16 citations · 69 across the 9 of their papers we have counts for
9 papers
ActionFormer: Localizing Moments of Actions with Transformers
Chenlin Zhang, Jianxin Wu, Yin Li
Self-attention based Transformer models have demonstrated impressive results for image classification and object detection, and more recently for video understanding. Inspired by t…
Weakly Supervised Foreground Learning for Weakly Supervised Localization and Detection
Chen-Lin Zhang, Yin Li, Jianxin Wu
Modern deep learning models require large amounts of accurately annotated data, which is often difficult to satisfy. Hence, weakly supervised tasks, including weakly supervised obj…
Salvage of Supervision in Weakly Supervised Object Detection
Lin Sui, Chen-Lin Zhang, Jianxin Wu
Weakly supervised object detection~(WSOD) has recently attracted much attention. However, the lack of bounding-box supervision makes its accuracy much lower than fully supervised o…
Rethinking the Route Towards Weakly Supervised Object Localization
Chen-Lin Zhang, Yun-Hao Cao, Jianxin Wu
Weakly supervised object localization (WSOL) aims to localize objects with only image-level labels. Previous methods often try to utilize feature maps and classification weights to…
Towards Real-Time Action Recognition on Mobile Devices Using Deep Models
Chen-Lin Zhang, Xin-Xin Liu, Jianxin Wu
Action recognition is a vital task in computer vision, and many methods are developed to push it to the limit. However, current action recognition models have huge computational co…
Coarse-to-fine: A RNN-based hierarchical attention model for vehicle re-identification
Xiu-Shen Wei, Chen-Lin Zhang, Lingqiao Liu +2
Vehicle re-identification is an important problem and becomes desirable with the rapid expansion of applications in video surveillance and intelligent transportation. By recalling…