Search Results for author: Jie Qin

Found 54 papers, 24 papers with code

Region Graph Embedding Network for Zero-Shot Learning

no code implementations ECCV 2020 Guo-Sen Xie, Li Liu, Fan Zhu, Fang Zhao, Zheng Zhang, Yazhou Yao, Jie Qin, Ling Shao

To exploit the progressive interactions among these regions, we represent them as a region graph, on which the parts relation reasoning is performed with graph convolutions, thus leading to our PRR branch.

Graph Embedding Relation +1

DiffusionGPT: LLM-Driven Text-to-Image Generation System

no code implementations18 Jan 2024 Jie Qin, Jie Wu, Weifeng Chen, Yuxi Ren, Huixia Li, Hefeng Wu, Xuefeng Xiao, Rui Wang, Shilei Wen

Diffusion models have opened up new avenues for the field of image generation, resulting in the proliferation of high-quality models shared on open-source platforms.

Model Selection Text-to-Image Generation

Transformer-based No-Reference Image Quality Assessment via Supervised Contrastive Learning

1 code implementation12 Dec 2023 Jinsong Shi, Pan Gao, Jie Qin

We first train a model on a large-scale synthetic dataset by SCL (no image subjective score is required) to extract degradation features of images with various distortion types and levels.

Contrastive Learning Inductive Bias +2

Relevant Intrinsic Feature Enhancement Network for Few-Shot Semantic Segmentation

no code implementations11 Dec 2023 Xiaoyi Bao, Jie Qin, Siyang Sun, Yun Zheng, Xingang Wang

To improve the semantic consistency of foreground instances, we propose an unlabeled branch as an efficient data utilization method, which teaches the model how to extract intrinsic features robust to intra-class differences.

Few-Shot Semantic Segmentation Semantic Segmentation

Generalizable Person Search on Open-world User-Generated Video Content

no code implementations16 Oct 2023 Junjie Li, Guanshuo Wang, Yichao Yan, Fufu Yu, Qiong Jia, Jie Qin, Shouhong Ding, Xiaokang Yang

Person search is a challenging task that involves detecting and retrieving individuals from a large set of un-cropped scene images.

Domain Generalization Person Search

SIDE: Self-supervised Intermediate Domain Exploration for Source-free Domain Adaptation

1 code implementation13 Oct 2023 Jiamei Liu, Han Sun, Yizhen Jia, Jie Qin, Huiyu Zhou, Ningzhong Liu

Domain adaptation aims to alleviate the domain shift when transferring the knowledge learned from the source domain to the target domain.

Self-Supervised Learning Source-Free Domain Adaptation

Video Frame Interpolation with Flow Transformer

no code implementations30 Jul 2023 Pan Gao, Haoyue Tian, Jie Qin

Specifically, we design a Flow Transformer Block that calculates the temporal self-attention in a matched local area with the guidance of flow, making our framework suitable for interpolating frames with large motion while maintaining reasonably low complexity.

Video Frame Interpolation

AlignDet: Aligning Pre-training and Fine-tuning in Object Detection

1 code implementation ICCV 2023 Ming Li, Jie Wu, Xionghui Wang, Chen Chen, Jie Qin, Xuefeng Xiao, Rui Wang, Min Zheng, Xin Pan

To this end, we propose AlignDet, a unified pre-training framework that can be adapted to various existing detectors to alleviate the discrepancies.

object-detection Object Detection

FreeSeg: Unified, Universal and Open-Vocabulary Image Segmentation

no code implementations CVPR 2023 Jie Qin, Jie Wu, Pengxiang Yan, Ming Li, Ren Yuxi, Xuefeng Xiao, Yitong Wang, Rui Wang, Shilei Wen, Xin Pan, Xingang Wang

Recently, open-vocabulary learning has emerged to accomplish segmentation for arbitrary categories of text-based descriptions, which popularizes the segmentation system to more general-purpose application scenarios.

Image Segmentation Instance Segmentation +3

Self-Paced Learning for Open-Set Domain Adaptation

no code implementations10 Mar 2023 Xinghong Liu, Yi Zhou, Tao Zhou, Jie Qin, Shengcai Liao

Open-set domain adaptation aims to not only recognize target samples belonging to common classes shared by source and target domains but also perceive unknown class samples.

