Search Results for author: Shaogang Gong

Found 81 papers, 17 papers with code

Video Activity Localisation with Uncertainties in Temporal Boundary

no code implementations26 Jun 2022 Jiabo Huang, Hailin Jin, Shaogang Gong, Yang Liu

Such uncertainties in temporal labelling are currently ignored in model training, resulting in learning mis-matched video-text correlation with poor generalisation in test.

Learning Unbiased Transferability for Domain Adaptation by Uncertainty Modeling

no code implementations2 Jun 2022 Jian Hu, Haowen Zhong, Junchi Yan, Shaogang Gong, Guile Wu, Fei Yang

However, due to the significant imbalance between the amount of annotated data in the source and target domains, usually only the target distribution is aligned to the source domain, leading to adapting unnecessary source specific knowledge to the target domain, i. e., biased domain adaptation.

Domain Adaptation Transfer Learning

Feature-Distribution Perturbation and Calibration for Generalized Person ReID

no code implementations23 May 2022 Qilei Li, Jiabo Huang, Jian Hu, Shaogang Gong

In this work, we propose a Feature-Distribution Perturbation and Calibration (PECA) method to derive generic feature representations for person ReID, which is not only discriminative across cameras but also agnostic and deployable to arbitrary unseen target domains.

Person Re-Identification

A Framework of Meta Functional Learning for Regularising Knowledge Transfer

no code implementations28 Mar 2022 Pan Li, Yanwei Fu, Shaogang Gong

The MFL computes meta-knowledge on functional regularisation generalisable to different learning tasks by which functional training on limited labelled data promotes more discriminative functions to be learned.

cross-domain few-shot learning Transfer Learning

Local-Global Associative Frame Assemble in Video Re-ID

no code implementations22 Oct 2021 Qilei Li, Jiabo Huang, Shaogang Gong

In this work, we explore jointly both local alignments and global correlations with further consideration of their mutual promotion/reinforcement so to better assemble complementary discriminative Re-ID information within all the relevant frames in video tracklets.

Decentralised Person Re-Identification with Selective Knowledge Aggregation

no code implementations21 Oct 2021 Shitong Sun, Guile Wu, Shaogang Gong

This helps to preserve model personalisation knowledge on each local client domain and learn instance-specific information.

Person Re-Identification

Cross-Sentence Temporal and Semantic Relations in Video Activity Localisation

no code implementations ICCV 2021 Jiabo Huang, Yang Liu, Shaogang Gong, Hailin Jin

Video activity localisation has recently attained increasing attention due to its practical values in automatically localising the most salient visual segments corresponding to their language descriptions (sentences) from untrimmed and unstructured videos.

Deep Clustering by Semantic Contrastive Learning

no code implementations3 Mar 2021 Jiabo Huang, Shaogang Gong

Whilst contrastive learning has recently brought notable benefits to deep clustering of unlabelled images by learning sample-specific discriminative visual features, its potential for explicitly inferring class decision boundaries is less well understood.

Contrastive Learning Deep Clustering +2

Unsupervised Noisy Tracklet Person Re-identification

no code implementations16 Jan 2021 Minxian Li, Xiatian Zhu, Shaogang Gong

Extensive comparative experiments demonstrate that the proposed STL model surpasses significantly the state-of-the-art unsupervised learning and one-shot learning re-id methods on three large tracklet person re-id benchmarks.

One-Shot Learning Person Re-Identification

A Simple Feature Augmentation for Domain Generalization

no code implementations ICCV 2021 Pan Li, Da Li, Wei Li, Shaogang Gong, Yanwei Fu, Timothy M. Hospedales

The topical domain generalization (DG) problem asks trained models to perform well on an unseen target domain with different data statistics from the source training domains.

Computer Vision Data Augmentation +1

Fewmatch: Dynamic Prototype Refinement for Semi-Supervised Few-Shot Learning

no code implementations1 Jan 2021 Xu Lan, Steven McDonagh, Shaogang Gong, Jiali Wang, Zhenguo Li, Sarah Parisot

Semi-Supervised Few-shot Learning (SS-FSL) investigates the benefit of incorporating unlabelled data in few-shot settings.

Few-Shot Learning

Striking a Balance Between Stability and Plasticity for Class-Incremental Learning

no code implementations ICCV 2021 Guile Wu, Shaogang Gong, Pan Li

With the reformulated baseline, we present two new approaches to CIL by learning class-independent knowledge and multi-perspective knowledge, respectively.

class-incremental learning Incremental Learning

Faster Person Re-Identification

1 code implementation ECCV 2020 Guan'an Wang, Shaogang Gong, Jian Cheng, Zeng-Guang Hou

In this work, we introduce a new solution for fast ReID by formulating a novel Coarse-to-Fine (CtF) hashing code search strategy, which complementarily uses short and long codes, achieving both faster speed and better accuracy.

