IPDiff: Diffusion-driven ORSI Salient Object Detection with Information Reconstruction and Multi-Prior Guidance

Dan Zeng; Gongyang Li; Runmin Cong; Weisi Lin; Xiao-Ping Zhang; Zhen Bai

REVIEW 2 major objections 6 minor 300 references

IPDiff: Diffusion-driven ORSI Salient Object Detection with Information Reconstruction and Multi-Prior Guidance

T0 review · 2 major / 6 minor · reviewed 2026-07-12 · grok-4.5

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Pith's one-line read A diffusion model turns one-shot remote-sensing saliency detection into iterative refinement that corrects errors and leads 46 methods on three public benchmarks.

desk verdict Solid empirical SOTA on the three standard ORSI-SOD benchmarks via a carefully engineered conditional-diffusion pipeline; the advance is real but stays inside the niche and is slow at test time. read the letter →

arxiv 2607.03696 v1 pith:SBLNTNZ3 submitted 2026-07-04 cs.CV

Gongyang Li , Zhen Bai , Runmin Cong , Dan Zeng , Weisi Lin , Xiao-Ping Zhang This is my paper

classification cs.CV

keywords salientobjectdetectionopticalremotesensingimagediffusionmodeldynamicoptimizationinformationreconstructionmulti-priorguidancespectraldomain

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

Existing methods that locate the most eye-catching objects in optical remote-sensing images make a single prediction with fixed parameters, so any mistakes stay locked in. This paper claims that casting the task as a conditional diffusion problem lets the model start from random noise and iteratively clean it into a saliency map, guided by image-derived priors, so later steps can fix earlier mistakes. IPDiff first extracts a saliency prior (location) and hierarchical priors (detail and semantics) with an attention module that reconstructs features in the spectral domain, then denoises under those priors while training with both spatial and spectral losses. On the ORSSD, EORSSD and ORSI-4199 datasets it reports the highest scores against 46 competitors. Readers who work with aerial or satellite imagery would care because the same iterative correction can make automatic scene understanding more reliable when objects vary wildly in size, orientation and contrast.

What carries the argument

IPDiff: a prior network (pyramid vision transformer blocks plus Information Reconstruction-driven Attention Modules that adaptively decouple and reweight low- and high-frequency spectral components) that produces the saliency and hierarchical priors, feeding a multi-prior guidance denoising network that iteratively denoises a noisy mask over a short horizon (default T=10), with training-only Information Perturbation Modules and a hybrid spatial-spectral loss.

What would settle it

Train and evaluate the identical architecture with T=1 (pure one-shot) versus T=10 on a held-out sensor or geographic region never seen in the three public datasets; if multi-step denoising no longer improves the four metrics, the dynamic-optimization claim fails for that domain.

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Extended reading notes

Core claim

Formulating optical remote-sensing salient object detection as a conditional diffusion process that is guided by a saliency prior and hierarchical priors, and that reconstructs information in the spectral domain, yields a dynamic optimization strategy able to iteratively correct saliency maps and to outperform 46 prior methods on the three standard public benchmarks.

Load-bearing premise

The method assumes that a fixed schedule of ten denoising steps, a twenty-percent feature-perturbation rate, and the spectral-decoupling parameters learned on the training split remain near-optimal for every new sensor and scene.

Editorial extensions

If this is right

Saliency maps that still contain errors after the first pass can be progressively cleaned without retraining or changing model weights.
Spectral-domain reconstruction of features can replace purely spatial attention for large-scale remote-sensing scenes that exhibit size and orientation variation.
Hybrid spatial-spectral supervision can be transferred to other dense prediction tasks that suffer from frequency imbalance.
Diffusion-style iterative refinement becomes a practical alternative to one-shot CNNs and transformers for ORSI-SOD whenever accuracy is valued over real-time speed.

Reading between the lines

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

The same multi-prior plus spectral reconstruction recipe could be dropped into other remote-sensing dense tasks (change detection, land-cover mapping) that also suffer from one-shot error lock-in.
Because performance plateaus after roughly ten steps, practitioners may treat the diffusion horizon as a cheap accuracy–speed dial rather than a free accuracy lever.
The large domain gap closed in the zero-shot 360° tests suggests the learned priors capture viewpoint-invariant saliency structure that could help multi-sensor fusion.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

This paper is the new high-water mark on ORSSD, EORSSD and ORSI-4199. Against 46 published methods (CNN, transformer, hybrid, lightweight, and three prior diffusion segmenters) it records the best Sα / Fmax / Emax and lowest MAE on every split (Table 1). Code and maps are released, so the ranking is checkable.