Domain Adaptation

Memory-aided Contrastive Consensus Learning for Co-salient Object Detection

2 code implementations28 Feb 2023 Peng Zheng, Jie Qin, Shuo Wang, Tian-Zhu Xiang, Huan Xiong

To learn better group consensus, we propose the Group Consensus Aggregation Module (GCAM) to abstract the common features of each image group; meanwhile, to make the consensus representation more discriminative, we introduce the Memory-based Contrastive Module (MCM), which saves and updates the consensus of images from different groups in a queue of memories.

Co-Salient Object Detection object-detection +1

iSmallNet: Densely Nested Network with Label Decoupling for Infrared Small Target Detection

no code implementations29 Oct 2022 Zhiheng Hu, Yongzhen Wang, Peng Li, Jie Qin, Haoran Xie, Mingqiang Wei

First, to maintain small targets in deep layers, we develop a multi-scale nested interaction module to explore a wide range of context information.

object-detection Small Object Detection

Multi-Granularity Distillation Scheme Towards Lightweight Semi-Supervised Semantic Segmentation

1 code implementation22 Aug 2022 Jie Qin, Jie Wu, Ming Li, Xuefeng Xiao, Min Zheng, Xingang Wang

Consequently, we offer the first attempt to provide lightweight SSSS models via a novel multi-granularity distillation (MGD) scheme, where multi-granularity is captured from three aspects: i) complementary teacher structure; ii) labeled-unlabeled data cooperative distillation; iii) hierarchical and multi-levels loss setting.

Knowledge Distillation Semi-Supervised Semantic Segmentation

Parallel Pre-trained Transformers (PPT) for Synthetic Data-based Instance Segmentation

no code implementations22 Jun 2022 Ming Li, Jie Wu, Jinhang Cai, Jie Qin, Yuxi Ren, Xuefeng Xiao, Min Zheng, Rui Wang, Xin Pan

Recently, Synthetic data-based Instance Segmentation has become an exceedingly favorable optimization paradigm since it leverages simulation rendering and physics to generate high-quality image-annotation pairs.

Instance Segmentation Segmentation +1

GCoNet+: A Stronger Group Collaborative Co-Salient Object Detector

2 code implementations30 May 2022 Peng Zheng, Huazhu Fu, Deng-Ping Fan, Qi Fan, Jie Qin, Yu-Wing Tai, Chi-Keung Tang, Luc van Gool

In this paper, we present a novel end-to-end group collaborative learning network, termed GCoNet+, which can effectively and efficiently (250 fps) identify co-salient objects in natural scenes.

Co-Salient Object Detection Object +2

RangeUDF: Semantic Surface Reconstruction from 3D Point Clouds

2 code implementations19 Apr 2022 Bing Wang, Zhengdi Yu, Bo Yang, Jie Qin, Toby Breckon, Ling Shao, Niki Trigoni, Andrew Markham

We present RangeUDF, a new implicit representation based framework to recover the geometry and semantics of continuous 3D scene surfaces from point clouds.

Semantic Segmentation Surface Reconstruction

ACGNet: Action Complement Graph Network for Weakly-supervised Temporal Action Localization

1 code implementation21 Dec 2021 Zichen Yang, Jie Qin, Di Huang

Weakly-supervised temporal action localization (WTAL) in untrimmed videos has emerged as a practical but challenging task since only video-level labels are available.

Weakly-supervised Temporal Action Localization Weakly Supervised Temporal Action Localization

Activation Modulation and Recalibration Scheme for Weakly Supervised Semantic Segmentation

1 code implementation16 Dec 2021 Jie Qin, Jie Wu, Xuefeng Xiao, Lujun Li, Xingang Wang

Extensive experiments show that AMR establishes a new state-of-the-art performance on the PASCAL VOC 2012 dataset, surpassing not only current methods trained with the image-level of supervision but also some methods relying on stronger supervision, such as saliency label.

Feature Importance Scene Understanding +3

MovieNet-PS: A Large-Scale Person Search Dataset in the Wild

1 code implementation5 Dec 2021 Jie Qin, Peng Zheng, Yichao Yan, Rong Quan, Xiaogang Cheng, Bingbing Ni

Person search aims to jointly localize and identify a query person from natural, uncropped images, which has been actively studied over the past few years.

Person Search

TAL: Two-stream Adaptive Learning for Generalizable Person Re-identification

no code implementations29 Nov 2021 Yichao Yan, Junjie Li, Shengcai Liao, Jie Qin, Bingbing Ni, Xiaokang Yang

In the meantime, we design an adaptive BN layer in the domain-invariant stream, to approximate the statistics of various unseen domains.

