Search Results for author: Jinqiao Wang

Found 51 papers, 19 papers with code

Blended Grammar Network for Human Parsing

no code implementations ECCV 2020 Xiaomei Zhang, Yingying Chen, Bingke Zhu, Jinqiao Wang, Ming Tang

Although human parsing has made great progress, it still faces a challenge, i. e., how to extract the whole foreground from similar or cluttered scenes effectively.

Human Parsing

Occlusion-Aware Siamese Network for Human Pose Estimation

no code implementations ECCV 2020 Lu Zhou, Yingying Chen, Yunze Gao, Jinqiao Wang, Hanqing Lu

To overcome the defects caused by the erasing operation, we perform feature reconstruction to recover the information destroyed by occlusion and details lost in cleaning procedure.

Pose Estimation

Large Batch Optimization for Object Detection: Training COCO in 12 Minutes

no code implementations ECCV 2020 Tong Wang, Yousong Zhu, Chaoyang Zhao, Wei Zeng, Yao-Wei Wang, Jinqiao Wang, Ming Tang

Most of existing object detectors usually adopt a small training batch size ( ~16), which severely hinders the whole community from exploring large-scale datasets due to the extremely long training procedure.

object-detection Object Detection

Griffon v2: Advancing Multimodal Perception with High-Resolution Scaling and Visual-Language Co-Referring

1 code implementation14 Mar 2024 Yufei Zhan, Yousong Zhu, Hongyin Zhao, Fan Yang, Ming Tang, Jinqiao Wang

Large Vision Language Models have achieved fine-grained object perception, but the limitation of image resolution remains a significant obstacle to surpass the performance of task-specific experts in complex and dense scenarios.

Object Object Counting +3

PFDM: Parser-Free Virtual Try-on via Diffusion Model

no code implementations5 Feb 2024 Yunfang Niu, Dong Yi, Lingxiang Wu, Zhiwei Liu, Pengxiang Cai, Jinqiao Wang

Virtual try-on can significantly improve the garment shopping experiences in both online and in-store scenarios, attracting broad interest in computer vision.

Virtual Try-on

Continual Instruction Tuning for Large Multimodal Models

no code implementations27 Nov 2023 Jinghan He, Haiyun Guo, Ming Tang, Jinqiao Wang

2) Are the existing three classes of continual learning methods still applicable to the continual instruction tuning of LMMs?

Continual Learning

Mitigating Hallucination in Visual Language Models with Visual Supervision

no code implementations27 Nov 2023 Zhiyang Chen, Yousong Zhu, Yufei Zhan, Zhaowen Li, Chaoyang Zhao, Jinqiao Wang, Ming Tang

Large vision-language models (LVLMs) suffer from hallucination a lot, generating responses that apparently contradict to the image content occasionally.


Griffon: Spelling out All Object Locations at Any Granularity with Large Language Models

1 code implementation24 Nov 2023 Yufei Zhan, Yousong Zhu, Zhiyang Chen, Fan Yang, Ming Tang, Jinqiao Wang

More importantly, we present $\textbf{Griffon}$, a purely LVLM-based baseline, which does not require the introduction of any special tokens, expert models, or additional detection modules.

Referring Expression Referring Expression Comprehension

Surgical Temporal Action-aware Network with Sequence Regularization for Phase Recognition

no code implementations21 Nov 2023 Zhen Chen, Yuhao Zhai, Jun Zhang, Jinqiao Wang

Specifically, we propose an efficient multi-scale surgical temporal action (MS-STA) module, which integrates visual features with spatial and temporal knowledge of surgical actions at the cost of 2D networks.

Surgical phase recognition

ChineseWebText: Large-scale High-quality Chinese Web Text Extracted with Effective Evaluation Model

1 code implementation2 Nov 2023 Jianghao Chen, Pu Jian, Tengxiao Xi, Dongyi Yi, Qianlong Du, Chenglin Ding, Guibo Zhu, Chengqing Zong, Jinqiao Wang, Jiajun Zhang

Using our proposed approach, we release the largest and latest large-scale high-quality Chinese web text ChineseWebText, which consists of 1. 42 TB and each text is associated with a quality score, facilitating the LLM researchers to choose the data according to the desired quality thresholds.

SSPFusion: A Semantic Structure-Preserving Approach for Infrared and Visible Image Fusion

no code implementations26 Sep 2023 Qiao Yang, Yu Zhang, Jian Zhang, Zijing Zhao, Shunli Zhang, Jinqiao Wang, Junzhe Chen

Most existing learning-based infrared and visible image fusion (IVIF) methods exhibit massive redundant information in the fusion images, i. e., yielding edge-blurring effect or unrecognizable for object detectors.

