Search Results for author: Yuxin Peng

Found 45 papers, 18 papers with code

FineParser: A Fine-grained Spatio-temporal Action Parser for Human-centric Action Quality Assessment

no code implementations11 May 2024 Jinglin Xu, Sibo Yin, Guohao Zhao, Zishuo Wang, Yuxin Peng

We argue that a fine-grained understanding of actions requires the model to perceive and parse actions in both time and space, which is also the key to the credibility and interpretability of the AQA technique.

Action Quality Assessment Action Understanding

Attribute-Aware Deep Hashing with Self-Consistency for Large-Scale Fine-Grained Image Retrieval

1 code implementation21 Nov 2023 Xiu-Shen Wei, Yang shen, Xuhao Sun, Peng Wang, Yuxin Peng

Our work focuses on tackling large-scale fine-grained image retrieval as ranking the images depicting the concept of interests (i. e., the same sub-category labels) highest based on the fine-grained details in the query.

Attribute Deep Hashing +2

Efficient Adaptive Human-Object Interaction Detection with Concept-guided Memory

1 code implementation ICCV 2023 Ting Lei, Fabian Caba, Qingchao Chen, Hailin Jin, Yuxin Peng, Yang Liu

This observation motivates us to design an HOI detector that can be trained even with long-tailed labeled data and can leverage existing knowledge from pre-trained models.

Human-Object Interaction Detection Retrieval

Towards Video Anomaly Retrieval from Video Anomaly Detection: New Benchmarks and Model

1 code implementation24 Jul 2023 Peng Wu, Jing Liu, Xiangteng He, Yuxin Peng, Peng Wang, Yanning Zhang

In this context, we propose a novel task called Video Anomaly Retrieval (VAR), which aims to pragmatically retrieve relevant anomalous videos by cross-modalities, e. g., language descriptions and synchronous audios.

Anomaly Detection Retrieval +2

MB-HGCN: A Hierarchical Graph Convolutional Network for Multi-behavior Recommendation

no code implementations19 Jun 2023 Mingshi Yan, Zhiyong Cheng, Jing Sun, Fuming Sun, Yuxin Peng

In this paper, we propose MB-HGCN, a novel multi-behavior recommendation model that uses a hierarchical graph convolutional network to learn user and item embeddings from coarse-grained on the global level to fine-grained on the behavior-specific level.

Collaborative Filtering Multi-Task Learning +1

Multi-Behavior Recommendation with Cascading Graph Convolution Networks

1 code implementation28 Mar 2023 Zhiyong Cheng, Sai Han, Fan Liu, Lei Zhu, Zan Gao, Yuxin Peng

Most existing multi-behavior models fail to capture such dependencies in a behavior chain for embedding learning.

PosterLayout: A New Benchmark and Approach for Content-aware Visual-Textual Presentation Layout

1 code implementation CVPR 2023 HsiaoYuan Hsu, Xiangteng He, Yuxin Peng, Hao Kong, Qing Zhang

Content-aware visual-textual presentation layout aims at arranging spatial space on the given canvas for pre-defined elements, including text, logo, and underlay, which is a key to automatic template-free creative graphic design.

Generative Adversarial Network

Confidence-aware Pseudo-label Learning for Weakly Supervised Visual Grounding

1 code implementation ICCV 2023 Yang Liu, Jiahua Zhang, Qingchao Chen, Yuxin Peng

Visual grounding aims at localizing the target object in image which is most related to the given free-form natural language query.

Descriptive Object +5

Masked Retraining Teacher-Student Framework for Domain Adaptive Object Detection

1 code implementation ICCV 2023 Zijing Zhao, Sitong Wei, Qingchao Chen, Dehui Li, Yifan Yang, Yuxin Peng, Yang Liu

This helps the student model capture target domain characteristics and become a more data-efficient learner to gain knowledge from the limited number of pseudo boxes.

Decoder object-detection +2

An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning

3 code implementations28 Sep 2022 Xiu-Shen Wei, He-Yang Xu, Faen Zhang, Yuxin Peng, Wei Zhou

Semi-supervised few-shot learning consists in training a classifier to adapt to new tasks with limited labeled data and a fixed quantity of unlabeled data.