Domain Generalization Generalizable Person Re-identification +1

Efficient Person Search: An Anchor-Free Approach

4 code implementations1 Sep 2021 Yichao Yan, Jinpeng Li, Jie Qin, Shengcai Liao, Xiaokang Yang

Third, by investigating the advantages of both anchor-based and anchor-free models, we further augment AlignPS with an ROI-Align head, which significantly improves the robustness of re-id features while still keeping our model highly efficient.

Person Search

Identity-aware Graph Memory Network for Action Detection

no code implementations26 Aug 2021 Jingcheng Ni, Jie Qin, Di Huang

Action detection plays an important role in high-level video understanding and media interpretation.

Action Detection Temporal Localization +1

You Never Cluster Alone

no code implementations NeurIPS 2021 Yuming Shen, Ziyi Shen, Menghan Wang, Jie Qin, Philip H. S. Torr, Ling Shao

On one hand, with the corresponding assignment variables being the weight, a weighted aggregation along the data points implements the set representation of a cluster.

Clustering Contrastive Learning +1

Learning Multi-Attention Context Graph for Group-Based Re-Identification

1 code implementation29 Apr 2021 Yichao Yan, Jie Qin, Bingbing Ni, Jiaxin Chen, Li Liu, Fan Zhu, Wei-Shi Zheng, Xiaokang Yang, Ling Shao

Extensive experiments on the novel dataset as well as three existing datasets clearly demonstrate the effectiveness of the proposed framework for both group-based re-id tasks.

Person Re-Identification

Anchor-Free Person Search

1 code implementation CVPR 2021 Yichao Yan, Jinpeng Li, Jie Qin, Song Bai, Shengcai Liao, Li Liu, Fan Zhu, Ling Shao

Person search aims to simultaneously localize and identify a query person from realistic, uncropped images, which can be regarded as the unified task of pedestrian detection and person re-identification (re-id).

Pedestrian Detection Person Re-Identification +1

S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration

1 code implementation CVPR 2021 Zhiqiang Shen, Zechun Liu, Jie Qin, Lei Huang, Kwang-Ting Cheng, Marios Savvides

In this paper, we focus on this more difficult scenario: learning networks where both weights and activations are binary, meanwhile, without any human annotated labels.

Contrastive Learning Self-Supervised Learning

Partial Is Better Than All: Revisiting Fine-tuning Strategy for Few-shot Learning

no code implementations8 Feb 2021 Zhiqiang Shen, Zechun Liu, Jie Qin, Marios Savvides, Kwang-Ting Cheng

A common practice for this task is to train a model on the base set first and then transfer to novel classes through fine-tuning (Here fine-tuning procedure is defined as transferring knowledge from base to novel data, i. e. learning to transfer in few-shot scenario.)

Few-Shot Learning

ResizeMix: Mixing Data with Preserved Object Information and True Labels

1 code implementation21 Dec 2020 Jie Qin, Jiemin Fang, Qian Zhang, Wenyu Liu, Xingang Wang, Xinggang Wang

Especially, CutMix uses a simple but effective method to improve the classifiers by randomly cropping a patch from one image and pasting it on another image.

Data Augmentation Image Classification +3

Contour Primitive of Interest Extraction Network Based on One-Shot Learning for Object-Agnostic Vision Measurement

no code implementations7 Oct 2020 Fangbo Qin, Jie Qin, Siyu Huang, De Xu

For the novel CPI extraction task, we built the Object Contour Primitives dataset using online public images, and the Robotic Object Contour Measurement dataset using a camera mounted on a robot.

Object One-Shot Learning +1

Normalization Techniques in Training DNNs: Methodology, Analysis and Application

no code implementations27 Sep 2020 Lei Huang, Jie Qin, Yi Zhou, Fan Zhu, Li Liu, Ling Shao

Normalization techniques are essential for accelerating the training and improving the generalization of deep neural networks (DNNs), and have successfully been used in various applications.

Invertible Zero-Shot Recognition Flows

1 code implementation ECCV 2020 Yuming Shen, Jie Qin, Lei Huang

Deep generative models have been successfully applied to Zero-Shot Learning (ZSL) recently.

