Pith. sign in

REVIEW 4 major objections 4 minor 39 references

FedEMA: Federated Exponential Moving Averaging with Negative Entropy Regularizer in Autonomous Driving

T0 review · 4 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

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

Pith's one-line read FedEMA claims temporal catastrophic forgetting in federated autonomous-driving perception is mitigated by a server-side exponential moving average plus a vehicle-side negative entropy regularizer, with a relative 7.12% mIoU gain on…

desk verdict FedEMA combines server-side EMA and negative entropy regularization into a simple federated segmentation method; the static benchmark gains look real, but the paper's headline claim about temporal catastrophic forgetting is never actually tested. read the letter →

arxiv 2505.00318 v1 pith:N2ZZTJ54 submitted 2025-05-01 cs.RO cs.LG

Wei-Bin Kou , Guangxu Zhu , Bingyang Cheng , Shuai Wang , Ming Tang , Yik-Chung Wu This is my paper · ORCID
classification cs.ROcs.LG
keywords federatedlearningautonomousdrivingsemanticsegmentationcatastrophicforgettingexponentialmovingaveragenegativeentropyregularizationDeepLabv3+TopFormer
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

This paper argues that temporal catastrophic forgetting in federated autonomous-driving perception can be countered without replay buffers or task-specific memory. Its recipe is two changes to ordinary federated training: the server broadcasts an exponential moving average of past global models instead of the latest aggregate, and each vehicle adds a negative entropy regularizer to the segmentation loss to suppress overconfident, temporally overfit predictions. The combined scheme, FedEMA, reaches 60.62% mean Intersection-over-Union (mIoU) on Cityscapes with DeepLabv3+, a relative 7.12% gain over the best comparison baseline, and it is the top result among the federated baselines on that benchmark. The paper also proves an $O(1/\sqrt{R})$ convergence rate that matches standard federated optimization, and the ablations show the EMA window size and regularizer coefficient are two tunable controls. A sympathetic reader would care because the recipe is simple, communication-friendly, and targets the round-by-round overwriting of historical knowledge that standard federated averaging does not address.

What carries the argument

The load-bearing object is the server-side EMA model $\omega^r_{EMA}$, updated as $\omega^r_{EMA} = \beta\omega^{r-1}_{EMA} + (1-\beta)\omega$ with $\beta = 2/(N+1)$, together with the vehicle-side negative entropy term $-\lambda H(\omega_c)$ in the local objective. The EMA acts as a slow-moving historical model that is broadcast to vehicles every round, so the current aggregate only contributes a fraction $1-\beta$ of the next starting point; the entropy term, defined on per-pixel softmax probabilities, keeps local models from becoming overconfident on the current temporal slice. The convergence proof treats the EMA update as momentum, bounds client drift using the 'negative entropy regularity' inequality $\|\nabla H(\omega)\| \le \gamma\mathbb{E}_x\|\nabla \log p_k(x;\omega)\|$, and then applies L-smoothness to obtain the $O(1/\sqrt{R})$ bound. In the ablations, window size $N$ governs how much history survives, and $\lambda$ controls how strongly complexity is penalized, giving the method its two tuning knobs.

What would settle it

Run FedEMA on a temporally ordered stream — train on scene group A, adapt on scene group B, then re-measure mIoU on A — and compare the drop with FedAvg's; if the EMA model forgets A at the same rate, the historical-preservation claim fails. Separately, compute $\|\nabla H(\omega)\|$ and $\mathbb{E}_x\|\nabla \log p_k(x;\omega)\|$ on the trained segmenters to check the assumed 'negative entropy regularity' inequality; a clean violation would leave the convergence bound unsupported.

Watch

Extended reading notes

Core claim

At its center, FedEMA claims that the overwriting update of federated averaging is the mechanism of temporal forgetting, and that a recursive server-side exponential moving average is sufficient to preserve historical fitting capability: $\omega^r_{EMA} = \beta\omega^{r-1}_{EMA} + (1-\beta)\omega$, with $\beta = 2/(N+1)$, where $N$ is the EMA window. Because the EMA model, not the freshly aggregated model, is distributed to vehicles, every local training round starts from a blend of old and new knowledge. The vehicle-side counterweight is the negative entropy regularizer $-\lambda H(\omega_c)$ with $H(\omega_c) = \mathbb{E}_x[\sum_{k=1}^K p_k(x;\omega_c)\log p_k(x;\omega_c)]$, which the paper interprets as penalizing model complexity and preventing the EMA-introduced temporal patterns from being overfit. The two mechanisms define the dual objective in Eq. (6), and the convergence analysis reformulates the EMA as momentum to show client drift is bounded and the rate is $O(1/\sqrt{R})$. On the paper's own terms, FedEMA is a demonstration that forgetting can be mitigated by changing only how models are averaged and regularized, without changing the underlying segmentation architecture or data sharing.

