5 citations · 18 across the 18 of their papers we have counts for
9 papers · 1 filter
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…
TuckA: Hierarchical Compact Tensor Experts for Efficient Fine-Tuning
Qifeng Lei, Zhiyong Yang, Qianqian Xu +3
Efficiently fine-tuning pre-trained models for downstream tasks is a key challenge in the era of foundation models. Parameter-efficient fine-tuning (PEFT) presents a promising solu…
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…
CSRM-LLM: Embracing Multilingual LLMs for Cold-Start Relevance Matching in Emerging E-commerce Markets
Yujing Wang, Yiren Chen, Huoran Li +18
As global e-commerce platforms continue to expand, companies are entering new markets where they encounter cold-start challenges due to limited human labels and user behaviors. In…
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…