Search Results for author: Fumin Shen

Found 48 papers, 13 papers with code

Prototype-supervised Adversarial Network for Targeted Attack of Deep Hashing

1 code implementation CVPR 2021 Xunguang Wang, Zheng Zhang, Baoyuan Wu, Fumin Shen, Guangming Lu

However, deep hashing networks are vulnerable to adversarial examples, which is a practical secure problem but seldom studied in hashing-based retrieval field.

Image Retrieval Representation Learning

Jo-SRC: A Contrastive Approach for Combating Noisy Labels

1 code implementation CVPR 2021 Yazhou Yao, Zeren Sun, Chuanyi Zhang, Fumin Shen, Qi Wu, Jian Zhang, Zhenmin Tang

Due to the memorization effect in Deep Neural Networks (DNNs), training with noisy labels usually results in inferior model performance.

Contrastive Learning

Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Noisy Samples and Utilizing Hard Ones

1 code implementation23 Jan 2021 Huafeng Liu, Chuanyi Zhang, Yazhou Yao, Xiushen Wei, Fumin Shen, Jian Zhang, Zhenmin Tang

Labeling objects at a subordinate level typically requires expert knowledge, which is not always available when using random annotators.

Fine-Grained Visual Recognition

Dual ResGCN for Balanced Scene GraphGeneration

no code implementations9 Nov 2020 Jingyi Zhang, Yong Zhang, Baoyuan Wu, Yanbo Fan, Fumin Shen, Heng Tao Shen

We propose to incorporate the prior about the co-occurrence of relation pairs into the graph to further help alleviate the class imbalance issue.

Graph Convolutional Network Graph Generation +1

Auto-Encoding Twin-Bottleneck Hashing

1 code implementation 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

Fast Large-Scale Discrete Optimization Based on Principal Coordinate Descent

no code implementations16 Sep 2019 Huan Xiong, Mengyang Yu, Li Liu, Fan Zhu, Fumin Shen, Ling Shao

Binary optimization, a representative subclass of discrete optimization, plays an important role in mathematical optimization and has various applications in computer vision and machine learning.

Quantization

Cooperative Cross-Stream Network for Discriminative Action Representation

no code implementations27 Aug 2019 Jingran Zhang, Fumin Shen, Xing Xu, Heng Tao Shen

It extracts this complementary information of different modality from a connection block, which aims at exploring correlations of different stream features.

Ranked #10 on Action Recognition on HMDB-51 (using extra training data)

Action Recognition

MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning

no code implementations27 Aug 2019 Zhijun Mai, Guosheng Hu, Dexiong Chen, Fumin Shen, Heng Tao Shen

Since deep networks are capable of memorizing the entire dataset, the corrupted samples generated by vanilla MixUp with a badly chosen interpolation policy will degrade the performance of networks.

Data Augmentation Domain Adaptation +1

Temporal Reasoning Graph for Activity Recognition

no code implementations27 Aug 2019 Jingran Zhang, Fumin Shen, Xing Xu, Heng Tao Shen

In this paper, we propose an efficient temporal reasoning graph (TRG) to simultaneously capture the appearance features and temporal relation between video sequences at multiple time scales.

Action Recognition Relation Extraction

Make a Face: Towards Arbitrary High Fidelity Face Manipulation

no code implementations ICCV 2019 Shengju Qian, Kwan-Yee Lin, Wayne Wu, Yangxiaokang Liu, Quan Wang, Fumin Shen, Chen Qian, Ran He

Recent studies have shown remarkable success in face manipulation task with the advance of GANs and VAEs paradigms, but the outputs are sometimes limited to low-resolution and lack of diversity.

Extracting Visual Knowledge from the Internet: Making Sense of Image Data

no code implementations7 Jun 2019 Yazhou Yao, Jian Zhang, Xian-Sheng Hua, Fumin Shen, Zhenmin Tang

Recent successes in visual recognition can be primarily attributed to feature representation, learning algorithms, and the ever-increasing size of labeled training data.

Representation Learning

Dynamically Visual Disambiguation of Keyword-based Image Search

no code implementations27 May 2019 Yazhou Yao, Zeren Sun, Fumin Shen, Li Liu, Li-Min Wang, Fan Zhu, Lizhong Ding, Gangshan Wu, Ling Shao

To address this issue, we present an adaptive multi-model framework that resolves polysemy by visual disambiguation.

