5 citations · 18 across the 18 of their papers we have counts for
13 papers · 1 filter
CODI: Improving Self-Supervised Vision Transformers with Cross Distillation
Siran Dai, Qianqian Xu, Peisong Wen +3
Self-supervised Vision Transformers learn strong image-level representations, but their patch-level representations can be unreliable for dense prediction. We study this mismatch a…
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…
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…
Semantic Concentration for Self-Supervised Dense Representations Learning
Peisong Wen, Qianqian Xu, Siran Dai +2
Recent advances in image-level self-supervised learning (SSL) have made significant progress, yet learning dense representations for patches remains challenging. Mainstream methods…
Learn Faster and Remember More: Balancing Exploration and Exploitation for Continual Test-time Adaptation
Pinci Yang, Peisong Wen, Ke Ma +1
Continual Test-Time Adaptation (CTTA) aims to adapt a source pre-trained model to continually changing target domains during inference. As a fundamental principle, an ideal CTTA me…
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…
When the Future Becomes the Past: Taming Temporal Correspondence for Self-supervised Video Representation Learning
Yang Liu, Qianqian Xu, Peisong Wen +2
The past decade has witnessed notable achievements in self-supervised learning for video tasks. Recent efforts typically adopt the Masked Video Modeling (MVM) paradigm, leading to…
AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation
Boyu Han, Qianqian Xu, Zhiyong Yang +4
The Area Under the ROC Curve (AUC) is a well-known metric for evaluating instance-level long-tail learning problems. In the past two decades, many AUC optimization methods have bee…
Not All Pairs are Equal: Hierarchical Learning for Average-Precision-Oriented Video Retrieval
Yang Liu, Qianqian Xu, Peisong Wen +2
The rapid growth of online video resources has significantly promoted the development of video retrieval methods. As a standard evaluation metric for video retrieval, Average Preci…
Seeking the Shape of Sound: An Adaptive Framework for Learning Voice-Face Association
Peisong Wen, Qianqian Xu, Yangbangyan Jiang +3
Nowadays, we have witnessed the early progress on learning the association between voice and face automatically, which brings a new wave of studies to the computer vision community…