thiemert, H

    S. Thiemert, H. Sahbi, and M. Steinebach. ”Using entropy for image and video authentication watermarks.” Security, Steganography, and Watermarking of Multimedia Contents VIII. Vol. 6072. SPIE, 2006

    2006

  81. [91]

    ”Deep kernel learning.” Artificial intelligence and statistics

    Wilson, Andrew Gordon, et al. ”Deep kernel learning.” Artificial intelligence and statistics. PMLR, 2016

    2016

  82. [92]

    Braun, and Klaus-Robert M ¨uller

    Montavon, Gr ´egoire, Mikio L. Braun, and Klaus-Robert M ¨uller. ”Kernel Analysis of Deep Networks.” Journal of Machine Learning Research 12.9 (2011)

    2011

  83. [93]

    Sahbi and N

    H. Sahbi and N. Boujemaa. ”Coarse-to-fine support vector classifiers for face detection.” Object recognition supported by user interaction for service robots. Vol. 3. IEEE, 2002

    2002

  84. [94]

    ”Kernel methods for deep learning.” Advances in neural information processing systems 22 (2009)

    Cho, Youngmin, and Lawrence Saul. ”Kernel methods for deep learning.” Advances in neural information processing systems 22 (2009)

    2009

  85. [95]

    ”Neural operator: Graph kernel network for partial differential equations.” arXiv preprint arXiv:2003.03485 (2020)

    Li, Zongyi, et al. ”Neural operator: Graph kernel network for partial differential equations.” arXiv preprint arXiv:2003.03485 (2020)

    2020 arXiv

  86. [96]

    H. Sahbi. Coarse-to-fine support vector machines for hierarchical face detection. Diss. PhD thesis, Versailles University, 2003

    2003

  87. [97]

    ”Efficient convolution kernels for dependency and constituent syntactic trees.” European Conference on Machine Learning

    Moschitti, Alessandro. ”Efficient convolution kernels for dependency and constituent syntactic trees.” European Conference on Machine Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006

    2006

  88. [98]

    Yanardag, Pinar, and S. V . N. Vishwanathan. ”Deep graph kernels.” Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. 2015

    2015

  89. [99]

    Boujemaa, F

    N. Boujemaa, F. Fleuret, V . Gouet, and H. Sahbi. ”Visual content extraction for automatic semantic annotation of video news.” In the proceedings of the SPIE Conference, San Jose, CA, vol. 6. 2004

    2004

  90. [100]

    ”Extracting relations with integrated information using kernel methods.” Proceedings of the 43rd annual meeting of the association for computational linguistics (acl’05)

    Zhao, Shubin, and Ralph Grishman. ”Extracting relations with integrated information using kernel methods.” Proceedings of the 43rd annual meeting of the association for computational linguistics (acl’05). 2005

    2005

  91. [101]

    ”Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition.” IEEE Transactions on audio, speech, and language processing 20.1 (2011): 30-42

    Dahl, George E., et al. ”Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition.” IEEE Transactions on audio, speech, and language processing 20.1 (2011): 30-42

    2011

  92. [102]

    H. Sahbi. ”Misalignment resilient cca for interactive satellite image change detection.” 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, 2016

    2016

  93. [103]

    ”A shortest path dependency kernel for relation extraction.” Proceedings of human language technology conference and conference on empirical methods in natural language processing

    Bunescu, Razvan, and Raymond Mooney. ”A shortest path dependency kernel for relation extraction.” Proceedings of human language technology conference and conference on empirical methods in natural language processing. 2005

    2005

  94. [104]

    ”Context-dependent sentiment analysis in user-generated videos.” Proceedings of the 55th annual meeting of the association for computational linguistics (volume 1: Long papers)

    Poria, Soujanya, et al. ”Context-dependent sentiment analysis in user-generated videos.” Proceedings of the 55th annual meeting of the association for computational linguistics (volume 1: Long papers). 2017

    2017

  95. [105]

    Napol ´eon and H

    T. Napol ´eon and H. Sahbi. ”From 2D silhouettes to 3D object retrieval: contributions and benchmarking.” EURASIP Journal on Image and Video Processing 2010 (2010): 1-17

    2010

  96. [106]

    ”Multi-label informed feature selection.” IJCAI

    Jian, Ling, et al. ”Multi-label informed feature selection.” IJCAI. Vol. 16. 2016. 11

    2016

  97. [107]

    (2007, September)

    Tsoumakas, G., Vlahavas, I. (2007, September). Random k-labelsets: An ensemble method for multilabel classification. In European conference on machine learning (pp. 406-417). Berlin, Heidelberg: Springer Berlin Heidelberg

    2007

  98. [108]

    Jiu and H

    M. Jiu and H. Sahbi. ”Semi supervised deep kernel design for image annotation.” 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015

    2015

  99. [109]

    ”Partially labeled classification with Markov random walks.” Advances in neural information processing systems 14 (2001)