Load-bearing premise

The load-bearing premise is that splitting static Cityscapes and CamVid samples across vehicles and running standard FL rounds reproduces temporal catastrophic forgetting — but the experiments introduce no temporal shift and re-test no old scenes, and the convergence proof depends on an unverified 'negative entropy regularity' inequality.

Editorial extensions

If this is right

  • On Cityscapes with DeepLabv3+, FedEMA reports 60.62% mIoU versus 56.59% for the best baseline FedProx, a relative 7.12% improvement, with mF1, mPrecision, and mRecall also higher.
  • The gains transfer at least partially across architectures and datasets: FedEMA reaches 74.73% mIoU on CamVid with DeepLabv3+, the best result in that table, and it is the best Cityscapes result with the TopFormer transformer backbone, though on CamVid with TopFormer its 41.79% trails FedProx's 42.10%.
  • Ablations show smaller EMA window sizes improve both convergence speed and final performance, while larger $\lambda$ accelerates convergence with little effect on final mIoU, giving two hyperparameters that trade adaptability against stability.
  • The proved $O(1/\sqrt{R})$ rate means adding the entropy regularizer does not degrade the asymptotic convergence order of federated optimization, and the analysis ties the regularizer to reduced client drift.

Reading between the lines

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

  • Because the paper validates on static splits rather than a temporal stream, the direct test of its forgetting claim would be to order Cityscapes or CamVid images by recording time or season, train in consecutive chunks, and re-measure old-chunk mIoU after adaptation; if FedEMA's old-scene accuracy declines as fast as or faster than FedAvg's, the memory mechanism is not doing the claimed work.
  • The EMA-as-momentum reformulation invites combining FedEMA with heterogeneity-aware client regularizers such as proximal or contrastive terms, addressing device skew and temporal drift simultaneously rather than separately.
  • A practical extension is to make the EMA window $N$ and the entropy coefficient $\lambda$ adaptive: shrink the window when a distribution-drift detector fires, and grow $\lambda$ when predictions become overconfident, removing the need to grid-search these two knobs.
  • If the mechanism is as general as the paper suggests, the same two changes should transfer to other federated dense-prediction tasks in autonomous driving, including depth estimation and panoptic segmentation, which share the overwriting failure mode.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The paper proposes FedEMA, a federated learning method for street-scene semantic segmentation in autonomous driving, combining a server-side exponential moving average of round-wise aggregated models with a vehicle-side entropy regularizer. The authors argue that the EMA preserves historical model fitting capability and the regularizer prevents overfitting to temporal patterns, thereby mitigating temporal catastrophic forgetting in dynamically evolving environments. They provide a convergence analysis (Section III-C) claiming O(1/√R) convergence and conduct experiments on Cityscapes and CamVid with two segmentation backbones, reporting that FedEMA outperforms several FL baselines, including a 7.12% relative mIoU improvement over FedProx(0.005) on Cityscapes with DeepLabv3+.

Significance. If the temporal-catastrophic-forgetting claim were actually demonstrated, the contribution would be a simple and potentially useful addition to federated semantic segmentation: a server-side EMA is easy to implement and the entropy regularizer is a standard regularizer, so the method could be adopted by practitioners. The paper also includes the useful discipline of comparing against multiple FL baselines on two datasets and two architectures, and it reports ablations over the two hyperparameters λ and N. However, the significance is currently conditional. The experiments are entirely static: data are distributed once over vehicles, there is no temporal ordering or distribution shift, and no forgetting metric is reported. The convergence analysis is a proof sketch whose displayed bound does not support the stated insights, and one of its assumptions is unproved. Thus the central advertised contribution, mitigating temporal catastrophic forgetting, is supported neither by the experiments nor by the theory as written.