Zero-Shot Learning

Auto-Encoding Twin-Bottleneck Hashing

2 code implementations CVPR 2020 Yuming Shen, Jie Qin, Jiaxin Chen, Mengyang Yu, Li Liu, Fan Zhu, Fumin Shen, Ling Shao

One bottleneck (i. e., binary codes) conveys the high-level intrinsic data structure captured by the code-driven graph to the other (i. e., continuous variables for low-level detail information), which in turn propagates the updated network feedback for the encoder to learn more discriminative binary codes.

graph construction Retrieval

Layer-wise Conditioning Analysis in Exploring the Learning Dynamics of DNNs

no code implementations ECCV 2020 Lei Huang, Jie Qin, Li Liu, Fan Zhu, Ling Shao

To this end, we propose layer-wise conditioning analysis, which explores the optimization landscape with respect to each layer independently.

Fully-Convolutional Intensive Feature Flow Neural Network for Text Recognition

no code implementations13 Dec 2019 Zhao Zhang, Zemin Tang, Zheng Zhang, Yang Wang, Jie Qin, Meng Wang

But existing CNNs based frameworks still have several drawbacks: 1) the traditaional pooling operation may lose important feature information and is unlearnable; 2) the tradi-tional convolution operation optimizes slowly and the hierar-chical features from different layers are not fully utilized.

Embarrassingly Simple Binary Representation Learning

1 code implementation26 Aug 2019 Yuming Shen, Jie Qin, Jiaxin Chen, Li Liu, Fan Zhu

Recent binary representation learning models usually require sophisticated binary optimization, similarity measure or even generative models as auxiliaries.

Representation Learning

Jointly Learning Structured Analysis Discriminative Dictionary and Analysis Multiclass Classifier

no code implementations27 May 2019 Zhao Zhang, Weiming Jiang, Jie Qin, Li Zhang, Fanzhang Li, Min Zhang, Shuicheng Yan

Then we compute a linear classifier based on the approximated sparse codes by an analysis mechanism to simultaneously consider the classification and representation powers.

Dictionary Learning General Classification

Scalable Block-Diagonal Locality-Constrained Projective Dictionary Learning

no code implementations25 May 2019 Zhao Zhang, Weiming Jiang, Zheng Zhang, Sheng Li, Guangcan Liu, Jie Qin

More importantly, LC-PDL avoids using the complementary data matrix to learn the sub-dictionary over each class.

Dictionary Learning

TBN: Convolutional Neural Network with Ternary Inputs and Binary Weights

1 code implementation ECCV 2018 Diwen Wan, Fumin Shen, Li Liu, Fan Zhu, Jie Qin, Ling Shao, Heng Tao Shen

Despite the remarkable success of Convolutional Neural Networks (CNNs) on generalized visual tasks, high computational and memory costs restrict their comprehensive applications on consumer electronics (e. g., portable or smart wearable devices).

object-detection Object Detection

stagNet: An Attentive Semantic RNN for Group Activity Recognition

no code implementations ECCV 2018 Mengshi Qi, Jie Qin, Annan Li, Yunhong Wang, Jiebo Luo, Luc van Gool

Group activity recognition plays a fundamental role in a variety of applications, e. g. sports video analysis and intelligent surveillance.

Group Activity Recognition

Adversarial Binary Coding for Efficient Person Re-identification

no code implementations29 Mar 2018 Zheng Liu, Jie Qin, Annan Li, Yunhong Wang, Luc van Gool

Specifically, instead of learning explicit projections or adding fully-connected mapping layers, the proposed Adversarial Binary Coding (ABC) framework guides the extraction of binary codes implicitly and effectively.

Person Re-Identification

Binary Coding for Partial Action Analysis With Limited Observation Ratios

no code implementations CVPR 2017 Jie Qin, Li Liu, Ling Shao, Bingbing Ni, Chen Chen, Fumin Shen, Yunhong Wang

Extensive experiments on four realistic action datasets in terms of three tasks (i. e., partial action retrieval, recognition and prediction) clearly show the superiority of PRBC over the state-of-the-art methods, along with significantly reduced memory load and computational costs during the online test.

Action Analysis Action Recognition +3

Fast Person Re-Identification via Cross-Camera Semantic Binary Transformation

no code implementations CVPR 2017 Jiaxin Chen, Yunhong Wang, Jie Qin, Li Liu, Ling Shao

Numerous methods have been proposed for person re-identification, most of which however neglect the matching efficiency.

Person Re-Identification

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