Code Search Person Re-Identification

Unsupervised Transfer Learning with Self-Supervised Remedy

no code implementations8 Jun 2020 Jiabo Huang, Shaogang Gong

In this work, we address this problem by transfer clustering that aims to learn a discriminative latent space of the unlabelled target data in a novel domain by knowledge transfer from labelled related domains.

Domain Adaptation Few-Shot Learning +4

Decentralised Learning from Independent Multi-Domain Labels for Person Re-Identification

no code implementations7 Jun 2020 Guile Wu, Shaogang Gong

Specifically, each local client receives global model updates from the server and trains a local model using its local data independent from all the other clients.

Computer Vision Person Re-Identification

Peer Collaborative Learning for Online Knowledge Distillation

1 code implementation7 Jun 2020 Guile Wu, Shaogang Gong

Meanwhile, we employ the temporal mean model of each peer as the peer mean teacher to collaboratively transfer knowledge among peers, which helps each peer to learn richer knowledge and facilitates to optimise a more stable model with better generalisation.

Knowledge Distillation

Intra-Camera Supervised Person Re-Identification

no code implementations12 Feb 2020 Xiangping Zhu, Xiatian Zhu, Minxian Li, Pietro Morerio, Vittorio Murino, Shaogang Gong

Existing person re-identification (re-id) methods mostly exploit a large set of cross-camera identity labelled training data.

Person Re-Identification

Characteristic Regularisation for Super-Resolving Face Images

no code implementations30 Dec 2019 Zhiyi Cheng, Xiatian Zhu, Shaogang Gong

Extensive evaluations demonstrate the performance superiority of our method over state-of-the-art SR and UDA models on both genuine and artificial LR facial imagery data.

Image Super-Resolution Unsupervised Domain Adaptation

Neural Operator Search

no code implementations25 Sep 2019 Wei Li, Shaogang Gong, Xiatian Zhu

We address this limitation by additionally exploiting feature self-calibration operations, resulting in a heterogeneous search space.

Neural Architecture Search

Intra-Camera Supervised Person Re-Identification: A New Benchmark

no code implementations27 Aug 2019 Xiangping Zhu, Xiatian Zhu, Minxian Li, Vittorio Murino, Shaogang Gong

Existing person re-identification (re-id) methods rely mostly on a large set of inter-camera identity labelled training data, requiring a tedious data collection and annotation process therefore leading to poor scalability in practical re-id applications.

Multi-Label Learning Person Re-Identification

Zero-Shot Crowd Behavior Recognition

no code implementations16 Aug 2019 Xun Xu, Shaogang Gong, Timothy Hospedales

To that end, we relax the common assumption that each individual crowd video instance is only associated with a single crowd attribute.

Computer Vision Zero-Shot Learning

Universal Person Re-Identification

no code implementations22 Jul 2019 Xu Lan, Xiatian Zhu, Shaogang Gong

Most state-of-the-art person re-identification (re-id) methods depend on supervised model learning with a large set of cross-view identity labelled training data.

Domain Generalization Person Re-Identification +1

Unsupervised Deep Learning by Neighbourhood Discovery

1 code implementation25 Apr 2019 Jiabo Huang, Qi Dong, Shaogang Gong, Xiatian Zhu

Deep convolutional neural networks (CNNs) have demonstrated remarkable success in computer vision by supervisedly learning strong visual feature representations.

Computer Vision Image Classification

Instance-level Sketch-based Retrieval by Deep Triplet Classification Siamese Network

no code implementations28 Nov 2018 Peng Lu, Hangyu Lin, Yanwei Fu, Shaogang Gong, Yu-Gang Jiang, xiangyang xue

Additionally, to study the tasks of sketch-based hairstyle retrieval, this paper contributes a new instance-level photo-sketch dataset - Hairstyle Photo-Sketch dataset, which is composed of 3600 sketches and photos, and 2400 sketch-photo pairs.

General Classification Sketch-Based Image Retrieval +1

Low-Resolution Face Recognition

no code implementations21 Nov 2018 Zhiyi Cheng, Xiatian Zhu, Shaogang Gong

Whilst recent face-recognition (FR) techniques have made significant progress on recognising constrained high-resolution web images, the same cannot be said on natively unconstrained low-resolution images at large scales.

Face Recognition Super-Resolution

Self-Referenced Deep Learning

no code implementations19 Nov 2018 Xu Lan, Xiatian Zhu, Shaogang Gong

Whilst being able to create stronger target networks compared to the vanilla non-teacher based learning strategy, this scheme needs to train additionally a large teacher model with expensive computational cost.

