115 citations · 775 across the 83 of their papers we have counts for
84 papers
ClickTrack: Towards Real-time Interactive Single Object Tracking
Kuiran Wang, Xuehui Yu, Wenwen Yu +5
Single object tracking(SOT) relies on precise object bounding box initialization. In this paper, we reconsidered the deficiencies in the current approaches to initializing single o…
Towards Deviation-Robust Agent Navigation via Perturbation-Aware Contrastive Learning
Bingqian Lin, Yanxin Long, Yi Zhu +4
Vision-and-language navigation (VLN) asks an agent to follow a given language instruction to navigate through a real 3D environment. Despite significant advances, conventional VLN…
CPR++: Object Localization via Single Coarse Point Supervision
Xuehui Yu, Pengfei Chen, Kuiran Wang +5
Point-based object localization (POL), which pursues high-performance object sensing under low-cost data annotation, has attracted increased attention. However, the point annotatio…
Spatial Transform Decoupling for Oriented Object Detection
Hongtian Yu, Yunjie Tian, Qixiang Ye +1
Vision Transformers (ViTs) have achieved remarkable success in computer vision tasks. However, their potential in rotation-sensitive scenarios has not been fully explored, and this…
Generative Prompt Model for Weakly Supervised Object Localization
Yuzhong Zhao, Qixiang Ye, Weijia Wu +2
Weakly supervised object localization (WSOL) remains challenging when learning object localization models from image category labels. Conventional methods that discriminatively tra…
Album Storytelling with Iterative Story-aware Captioning and Large Language Models
Munan Ning, Yujia Xie, Dongdong Chen +5
This work studies how to transform an album to vivid and coherent stories, a task we refer to as "album storytelling". While this task can help preserve memories and facilitate exp…
Generic-to-Specific Distillation of Masked Autoencoders
Wei Huang, Zhiliang Peng, Li Dong +3
Large vision Transformers (ViTs) driven by self-supervised pre-training mechanisms achieved unprecedented progress. Lightweight ViT models limited by the model capacity, however, b…
Spectral Aware Softmax for Visible-Infrared Person Re-Identification
Lei Tan, Pingyang Dai, Qixiang Ye +3
Visible-infrared person re-identification (VI-ReID) aims to match specific pedestrian images from different modalities. Although suffering an extra modality discrepancy, existing m…
Unsupervised Domain Adaptation on Person Re-Identification via Dual-level Asymmetric Mutual Learning
Qiong Wu, Jiahan Li, Pingyang Dai +4
Unsupervised domain adaptation person re-identification (Re-ID) aims to identify pedestrian images within an unlabeled target domain with an auxiliary labeled source-domain dataset…
DQnet: Cross-Model Detail Querying for Camouflaged Object Detection
Wei Sun, Chengao Liu, Linyan Zhang +6
Camouflaged objects are seamlessly blended in with their surroundings, which brings a challenging detection task in computer vision. Optimizing a convolutional neural network (CNN)…
Proposal Distribution Calibration for Few-Shot Object Detection
Bohao Li, Chang Liu, Mengnan Shi +3
Adapting object detectors learned with sufficient supervision to novel classes under low data regimes is charming yet challenging. In few-shot object detection (FSOD), the two-step…
Feature Calibration Network for Occluded Pedestrian Detection
Tianliang Zhang, Qixiang Ye, Baochang Zhang +3
Pedestrian detection in the wild remains a challenging problem especially for scenes containing serious occlusion. In this paper, we propose a novel feature learning method in the…
CircleNet: Reciprocating Feature Adaptation for Robust Pedestrian Detection
Tianliang Zhang, Zhenjun Han, Huijuan Xu +2
Pedestrian detection in the wild remains a challenging problem especially when the scene contains significant occlusion and/or low resolution of the pedestrians to be detected. Exi…
Beyond Instance Discrimination: Relation-aware Contrastive Self-supervised Learning
Yifei Zhang, Chang Liu, Yu Zhou +3
Contrastive self-supervised learning (CSL) based on instance discrimination typically attracts positive samples while repelling negatives to learn representations with pre-defined…
A Unified View of Masked Image Modeling
Zhiliang Peng, Li Dong, Hangbo Bao +2
Masked image modeling has demonstrated great potential to eliminate the label-hungry problem of training large-scale vision Transformers, achieving impressive performance on variou…
Multi-Agent Automated Machine Learning
Zhaozhi Wang, Kefan Su, Jian Zhang +4
In this paper, we propose multi-agent automated machine learning (MA2ML) with the aim to effectively handle joint optimization of modules in automated machine learning (AutoML). MA…
Learnable Distribution Calibration for Few-Shot Class-Incremental Learning
Binghao Liu, Boyu Yang, Lingxi Xie +3
Few-shot class-incremental learning (FSCIL) faces challenges of memorizing old class distributions and estimating new class distributions given few training samples. In this study,…
BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers
Zhiliang Peng, Li Dong, Hangbo Bao +2
Masked image modeling (MIM) has demonstrated impressive results in self-supervised representation learning by recovering corrupted image patches. However, most existing studies ope…
Point-to-Box Network for Accurate Object Detection via Single Point Supervision
Pengfei Chen, Xuehui Yu, Xumeng Han +7
Object detection using single point supervision has received increasing attention over the years. However, the performance gap between point supervised object detection (PSOD) and…
HiViT: Hierarchical Vision Transformer Meets Masked Image Modeling
Xiaosong Zhang, Yunjie Tian, Wei Huang +4
Recently, masked image modeling (MIM) has offered a new methodology of self-supervised pre-training of vision transformers. A key idea of efficient implementation is to discard the…
Integrally Migrating Pre-trained Transformer Encoder-decoders for Visual Object Detection
Feng Liu, Xiaosong Zhang, Zhiliang Peng +4
Modern object detectors have taken the advantages of backbone networks pre-trained on large scale datasets. Except for the backbone networks, however, other components such as the…
Beyond Masking: Demystifying Token-Based Pre-Training for Vision Transformers
Yunjie Tian, Lingxi Xie, Jiemin Fang +6
The past year has witnessed a rapid development of masked image modeling (MIM). MIM is mostly built upon the vision transformers, which suggests that self-supervised visual represe…
Object Localization under Single Coarse Point Supervision
Xuehui Yu, Pengfei Chen, Di Wu +6
Point-based object localization (POL), which pursues high-performance object sensing under low-cost data annotation, has attracted increased attention. However, the point annotatio…
Global2Local: A Joint-Hierarchical Attention for Video Captioning
Chengpeng Dai, Fuhai Chen, Xiaoshuai Sun +3
Recently, automatic video captioning has attracted increasing attention, where the core challenge lies in capturing the key semantic items, like objects and actions as well as thei…