401 citations · 1.3k across the 36 of their papers we have counts for
22 papers · 1 filter
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…
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…
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…
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…
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…
Fine-graind Image Classification via Combining Vision and Language
Xiangteng He, Yuxin Peng
Fine-grained image classification is a challenging task due to the large intra-class variance and small inter-class variance, aiming at recognizing hundreds of sub-categories belon…
Object-Part Attention Model for Fine-grained Image Classification
Yuxin Peng, Xiangteng He, Junjie Zhao
Fine-grained image classification is to recognize hundreds of subcategories belonging to the same basic-level category, such as 200 subcategories belonging to the bird, which is hi…
Saliency-guided video classification via adaptively weighted learning
Yunzhen Zhao, Yuxin Peng
Video classification is productive in many practical applications, and the recent deep learning has greatly improved its accuracy. However, existing works often model video frames…
Query-adaptive Image Retrieval by Deep Weighted Hashing
Jian Zhang, Yuxin Peng
Hashing methods have attracted much attention for large scale image retrieval. Some deep hashing methods have achieved promising results by taking advantage of the strong represent…
SSDH: Semi-supervised Deep Hashing for Large Scale Image Retrieval
Jian Zhang, Yuxin Peng
Hashing methods have been widely used for efficient similarity retrieval on large scale image database. Traditional hashing methods learn hash functions to generate binary codes fr…
The Application of Two-level Attention Models in Deep Convolutional Neural Network for Fine-grained Image Classification
Tianjun Xiao, Yichong Xu, Kuiyuan Yang +3
Fine-grained classification is challenging because categories can only be discriminated by subtle and local differences. Variances in the pose, scale or rotation usually make the p…