OSDTW decomposes long-tailed recognition into head and tail tasks with shared encoder and derives a Fisher-based bias-variance proxy to select shared depth and task weights.
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
A competition-winning multi-modal model for hidden emotion recognition integrates static and dynamic pose features via cross-attention and MIL pooling while noting representation collapse in vision foundation models on micro-dynamic tasks.
citing papers explorer
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OSDTW: Optimal Shared Depth and Task Weighting for Long-Tailed Recognition
OSDTW decomposes long-tailed recognition into head and tail tasks with shared encoder and derives a Fisher-based bias-variance proxy to select shared depth and task weights.
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Rethinking the Role of Feature Engineering and Learning Strategies in Few-Shot Hidden Emotion Recognition
A competition-winning multi-modal model for hidden emotion recognition integrates static and dynamic pose features via cross-attention and MIL pooling while noting representation collapse in vision foundation models on micro-dynamic tasks.