Knowledge Distillation

Vehicle Re-Identification in Context

no code implementations25 Sep 2018 Aytaç Kanacı, Xiatian Zhu, Shaogang Gong

Existing vehicle re-identification (re-id) evaluation benchmarks consider strongly artificial test scenarios by assuming the availability of high quality images and fine-grained appearance at an almost constant image scale, reminiscent to images required for Automatic Number Plate Recognition, e. g. VeRi-776.

Vehicle Re-Identification

Semi-Supervised Deep Learning with Memory

1 code implementation ECCV 2018 Yanbei Chen, Xiatian Zhu, Shaogang Gong

We consider the semi-supervised multi-class classification problem of learning from sparse labelled and abundant unlabelled training data.

General Classification Multi-class Classification +1

Deep Association Learning for Unsupervised Video Person Re-identification

1 code implementation22 Aug 2018 Yanbei Chen, Xiatian Zhu, Shaogang Gong

In this work, to address the video person re-id task, we formulate a novel Deep Association Learning (DAL) scheme, the first end-to-end deep learning method using none of the identity labels in model initialisation and training.

Unsupervised Person Re-Identification Video-Based Person Re-Identification

Person Search by Multi-Scale Matching

no code implementations ECCV 2018 Xu Lan, Xiatian Zhu, Shaogang Gong

In contrast to previous studies, we show that sufficiently reliable person instance cropping is achievable by slightly improved state-of-the-art deep learning object detectors (e. g. Faster-RCNN), and the under-studied multi-scale matching problem in person search is a more severe barrier.

Human Detection Person Search

Open Logo Detection Challenge

2 code implementations5 Jul 2018 Hang Su, Xiatian Zhu, Shaogang Gong

In this work, we introduce a more realistic and challenging logo detection setting, called Open Logo Detection.

Person Re-Identification in Identity Regression Space

no code implementations25 Jun 2018 Hanxiao Wang, Xiatian Zhu, Shaogang Gong, Tao Xiang

Most existing person re-identification (re-id) methods are unsuitable for real-world deployment due to two reasons: Unscalability to large population size, and Inadaptability over time.

Incremental Learning Person Re-Identification

Knowledge Distillation by On-the-Fly Native Ensemble

3 code implementations NeurIPS 2018 Xu Lan, Xiatian Zhu, Shaogang Gong

Knowledge distillation is effective to train small and generalisable network models for meeting the low-memory and fast running requirements.

Knowledge Distillation

Imbalanced Deep Learning by Minority Class Incremental Rectification

1 code implementation28 Apr 2018 Qi Dong, Shaogang Gong, Xiatian Zhu

In particular, existing deep learning methods consider mostly either class balanced data or moderately imbalanced data in model training, and ignore the challenge of learning from significantly imbalanced training data.

Facial Attribute Classification

Surveillance Face Recognition Challenge

1 code implementation25 Apr 2018 Zhiyi Cheng, Xiatian Zhu, Shaogang Gong

To facilitate more studies on developing FR models that are effective and robust for low-resolution surveillance facial images, we introduce a new Surveillance Face Recognition Challenge, which we call the QMUL-SurvFace benchmark.

Computer Vision Face Recognition

Scalable Deep Learning Logo Detection

2 code implementations30 Mar 2018 Hang Su, Shaogang Gong, Xiatian Zhu

Existing logo detection methods usually consider a small number of logo classes and limited images per class with a strong assumption of requiring tedious object bounding box annotations, therefore not scalable to real-world dynamic applications.

Incremental Learning

Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-Identification

no code implementations CVPR 2018 Jingya Wang, Xiatian Zhu, Shaogang Gong, Wei Li

Most existing person re-identification (re-id) methods require supervised model learning from a separate large set of pairwise labelled training data for every single camera pair.

Unsupervised Domain Adaptation Unsupervised Person Re-Identification

Harmonious Attention Network for Person Re-Identification

1 code implementation CVPR 2018 Wei Li, Xiatian Zhu, Shaogang Gong

Existing person re-identification (re-id) methods either assume the availability of well-aligned person bounding box images as model input or rely on constrained attention selection mechanisms to calibrate misaligned images.

Person Re-Identification

Class Rectification Hard Mining for Imbalanced Deep Learning

1 code implementation ICCV 2017 Qi Dong, Shaogang Gong, Xiatian Zhu

Recognising detailed facial or clothing attributes in images of people is a challenging task for computer vision, especially when the training data are both in very large scale and extremely imbalanced among different attribute classes.

