Jul 2026· Signal Processing and Communications Applications Conference· pp. 1-4· 0 citations· 23 references
Abstract
Video action recognition tasks require large-scale labeled data to achieve high performance when pretraining is not used. However, annotating video data is costly and time-consuming. To reduce this dependency on labeled data, transfer learning approaches are commonly employed. Vision–language models, which learn generalizable visual representations from large-scale data, are effective at capturing spatial information and thus serve as suitable teacher models for spatial knowledge distillation. In this work, we propose a knowledge distillation-based pretraining approach that leverages zero-shot predictions from vision–language models to initialize video models. In this framework, the resulting soft class distributions are used as supervisory signals and transferred to the student video model. Experimental results on the UCF101 and HMDB51 datasets demonstrate that the proposed method provides an effective weight initialization strategy and yields consistent performance improvements.
Compared to prior CLIP-enhancement methods, MLLMCLIP achieves state-of-the-art compositional accuracy while delivering consistent gains on standard zero-shot classification and image-text retrieval, showing that feature-level distillation strengthens both compositional and general vision-language representation capabil...
Jongsuk Kim, Qi-Yu Wu, Zhuoyuan Mao et al.· 0 citations
This work introduces \method, a Multi-scale Adaptive Vision Encoder, a Multi-scale Adaptive Vision Encoder that uses position-dependent gates to fuse shallow, intermediate, and deep features from a vision Transformer, preserving global semantics while enhancing edges, text, and local structure.
We introduce S$^3$T (Self-Supervised Self-Distillation over Time), which, to the best of our knowledge, is the first fully self-contained framework for continuous video state tracking. Our method treats temporal sampling density as privileged information, based on the hypothesis that a denser view of the same clip reco...
Shravan Venkatraman, Wen-Shuai Zhao, Mohammad Hassan Vali et al.· 0 citations
OnPoKD is the first framework that applies on-policy distillation to vision-language model adaptation by learning target construction as a policy decision, and is the first framework that applies on-policy distillation to vision-language model adaptation by learning target construction as a policy decision.
Hong-Yuan Zhang, Xian-Da Guo, Yan-Lun Peng et al.· Information Fusion· 0 citations
This work introduces ActionLMM, a memory-augmented vision-language model for long-video action summarization that aligns visual and motion modalities through joint representation learning and leverages a novel dual-memory mechanism to retain both local motion details and global temporal structure.
Rui-Rui Li, Dari Abdullah Alrwoaily, Turgut Sofuyev et al.· International Conference on...· 0 citations
Across seven benchmarks, TimeLens2-2B outperforms all size-matched baselines on every benchmark, while the 4B and 8B variants achieve state-of-the-art performance, surpassing open-source models with up to 397B parameters.