What is actually new is the combination, not the diffusion idea itself. They treat ORSI-SOD as conditional denoising, extract a saliency prior plus hierarchical Fourier-domain priors with a learnable spectrum-decoupling attention block (IRAM), inject them hierarchically into a U-Net-style denoiser, and add training-only random feature masking (IPM) plus a spatial-spectral hybrid loss. The dynamic-optimization framing is honest: T=10 steps let the model correct false positives and recover missing structure (Fig. 8 and the per-step numbers in Table 7). Ablations (Tables 2–8) and the cross-dataset / zero-shot 360° tests (Table 9) are thorough; nothing looks cooked.

Soft spots are the usual ones for this genre and are already visible in the paper. Inference is 4 fps because of the iterative loop; T, the 20 % perturbation rate, and the learned spectral hyperparameters are fixed after a single search. No error bars over seeds. None of that reverses the ranking on the public test sets, but it does mean the method is a strong specialist detector rather than a general recipe you would drop into every remote-sensing pipeline tomorrow.

Math is standard DDPM reverse process; citations cover the relevant ORSI-SOD and diffusion-segmentation literature without obvious gaps or self-citation inflation. For anyone working on remote-sensing saliency or wanting a well-documented multi-prior diffusion baseline, this is worth reading and citing. A serious editor should send it to referees; the empirical claim is solid enough to deserve that time.

Referee Report

2 major / 6 minor

Summary. The paper proposes IPDiff, a conditional diffusion framework for salient object detection in optical remote sensing images (ORSI-SOD). It replaces the conventional static one-shot inference used by CNN/transformer ORSI-SOD models with a dynamic optimization strategy: a prior network (PVT-v2 + Information Reconstruction-driven Attention Modules) extracts a saliency prior and hierarchical priors; a multi-prior guidance denoising network then iteratively denoises a noisy mask under those priors, with Information Perturbation Modules used only at training time. A hybrid spatial-spectral loss supervises both the intermediate saliency prior and the final prediction. On ORSSD, EORSSD and ORSI-4199 the method reports the best Sα, Fmaxβ, Emaxξ and MAE among 46 published baselines (Table 1), supported by component ablations (Tables 2–8), qualitative error-correction trajectories (Fig. 8), and cross-dataset / zero-shot transfer (Table 9). Code and results are released.

Significance. If the ranking holds, the work is a clear empirical advance for ORSI-SOD: it is the first diffusion-driven method tailored to this domain that systematically outperforms recent transformer, hybrid and diffusion baselines on the three standard public benchmarks. The multi-prior conditioning design, spectral-domain IRAM, training-only IPM, and spatial-spectral hybrid loss are concrete, ablated contributions rather than pure re-application of DDPM. Released code and exhaustive tables make the central performance claim falsifiable and reproducible. The main practical cost—iterative inference at ~4 fps—is acknowledged and does not negate the accuracy gains.

major comments (2)

Table 1 and §4.1 report point estimates only; no standard deviations over multiple random seeds or training runs are given. For a claim of strict superiority over 46 methods (including several that are within 0.5–1% on individual metrics), at least 3–5 independent runs with mean±std on the primary metrics would strengthen that the ranking is stable rather than a single-run artifact.
§4.1 and Tables 4/8 fix T=10 and r=20% after grid search on EORSSD. While the ablations show these values are near-optimal on that split, the manuscript does not report whether the same (T,r) pair remains optimal when the model is trained on ORSSD or ORSI-4199 and tested cross-dataset (Table 9). A short sensitivity check under the transfer settings would address the weakest practical assumption without changing the central claim.

minor comments (6)

Fig. 1 caption and surrounding text use inconsistent notation for the predicted clean mask (bxt vs. bx0); unify with Eq. (5).
Eq. (11) writes {η1,η1}∈(0,2); the second index should be η2.
In §3.4 the encoder-block footnotes (EncBlk1–4, FuBlk, etc.) are dense; a small architecture table would improve readability.
Table 1 marks several second-best results in italic; a few cells (e.g., EORSSD Emaxξ of DAFNet) appear tied or nearly tied—clarify the ranking rule when values are equal to three decimals.
The inference-speed column (4 fps) is honest but could briefly note wall-clock time for T=5 vs. T=10 so readers can trade accuracy for speed.
A few typographical slips remain (e.g., “W ork”, “IPdiff”, “dateset” in Table 9 caption).