Infrared And Visible Image Fusion

IAIFNet: An Illumination-Aware Infrared and Visible Image Fusion Network

no code implementations26 Sep 2023 Qiao Yang, Yu Zhang, Jian Zhang, Zijing Zhao, Shunli Zhang, Jinqiao Wang, Junzhe Chen

Infrared and visible image fusion (IVIF) is used to generate fusion images with comprehensive features of both images, which is beneficial for downstream vision tasks.

Infrared And Visible Image Fusion

AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models

1 code implementation29 Aug 2023 Zhaopeng Gu, Bingke Zhu, Guibo Zhu, Yingying Chen, Ming Tang, Jinqiao Wang

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 tasks.

Anomaly Detection In-Context Learning

Fast Segment Anything

1 code implementation21 Jun 2023 Xu Zhao, Wenchao Ding, Yongqi An, Yinglong Du, Tao Yu, Min Li, Ming Tang, Jinqiao Wang

In this paper, we propose a speed-up alternative method for this fundamental task with comparable performance.

Edge Detection Image Segmentation +6

FreConv: Frequency Branch-and-Integration Convolutional Networks

no code implementations10 Apr 2023 Zhaowen Li, Xu Zhao, Peigeng Ding, Zongxin Gao, Yuting Yang, Ming Tang, Jinqiao Wang

In the high-frequency branch, a derivative-filter-like architecture is designed to extract the high-frequency information while a light extractor is employed in the low-frequency branch because the low-frequency information is usually redundant.

ZBS: Zero-shot Background Subtraction via Instance-level Background Modeling and Foreground Selection

1 code implementation CVPR 2023 Yongqi An, Xu Zhao, Tao Yu, Haiyun Guo, Chaoyang Zhao, Ming Tang, Jinqiao Wang

However, previous unsupervised deep learning BGS algorithms perform poorly in sophisticated scenarios such as shadows or night lights, and they cannot detect objects outside the pre-defined categories.

Foreground Segmentation Object +2

Efficient Masked Autoencoders with Self-Consistency

no code implementations28 Feb 2023 Zhaowen Li, Yousong Zhu, Zhiyang Chen, Wei Li, Chaoyang Zhao, Liwei Wu, Rui Zhao, Ming Tang, Jinqiao Wang

However, its high random mask ratio would result in two serious problems: 1) the data are not efficiently exploited, which brings inefficient pre-training (\eg, 1600 epochs for MAE $vs.$ 300 epochs for the supervised), and 2) the high uncertainty and inconsistency of the pre-trained model, \ie, the prediction of the same patch may be inconsistent under different mask rounds.

Language Modelling Masked Language Modeling +3

Temporal-Channel Topology Enhanced Network for Skeleton-Based Action Recognition

1 code implementation25 Feb 2023 Jinzhao Luo, Lu Zhou, Guibo Zhu, Guojing Ge, Beiying Yang, Jinqiao Wang

Most current methods adopt graph convolutional network (GCN) for topology modeling, but GCN-based methods are limited in long-distance correlation modeling and generalizability.

Action Recognition Skeleton Based Action Recognition

Masked Contrastive Pre-Training for Efficient Video-Text Retrieval

1 code implementation2 Dec 2022 Fangxun Shu, Biaolong Chen, Yue Liao, Shuwen Xiao, Wenyu Sun, Xiaobo Li, Yousong Zhu, Jinqiao Wang, Si Liu

Our MAC aims to reduce video representation's spatial and temporal redundancy in the VidLP model by a mask sampling mechanism to improve pre-training efficiency.

Ranked #36 on Video Retrieval on MSR-VTT-1kA (using extra training data)

Retrieval Text Retrieval +1

Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks

2 code implementations28 Sep 2022 Zhiyang Chen, Yousong Zhu, Zhaowen Li, Fan Yang, Wei Li, Haixin Wang, Chaoyang Zhao, Liwei Wu, Rui Zhao, Jinqiao Wang, Ming Tang

Obj2Seq is able to flexibly determine input categories to satisfy customized requirements, and be easily extended to different visual tasks.

Multi-Label Classification Object +2

Transfering Low-Frequency Features for Domain Adaptation

no code implementations31 Aug 2022 Zhaowen Li, Xu Zhao, Chaoyang Zhao, Ming Tang, Jinqiao Wang

Previous unsupervised domain adaptation methods did not handle the cross-domain problem from the perspective of frequency for computer vision.

