Search Results for author: Yuxin Peng

Found 31 papers, 7 papers with code

Disentangled Graph Neural Networks for Session-based Recommendation

no code implementations10 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.

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.

Computer Vision Fine-Grained Image Recognition +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

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.

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.

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

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.

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).

reinforcement-learning

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 Representation Learning

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 +1

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

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

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 +1

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.

Classification Fine-Grained Image Classification +1

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

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 +2

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.

Classification Fine-Grained Image Classification +1

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 +1

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 +3

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.

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.

graph construction Image Retrieval +2

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 +1

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

1 code implementation22 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.

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