Search Results for author: Jinhong Deng

Found 6 papers, 4 papers with code

Revisiting AP Loss for Dense Object Detection: Adaptive Ranking Pair Selection

1 code implementation CVPR 2022 Dongli Xu, Jinhong Deng, Wen Li

However, a deep understanding of how AP loss affects the detector from a pairwise ranking perspective has not yet been developed. In this work, we revisit the average precision (AP)loss and reveal that the crucial element is that of selecting the ranking pairs between positive and negative samples. Based on this observation, we propose two strategies to improve the AP loss.

Dense Object Detection object-detection

Cross-domain Detection Transformer based on Spatial-aware and Semantic-aware Token Alignment

no code implementations1 Jun 2022 Jinhong Deng, Xiaoyue Zhang, Wen Li, Lixin Duan

In particular, we take advantage of the characteristics of cross-attention as used in detection transformer and propose the spatial-aware token alignment (SpaTA) and the semantic-aware token alignment (SemTA) strategies to guide the token alignment across domains.

Domain Adaptation object-detection +1

Undoing the Damage of Label Shift for Cross-domain Semantic Segmentation

1 code implementation CVPR 2022 Yahao Liu, Jinhong Deng, Jiale Tao, Tong Chu, Lixin Duan, Wen Li

Existing works typically treat cross-domain semantic segmentation (CDSS) as a data distribution mismatch problem and focus on aligning the marginal distribution or conditional distribution.

Semantic Segmentation

BAPA-Net: Boundary Adaptation and Prototype Alignment for Cross-Domain Semantic Segmentation

1 code implementation ICCV 2021 Yahao Liu, Jinhong Deng, Xinchen Gao, Wen Li, Lixin Duan

By integrating the boundary adaptation and prototype alignment, we are able to train a discriminative and domain-invariant model for cross-domain semantic segmentation.

Semantic Segmentation Unsupervised Domain Adaptation

Unbiased Mean Teacher for Cross-domain Object Detection

1 code implementation CVPR 2021 Jinhong Deng, Wen Li, Yu-Hua Chen, Lixin Duan

We reveal that there often exists a considerable model bias for the simple mean teacher (MT) model in cross-domain scenarios, and eliminate the model bias with several simple yet highly effective strategies.

object-detection Object Detection +1

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