167 citations · 580 across the 23 of their papers we have counts for
24 papers
Large-Scale Pre-training for Person Re-identification with Noisy Labels
Dengpan Fu, Dongdong Chen, Hao Yang +6
This paper aims to address the problem of pre-training for person re-identification (Re-ID) with noisy labels. To setup the pre-training task, we apply a simple online multi-object…
Online Multi-Object Tracking with Unsupervised Re-Identification Learning and Occlusion Estimation
Qiankun Liu, Dongdong Chen, Qi Chu +4
Occlusion between different objects is a typical challenge in Multi-Object Tracking (MOT), which often leads to inferior tracking results due to the missing detected objects. The c…
Grounded Language-Image Pre-training
Liunian Harold Li, Pengchuan Zhang, Haotian Zhang +9
This paper presents a grounded language-image pre-training (GLIP) model for learning object-level, language-aware, and semantic-rich visual representations. GLIP unifies object det…
Unsupervised Finetuning
Suichan Li, Dongdong Chen, Yinpeng Chen +5
This paper studies "unsupervised finetuning", the symmetrical problem of the well-known "supervised finetuning". Given a pretrained model and small-scale unlabeled target data, uns…
MicroNet: Improving Image Recognition with Extremely Low FLOPs
Yunsheng Li, Yinpeng Chen, Xiyang Dai +6
This paper aims at addressing the problem of substantial performance degradation at extremely low computational cost (e.g. 5M FLOPs on ImageNet classification). We found that two f…
Image Scene Graph Generation (SGG) Benchmark
Xiaotian Han, Jianwei Yang, Houdong Hu +3
There is a surge of interest in image scene graph generation (object, attribute and relationship detection) due to the need of building fine-grained image understanding models that…
Improve Unsupervised Pretraining for Few-label Transfer
Suichan Li, Dongdong Chen, Yinpeng Chen +5
Unsupervised pretraining has achieved great success and many recent works have shown unsupervised pretraining can achieve comparable or even slightly better transfer performance th…
Gradual Domain Adaptation via Self-Training of Auxiliary Models
Yabin Zhang, Bin Deng, Kui Jia +1
Domain adaptation becomes more challenging with increasing gaps between source and target domains. Motivated from an empirical analysis on the reliability of labeled source data fo…
Dynamic Head: Unifying Object Detection Heads with Attentions
Xiyang Dai, Yinpeng Chen, Bin Xiao +4
The complex nature of combining localization and classification in object detection has resulted in the flourished development of methods. Previous works tried to improve the perfo…
Chasing Sparsity in Vision Transformers: An End-to-End Exploration
Tianlong Chen, Yu Cheng, Zhe Gan +3
Vision transformers (ViTs) have recently received explosive popularity, but their enormous model sizes and training costs remain daunting. Conventional post-training pruning often…
Semi-supervised Models are Strong Unsupervised Domain Adaptation Learners
Yabin Zhang, Haojian Zhang, Bin Deng +3
Unsupervised domain adaptation (UDA) and semi-supervised learning (SSL) are two typical strategies to reduce expensive manual annotations in machine learning. In order to learn eff…
SA-ConvONet: Sign-Agnostic Optimization of Convolutional Occupancy Networks
Jiapeng Tang, Jiabao Lei, Dan Xu +3
Surface reconstruction from point clouds is a fundamental problem in the computer vision and graphics community. Recent state-of-the-arts solve this problem by individually optimiz…
Lite-HRNet: A Lightweight High-Resolution Network
Changqian Yu, Bin Xiao, Changxin Gao +4
We present an efficient high-resolution network, Lite-HRNet, for human pose estimation. We start by simply applying the efficient shuffle block in ShuffleNet to HRNet (high-resolut…
CUPID: Adaptive Curation of Pre-training Data for Video-and-Language Representation Learning
Luowei Zhou, Jingjing Liu, Yu Cheng +2
This work concerns video-language pre-training and representation learning. In this now ubiquitous training scheme, a model first performs pre-training on paired videos and text (e…
Spatial Feature Calibration and Temporal Fusion for Effective One-stage Video Instance Segmentation
Minghan Li, Shuai Li, Lida Li +1
Modern one-stage video instance segmentation networks suffer from two limitations. First, convolutional features are neither aligned with anchor boxes nor with ground-truth boundin…
DAP: Detection-Aware Pre-training with Weak Supervision
Yuanyi Zhong, Jianfeng Wang, Lijuan Wang +3
This paper presents a detection-aware pre-training (DAP) approach, which leverages only weakly-labeled classification-style datasets (e.g., ImageNet) for pre-training, but is speci…
Learning Parallel Dense Correspondence from Spatio-Temporal Descriptors for Efficient and Robust 4D Reconstruction
Jiapeng Tang, Dan Xu, Kui Jia +1
This paper focuses on the task of 4D shape reconstruction from a sequence of point clouds. Despite the recent success achieved by extending deep implicit representations into 4D sp…
CvT: Introducing Convolutions to Vision Transformers
Haiping Wu, Bin Xiao, Noel Codella +4
We present in this paper a new architecture, named Convolutional vision Transformer (CvT), that improves Vision Transformer (ViT) in performance and efficiency by introducing convo…
Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding
Pengchuan Zhang, Xiyang Dai, Jianwei Yang +4
This paper presents a new Vision Transformer (ViT) architecture Multi-Scale Vision Longformer, which significantly enhances the ViT of \cite{dosovitskiy2020image} for encoding high…
SEED: Self-supervised Distillation For Visual Representation
Zhiyuan Fang, Jianfeng Wang, Lijuan Wang +3
This paper is concerned with self-supervised learning for small models. The problem is motivated by our empirical studies that while the widely used contrastive self-supervised lea…
VinVL: Revisiting Visual Representations in Vision-Language Models
Pengchuan Zhang, Xiujun Li, Xiaowei Hu +5
This paper presents a detailed study of improving visual representations for vision language (VL) tasks and develops an improved object detection model to provide object-centric re…
TAP: Text-Aware Pre-training for Text-VQA and Text-Caption
Zhengyuan Yang, Yijuan Lu, Jianfeng Wang +6
In this paper, we propose Text-Aware Pre-training (TAP) for Text-VQA and Text-Caption tasks. These two tasks aim at reading and understanding scene text in images for question answ…
Unsupervised Pre-training for Person Re-identification
Dengpan Fu, Dongdong Chen, Jianmin Bao +5
In this paper, we present a large scale unlabeled person re-identification (Re-ID) dataset "LUPerson" and make the first attempt of performing unsupervised pre-training for improvi…
MicroNet: Towards Image Recognition with Extremely Low FLOPs
Yunsheng Li, Yinpeng Chen, Xiyang Dai +6
In this paper, we present MicroNet, which is an efficient convolutional neural network using extremely low computational cost (e.g. 6 MFLOPs on ImageNet classification). Such a low…