3 papers
cs.CV2026
UPLiFT: Efficient Pixel-Dense Feature Upsampling with Local Attenders
Matthew Walmer, Saksham Suri, Anirud Aggarwal +1
The space of task-agnostic feature upsampling has emerged as a promising area of research to efficiently create denser features from pre-trained visual backbones. These methods act…
cs.CV2025
Trokens: Semantic-Aware Relational Trajectory Tokens for Few-Shot Action Recognition
Pulkit Kumar, Shuaiyi Huang, Matthew Walmer +2
Video understanding requires effective modeling of both motion and appearance information, particularly for few-shot action recognition. While recent advances in point tracking hav…
cs.CV2025
Multi-entity Video Transformers for Fine-Grained Video Representation Learning
Matthew Walmer, Rose Kanjirathinkal, Kai Sheng Tai +3
The area of temporally fine-grained video representation learning focuses on generating frame-by-frame representations for temporally dense tasks, such as fine-grained action phase…