FineTec: Robust Fine-Grained Action Recognition with Temporal Corruption Handling
Research Paper#Action Recognition, Computer Vision, Deep Learning🔬 Research|Analyzed: Jan 3, 2026 06:33•
Published: Dec 31, 2025 18:59
•1 min read
•ArXivAnalysis
This paper addresses the critical problem of recognizing fine-grained actions from corrupted skeleton sequences, a common issue in real-world applications. The proposed FineTec framework offers a novel approach by combining context-aware sequence completion, spatial decomposition, physics-driven estimation, and a GCN-based recognition head. The results on both coarse-grained and fine-grained benchmarks, especially the significant performance gains under severe temporal corruption, highlight the effectiveness and robustness of the proposed method. The use of physics-driven estimation is particularly interesting and potentially beneficial for capturing subtle motion cues.
Key Takeaways
- •Proposes FineTec, a unified framework for fine-grained action recognition under temporal corruption.
- •Employs context-aware sequence completion, spatial decomposition, and physics-driven estimation.
- •Achieves state-of-the-art results on both coarse-grained and fine-grained action recognition benchmarks, especially under severe temporal corruption.
- •Demonstrates robustness and generalizability.
Reference / Citation
View Original"FineTec achieves top-1 accuracies of 89.1% and 78.1% on the challenging Gym99-severe and Gym288-severe settings, respectively, demonstrating its robustness and generalizability."