activity
20162025
most citedTemporal Segment Networks: Towards Good Practices for Deep Action Recognition

289 citations · 917 across the 73 of their papers we have counts for

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59 papers · 1 filter

cs.CV2024

Empowering Image Recovery_ A Multi-Attention Approach

Juan Wen, Yawei Li, Chao Zhang +3

We propose Diverse Restormer (DART), a novel image restoration method that effectively integrates information from various sources (long sequences, local and global regions, featur…

cs.CV20241 cited

Self-Explainable Affordance Learning with Embodied Caption

Zhipeng Zhang, Zhimin Wei, Guolei Sun +2

In the field of visual affordance learning, previous methods mainly used abundant images or videos that delineate human behavior patterns to identify action possibility regions for…

cs.CV2024

Investigating the Effectiveness of Cross-Attention to Unlock Zero-Shot Editing of Text-to-Video Diffusion Models

Saman Motamed, Wouter Van Gansbeke, Luc Van Gool

With recent advances in image and video diffusion models for content creation, a plethora of techniques have been proposed for customizing their generated content. In particular, m…

cs.CV2024

Know Your Neighbors: Improving Single-View Reconstruction via Spatial Vision-Language Reasoning

Rui Li, Tobias Fischer, Mattia Segu +3

Recovering the 3D scene geometry from a single view is a fundamental yet ill-posed problem in computer vision. While classical depth estimation methods infer only a 2.5D scene repr…

cs.CV20241 cited

HandDiff: 3D Hand Pose Estimation with Diffusion on Image-Point Cloud

Wencan Cheng, Hao Tang, Luc Van Gool +1

Extracting keypoint locations from input hand frames, known as 3D hand pose estimation, is a critical task in various human-computer interaction applications. Essentially, the 3D h…

cs.CV2024

A Unified and Interpretable Emotion Representation and Expression Generation

Reni Paskaleva, Mykyta Holubakha, Andela Ilic +3

Canonical emotions, such as happy, sad, and fearful, are easy to understand and annotate. However, emotions are often compound, e.g. happily surprised, and can be mapped to the act…