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; purely empirical architecture paper whose SOTA claims rest on held-out public benchmarks, not on any self-referential derivation.

full rationale

IPDiff is a standard deep-learning method paper: it defines a conditional diffusion pipeline (prior network with IRAM + multi-prior denoising network with IPMs), trains it with a hybrid spatial-spectral loss, and reports superior Sα / Fmaxβ / Emaxξ / MAE on the public test splits of ORSSD, EORSSD and ORSI-4199 against 46 external baselines (Table 1). All supporting evidence consists of independent measurements—component ablations (Tables 2–8), cross-dataset / zero-shot transfer (Table 9), and qualitative error-correction trajectories (Fig. 8)—none of which reduce a claimed prediction to a fitted constant or to a self-citation by construction. Self-citations appear only as ordinary related-work or baseline references to the authors’ earlier ORSI-SOD models; they do not supply a uniqueness theorem, an ansatz, or any load-bearing premise of the present architecture. Hyper-parameters (T=10, r=20 %, spectral masks) are selected by ablation and treated as fixed engineering choices, not as derived predictions. Consequently none of the six circularity patterns is present.

Assumptions & free parameters 5 free parameters · 3 assumptions · 3 invented entities

The paper rests on the standard DDPM forward/reverse process, a pre-trained PVT-v2 backbone, and a handful of hand-chosen or validation-tuned scalars (T, r, loss weights, input resolution). The novel modules (IRAM, IPM, hybrid loss) are engineering constructs whose utility is demonstrated only by ablation on the same data; they introduce no new physical entities.

free parameters (5)

total diffusion timesteps T = 10
Set to 10 after ablation; controls both accuracy and inference cost.
IPM perturbation rate r = 0.20
Fraction of feature channels randomly zeroed during training; chosen by validation.
loss weight on saliency prior St = 0.5
Coefficient 0.5 in total loss (Eq. 18) to balance the two supervised heads.
learnable spectrum-decoupling γ and aggregation η1,η2 = data-driven
Adaptive masks and soft weights inside IRAM; optimized end-to-end on training data.
input resolution / learning rate / epochs / batch size = 352 / 1e-4 / 150 / 16
Standard training hyper-parameters fixed at 352², 1e-4, 150, 16.

assumptions (3)

domain assumption The standard DDPM Markov noising and reverse-process equations (Ho et al. 2020) correctly describe the distribution of binary saliency masks.
Invoked throughout Section 3.1; no proof that the Gaussian schedule is optimal for sparse remote-sensing masks.
domain assumption Low-frequency spectral components predominantly encode large objects and background redundancy while high-frequency components encode edges and small objects.
Stated in Section 3.3 as justification for IRAM’s spectrum decoupling; treated as given rather than measured.
ad hoc to paper A single forward pass of a trained network cannot correct its own errors (static-inference limitation).
Used in the introduction and Fig. 1 to motivate the dynamic strategy; true by definition for non-iterative models but not a universal law.

invented entities (3)

Information Reconstruction-driven Attention Module (IRAM)
purpose: Adaptively decouple, weight and re-aggregate spectral components to produce more informative hierarchical priors.
New architectural block; utility shown only by ablation on the same three datasets; no external theoretical guarantee.
Information Perturbation Module (IPM)
purpose: Randomly mask encoder features during training to improve anti-interference capacity.
Training-only regularizer; effectiveness demonstrated solely by internal ablations.
Spatial-spectral collaborative hybrid loss
purpose: Supervise both pixel-space and Fourier-space alignment of predicted masks.
Novel combination of weighted BCE/IoU, edge BCE and SSIM on real/imaginary FFT parts; no external validation outside the paper’s experiments.

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Cite this review

Pith. "Pith review of IPDiff: Diffusion-driven ORSI Salient Object Detection with Information Reconstruction and Multi-Prior Guidance." pith.science (2026). https://pith.science/paper/SBLNTNZ3