major comments (4)
  1. [IV-A, IV-B] The paper's central claim is that FedEMA mitigates temporal catastrophic forgetting in dynamically evolving AD environments, but the experimental protocol contains no temporal component. The Cityscapes and CamVid training sets are described as being distributed among vehicles once, and all reported results in Tables IV and V compare methods on the same fixed test sets. There is no task ordering, no distribution shift between rounds, no re-evaluation of previously seen scene types after later training, and no forgetting metric such as backward transfer or old-task accuracy. The 7.12% mIoU advantage on Cityscapes with DeepLabv3+ is therefore evidence of better accuracy on a static data mixture, not of preserving historical fitting capability, and it does not support the abstract's or Section I's catastrophic-forgetting motivation. A temporal continual-FL experiment, such as sequentially introducing scene conditions and measuring old-condition performance, is needed before the central claim can be evaluated.
  2. [III-C, Eqs. (9), (11), (13)] The convergence analysis as written does not imply the stated insights. Theorem 1's bound in Eqs. (9) and (13) contains a positive +λ²γ² term in the numerator; as λ grows, the displayed upper bound becomes looser, and there is no term in which λ multiplies σ². The text's insight (III) that 'larger λ suppresses heterogeneity σ², which leads to faster convergence' is therefore not a consequence of the displayed theorem. In addition, the 'Negative Entropy regularity' assumption, ∥∇H(ω)∥≤γ E_x∥∇log p_k(x;ω)∥, is introduced without proof or citation, and the proof sketch in Steps 2–4 omits the derivation of the client-drift bound in Eq. (11) and the descent lemma in Eq. (12); the step from Eq. (12) to the final bound is not shown. At minimum, the theorem statement and insights must be reconciled with the displayed bound, or the theoretical claims should be withdrawn.
  3. [Eq. (6), Algorithm 1] There is an unresolved sign inconsistency in the problem definition. Equation (5) and Algorithm 1 line 7 define the vehicle objective as min Lc(ωc)−λH(ωc), which is equivalent to maximizing H(ωc), but Eq. (6) states the constraint ωc = arg min [Lc(ω)+λH(ω)], which minimizes H(ω). These two formulations have opposite effects on the prediction distribution: the latter would sharpen predictions, contradicting the stated goal of preventing overconfident predictions. The formal optimization problem must be corrected and aligned with the implemented update before the method is well defined.
  4. [Table V] The empirical superiority statement is broader than the data support. The headline 7.12% improvement is computed only for Cityscapes with DeepLabv3+ against FedProx(0.005). In Table V (TopFormer on CamVid), FedEMA's mIoU is 41.79±0.36, numerically below FedProx(0.005)'s 42.10±0.31, and on several other rows FedEMA is not the best, such as CamVid mPrecision with DeepLabv3+ in Table IV. The claim of achieving 'almost the best performance' should be qualified by the configurations where the proposed method is not statistically distinguishable from or is worse than a baseline, and the abstract's unconditional '7.12% higher mIoU' should be tied to the specific configuration.
minor comments (4)
  1. [III-B, Eq. (5)] The terminology for H(ω) is confusing: the paper calls H(ω)=E_x[Σ_k p_k(x;ω) log p_k(x;ω)] the 'negative entropy', but this quantity is the negative of the standard Shannon entropy. Since the algorithm maximizes H, it is actually encouraging flatter, less confident predictions. Please clarify the sign convention so that the text, equations, and algorithm are mutually consistent.
  2. [IV-A] The experimental setup is under-specified for reproduction: the number of vehicles, the data partitioning strategy (IID versus non-IID, number of classes per vehicle, overlap), the total number of communication rounds, and the exact values of λ and N used for the main results in Tables IV and V are not reported.
  3. [IV-C, Figs. 4 and 5] The captions of Figures 4 and 5 do not state which dataset and model are used, whether the curves are averaged over multiple seeds, or how 'faster convergence' is quantified. The claim in Section IV-C that a higher λ results in quicker convergence appears to rest on a visual impression; please provide a quantitative convergence criterion, especially because the theoretical bound in Eq. (9) suggests the opposite dependence on λ.
  4. [Table V] MOON with TopFormer achieves near-zero mIoU (0.20 on Cityscapes and 1.03 on CamVid), which likely indicates training instability rather than a meaningful algorithmic comparison. Please add a brief explanation or report adjusted hyperparameters for that configuration.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: FedEMA's components are defined directly and benchmarked externally; the temporal-forgetting claim is untested, not circular.