    Szummer, Martin, and Tommi Jaakkola. ”Partially labeled classification with Markov random walks.” Advances in neural information processing systems 14 (2001)

    2001

  100. [110]

    ”Metrics for multi-class classification: an overview.” arXiv preprint arXiv:2008.05756 (2020)

    Grandini, Margherita, Enrico Bagli, and Giorgio Visani. ”Metrics for multi-class classification: an overview.” arXiv preprint arXiv:2008.05756 (2020)

    2020 arXiv

  101. [111]

    Jiu and H

    M. Jiu and H. Sahbi. ”Deep kernel map networks for image annotation.” 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016

    2016

  102. [112]

    ”Multi-class active learning for image classification.” 2009 ieee conference on computer vision and pattern recognition

    Joshi, Ajay J., Fatih Porikli, and Nikolaos Papanikolopoulos. ”Multi-class active learning for image classification.” 2009 ieee conference on computer vision and pattern recognition. IEEE, 2009

    2009

  103. [113]

    ”A kernel method for multi-labelled classification.” Advances in neural information processing systems 14 (2001)

    Elisseeff, Andr ´e, and Jason Weston. ”A kernel method for multi-labelled classification.” Advances in neural information processing systems 14 (2001)

    2001

  104. [114]

    ”ML-KNN: A lazy learning approach to multi-label learning.” Pattern recognition 40.7 (2007): 2038-2048

    Zhang, Min-Ling, and Zhi-Hua Zhou. ”ML-KNN: A lazy learning approach to multi-label learning.” Pattern recognition 40.7 (2007): 2038-2048

    2007

  105. [115]

    ”An extensive experimental comparison of methods for multi-label learning.” Pattern recognition 45.9 (2012): 3084-3104

    Madjarov, Gjorgji, et al. ”An extensive experimental comparison of methods for multi-label learning.” Pattern recognition 45.9 (2012): 3084-3104

    2012

  106. [116]

    Sahbi and F

    H. Sahbi and F. Fleuret. Kernel methods and scale invariance using the triangular kernel. Diss. INRIA, 2004

    2004

  107. [117]

    ”Multilabel classification via calibrated label ranking.” Machine learning 73 (2008): 133-153

    F ¨urnkranz, Johannes, et al. ”Multilabel classification via calibrated label ranking.” Machine learning 73 (2008): 133-153

    2008

  108. [118]

    ”Learning multi-label scene classification.” Pattern recognition 37.9 (2004): 1757-1771

    Boutell, Matthew R., et al. ”Learning multi-label scene classification.” Pattern recognition 37.9 (2004): 1757-1771

    2004

  109. [119]

    Vo and H

    P . Vo and H. Sahbi. ”Transductive kernel map learning and its application to image annotation.” BMVC. 2012

    2012

  110. [120]

    ”A review on multi-label learning algorithms.” IEEE transactions on knowledge and data engineering 26.8 (2013): 1819-1837

    Zhang, Min-Ling, and Zhi-Hua Zhou. ”A review on multi-label learning algorithms.” IEEE transactions on knowledge and data engineering 26.8 (2013): 1819-1837

    2013

  111. [121]

    ”Discriminative methods for multi-labeled classification.” Pacific-Asia conference on knowledge discovery and data mining

    Godbole, Shantanu, and Sunita Sarawagi. ”Discriminative methods for multi-labeled classification.” Pacific-Asia conference on knowledge discovery and data mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004

    2004

  112. [122]

    Ferecatu and H

    M. Ferecatu and H. Sahbi. ”Multi-view object matching and tracking using canonical correlation analysis.” 2009 16th IEEE International Conference on Image Processing (ICIP). IEEE, 2009

    2009

  113. [123]

    ”Decision trees for hierarchical multi-label classification.” Machine learning 73 (2008): 185-214

    Vens, Celine, et al. ”Decision trees for hierarchical multi-label classification.” Machine learning 73 (2008): 185-214

    2008

  114. [124]

    In: Asian Conference on Computer Vision

    Sahbi, H., Li, X.: Context-based support vector machines for interconnected image annotation. In: Asian Conference on Computer Vision. pp. 214–227. Springer (2010)

    2010

  115. [125]

    ”Classifier chains for multi-label classification.” Machine learning 85 (2011): 333-359

    Read, Jesse, et al. ”Classifier chains for multi-label classification.” Machine learning 85 (2011): 333-359

    2011

  116. [126]

    Tsoumakas and I

    G. Tsoumakas and I. Katakis (2008). Multi-label classification: An overview. Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications, 64-74

    2008

  117. [127]

    H. Sahbi. ”Learning Connectivity with Graph Convolutional Networks.” 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021

    2020

  118. [2022]

    Available: https://api.semanticscholar.org/CorpusID:250073162

    [Online]. Available: https://api.semanticscholar.org/CorpusID:250073162

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.