General Classification Image Retrieval

Exact Adversarial Attack to Image Captioning via Structured Output Learning with Latent Variables

1 code implementation CVPR 2019 Yan Xu, Baoyuan Wu, Fumin Shen, Yanbo Fan, Yong Zhang, Heng Tao Shen, Wei Liu

Due to the sequential dependencies among words in a caption, we formulate the generation of adversarial noises for targeted partial captions as a structured output learning problem with latent variables.

Adversarial Attack Image Captioning

Collaborative Learning for Extremely Low Bit Asymmetric Hashing

1 code implementation25 Sep 2018 Yadan Luo, Zi Huang, Yang Li, Fumin Shen, Yang Yang, Peng Cui

Hashing techniques are in great demand for a wide range of real-world applications such as image retrieval and network compression.

Image Retrieval

TBN: Convolutional Neural Network with Ternary Inputs and Binary Weights

no code implementations 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

Generative Domain-Migration Hashing for Sketch-to-Image Retrieval

1 code implementation ECCV 2018 Jingyi Zhang, Fumin Shen, Li Liu, Fan Zhu, Mengyang Yu, Ling Shao, Heng Tao Shen, Luc van Gool

The generative model learns a mapping that the distributions of sketches can be indistinguishable from the distribution of natural images using an adversarial loss, and simultaneously learns an inverse mapping based on the cycle consistency loss in order to enhance the indistinguishability.

Multi-Task Learning Sketch-Based Image Retrieval

Zero-Shot Sketch-Image Hashing

1 code implementation CVPR 2018 Yuming Shen, Li Liu, Fumin Shen, Ling Shao

As an important part of ZSIH, we formulate a generative hashing scheme in reconstructing semantic knowledge representations for zero-shot retrieval.

Representation Learning Sketch-Based Image Retrieval

Neural Stereoscopic Image Style Transfer

no code implementations ECCV 2018 Xinyu Gong, HaoZhi Huang, Lin Ma, Fumin Shen, Wei Liu, Tong Zhang

While each view of the stereoscopic pair is processed in an individual path, a novel feature aggregation strategy is proposed to effectively share information between the two paths.

Style Transfer

Towards Automatic Construction of Diverse, High-quality Image Dataset

no code implementations22 Aug 2017 Yazhou Yao, Jian Zhang, Fumin Shen, Li Liu, Fan Zhu, Dongxiang Zhang, Heng-Tao Shen

To eliminate manual annotation, in this work, we propose a novel image dataset construction framework by employing multiple textual queries.

Image Classification Object Detection

Discretely Coding Semantic Rank Orders for Supervised Image Hashing

no code implementations CVPR 2017 Li Liu, Ling Shao, Fumin Shen, Mengyang Yu

Learning to hash has been recognized to accomplish highly efficient storage and retrieval for large-scale visual data.

Word Embeddings

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 +1

Matrix Tri-Factorization With Manifold Regularizations for Zero-Shot Learning

no code implementations CVPR 2017 Xing Xu, Fumin Shen, Yang Yang, Dongxiang Zhang, Heng Tao Shen, Jingkuan Song

By additionally introducing manifold regularizations on visual data and semantic embeddings, the learned projection can effectively captures the geometrical manifold structure residing in both visual and semantic spaces.

Transfer Learning Zero-Shot Learning

From Zero-shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis

no code implementations CVPR 2017 Yang Long, Li Liu, Ling Shao, Fumin Shen, Guiguang Ding, Jungong Han

Using the proposed Unseen Visual Data Synthesis (UVDS) algorithm, semantic attributes are effectively utilised as an intermediate clue to synthesise unseen visual features at the training stage.

General Classification Object Recognition +1

Refining Image Categorization by Exploiting Web Images and General Corpus

no code implementations16 Mar 2017 Yazhou Yao, Jian Zhang, Fumin Shen, Xian-Sheng Hua, Wankou Yang, Zhenmin Tang

To tackle these problems, in this work, we exploit general corpus information to automatically select and subsequently classify web images into semantic rich (sub-)categories.

