203 citations · 293 across the 13 of their papers we have counts for
13 papers
LINK: Adaptive Modality Interaction for Audio-Visual Video Parsing
Langyu Wang, Bingke Zhu, Yingying Chen +1
Audio-visual video parsing focuses on classifying videos through weak labels while identifying events as either visible, audible, or both, alongside their respective temporal bound…
UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly Detection
Zhaopeng Gu, Bingke Zhu, Guibo Zhu +3
Visual Anomaly Detection (VAD) aims to identify abnormal samples in images that deviate from normal patterns, covering multiple domains, including industrial, logical, and medical…
Friend or Foe? Harnessing Controllable Overfitting for Anomaly Detection
Long Qian, Bingke Zhu, Yingying Chen +2
Overfitting has traditionally been viewed as detrimental to anomaly detection, where excessive generalization often limits models' sensitivity to subtle anomalies. Our work challen…
The BRAVO Semantic Segmentation Challenge Results in UNCV2024
Tuan-Hung Vu, Eduardo Valle, Andrei Bursuc +16
We propose the unified BRAVO challenge to benchmark the reliability of semantic segmentation models under realistic perturbations and unknown out-of-distribution (OOD) scenarios. W…
MROVSeg: Breaking the Resolution Curse of Vision-Language Models in Open-Vocabulary Image Segmentation
Yuanbing Zhu, Bingke Zhu, Yingying Chen +3
Pretrained vision-language models (VLMs), \eg CLIP, are increasingly used to bridge the gap between open- and close-vocabulary recognition in open-vocabulary image segmentation. As…
Optimization of Prompt Learning via Multi-Knowledge Representation for Vision-Language Models
Enming Zhang, Bingke Zhu, Yingying Chen +3
Vision-Language Models (VLMs), such as CLIP, play a foundational role in various cross-modal applications. To fully leverage VLMs' potential in adapting to downstream tasks, contex…
AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models
Zhaopeng Gu, Bingke Zhu, Guibo Zhu +3
Large Vision-Language Models (LVLMs) such as MiniGPT-4 and LLaVA have demonstrated the capability of understanding images and achieved remarkable performance in various visual task…
UniVIP: A Unified Framework for Self-Supervised Visual Pre-training
Zhaowen Li, Yousong Zhu, Fan Yang +9
Self-supervised learning (SSL) holds promise in leveraging large amounts of unlabeled data. However, the success of popular SSL methods has limited on single-centric-object images…
Recurrent Calibration Network for Irregular Text Recognition
Yunze Gao, Yingying Chen, Jinqiao Wang +3
Scene text recognition has received increased attention in the research community. Text in the wild often possesses irregular arrangements, typically including perspective text, cu…
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
Fisher Yu, Haofeng Chen, Xin Wang +5
Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving. Re…
Reading Scene Text with Attention Convolutional Sequence Modeling
Yunze Gao, Yingying Chen, Jinqiao Wang +1
Reading text in the wild is a challenging task in the field of computer vision. Existing approaches mainly adopted Connectionist Temporal Classification (CTC) or Attention models b…
Fast Deep Matting for Portrait Animation on Mobile Phone
Bingke Zhu, Yingying Chen, Jinqiao Wang +3
Image matting plays an important role in image and video editing. However, the formulation of image matting is inherently ill-posed. Traditional methods usually employ interaction…
Joint Background Reconstruction and Foreground Segmentation via A Two-stage Convolutional Neural Network
Xu Zhao, Yingying Chen, Ming Tang +1
Foreground segmentation in video sequences is a classic topic in computer vision. Due to the lack of semantic and prior knowledge, it is difficult for existing methods to deal with…