17 citations · 34 across the 7 of their papers we have counts for
10 papers · 1 filter
Dual Cross-Attention Learning for Fine-Grained Visual Categorization and Object Re-Identification
Haowei Zhu, Wenjing Ke, Dong Li +3
Recently, self-attention mechanisms have shown impressive performance in various NLP and CV tasks, which can help capture sequential characteristics and derive global information.…
Dynamic Sparse R-CNN
Qinghang Hong, Fengming Liu, Dong Li +3
Sparse R-CNN is a recent strong object detection baseline by set prediction on sparse, learnable proposal boxes and proposal features. In this work, we propose to improve Sparse R-…
Compare learning: bi-attention network for few-shot learning
Li Ke, Meng Pan, Weigao Wen +1
Learning with few labeled data is a key challenge for visual recognition, as deep neural networks tend to overfit using a few samples only. One of the Few-shot learning methods cal…
Representing Videos as Discriminative Sub-graphs for Action Recognition
Dong Li, Zhaofan Qiu, Yingwei Pan +3
Human actions are typically of combinatorial structures or patterns, i.e., subjects, objects, plus spatio-temporal interactions in between. Discovering such structures is therefore…
Towards Discriminative Representation Learning for Unsupervised Person Re-identification
Takashi Isobe, Dong Li, Lu Tian +3
In this work, we address the problem of unsupervised domain adaptation for person re-ID where annotations are available for the source domain but not for target. Previous methods t…
Towards Optimal Filter Pruning with Balanced Performance and Pruning Speed
Dong Li, Sitong Chen, Xudong Liu +2
Filter pruning has drawn more attention since resource constrained platform requires more compact model for deployment. However, current pruning methods suffer either from the infe…