arXiv Computer Vision
Hierarchical Relation-augmented Representation Generalization for Few-shot Action Recognition
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
Sep 7
Few-Shot Video Recognition via Hierarchical Metric Learning
The paper introduces HML-FSAR, a hierarchical metric learning framework for few-shot action recognition. It incorporates a spatial‑enhanced module, temporal MHA, heterogeneous alignment, spatial‑temporal fusion, and dictionary learning to build a comprehensive feature pipeline. Progressive constraints—center, alignment, contrastive, dictionary, and prototype metrics—are applied from frame‑level representations to final prototypes, improving feature compactness, alignment, discriminability, and robustness.
arXiv AI
Jul 7
Learning to Visually Connect Actions and their Effects
arXiv:2401. 10805v4 Announce Type: replace-cross Abstract: We introduce the novel concept of visually Connecting Actions and Their Effects (CATE) in video understanding.
Hugging Face Trending Papers
Jul 22
MoAKE: Toward Unified All-in-One Action Quality Assessment via Mixture of Action Knowledge Experts
Action Quality Assessment (AQA) aims to objectively evaluate performance quality from action videos. Most existing methods follow a ``one-by-one'' paradigm, training a separate model for each action type.
arXiv Computer Vision
Sep 16
Exploring the Temporal Consistency for Point-Level Weakly-Supervised Temporal Action Localization
arXiv:2602.05718v2 Announce Type: replace Abstract: Point-supervised Temporal Action Localization (PTAL) adopts a lightly frame-annotated paradigm (\textit{i.e.}, labeling only a single frame per act...
arXiv Computer Vision
Sep 10
IAE-VTG: Interaction-Aligned Action-Entity Video Temporal Grounding
arXiv:2609.09736v1 Announce Type: new Abstract: Video Temporal Grounding (VTG) localizes the video segment that matches a natural-language query. Many queries describe an action performed by a partic...
Hugging Face Trending Papers
Jun 11
OR-Action: Multi-Role Video Understanding with Fine-Grained Actions
Fine-grained understanding of operating room (OR) activity could enable workflow-aware assistance, yet remains difficult due to clutter, occlusions, and limited sensing. The prevailing approach to model this environment is scene graphs as an interpretable representation of OR interactions.