Image Classification object-detection +2

Plug-and-Play Pseudo Label Correction Network for Unsupervised Person Re-identification

no code implementations14 Jun 2022 Tianyi Yan, Kuan Zhu, Haiyun Guo, Guibo Zhu, Ming Tang, Jinqiao Wang

Clustering-based methods, which alternate between the generation of pseudo labels and the optimization of the feature extraction network, play a dominant role in both unsupervised learning (USL) and unsupervised domain adaptive (UDA) person re-identification (Re-ID).

Clustering Pseudo Label +1

UniVIP: A Unified Framework for Self-Supervised Visual Pre-training

no code implementations CVPR 2022 Zhaowen Li, Yousong Zhu, Fan Yang, Wei Li, Chaoyang Zhao, Yingying Chen, Zhiyang Chen, Jiahao Xie, Liwei Wu, Rui Zhao, Ming Tang, Jinqiao Wang

Furthermore, our method can also exploit single-centric-object dataset such as ImageNet and outperforms BYOL by 2. 5% with the same pre-training epochs in linear probing, and surpass current self-supervised object detection methods on COCO dataset, demonstrating its universality and potential.

Image Classification Object +4

Pruning-aware Sparse Regularization for Network Pruning

1 code implementation18 Jan 2022 Nanfei Jiang, Xu Zhao, Chaoyang Zhao, Yongqi An, Ming Tang, Jinqiao Wang

MaskSparsity imposes the fine-grained sparse regularization on the specific filters selected by a pruning mask, rather than all the filters of the model.

Network Pruning

Multi-initialization Optimization Network for Accurate 3D Human Pose and Shape Estimation

no code implementations24 Dec 2021 Zhiwei Liu, Xiangyu Zhu, Lu Yang, Xiang Yan, Ming Tang, Zhen Lei, Guibo Zhu, Xuetao Feng, Yan Wang, Jinqiao Wang

In the second stage, we design a mesh refinement transformer (MRT) to respectively refine each coarse reconstruction result via a self-attention mechanism.

Ranked #65 on 3D Human Pose Estimation on 3DPW (MPJPE metric)

3D human pose and shape estimation 3D Reconstruction

DPT: Deformable Patch-based Transformer for Visual Recognition

1 code implementation30 Jul 2021 Zhiyang Chen, Yousong Zhu, Chaoyang Zhao, Guosheng Hu, Wei Zeng, Jinqiao Wang, Ming Tang

To address this problem, we propose a new Deformable Patch (DePatch) module which learns to adaptively split the images into patches with different positions and scales in a data-driven way rather than using predefined fixed patches.

Image Classification object-detection +2

OPT: Omni-Perception Pre-Trainer for Cross-Modal Understanding and Generation

2 code implementations1 Jul 2021 Jing Liu, Xinxin Zhu, Fei Liu, Longteng Guo, Zijia Zhao, Mingzhen Sun, Weining Wang, Hanqing Lu, Shiyu Zhou, Jiajun Zhang, Jinqiao Wang

In this paper, we propose an Omni-perception Pre-Trainer (OPT) for cross-modal understanding and generation, by jointly modeling visual, text and audio resources.

Audio to Text Retrieval Cross-Modal Retrieval +3

Improving Multiple Object Tracking With Single Object Tracking

no code implementations CVPR 2021 Linyu Zheng, Ming Tang, Yingying Chen, Guibo Zhu, Jinqiao Wang, Hanqing Lu

Despite considerable similarities between multiple object tracking (MOT) and single object tracking (SOT) tasks, modern MOT methods have not benefited from the development of SOT ones to achieve satisfactory performance.

Multiple Object Tracking Object +2

MST: Masked Self-Supervised Transformer for Visual Representation

no code implementations NeurIPS 2021 Zhaowen Li, Zhiyang Chen, Fan Yang, Wei Li, Yousong Zhu, Chaoyang Zhao, Rui Deng, Liwei Wu, Rui Zhao, Ming Tang, Jinqiao Wang

More importantly, the masked tokens together with the remaining tokens are further recovered by a global image decoder, which preserves the spatial information of the image and is more friendly to the downstream dense prediction tasks.

Language Modelling Masked Language Modeling +3

AAformer: Auto-Aligned Transformer for Person Re-Identification

no code implementations2 Apr 2021 Kuan Zhu, Haiyun Guo, Shiliang Zhang, YaoWei Wang, Gaopan Huang, Honglin Qiao, Jing Liu, Jinqiao Wang, Ming Tang

In this paper, we introduce an alignment scheme in Transformer architecture for the first time and propose the Auto-Aligned Transformer (AAformer) to automatically locate both the human parts and non-human ones at patch-level.