full rationale

No load-bearing circular step is present. FedEMA's server-side EMA (Eq. 4) and vehicle-side negative-entropy regularizer (Eq. 5) are method definitions, and the 7.12% mIoU gain is measured against external baselines (FedProx, FedDyn, MOON, FedIR) on Cityscapes/CamVid; lambda and window size N are swept in ablations (Figs. 4-5) rather than fitted to the reported test metric. The convergence theorem is derived from stated assumptions; the unproved 'Negative Entropy regularity' assumption (Section III-C) and the tension between the bound's +lambda^2*gamma^2 term and the claim that larger lambda accelerates convergence are proof/consistency defects, not reductions of the result to its inputs. The main support gap is that Section IV-A distributes static Cityscapes/CamVid frames across vehicles and never introduces temporal shift or re-tests old scenes, so the headline 'temporal catastrophic forgetting' mitigation is not directly measured; this is an experimental-validity issue, not circularity. Self-citations ([4], [10], [12]-[14], [30], [31]) appear in related work and motivation, e.g., 'this catastrophic forgetting phenomenon stems from FL's overwriting training scheme ... [13], [14]', but the method's empirical numbers and hyperparameters do not come from those citations, and no uniqueness theorem is imported. Thus the paper is self-contained against external benchmarks; score 2 reflects only minor non-load-bearing self-citation.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The method depends on two hand-tuned hyperparameters (N via beta, lambda) and on four regularity assumptions for the convergence theorem, one of which is ad hoc. No new physical or conceptual entities are introduced.

free parameters (2)
  • EMA window size N (via beta = 2/(N+1)) = not stated for main results; ablation uses N = 5
    Controls how much historical model is retained; selected by validation and ablation, not derived from first principles.
  • negative entropy coefficient lambda = ablation uses lambda = 0.002; main-run values not reported
    Regularization strength that penalizes overconfident predictions; chosen by hand rather than learned.
assumptions (4)
  • domain assumption Local losses are L-smooth
    Assumed for Theorem 1 in Section III-C.
  • domain assumption Bounded gradients: E||grad L_c(w)||^2 <= G^2
    Assumed for Theorem 1 in Section III-C.
  • domain assumption Bounded heterogeneity: E_c||grad L_c(w) - grad L(w)||^2 <= sigma^2
    Assumed for Theorem 1 in Section III-C.
  • ad hoc to paper Negative entropy regularity: ||grad H(w)|| <= gamma E_x||grad log p_k(x;w)||
    Introduced solely for the proof in Section III-C; no justification or standard precedent is given for this functional inequality.

how reviews work

0 comments
Cite this review

Pith. "Pith review of FedEMA: Federated Exponential Moving Averaging with Negative Entropy Regularizer in Autonomous Driving." pith.science (2026). https://pith.science/paper/N2ZZTJ54

@misc{pith2026250500318,
  author       = {Pith},
  title        = {Pith review of: FedEMA: Federated Exponential Moving Averaging with Negative Entropy Regularizer in Autonomous Driving},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/N2ZZTJ54}},
  note         = {Machine review of arXiv:2505.00318}
}
read the original abstract

Street Scene Semantic Understanding (denoted as S3U) is a crucial but complex task for autonomous driving (AD) vehicles. Their inference models typically face poor generalization due to domain-shift. Federated Learning (FL) has emerged as a promising paradigm for enhancing the generalization of AD models through privacy-preserving distributed learning. However, these FL AD models face significant temporal catastrophic forgetting when deployed in dynamically evolving environments, where continuous adaptation causes abrupt erosion of historical knowledge. This paper proposes Federated Exponential Moving Average (FedEMA), a novel framework that addresses this challenge through two integral innovations: (I) Server-side model's historical fitting capability preservation via fusing current FL round's aggregation model and a proposed previous FL round's exponential moving average (EMA) model; (II) Vehicle-side negative entropy regularization to prevent FL models' possible overfitting to EMA-introduced temporal patterns. Above two strategies empower FedEMA a dual-objective optimization that balances model generalization and adaptability. In addition, we conduct theoretical convergence analysis for the proposed FedEMA. Extensive experiments both on Cityscapes dataset and Camvid dataset demonstrate FedEMA's superiority over existing approaches, showing 7.12% higher mean Intersection-over-Union (mIoU).

Figures

Figures reproduced from arXiv: 2505.00318 by the authors.

