147 citations · 283 across the 36 of their papers we have counts for
13 papers · 1 filter
FoPro: Few-Shot Guided Robust Webly-Supervised Prototypical Learning
Yulei Qin, Xingyu Chen, Chao Chen +5
Recently, webly supervised learning (WSL) has been studied to leverage numerous and accessible data from the Internet. Most existing methods focus on learning noise-robust models f…
ECO-TR: Efficient Correspondences Finding Via Coarse-to-Fine Refinement
Dongli Tan, Jiang-Jiang Liu, Xingyu Chen +5
Modeling sparse and dense image matching within a unified functional correspondence model has recently attracted increasing research interest. However, existing efforts mainly focu…
LAB-Net: LAB Color-Space Oriented Lightweight Network for Shadow Removal
Hong Yang, Gongrui Nan, Mingbao Lin +4
This paper focuses on the limitations of current over-parameterized shadow removal models. We present a novel lightweight deep neural network that processes shadow images in the LA…
Efficient Decoder-free Object Detection with Transformers
Peixian Chen, Mengdan Zhang, Yunhang Shen +5
Vision transformers (ViTs) are changing the landscape of object detection approaches. A natural usage of ViTs in detection is to replace the CNN-based backbone with a transformer-b…
MISSU: 3D Medical Image Segmentation via Self-distilling TransUNet
Nan Wang, Shaohui Lin, Xiaoxiao Li +4
U-Nets have achieved tremendous success in medical image segmentation. Nevertheless, it may suffer limitations in global (long-range) contextual interactions and edge-detail preser…
Open Vocabulary Object Detection with Proposal Mining and Prediction Equalization
Peixian Chen, Kekai Sheng, Mengdan Zhang +5
Open-vocabulary object detection (OVD) aims to scale up vocabulary size to detect objects of novel categories beyond the training vocabulary. Recent work resorts to the rich knowle…
Super Vision Transformer
Mingbao Lin, Mengzhao Chen, Yuxin Zhang +3
We attempt to reduce the computational costs in vision transformers (ViTs), which increase quadratically in the token number. We present a novel training paradigm that trains only…
Shadow-Aware Dynamic Convolution for Shadow Removal
Yimin Xu, Mingbao Lin, Hong Yang +2
With a wide range of shadows in many collected images, shadow removal has aroused increasing attention since uncontaminated images are of vital importance for many downstream multi…
End-to-End Zero-Shot HOI Detection via Vision and Language Knowledge Distillation
Mingrui Wu, Jiaxin Gu, Yunhang Shen +3
Most existing Human-Object Interaction~(HOI) Detection methods rely heavily on full annotations with predefined HOI categories, which is limited in diversity and costly to scale fu…
PixelFolder: An Efficient Progressive Pixel Synthesis Network for Image Generation
Jing He, Yiyi Zhou, Qi Zhang +5
Pixel synthesis is a promising research paradigm for image generation, which can well exploit pixel-wise prior knowledge for generation. However, existing methods still suffer from…
SeqTR: A Simple yet Universal Network for Visual Grounding
Chaoyang Zhu, Yiyi Zhou, Yunhang Shen +7
In this paper, we propose a simple yet universal network termed SeqTR for visual grounding tasks, e.g., phrase localization, referring expression comprehension (REC) and segmentati…
Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution Networks
Yunshan Zhong, Mingbao Lin, Xunchao Li +5
Light-weight super-resolution (SR) models have received considerable attention for their serviceability in mobile devices. Many efforts employ network quantization to compress SR m…
CF-ViT: A General Coarse-to-Fine Method for Vision Transformer
Mengzhao Chen, Mingbao Lin, Ke Li +4
Vision Transformers (ViT) have made many breakthroughs in computer vision tasks. However, considerable redundancy arises in the spatial dimension of an input image, leading to mass…