Few-Shot Learning

SIM-Trans: Structure Information Modeling Transformer for Fine-grained Visual Categorization

1 code implementation31 Aug 2022 Hongbo Sun, Xiangteng He, Yuxin Peng

To address the above limitations, we propose the Structure Information Modeling Transformer (SIM-Trans) to incorporate object structure information into transformer for enhancing discriminative representation learning to contain both the appearance information and structure information.

Contrastive Learning Fine-Grained Image Classification +2

Team PKU-WICT-MIPL PIC Makeup Temporal Video Grounding Challenge 2022 Technical Report

no code implementations6 Jul 2022 Minghang Zheng, Dejie Yang, Zhongjie Ye, Ting Lei, Yuxin Peng, Yang Liu

In this technical report, we briefly introduce the solutions of our team `PKU-WICT-MIPL' for the PIC Makeup Temporal Video Grounding (MTVG) Challenge in ACM-MM 2022.

Sentence Temporal Localization +1

Disentangled Graph Neural Networks for Session-based Recommendation

1 code implementation10 Jan 2022 Ansong Li, Zhiyong Cheng, Fan Liu, Zan Gao, Weili Guan, Yuxin Peng

The session embedding is then generated by aggregating the item embeddings with attention weights of each item's factors.

Session-Based Recommendations

Weakly Supervised Temporal Sentence Grounding With Gaussian-Based Contrastive Proposal Learning

1 code implementation CVPR 2022 Minghang Zheng, Yanjie Huang, Qingchao Chen, Yuxin Peng, Yang Liu

Moreover, they train their model to distinguish positive visual-language pairs from negative ones randomly collected from other videos, ignoring the highly confusing video segments within the same video.

Model Optimization Sentence +1

Fine-Grained Image Analysis with Deep Learning: A Survey

no code implementations11 Nov 2021 Xiu-Shen Wei, Yi-Zhe Song, Oisin Mac Aodha, Jianxin Wu, Yuxin Peng, Jinhui Tang, Jian Yang, Serge Belongie

Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications.

Fine-Grained Image Recognition Image Retrieval +1

A New Benchmark and Approach for Fine-grained Cross-media Retrieval

1 code implementation10 Jul 2019 Xiangteng He, Yuxin Peng, Liu Xie

To the best of our knowledge, it is the first benchmark with 4 media types for fine-grained cross-media retrieval.

Representation Learning Retrieval

Object-aware Aggregation with Bidirectional Temporal Graph for Video Captioning

no code implementations CVPR 2019 Junchao Zhang, Yuxin Peng

The main novelties and advantages are: (1) Bidirectional temporal graph: A bidirectional temporal graph is constructed along and reversely along the temporal order, which provides complementary ways to capture the temporal trajectories for each salient object.

Object Video Captioning

Text-to-image Synthesis via Symmetrical Distillation Networks

no code implementations21 Aug 2018 Mingkuan Yuan, Yuxin Peng

For addressing these problems, we exploit the excellent capability of generic discriminative models (e. g. VGG19), which can guide the training process of a new generative model on multiple levels to bridge the two gaps.

Image Generation

Visual Data Synthesis via GAN for Zero-Shot Video Classification

no code implementations26 Apr 2018 Chenrui Zhang, Yuxin Peng

First, we propose multi-level semantic inference to boost video feature synthesis, which captures the discriminative information implied in joint visual-semantic distribution via feature-level and label-level semantic inference.

Classification General Classification +2

Cross-media Multi-level Alignment with Relation Attention Network

1 code implementation25 Apr 2018 Jinwei Qi, Yuxin Peng, Yuxin Yuan

First, we propose visual-language relation attention model to explore both fine-grained patches and their relations of different media types.

Relation Retrieval

Deep Cross-media Knowledge Transfer

no code implementations CVPR 2018 Xin Huang, Yuxin Peng

For achieving the goal, this paper proposes deep cross-media knowledge transfer (DCKT) approach, which transfers knowledge from a large-scale cross-media dataset to promote the model training on another small-scale cross-media dataset.