Figure 1
Figure 1. Illustration of Federated AD (FedAD) system [12]. The [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of the proposed FedEMA method. To mitigate such challenges, we propose Federated Exponential Moving Average (FedEMA), a novel frame￾work that addresses these challenges through two synergistic strategies. First, an exponential moving average (EMA) ap￾proach with adjustable window size is proposed to fuse the currently aggregated model and the historical model. Specif￾ically, in each FL round, an EMA model o… view at source ↗
Figure 3
Figure 3. Metric comparison of DeepLabv3+ on Cityscapes dataset. Results show FedEMA outperform other FL methods across all metrics. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Performance and convergence comparison with different [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Performance and convergence comparison with different [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

39 extracted references · 21 canonical work pages

  1. [1]

    Semi-supervised active learning for semantic segmentation in unknown environments using informative path planning,

    J. Rückin, F. Magistri, C. Stachniss, and M. Popovi ´c, “Semi-supervised active learning for semantic segmentation in unknown environments using informative path planning,” IEEE Robotics and Automation Letters, vol. 9, no. 3, pp. 2662–2669, 2024

    2024

  2. [2]

    Wedge: Web-image assisted domain generalization for semantic segmentation,

    N. Kim, T. Son, J. Pahk, C. Lan, W. Zeng, and S. Kwak, “Wedge: Web-image assisted domain generalization for semantic segmentation,” in 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023, pp. 9281–9288

    2023

  3. [3]

    Cekd: Cross-modal edge-privileged knowledge distillation for semantic scene understanding using only thermal images,

    Z. Feng, Y . Guo, and Y . Sun, “Cekd: Cross-modal edge-privileged knowledge distillation for semantic scene understanding using only thermal images,” IEEE Robotics and Automation Letters, vol. 8, no. 4, pp. 2205–2212, 2023

    work page 2023

  4. [4]

    Enhancing large vision model in street scene semantic understanding through leveraging posterior optimization trajectory,

    W.-B. Kou, Q. Lin, M. Tang, S. Wang, R. Ye, G. Zhu, and Y .-C. Wu, “Enhancing large vision model in street scene semantic understanding through leveraging posterior optimization trajectory,” arXiv preprint arXiv:2501.01710, 2025

    arXiv 2025

  5. [5]

    Lightweight semantic segmentation network for semantic scene understanding on low-compute devices,

    H. Son and J. Weiland, “Lightweight semantic segmentation network for semantic scene understanding on low-compute devices,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023, pp. 62–69

    2023

  6. [6]

    Ida: Informed domain adaptive semantic segmentation,

    Z. Chen, Z. Ding, J. M. Gregory, and L. Liu, “Ida: Informed domain adaptive semantic segmentation,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS), 2023, pp. 90–97

    work page 2023

  7. [7]

    Featdanet: Feature-level domain adaptation network for semantic segmentation,

    J. Li, W. Shi, D. Zhu, G. Zhang, X. Zhang, and J. Li, “Featdanet: Feature-level domain adaptation network for semantic segmentation,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023, pp. 3873–3880

    2023

  8. [8]

    Vision-based semantic segmentation in scene understanding for autonomous driving: Recent achievements, challenges, and outlooks,

    K. Muhammad, T. Hussain, H. Ullah, J. Del Ser, M. Rezaei, N. Kumar, M. Hijji, P. Bellavista, and V . H. C. de Albuquerque, “Vision-based semantic segmentation in scene understanding for autonomous driving: Recent achievements, challenges, and outlooks,”IEEE Transactions on Intelligent Transportation Systems, 2022

    work page 2022

Show all 39 references
  1. [9]

    Communication-efficient learning of deep networks from decentralized data,

    H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y. Arcas, “Communication-efficient learning of deep networks from decentralized data,” 2016

    2016

  2. [10]

    Communication resources constrained hierarchical feder- ated learning for end-to-end autonomous driving,

    W.-B. Kou, S. Wang, G. Zhu, B. Luo, Y . Chen, D. W. Kwan Ng, and Y .-C. Wu, “Communication resources constrained hierarchical feder- ated learning for end-to-end autonomous driving,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2023, pp....

