401 citations · 1.3k across the 36 of their papers we have counts for
36 papers
Attribute-Aware Deep Hashing with Self-Consistency for Large-Scale Fine-Grained Image Retrieval
Xiu-Shen Wei, Yang Shen, Xuhao Sun +2
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…
Towards Video Anomaly Retrieval from Video Anomaly Detection: New Benchmarks and Model
Peng Wu, Jing Liu, Xiangteng He +3
Video anomaly detection (VAD) has been paid increasing attention due to its potential applications, its current dominant tasks focus on online detecting anomalies% at the frame lev…
Enhancing Unsupervised Audio Representation Learning via Adversarial Sample Generation
Yulin Pan, Xiangteng He, Biao Gong +2
Existing audio analysis methods generally first transform the audio stream to spectrogram, and then feed it into CNN for further analysis. A standard CNN recognizes specific visual…
An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning
Xiu-Shen Wei, He-Yang Xu, Faen Zhang +2
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. Many sophisticated metho…
SIM-Trans: Structure Information Modeling Transformer for Fine-grained Visual Categorization
Hongbo Sun, Xiangteng He, Yuxin Peng
Fine-grained visual categorization (FGVC) aims at recognizing objects from similar subordinate categories, which is challenging and practical for human's accurate automatic recogni…
Fine-Grained Image Analysis with Deep Learning: A Survey
Xiu-Shen Wei, Yi-Zhe Song, Oisin Mac Aodha +5
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. The…
A New Benchmark and Approach for Fine-grained Cross-media Retrieval
Xiangteng He, Yuxin Peng, Liu Xie
Cross-media retrieval is to return the results of various media types corresponding to the query of any media type. Existing researches generally focus on coarse-grained cross-medi…
Object-aware Aggregation with Bidirectional Temporal Graph for Video Captioning
Junchao Zhang, Yuxin Peng
Video captioning aims to automatically generate natural language descriptions of video content, which has drawn a lot of attention recent years. Generating accurate and fine-graine…
Text-to-image Synthesis via Symmetrical Distillation Networks
Mingkuan Yuan, Yuxin Peng
Text-to-image synthesis aims to automatically generate images according to text descriptions given by users, which is a highly challenging task. The main issues of text-to-image sy…
Visual Data Synthesis via GAN for Zero-Shot Video Classification
Chenrui Zhang, Yuxin Peng
Zero-Shot Learning (ZSL) in video classification is a promising research direction, which aims to tackle the challenge from explosive growth of video categories. Most existing meth…
Better and Faster: Knowledge Transfer from Multiple Self-supervised Learning Tasks via Graph Distillation for Video Classification
Chenrui Zhang, Yuxin Peng
Video representation learning is a vital problem for classification task. Recently, a promising unsupervised paradigm termed self-supervised learning has emerged, which explores in…
Cross-media Multi-level Alignment with Relation Attention Network
Jinwei Qi, Yuxin Peng, Yuxin Yuan
With the rapid growth of multimedia data, such as image and text, it is a highly challenging problem to effectively correlate and retrieve the data of different media types. Natura…
Deep Cross-media Knowledge Transfer
Xin Huang, Yuxin Peng
Cross-media retrieval is a research hotspot in multimedia area, which aims to perform retrieval across different media types such as image and text. The performance of existing met…
SCH-GAN: Semi-supervised Cross-modal Hashing by Generative Adversarial Network
Jian Zhang, Yuxin Peng, Mingkuan Yuan
Cross-modal hashing aims to map heterogeneous multimedia data into a common Hamming space, which can realize fast and flexible retrieval across different modalities. Supervised cro…
Deep Reinforcement Learning for Image Hashing
Yuxin Peng, Jian Zhang, Zhaoda Ye
Deep hashing methods have received much attention recently, which achieve promising results by taking advantage of the strong representation power of deep networks. However, most e…
Unsupervised Generative Adversarial Cross-modal Hashing
Jian Zhang, Yuxin Peng, Mingkuan Yuan
Cross-modal hashing aims to map heterogeneous multimedia data into a common Hamming space, which can realize fast and flexible retrieval across different modalities. Unsupervised c…
Two-stream Collaborative Learning with Spatial-Temporal Attention for Video Classification
Yuxin Peng, Yunzhen Zhao, Junchao Zhang
Video classification is highly important with wide applications, such as video search and intelligent surveillance. Video naturally consists of static and motion information, which…
CM-GANs: Cross-modal Generative Adversarial Networks for Common Representation Learning
Yuxin Peng, Jinwei Qi, Yuxin Yuan
It is known that the inconsistent distribution and representation of different modalities, such as image and text, cause the heterogeneity gap that makes it challenging to correlat…
Fast Fine-grained Image Classification via Weakly Supervised Discriminative Localization
Xiangteng He, Yuxin Peng, Junjie Zhao
Fine-grained image classification is to recognize hundreds of subcategories in each basic-level category. Existing methods employ discriminative localization to find the key distin…
Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN
Xiangteng He, Yuxin Peng, Junjie Zhao
Discriminative localization is essential for fine-grained image classification task, which devotes to recognizing hundreds of subcategories in the same basic-level category. Reflec…
Fine-grained Visual-textual Representation Learning
Xiangteng He, Yuxin Peng
Fine-grained visual categorization is to recognize hundreds of subcategories belonging to the same basic-level category, which is a highly challenging task due to the quite subtle…
Modality-specific Cross-modal Similarity Measurement with Recurrent Attention Network
Yuxin Peng, Jinwei Qi, Yuxin Yuan
Nowadays, cross-modal retrieval plays an indispensable role to flexibly find information across different modalities of data. Effectively measuring the similarity between different…
MHTN: Modal-adversarial Hybrid Transfer Network for Cross-modal Retrieval
Xin Huang, Yuxin Peng, Mingkuan Yuan
Cross-modal retrieval has drawn wide interest for retrieval across different modalities of data. However, existing methods based on DNN face the challenge of insufficient cross-mod…
Cross-modal Common Representation Learning by Hybrid Transfer Network
Xin Huang, Yuxin Peng, Mingkuan Yuan
DNN-based cross-modal retrieval is a research hotspot to retrieve across different modalities as image and text, but existing methods often face the challenge of insufficient cross…