Multimedia

Deep Reinforcement Learning for Image Hashing

no code implementations7 Feb 2018 Yuxin Peng, Jian Zhang, Zhaoda Ye

Inspired by the sequential decision ability of deep reinforcement learning, we propose a new Deep Reinforcement Learning approach for Image Hashing (DRLIH).

Deep Hashing reinforcement-learning +1

SCH-GAN: Semi-supervised Cross-modal Hashing by Generative Adversarial Network

no code implementations7 Feb 2018 Jian Zhang, Yuxin Peng, Mingkuan Yuan

(2) Ignore the rich information contained in the large amount of unlabeled data across different modalities, especially the margin examples that are easily to be incorrectly retrieved, which can help to model the correlations.

Generative Adversarial Network Retrieval

Unsupervised Generative Adversarial Cross-modal Hashing

no code implementations1 Dec 2017 Jian Zhang, Yuxin Peng, Mingkuan Yuan

To address the above problem, in this paper we propose an Unsupervised Generative Adversarial Cross-modal Hashing approach (UGACH), which makes full use of GAN's ability for unsupervised representation learning to exploit the underlying manifold structure of cross-modal data.

Cross-Modal Retrieval Generative Adversarial Network +2

Two-stream Collaborative Learning with Spatial-Temporal Attention for Video Classification

no code implementations9 Nov 2017 Yuxin Peng, Yunzhen Zhao, Junchao Zhang

Recently, researchers generally adopt the deep networks to capture the static and motion information \textbf{\emph{separately}}, which mainly has two limitations: (1) Ignoring the coexistence relationship between spatial and temporal attention, while they should be jointly modelled as the spatial and temporal evolutions of video, thus discriminative video features can be extracted.

General Classification Optical Flow Estimation +2

CM-GANs: Cross-modal Generative Adversarial Networks for Common Representation Learning

no code implementations14 Oct 2017 Yuxin Peng, Jinwei Qi, Yuxin Yuan

They can not only exploit cross-modal correlation for learning common representation, but also preserve reconstruction information for capturing semantic consistency within each modality.

Cross-Modal Retrieval Representation Learning +1

Fast Fine-grained Image Classification via Weakly Supervised Discriminative Localization

no code implementations30 Sep 2017 Xiangteng He, Yuxin Peng, Junjie Zhao

Therefore, we propose a weakly supervised discriminative localization approach (WSDL) for fast fine-grained image classification to address the two limitations at the same time, and its main advantages are: (1) n-pathway end-to-end discriminative localization network is designed to improve classification speed, which simultaneously localizes multiple different discriminative regions for one image to boost classification accuracy, and shares full-image convolutional features generated by region proposal network to accelerate the process of generating region proposals as well as reduce the computation of convolutional operation.

Classification Fine-Grained Image Classification +2

Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN

no code implementations25 Sep 2017 Xiangteng He, Yuxin Peng, Junjie Zhao

Existing methods generally adopt a two-stage learning framework: The first stage is to localize the discriminative regions of objects, and the second is to encode the discriminative features for training classifiers.

Classification Fine-Grained Image Classification +1

Fine-grained Visual-textual Representation Learning

1 code implementation31 Aug 2017 Xiangteng He, Yuxin Peng

As is known to all, when we describe the object of an image via textual descriptions, we mainly focus on the pivotal characteristics, and rarely pay attention to common characteristics as well as the background areas.

Fine-Grained Visual Categorization Representation Learning

Modality-specific Cross-modal Similarity Measurement with Recurrent Attention Network

1 code implementation16 Aug 2017 Yuxin Peng, Jinwei Qi, Yuxin Yuan

Effectively measuring the similarity between different modalities of data is the key of cross-modal retrieval.

Cross-Modal Retrieval Retrieval +1

MHTN: Modal-adversarial Hybrid Transfer Network for Cross-modal Retrieval

no code implementations8 Aug 2017 Xin Huang, Yuxin Peng, Mingkuan Yuan

Transfer learning is for relieving the problem of insufficient training data, but it mainly focuses on knowledge transfer only from large-scale datasets as single-modal source domain to single-modal target domain.