    2023

  3. [11]

    A hierarchical federated learning framework for collaborative quality de- fect inspection in construction,

    H.-T. Wu, H. Li, H.-L. Chi, W.-B. Kou, Y .-C. Wu, and S. Wang, “A hierarchical federated learning framework for collaborative quality de- fect inspection in construction,” Engineering Applications of Artificial Intelligence, vol. 133, p. 108218, 2024

    2024

  4. [12]

    pfedlvm: A large vision model (lvm)-driven and latent feature-based personalized federated learning framework in autonomous driving,

    W.-B. Kou, Q. Lin, M. Tang, S. Xu, R. Ye, Y . Leng, S. Wang, Z. Chen, G. Zhu, and Y .-C. Wu, “pfedlvm: A large vision model (lvm)-driven and latent feature-based personalized federated learning framework in autonomous driving,” arXiv preprint arXiv:2405.04146 , 2024

    2024 arXiv

  5. [13]

    Fe- drc: A rapid-converged hierarchical federated learning framework in street scene semantic understanding,

    W.-B. Kou, Q. Lin, M. Tang, S. Wang, G. Zhu, and Y .-C. Wu, “Fe- drc: A rapid-converged hierarchical federated learning framework in street scene semantic understanding,” in 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2024, pp. 2578–2585

    2024

  6. [14]

    Fast-convergent and communication-alleviated heterogeneous hierar- chical federated learning in autonomous driving,

    W.-B. Kou, Q. Lin, M. Tang, R. Ye, S. Wang, G. Zhu, and Y .-C. Wu, “Fast-convergent and communication-alleviated heterogeneous hierar- chical federated learning in autonomous driving,” IEEE Transactions on Intelligent Transportation Systems , 2025

    2025

  7. [15]

    The cityscapes dataset for semantic urban scene understanding,

    M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benen- son, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,” in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016

    2016

  8. [16]

    Segmentation and recognition using structure from motion point clouds,

    G. J. Brostow, J. Shotton, J. Fauqueur, and R. Cipolla, “Segmentation and recognition using structure from motion point clouds,” in Proc. European Conference on Computer Vision of the (ECCV) , 2008

    2008

  9. [17]

    Multi-robot planning with conflicts and synergies,

    Y . Jiang, H. Yedidsion, S. Zhang, G. Sharon, and P. Stone, “Multi-robot planning with conflicts and synergies,” Autonomous Robots , vol. 43, no. 8, pp. 2011–2032, 2019

    2011

  10. [18]

    Ffinet: Future feedback interaction network for motion forecasting,

    M. Kang, S. Wang, S. Zhou, K. Ye, J. Jiang, and N. Zheng, “Ffinet: Future feedback interaction network for motion forecasting,” IEEE Transactions on Intelligent Transportation Systems , pp. 1–12, 2024

    2024

  11. [19]

    Neural semantic map-learning for autonomous vehicles,

    M. Herb, N. Navab, and F. Tombari, “Neural semantic map-learning for autonomous vehicles,” in 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2024, pp. 1062–1069

    2024

  12. [20]

    Goal- oriented pedestrian motion prediction,

    J. Wu, J. Ruenz, H. Berkemeyer, L. Dixon, and M. Althoff, “Goal- oriented pedestrian motion prediction,” IEEE Transactions on Intelli- gent Transportation Systems, vol. 25, no. 6, pp. 5282–5298, 2024

    2024

  13. [21]

    Multimodal end-to-end autonomous driving,

    Y . Xiao, F. Codevilla, A. Gurram, O. Urfalioglu, and A. M. López, “Multimodal end-to-end autonomous driving,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 1, pp. 537–547, 2022

    2022

  14. [22]

    Tv-net: A structure-level feature fusion network based on tensor voting for road crack seg- mentation,

    W. Zheng, X. Jiang, Z. Fang, and Y . Gao, “Tv-net: A structure-level feature fusion network based on tensor voting for road crack seg- mentation,” IEEE Transactions on Intelligent Transportation Systems , vol. 25, no. 6, pp. 5743–5754, 2024

    2024

  15. [23]

    Federated class-incremental learning,

    J. Dong, L. Wang, Z. Fang, G. Sun, S. Xu, X. Wang, and Q. Zhu, “Federated class-incremental learning,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2022

    2022

  16. [24]

    Communication-efficient activity detection for cell-free massive mimo: An augmented model-driven end-to-end learning framework,

    Q. Lin, Y . Li, W.-B. Kou, T.-H. Chang, and Y .-C. Wu, “Communication-efficient activity detection for cell-free massive mimo: An augmented model-driven end-to-end learning framework,” IEEE Transactions on Wireless Communications , pp. 1–1, 2024

