Temporal Reasoning

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Hierarchical Relation-augmented Representation Generalization for Few-shot Action Recognition
Apr 14, 2025Hongyu Qu, Ling Xing, Jiachao Zhang +3Temporal Reasoning Hierarchical Representation Learning
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China · Artificial Intelligence Industrial Technology Research Institute, Nanjing Institute of Technology, Nanjing 211167, China
Goal-Driven Reasoning in DatalogMTL with Magic Sets
Dec 10, 2024Shaoyu Wang, Kaiyue Zhao, Dongliang Wei +4Logic Programming Logical Reasoning
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, China · Department of Computer Science, University of Oxford, UK · School of Electronic Engineering and Computer Science, Queen Mary University of London, UK

Apr 14, 2025, cs.CVJ/K move · Enter open · S save

Hierarchical Relation-augmented Representation Generalization for Few-shot Action Recognition

Hongyu Qu, Ling Xing, Jiachao Zhang, Rui Yan+2

School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China · Artificial Intelligence Industrial Technology Research Institute, Nanjing Institute of Technology, Nanjing 211167, China

Temporal Reasoning Hierarchical Representation Learning Few-Shot Learning Video Action Recognition

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Few-shot action recognition (FSAR) aims to recognize novel action categories with few exemplars. Existing methods typically learn frame-level representations for each video by designing inter-frame temporal modeling strategies or inter-video interaction at the coarse video-level granularity. However, they treat each episode task in isolation and neglect fine-grained temporal relation modeling between videos, thus failing to capture shared fine-grained temporal patterns across videos and reuse temporal knowledge from historical tasks. In light of this, we propose HR2G-shot, a Hierarchical Relation-augmented Representation Generalization framework for FSAR, which unifies three types of relation modeling (inter-frame, inter-video, and inter-task) to learn task-specific temporal patterns from a holistic view. Going beyond conducting inter-frame temporal interactions, we further devise two components to respectively explore inter-video and inter-task relationships: i) Inter-video Semantic Correlation (ISC) performs cross-video frame-level interactions in a fine-grained manner, thereby capturing task-specific query features and enhancing both intra-class consistency and inter-class separability; ii) Inter-task Knowledge Transfer (IKT) retrieves and aggregates relevant temporal knowledge from the bank, which stores diverse temporal patterns from historical episode tasks. Extensive experiments on five benchmarks show that HR2G-shot outperforms current top-leading FSAR methods.

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Latest papers 32

Hierarchical Relation-augmented Representation Generalization for Few-shot Action Recognition

Goal-Driven Reasoning in DatalogMTL with Magic Sets