Cross-Modal Retrieval Representation Learning +2

Fine-Grained Image Classification via Combining Vision and Language

no code implementations CVPR 2017 Xiangteng He, Yuxin Peng

Most existing fine-grained image classification methods generally learn part detection models to obtain the semantic parts for better classification accuracy.

Attribute Classification +2

Cross-modal Common Representation Learning by Hybrid Transfer Network

no code implementations1 Jun 2017 Xin Huang, Yuxin Peng, Mingkuan Yuan

Knowledge in source domain cannot be directly transferred to both two different modalities in target domain, and the inherent cross-modal correlation contained in target domain provides key hints for cross-modal retrieval which should be preserved during transfer process.

Cross-Modal Retrieval Representation Learning +1

Cross-media Similarity Metric Learning with Unified Deep Networks

no code implementations14 Apr 2017 Jinwei Qi, Xin Huang, Yuxin Peng

Motivated by the strong ability of deep neural network in feature representation and comparison functions learning, we propose the Unified Network for Cross-media Similarity Metric (UNCSM) to associate cross-media shared representation learning with distance metric in a unified framework.

Metric Learning Representation Learning +3

Fine-graind Image Classification via Combining Vision and Language

no code implementations10 Apr 2017 Xiangteng He, Yuxin Peng

Most existing fine-grained image classification methods generally learn part detection models to obtain the semantic parts for better classification accuracy.

Attribute Classification +2

Object-Part Attention Model for Fine-grained Image Classification

1 code implementation6 Apr 2017 Yuxin Peng, Xiangteng He, Junjie Zhao

Both are jointly employed to exploit the subtle and local differences for distinguishing the subcategories.

Classification Fine-Grained Image Classification +2

Saliency-guided video classification via adaptively weighted learning

no code implementations23 Mar 2017 Yunzhen Zhao, Yuxin Peng

Then two streams of 3D CNN are trained individually for raw frames and optical flow on salient areas, and another 2D CNN is trained for raw frames on non-salient areas.

Classification General Classification +2

Cross-modal Deep Metric Learning with Multi-task Regularization

no code implementations21 Mar 2017 Xin Huang, Yuxin Peng

The quadruplet ranking loss can model the semantically similar and dissimilar constraints to preserve cross-modal relative similarity ranking information.

Cross-Modal Retrieval Metric Learning +4

Query-adaptive Image Retrieval by Deep Weighted Hashing

no code implementations8 Dec 2016 Jian Zhang, Yuxin Peng

On the other hand, different hash bits actually contribute to the image retrieval differently, and treating them equally greatly affects the retrieval accuracy of image.

Deep Hashing Image Retrieval

SSDH: Semi-supervised Deep Hashing for Large Scale Image Retrieval

no code implementations28 Jul 2016 Jian Zhang, Yuxin Peng

(2) A semi-supervised deep hashing network is designed to extensively exploit both labeled and unlabeled data, in which we propose an online graph construction method to benefit from the evolving deep features during training to better capture semantic neighbors.

Deep Hashing graph construction +3

The Application of Two-level Attention Models in Deep Convolutional Neural Network for Fine-grained Image Classification

no code implementations CVPR 2015 Tianjun Xiao, Yichong Xu, Kuiyuan Yang, Jiaxing Zhang, Yuxin Peng, Zheng Zhang

Our pipeline integrates three types of attention: the bottom-up attention that propose candidate patches, the object-level top-down attention that selects relevant patches to a certain object, and the part-level top-down attention that localizes discriminative parts.

Classification Fine-Grained Image Classification +2

Exhaustive and Efficient Constraint Propagation: A Semi-Supervised Learning Perspective and Its Applications

no code implementations22 Sep 2011 Zhiwu Lu, Horace H. S. Ip, Yuxin Peng

This paper presents a novel pairwise constraint propagation approach by decomposing the challenging constraint propagation problem into a set of independent semi-supervised learning subproblems which can be solved in quadratic time using label propagation based on k-nearest neighbor graphs.

Retrieval

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