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Label-Guided Knowledge Distillation for 3D-CNNs in Action Recognition

Paper recorded by Signals 4 on 2026-09-11 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-11 on arXiv · recorded by Signals 4 on 2026-09-14

Category: cs.AI · 人工智能 · first seen 2026-09-14

Abstract

As a key model compression technique, knowledge distillation aims to transfer knowledge from a high-capacity teacher model to a lightweight student model for enhancing the latter's performance. In this work, we reviewed the feature knowledge distillation for 3D-CNNs and observed that most feature distillation methods in video analysis are simple adaptations of those used in image analysis, often n

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#97 most recent of 300 cs.AI papers we have recorded · ↑ newer: Attention Quantization for Tabular Foundation Models · ↓ older: TileNet: Tile-Based CNN-SVM Architecture for Autonomous Unmanned Aeria
Cite this page: Label-Guided Knowledge Distillation for 3D-CNNs in Action Recognition: the #97 most recent of 300 cs.AI papers we have recorded (as of 2026-09-11). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/label-guided-knowledge-distillation-for-3d-cnns-in-action-recognition.html
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