SeFAR: Semi-supervised Fine-grained Action Recognition with Temporal
  Perturbation and Learning Stabilization
Human action understanding is crucial for the advancement of multimodal
systems. While recent developments, driven by powerful large language models
(LLMs), aim to be general enough to cover a wide range of categories, they
often overlook the need for more specific capabilities. In this work, we
address the more challenging task of Fine-grained Action Recognition (FAR),
which focuses on detailed semantic labels within shorter temporal duration
(e.g., "salto backward tucked with 1 turn"). Given the high costs of annotating
fine-grained labels and the substantial data needed for fine-tuning LLMs, we
propose to adopt semi-supervised learning (SSL). Our framework, SeFAR,
incorporates several innovative designs to tackle these challenges.
Specifically, to capture sufficient visual details, we construct Dual-level
temporal elements as more effective representations, based on which we design a
new strong augmentation strategy for the Teacher-Student learning paradigm
through involving moderate temporal perturbation. Furthermore, to handle the
high uncertainty within the teacher model's predictions for FAR, we propose the
Adaptive Regulation to stabilize the learning process. Experiments show that
SeFAR achieves state-of-the-art performance on two FAR datasets, FineGym and
FineDiving, across various data scopes. It also outperforms other
semi-supervised methods on two classical coarse-grained datasets, UCF101 and
HMDB51. Further analysis and ablation studies validate the effectiveness of our
designs. Additionally, we show that the features extracted by our SeFAR could
largely promote the ability of multimodal foundation models to understand
fine-grained and domain-specific semantics.
67776b23b2a6ac5157bafa60
Yongle Huang
claimed_verified
2025-06-04T09:05:56.463Z
655b39f5a296e5c74c72cc6d
/avatars/48aa1874526cda3035bce919fd459e69.svg
Huang
user
KyleHuang9
67776b23b2a6ac5157bafa61
Haodong Chen
claimed_verified
2025-01-03T14:03:10.538Z
6570450a78d7aca0c361a177
https://cdn-avatars.huggingface.co/v1/production/uploads/6570450a78d7aca0c361a177/z0GrnXEsjK2_G-hFfQhKv.jpeg
Harold Chen
user
Harold328
67776b23b2a6ac5157bafa62
Zhenbang Xu
67776b23b2a6ac5157bafa63
Zihan Jia
67776b23b2a6ac5157bafa64
Haozhou Sun
67776b23b2a6ac5157bafa65
Dian Shao
SeFAR: Semi-supervised Fine-grained Action Recognition with Temporal
  Perturbation and Learning Stabilization
Human action understanding is crucial for the advancement of multimodal
systems. While recent developments, driven by powerful large language models
(LLMs), aim to be general enough to cover a wide range of categories, they
often overlook the need for more specific capabilities. In this work, we
address the more challenging task of Fine-grained Action Recognition (FAR),
which focuses on detailed semantic labels within shorter temporal duration
(e.g., "salto backward tucked with 1 turn"). Given the high costs of annotating
fine-grained labels and the substantial data needed for fine-tuning LLMs, we
propose to adopt semi-supervised learning (SSL). Our framework, SeFAR,
incorporates several innovative designs to tackle these challenges.
Specifically, to capture sufficient visual details, we construct Dual-level
temporal elements as more effective representations, based on which we design a
new strong augmentation strategy for the Teacher-Student learning paradigm
through involving moderate temporal perturbation. Furthermore, to handle the
high uncertainty within the teacher model's predictions for FAR, we propose the
Adaptive Regulation to stabilize the learning process. Experiments show that
SeFAR achieves state-of-the-art performance on two FAR datasets, FineGym and
FineDiving, across various data scopes. It also outperforms other
semi-supervised methods on two classical coarse-grained datasets, UCF101 and
HMDB51. Further analysis and ablation studies validate the effectiveness of our
designs. Additionally, we show that the features extracted by our SeFAR could
largely promote the ability of multimodal foundation models to understand
fine-grained and domain-specific semantics.
SeFAR, a semi-supervised learning framework using dual-level temporal elements and adaptive regulation, achieves state-of-the-art performance in fine-grained action recognition and enhances multimodal foundation models' understanding of fine-grained semantics.
fine-grained action recognition semi-supervised learning dual-level temporal elements teacher-student learning paradigm temporal perturbation adaptive regulation FineGym FineDiving UCF101 HMDB51