1 citations · 1 across the 28 of their papers we have counts for
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VideoCompressa: Data-Efficient Video Understanding via Joint Temporal Compression and Spatial Reconstruction
Shaobo Wang, Tianle Niu, Runkang Yang +6
The scalability of video understanding models is increasingly limited by the prohibitive storage and computational costs of large-scale video datasets. While data synthesis has imp…
UNSEEN: Enhancing Dataset Pruning from a Generalization Perspective
Furui Xu, Shaobo Wang, Jiajun Zhang +3
The growing scale of datasets in deep learning has introduced significant computational challenges. Dataset pruning addresses this challenge by constructing a compact but informati…
ImagebindDC: Compressing Multi-modal Data with Imagebind-based Condensation
Yue Min, Shaobo Wang, Jiaze Li +5
Data condensation techniques aim to synthesize a compact dataset from a larger one to enable efficient model training, yet while successful in unimodal settings, they often fail in…
Efficient Multi-modal Large Language Models via Progressive Consistency Distillation
Zichen Wen, Shaobo Wang, Yufa Zhou +8
Visual tokens consume substantial computational resources in multi-modal large models (MLLMs), significantly compromising their efficiency. Recent works have attempted to improve e…
Compute Only 16 Tokens in One Timestep: Accelerating Diffusion Transformers with Cluster-Driven Feature Caching
Zhixin Zheng, Xinyu Wang, Chang Zou +2
Diffusion transformers have gained significant attention in recent years for their ability to generate high-quality images and videos, yet still suffer from a huge computational co…
DD-Ranking: Rethinking the Evaluation of Dataset Distillation
Zekai Li, Xinhao Zhong, Samir Khaki +49
In recent years, dataset distillation has provided a reliable solution for data compression, where models trained on the resulting smaller synthetic datasets achieve performance co…
Dataset Distillation with Neural Characteristic Function: A Minmax Perspective
Shaobo Wang, Yicun Yang, Zhiyuan Liu +4
Dataset distillation has emerged as a powerful approach for reducing data requirements in deep learning. Among various methods, distribution matching-based approaches stand out for…