activity
20182026
most citedCLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text Labels

9 citations · 24 across the 15 of their papers we have counts for

collaborators

16 papers

cs.CV2026

Order Matters: Competition-Guided Query Ordering for RNN-Based Object Detection

Shengjian Wu, Li Sun, Yu Shangguan +1

DETR-style detectors use one-to-one bipartite matching during training to assign object queries to ground-truth objects, enabling end-to-end set prediction without non-maximum supp…

cs.CV2025

DragLoRA: Online Optimization of LoRA Adapters for Drag-based Image Editing in Diffusion Model

Siwei Xia, Li Sun, Tiantian Sun +1

Drag-based editing within pretrained diffusion model provides a precise and flexible way to manipulate foreground objects. Traditional methods optimize the input feature obtained f…

cs.CV2024★ 1 cited

RefineStyle: Dynamic Convolution Refinement for StyleGAN

Siwei Xia, Xueqi Hu, Li Sun +1

In StyleGAN, convolution kernels are shaped by both static parameters shared across images and dynamic modulation factors specific to each image. Therefore, $…

cs.CV2024

OD-DETR: Online Distillation for Stabilizing Training of Detection Transformer

Shengjian Wu, Li Sun, Qingli Li

DEtection TRansformer (DETR) becomes a dominant paradigm, mainly due to its common architecture with high accuracy and no post-processing. However, DETR suffers from unstable train…

cs.CV2023

RecursiveDet: End-to-End Region-based Recursive Object Detection

Jing Zhao, Li Sun, Qingli Li

End-to-end region-based object detectors like Sparse R-CNN usually have multiple cascade bounding box decoding stages, which refine the current predictions according to their previ…

cs.CV2023★ 9 cited

CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text Labels

Siyuan Li, Li Sun, Qingli Li

Pre-trained vision-language models like CLIP have recently shown superior performances on various downstream tasks, including image classification and segmentation. However, in fin…