@misc{pith2026260703696,
  author       = {Pith},
  title        = {Pith review of: IPDiff: Diffusion-driven ORSI Salient Object Detection with Information Reconstruction and Multi-Prior Guidance},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SBLNTNZ3}},
  note         = {Machine review of arXiv:2607.03696}
}

read the original abstract

Existing Salient Object Detection in Optical Remote Sensing Image (ORSI-SOD) methods mainly adopt the static inference strategy, which uses fixed trained model parameters for saliency inference in the testing phase. This means that even if the generated saliency map has errors, it cannot be further optimized. In this paper, we propose the novel IPDiff, a Diffusion-driven ORSI-SOD method with Information Reconstruction and Multi-Prior Guidance. We build IPDiff based on a unique dynamic optimization strategy, which endows IPDiff with the ability to iteratively optimize saliency maps with a dynamic parameter. Specifically, we formulate ORSI-SOD as a conditional diffusion problem in IPDiff. IPDiff first extracts informative conditional priors from ORSIs, including the saliency prior and the hierarchical priors, in the prior network with the assistance of the information reconstruction-driven attention module. The saliency prior can provide positional information of salient objects, while the hierarchical priors can provide specific detail and semantic information of salient objects. Under the guidance of these priors, IPDiff then iteratively denoises random noise as the timestep dynamically changes in the denoising network, generating saliency maps that are close to ground truths. Notably, we simultaneously supervise IPDiff in both spatial and spectral domains through a hybrid loss function to achieve efficient network training. Comprehensive experiments on public ORSSD, EORSSD, and ORSI-4199 datasets demonstrate that our proposed IPDiff achieves the best performance compared to 46 state-of-the-art methods. The code and results of our method are available at https://github.com/MathLee/IPDiff.

Figures

Figures reproduced from arXiv: 2607.03696 by the authors.

**Figure 2.** Illustration of the forward diffusion process and the reverse denoising process. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** The overall framework of the proposed IPDiff. IPDiff consists of a prior network and a denoising network. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (5 more)

**Figure 4.** Figure 4: Illustration of the Information Reconstruction-driven Attention Module. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

**Figure 5.** Figure 5: Visualization of the saliency prior and the hier [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]

**Figure 6.** Figure 6: Visualization of operations and features in the [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]

**Figure 7.** Figure 7: Qualitative comparisons with nine representative state-of-the-art methods. The source dataset is shown on [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]

**Figure 8.** Figure 8: Visualization of the dynamic optimization from [PITH_FULL_IMAGE:figures/full_fig_p017_8.png]

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arXiv preprint arXiv:2203.04708 , year=

A Unified Transformer Framework for Group-based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detection , author=. arXiv preprint arXiv:2203.04708 , year=

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Fang, Xian and Zhu, Jinshao and Shao, Xiuli and Wang, Hongpeng , journal=
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2020 , pages=

Wang, Qilong and Wu, Banggu and Zhu, Pengfei and Li, Peihua and Zuo, Wangmeng and Hu, Qinghua , booktitle=CVPR, title=. 2020 , pages=

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arXiv preprint arXiv:1905.09646 , year=

Spatial group-wise enhance: Improving semantic feature learning in convolutional networks , author=. arXiv preprint arXiv:1905.09646 , year=

1905 arXiv
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2021 , pages=

Zhang, Qing-Long and Yang, Yu-Bin , booktitle=ICASSP, title=. 2021 , pages=

2021
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2020 , volume=

Jie Hu and Li Shen and Samuel Albanie and Gang Sun and Enhua Wu , journal=TPAMI, title=. 2020 , volume=

2020
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2018 , pages=

Sanghyun Woo and Jongchan Park and Joon-Young Lee and In So Kweon , title =. 2018 , pages=

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2021 , pages=

Ze Liu and Yutong Lin and Yue Cao and Han Hu and Yixuan Wei and Zheng Zhang and Stephen Lin and Baining Guo , booktitle=ICCV, title=. 2021 , pages=

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Yuan, Li and Chen, Yunpeng and Wang, Tao and Yu, Weihao and Shi, Yujun and Jiang, Zihang and Tay, Francis E. H. and Feng, Jiashi and Yan, Shuicheng , booktitle=ICCV, title=. 2021 , pages=

2021
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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale , author=
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2021 , volume=

Mei, Jie and Li, Rou-Jing and Gao, Wang and Cheng, Ming-Ming , journal=TIP, title=. 2021 , volume=

2021
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2018 , pages=

Zhang, Xiangyu and Zhou, Xinyu and Lin, Mengxiao and Sun, Jian , booktitle=CVPR, title=. 2018 , pages=

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2022 , volume=

Wang, Qi and Liu, Yanfeng and Xiong, Zhitong and Yuan, Yuan , journal=TGRS, title=. 2022 , volume=

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2022 , month=

Tu, Zhengzheng and Li, Zhun and Li, Chenglong and Tang, Jin , journal=TIP, title=. 2022 , month=

2022
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2021 , month=

Zhengzheng Tu and Zhun Li and Chenglong Li and Yang Lang and Jin Tang , journal=TIP, title=. 2021 , month=

