Wasserstein Loss With Alternative Reinforcement Learning for Severity-Aware Semantic Segmentation

Liu, Xiaofeng; Lu, Yunhong; Liu, Xiongchang; Bai, Song; Li, Site; You, Jane

doi:10.1109/tits.2020.3014137

Cited by 19 publications

(10 citation statements)

References 43 publications

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“…In this paper, we resort to the optimal transport distance as an alternative for empirical risk minimization [9,10,11,12]. With the low-cost modification of the loss function perspective, our solution can be added on any up-to-date general deep networks in a plug-and-play fashion.…”

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confidence: 99%

Embedding Semantic Hierarchy in Discrete Optimal Transport for Risk Minimization

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The widely-used cross-entropy (CE) loss-based deep networks achieved significant progress w.r.t. the classification accuracy. However, the CE loss can essentially ignore the risk of misclassification which is usually measured by the distance between the prediction and label in a semantic hierarchical tree. In this paper, we propose to incorporate the risk-aware inter-class correlation in a discrete optimal transport (DOT) training framework by configuring its ground distance matrix. The ground distance matrix can be pre-defined following a priori of hierarchical semantic risk. Specifically, we define the tree induced error (TIE) on a hierarchical semantic tree and extend it to its increasing function from the optimization perspective. The semantic similarity in each level of a tree is integrated with the information gain. We achieve promising results on several large scale image classification tasks with a semantic tree structure in a plug and play manner.

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confidence: 99%

Embedding Semantic Hierarchy in Discrete Optimal Transport for Risk Minimization

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Video-based FER. With the fast development of deep learning [24], [25], [26], [27], [28], [29], [30], [31], both the frame aggregation and spatiotemporal FER networks are developed and outperforms the conventional methods [32], [33]. The frame aggregation methods can utilize the image-based FER networks by making the decision-level [34] or the featurelevel frame-wise aggregation [35].…”

Section: Related Workmentioning

confidence: 99%

Identity-aware Facial Expression Recognition in Compressed Video

Liu

Jin

Han

et al. 2021

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“…Any ambiguity in machine vision algorithms may cause fatal consequences in autonomous driving [45], thus robustness testing in diverse driving conditions is essential. For this reason, WildDash [20] provided ten different hazards, such as blurs, underexposures or lens distortions, as well as negative test cases against the overreaction of segmentation algorithms.…”

Section: B Semantic Segmentationmentioning

confidence: 99%

Exploring Event-driven Dynamic Context for Accident Scene Segmentation

Zhang¹,

Yang²,

Stiefelhagen³

2021

Preprint

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The robustness of semantic segmentation on edge cases of traffic scene is a vital factor for the safety of intelligent transportation. However, most of the critical scenes of traffic accidents are extremely dynamic and previously unseen, which seriously harm the performance of semantic segmentation methods. In addition, the delay of the traditional camera during highspeed driving will further reduce the contextual information in the time dimension. Therefore, we propose to extract dynamic context from event-based data with a higher temporal resolution to enhance static RGB images, even for those from traffic accidents with motion blur, collisions, deformations, overturns, etc. Moreover, in order to evaluate the segmentation performance in traffic accidents, we provide a pixel-wise annotated accident dataset, namely DADA-seg, which contains a variety of critical scenarios from traffic accidents. Our experiments indicate that event-based data can provide complementary information to stabilize semantic segmentation under adverse conditions by preserving fine-grained motion of fast-moving foreground (crash objects) in accidents. Our approach achieves +8.2% performance gain on the proposed accident dataset, exceeding more than 20 state-of-the-art semantic segmentation methods. The proposal has been demonstrated to be consistently effective for models learned on multiple source databases including Cityscapes, KITTI-360, BDD, and ApolloScape.

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Wasserstein Loss With Alternative Reinforcement Learning for Severity-Aware Semantic Segmentation

Cited by 19 publications

References 43 publications

Embedding Semantic Hierarchy in Discrete Optimal Transport for Risk Minimization

Embedding Semantic Hierarchy in Discrete Optimal Transport for Risk Minimization

Identity-aware Facial Expression Recognition in Compressed Video

Exploring Event-driven Dynamic Context for Accident Scene Segmentation

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