Image Categorization

Deep Sketch Hashing: Fast Free-hand Sketch-Based Image Retrieval

1 code implementation CVPR 2017 Li Liu, Fumin Shen, Yuming Shen, Xianglong Liu, Ling Shao

Free-hand sketch-based image retrieval (SBIR) is a specific cross-view retrieval task, in which queries are abstract and ambiguous sketches while the retrieval database is formed with natural images.

Sketch-Based Image Retrieval

Recurrent Image Captioner: Describing Images with Spatial-Invariant Transformation and Attention Filtering

no code implementations15 Dec 2016 Hao Liu, Yang Yang, Fumin Shen, Lixin Duan, Heng Tao Shen

Along with the prosperity of recurrent neural network in modelling sequential data and the power of attention mechanism in automatically identify salient information, image captioning, a. k. a., image description, has been remarkably advanced in recent years.

Image Captioning Variational Inference

Binary Subspace Coding for Query-by-Image Video Retrieval

no code implementations6 Dec 2016 Ruicong Xu, Yang Yang, Yadan Luo, Fumin Shen, Zi Huang, Heng Tao Shen

The first approach, termed Inner-product Binary Coding (IBC), preserves the inner relationships of images and videos in a common Hamming space.

Video Retrieval

Exploiting Web Images for Dataset Construction: A Domain Robust Approach

no code implementations22 Nov 2016 Yazhou Yao, Jian Zhang, Fumin Shen, Xian-Sheng Hua, Jingsong Xu, Zhenmin Tang

To reduce the cost of manual labelling, there has been increased research interest in automatically constructing image datasets by exploiting web images.

Domain Adaptation Image Classification +1

Zero-Shot Hashing via Transferring Supervised Knowledge

no code implementations16 Jun 2016 Yang Yang, Wei-Lun Chen, Yadan Luo, Fumin Shen, Jie Shao, Heng Tao Shen

Supervised knowledge e. g. semantic labels or pair-wise relationship) associated to data is capable of significantly improving the quality of hash codes and hash functions.

Image Retrieval

Learning Binary Codes for Maximum Inner Product Search

no code implementations ICCV 2015 Fumin Shen, Wei Liu, Shaoting Zhang, Yang Yang, Heng Tao Shen

Inspired by the latest advance in asymmetric hashing schemes, we propose an asymmetric binary code learning framework based on inner product fitting.

Hashing on Nonlinear Manifolds

no code implementations2 Dec 2014 Fumin Shen, Chunhua Shen, Qinfeng Shi, Anton Van Den Hengel, Zhenmin Tang, Heng Tao Shen

In addition, a supervised inductive manifold hashing framework is developed by incorporating the label information, which is shown to greatly advance the semantic retrieval performance.

Image Classification Quantization +1

Face Image Classification by Pooling Raw Features

no code implementations26 Jun 2014 Fumin Shen, Chunhua Shen, Heng Tao Shen

We propose a very simple, efficient yet surprisingly effective feature extraction method for face recognition (about 20 lines of Matlab code), which is mainly inspired by spatial pyramid pooling in generic image classification.

Face Recognition General Classification +1

Face Identification with Second-Order Pooling

no code implementations26 Jun 2014 Fumin Shen, Chunhua Shen, Heng Tao Shen

Spatial pyramid pooling of features encoded by an over-complete dictionary has been the key component of many state-of-the-art image classification systems.

Face Identification Face Recognition +3

Generic Image Classification Approaches Excel on Face Recognition

no code implementations22 Sep 2013 Fumin Shen, Chunhua Shen

The main finding of this work is that the standard image classification pipeline, which consists of dictionary learning, feature encoding, spatial pyramid pooling and linear classification, outperforms all state-of-the-art face recognition methods on the tested benchmark datasets (we have tested on AR, Extended Yale B, the challenging FERET, and LFW-a datasets).

Dictionary Learning Face Recognition +2

Fast Approximate L_infty Minimization: Speeding Up Robust Regression

no code implementations4 Apr 2013 Fumin Shen, Chunhua Shen, Rhys Hill, Anton Van Den Hengel, Zhenmin Tang

Minimization of the $L_\infty$ norm, which can be viewed as approximately solving the non-convex least median estimation problem, is a powerful method for outlier removal and hence robust regression.

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