2021
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2020 , volume=

Jin Tang and Dongzhe Fan and Xiaoxiao Wang and Zhengzheng Tu and Chenglong Li , journal=TCSVT, title=. 2020 , volume=

2020
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Li, Gongyang and Liu, Zhi and Zhang, Xinpeng and Lin, Weisi , title=
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2023 , volume=

Zhuge, Mingchen and Fan, Deng-Ping and Liu, Nian and Zhang, Dingwen and Xu, Dong and Shao, Ling , journal=TPAMI, title=. 2023 , volume=

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2022 , volume=

Liu, Yi and Zhang, Dingwen and Liu, Nian and Xu, Shoukun and Han, Jungong , journal=TIP, title=. 2022 , volume=

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2022 , volume=

Chaowei Fang and Haibin Tian and Dingwen Zhang and Qiang Zhang and Jungong Han and Junwei Han , journal=SCIS, title=. 2022 , volume=

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2023 , month=

Gongyang Li and Zhen Bai and Zhi Liu and Xinpeng Zhang and Haibin Ling , title =. 2023 , month=

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IEEE Trans

Gongyang Li and Zhen Bai and Zhi Liu , title =. IEEE Trans. Circuits Syst. II-Express Briefs , volume=. 2024 , month=

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Cattin , booktitle=MIDL, title=

Julia Wolleb and Robin Sandk\"uhler and Florentin Bieder and Philippe Valmaggia and Philippe C. Cattin , booktitle=MIDL, title=. 2022 , volume =

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2024 , pages=

Junde Wu and Wei Ji and Huazhu Fu and Min Xu and Yueming Jin and Yanwu Xu , booktitle=AAAI, title=. 2024 , pages=

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2024 , pages=

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2025 , volume=

Sun, Ke and Chen, Zhongxi and Lin, Xianming and Sun, Xiaoshuai and Liu, Hong and Ji, Rongrong , journal=TPAMI, title=. 2025 , volume=

2025
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2024 , volume=

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Gu, Shengyu and Song, Yong and Zhou, Ya and Bai, Yashuo and Yang, Xin and He, Yuxin , journal=GRSL, title=. 2024 , volume=

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2024 , volume=

Zhao, Jie and Jia, Yun and Ma, Lin and Yu, Lidan , journal=TGRS, title=. 2024 , volume=

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2024 , volume=

Quan, Yueqian and Xu, Honghui and Wang, Renfang and Guan, Qiu and Zheng, Jianwei , journal=TGRS, title=. 2024 , volume=

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2024 , volume=

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2025 , volume=

Jie Liu and Jinpeng He and Huaixin Chen and Ruoyu Yang and Ying Huang , journal=RS, title=. 2025 , volume=

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2024 , author =

Transformer with large convolution kernel decoder network for salient object detection in optical remote sensing images , journal = CVIU, volume =. 2024 , author =

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Li, Hongli and Chen, Xuhui and Mei, Liye and Yang, Wei , TITLE =. Electronics , VOLUME =. 2024 , NUMBER =

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Li, Hongli and Chen, Xuhui and Yang, Wei and Huang, Jian and Sun, Kaimin and Wang, Ying and Huang, Andong and Mei, Liye , TITLE =. Entropy , VOLUME =. 2024 , NUMBER =

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Hao Chen and Youfu Li and Yongjian Deng and Guosheng Lin , journal=IJCV, title=. 2021 , volume=

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2021 , volume=

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2022 , volume=

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2023 , volume=

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2024 , volume=

Xihang Hu and Fuming Sun and Jing Sun and Fasheng Wang and Haojie Li , journal=IJCV, title=. 2024 , volume=

2024

Pith tools

Reviewed July 12, 2026 · model on record in the stance chip above.

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Laurent Itti and Christof Koch and Ernst Niebur , journal=TPAMI, title=. 1998 , volume=

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Ye, Linwei and Rochan, Mrigank and Liu, Zhi and Wang, Yang , booktitle=CVPR, title=. 2019 , pages=

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Xuebin Qin and Zichen Zhang and Chenyang Huang and Chao Gao and Masood Dehghan and Martin Jagersand , booktitle=CVPR, title=. 2019 , pages=

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Deng-Ping Fan and Wenguan Wang and Ming-Ming Cheng and Jianbing Shen , booktitle=CVPR, title=. 2019 , pages=

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Ran Shi and King Ngi Ngan and Songnan Li and Raveendran Paramesran and Hongliang Li , journal=TIP, title=. 2015 , volume=

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