8 papers
Training-Free Composed Video Retrieval via Visual Representation-Guided Video-LLM Reasoning
Yang Liu, Qianqian Xu, Peisong Wen +2
Recent advances in large vision-language models have expanded video retrieval from simple text-based search to more flexible scenarios, where users may specify the desired result t…
From Static to Dynamic: Exploring Self-supervised Image-to-Video Representation Transfer Learning
Yang Liu, Qianqian Xu, Peisong Wen +3
Recent studies have made notable progress in video representation learning by transferring image-pretrained models to video tasks, typically with complex temporal modules and video…
HiGFA: Hierarchical Guidance for Fine-grained Data Augmentation with Diffusion Models
Zhiguang Lu, Qianqian Xu, Peisong Wen +2
Generative diffusion models show promise for data augmentation. However, applying them to fine-grained tasks presents a significant challenge: ensuring synthetic images accurately…
Bootstrapping Physics-Grounded Video Generation through VLM-Guided Iterative Self-Refinement
Yang Liu, Xilin Zhao, Peisong Wen +2
Recent progress in video generation has led to impressive visual quality, yet current models still struggle to produce results that align with real-world physical principles. To th…
Self-supervised Representation Learning with Local Aggregation for Image-based Profiling
Siran Dai, Qianqian Xu, Peisong Wen +2
Image-based cell profiling aims to create informative representations of cell images. This technique is critical in drug discovery and has greatly advanced with recent improvements…
Exploring Structural Degradation in Dense Representations for Self-supervised Learning
Siran Dai, Qianqian Xu, Peisong Wen +2
In this work, we observe a counterintuitive phenomenon in self-supervised learning (SSL): longer training may impair the performance of dense prediction tasks (e.g., semantic segme…