Human Parsing Image Classification +3

Adaptive Class Suppression Loss for Long-Tail Object Detection

1 code implementation CVPR 2021 Tong Wang, Yousong Zhu, Chaoyang Zhao, Wei Zeng, Jinqiao Wang, Ming Tang

To address the problem of long-tail distribution for the large vocabulary object detection task, existing methods usually divide the whole categories into several groups and treat each group with different strategies.

Object object-detection +1

High-Performance Discriminative Tracking With Transformers

no code implementations ICCV 2021 Bin Yu, Ming Tang, Linyu Zheng, Guibo Zhu, Jinqiao Wang, Hao Feng, Xuetao Feng, Hanqing Lu

End-to-end discriminative trackers improve the state of the art significantly, yet the improvement in robustness and efficiency is restricted by the conventional discriminative model, i. e., least-squares based regression.

Object Visual Tracking +1

Task Decoupled Knowledge Distillation For Lightweight Face Detectors

1 code implementation14 Oct 2020 Xiaoqing Liang, Xu Zhao, Chaoyang Zhao, Nanfei Jiang, Ming Tang, Jinqiao Wang

This method decouples the distillation task of face detection into two subtasks, i. e., the classification distillation subtask and the regression distillation subtask.

Face Detection Knowledge Distillation +1

Identity-Guided Human Semantic Parsing for Person Re-Identification

1 code implementation ECCV 2020 Kuan Zhu, Haiyun Guo, Zhiwei Liu, Ming Tang, Jinqiao Wang

In this paper, we propose the identity-guided human semantic parsing approach (ISP) to locate both the human body parts and personal belongings at pixel-level for aligned person re-ID only with person identity labels.

Clustering Human Parsing +3

Learning Feature Embeddings for Discriminant Model based Tracking

no code implementations ECCV 2020 Linyu Zheng, Ming Tang, Yingying Chen, Jinqiao Wang, Hanqing Lu

After observing that the features used in most online discriminatively trained trackers are not optimal, in this paper, we propose a novel and effective architecture to learn optimal feature embeddings for online discriminative tracking.

Visual Tracking

Fast Kernelized Correlation Filters without Boundary Effect

no code implementations17 Jun 2018 Ming Tang, Linyu Zheng, Bin Yu, Jinqiao Wang

To achieve the fast training and detection, a set of cyclic bases is introduced to construct the filter.

Visual Tracking

On the Relations of Correlation Filter Based Trackers and Struck

no code implementations25 Nov 2017 Jinqiao Wang, Ming Tang, Linyu Zheng, Jiayi Feng

In recent years, two types of trackers, namely correlation filter based tracker (CF tracker) and structured output tracker (Struck), have exhibited the state-of-the-art performance.


CoupleNet: Coupling Global Structure with Local Parts for Object Detection

3 code implementations ICCV 2017 Yousong Zhu, Chaoyang Zhao, Jinqiao Wang, Xu Zhao, Yi Wu, Hanqing Lu

To fully explore the local and global properties, in this paper, we propose a novel fully convolutional network, named as CoupleNet, to couple the global structure with local parts for object detection.

Object object-detection +3

Fast Deep Matting for Portrait Animation on Mobile Phone

1 code implementation26 Jul 2017 Bingke Zhu, Yingying Chen, Jinqiao Wang, Si Liu, Bo Zhang, Ming Tang

Finally, an automatic portrait animation system based on fast deep matting is built on mobile devices, which does not need any interaction and can realize real-time matting with 15 fps.

Image Matting Video Editing

Joint Background Reconstruction and Foreground Segmentation via A Two-stage Convolutional Neural Network

no code implementations24 Jul 2017 Xu Zhao, Yingying Chen, Ming Tang, Jinqiao Wang

In the first stage, a convolutional encoder-decoder sub-network is employed to reconstruct the background images and encode rich prior knowledge of background scenes.

Foreground Segmentation Segmentation

Learning Adaptive Receptive Fields for Deep Image Parsing Network

no code implementations CVPR 2017 Zhen Wei, Yao Sun, Jinqiao Wang, Hanjiang Lai, Si Liu

In this paper, we introduce a novel approach to regulate receptive field in deep image parsing network automatically.

Face Parsing

Relaxing From Vocabulary: Robust Weakly-Supervised Deep Learning for Vocabulary-Free Image Tagging

no code implementations ICCV 2015 Jianlong Fu, Yue Wu, Tao Mei, Jinqiao Wang, Hanqing Lu, Yong Rui

The development of deep learning has empowered machines with comparable capability of recognizing limited image categories to human beings.

Cannot find the paper you are looking for? You can Submit a new open access paper.