    2024

  17. [25]

    Communication- efficient joint signal compression and activity detection in cell-free massive mimo,

    Q. Lin, Y . Li, W. Kou, T.-H. Chang, and Y .-C. Wu, “Communication- efficient joint signal compression and activity detection in cell-free massive mimo,” in ICC 2023-IEEE International Conference on Communications. IEEE, 2023, pp. 5030–5035

    2023

  18. [26]

    Addressing class imbalance in federated learning,

    L. Wang, S. Xu, X. Wang, and Q. Zhu, “Addressing class imbalance in federated learning,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 11, 2021, pp. 10 165–10 173

    2021

  19. [27]

    Personalized cross-silo federated learning on non-iid data,

    Y . Huang, L. Chu, Z. Zhou, L. Wang, J. Liu, J. Pei, and Y . Zhang, “Personalized cross-silo federated learning on non-iid data,” 2021

    2021

  20. [28]

    Federated optimization in heterogeneous networks,

    T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V . Smith, “Federated optimization in heterogeneous networks,” 2020

    2020

  21. [29]

    Federated learning based on dynamic regularization,

    D. A. E. Acar, Y . Zhao, R. Matas, M. Mattina, P. Whatmough, and V . Saligrama, “Federated learning based on dynamic regularization,” in International Conference on Learning Representations , 2021

    2021

  22. [30]

    An adverse weather-immune scheme with unfolded regularization and foundation model knowledge distillation for street scene understand- ing,

    W.-B. Kou, G. Zhu, R. Ye, S. Wang, Q. Lin, M. Tang, and Y .-C. Wu, “An adverse weather-immune scheme with unfolded regularization and foundation model knowledge distillation for street scene understand- ing,” arXiv preprint arXiv:2409.14737 , 2024

    2024 arXiv

  23. [31]

    Label anything: An interpretable, high-fidelity and prompt-free annotator,

    W.-B. Kou, G. Zhu, R. Ye, S. Wang, M. Tang, and Y .-C. Wu, “Label anything: An interpretable, high-fidelity and prompt-free annotator,” arXiv preprint arXiv:2502.02972 , 2025

    2025 arXiv

  24. [32]

    Compuda: Com- positional unsupervised domain adaptation for semantic segmentation under adverse conditions,

    Z. Zhengl, Y . Chen, B.-S. Hua, and S.-K. Yeung, “Compuda: Com- positional unsupervised domain adaptation for semantic segmentation under adverse conditions,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2023, pp. 7675–7681

    2023

  25. [33]

    Rethinking semantic segmentation: A prototype view,

    T. Zhou, W. Wang, E. Konukoglu, and L. Van Gool, “Rethinking semantic segmentation: A prototype view,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 2582–2593

    2022

  26. [34]

    Superpixel transformers for efficient semantic seg- mentation,

    A. Z. Zhu, J. Mei, S. Qiao, H. Yan, Y . Zhu, L.-C. Chen, and H. Kretzschmar, “Superpixel transformers for efficient semantic seg- mentation,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2023, pp. 7651–7658

    2023

  27. [35]

    Understanding bird’s-eye view of road semantics using an onboard camera,

    Y . B. Can, A. Liniger, O. Unal, D. Paudel, and L. Van Gool, “Understanding bird’s-eye view of road semantics using an onboard camera,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 3302–3309, 2022

    2022

  28. [36]

    Encoder-decoder with atrous separable convolution for semantic image segmentation,

    L.-C. Chen, Y . Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” 2018

    2018

  29. [37]

    Topformer: Token pyramid transformer for mobile semantic segmentation,

    W. Zhang, Z. Huang, G. Luo, T. Chen, X. Wang, W. Liu, G. Yu, and C. Shen, “Topformer: Token pyramid transformer for mobile semantic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 12 083–12 093

    2022

  30. [38]

    Federated visual classification with real-world data distribution,

    T. M. H. Hsu, H. Qi, and M. Brown, “Federated visual classification with real-world data distribution,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Pro- ceedings, Part X 16 . Springer, 2020, pp. 76–92

    2020

  31. [39]

    Model-contrastive federated learning,

    Q. Li, B. He, and D. Song, “Model-contrastive federated learning,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 10 713–10 722

    2021

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.