Computer Vision

Recent Advances in Zero-shot Recognition

no code implementations13 Oct 2017 Yanwei Fu, Tao Xiang, Yu-Gang Jiang, xiangyang xue, Leonid Sigal, Shaogang Gong

With the recent renaissance of deep convolution neural networks, encouraging breakthroughs have been achieved on the supervised recognition tasks, where each class has sufficient training data and fully annotated training data.

Open Set Learning Zero-Shot Learning

RGB-Infrared Cross-Modality Person Re-Identification

no code implementations ICCV 2017 Ancong Wu, Wei-Shi Zheng, Hong-Xing Yu, Shaogang Gong, Jian-Huang Lai

To that end, matching RGB images with infrared images is required, which are heterogeneous with very different visual characteristics.

Ranked #3 on Cross-Modal Person Re-Identification on SYSU-MM01 (mAP (All-search & Single-shot) metric)

Cross-Modality Person Re-identification Cross-Modal Person Re-Identification

Attribute Recognition by Joint Recurrent Learning of Context and Correlation

no code implementations ICCV 2017 Jingya Wang, Xiatian Zhu, Shaogang Gong, Wei Li

Recognising semantic pedestrian attributes in surveillance images is a challenging task for computer vision, particularly when the imaging quality is poor with complex background clutter and uncontrolled viewing conditions, and the number of labelled training data is small.

Computer Vision Multi-Label Image Classification +1

Deep Reinforcement Learning Attention Selection for Person Re-Identification

no code implementations10 Jul 2017 Xu Lan, Hanxiao Wang, Shaogang Gong, Xiatian Zhu

Existing person re-identification (re-id) methods assume the provision of accurately cropped person bounding boxes with minimum background noise, mostly by manually cropping.

Person Re-Identification reinforcement-learning

Actor-Critic Sequence Training for Image Captioning

no code implementations29 Jun 2017 Li Zhang, Flood Sung, Feng Liu, Tao Xiang, Shaogang Gong, Yongxin Yang, Timothy M. Hospedales

Generating natural language descriptions of images is an important capability for a robot or other visual-intelligence driven AI agent that may need to communicate with human users about what it is seeing.

Image Captioning reinforcement-learning

Discovering Visual Concept Structure with Sparse and Incomplete Tags

no code implementations30 May 2017 Jingya Wang, Xiatian Zhu, Shaogang Gong

As a result, our model is able to discover more accurate semantic correlation between textual tags and visual features, and finally providing favourable visual semantics interpretation even with highly sparse and incomplete tags.

TAG

Person Re-Identification by Deep Joint Learning of Multi-Loss Classification

no code implementations12 May 2017 Wei Li, Xiatian Zhu, Shaogang Gong

Existing person re-identification (re-id) methods rely mostly on either localised or global feature representation alone.

feature selection General Classification +1

Semantic Autoencoder for Zero-Shot Learning

4 code implementations CVPR 2017 Elyor Kodirov, Tao Xiang, Shaogang Gong

We show that with this additional reconstruction constraint, the learned projection function from the seen classes is able to generalise better to the new unseen classes.

Zero-Shot Learning

Deep Learning Logo Detection with Data Expansion by Synthesising Context

no code implementations29 Dec 2016 Hang Su, Xiatian Zhu, Shaogang Gong

Logo detection in unconstrained images is challenging, particularly when only very sparse labelled training images are accessible due to high labelling costs.

Highly Efficient Regression for Scalable Person Re-Identification

no code implementations5 Dec 2016 Hanxiao Wang, Shaogang Gong, Tao Xiang

Existing person re-identification models are poor for scaling up to large data required in real-world applications due to: (1) Complexity: They employ complex models for optimal performance resulting in high computational cost for training at a large scale; (2) Inadaptability: Once trained, they are unsuitable for incremental update to incorporate any new data available.

Active Learning Person Re-Identification

Human-In-The-Loop Person Re-Identification

no code implementations5 Dec 2016 Hanxiao Wang, Shaogang Gong, Xiatian Zhu, Tao Xiang

Current person re-identification (re-id) methods assume that (1) pre-labelled training data are available for every camera pair, (2) the gallery size for re-identification is moderate.

Ensemble Learning Incremental Learning +1

Multi-Task Zero-Shot Action Recognition with Prioritised Data Augmentation

no code implementations26 Nov 2016 Xun Xu, Timothy M. Hospedales, Shaogang Gong

In this work, we improve the ability of ZSL to generalise across this domain shift in both model- and data-centric ways by formulating a visual-semantic mapping with better generalisation properties and a dynamic data re-weighting method to prioritise auxiliary data that are relevant to the target classes.

Action Recognition Data Augmentation +1

Person Re-Identification by Unsupervised Video Matching

no code implementations25 Nov 2016 Xiaolong Ma, Xiatian Zhu, Shaogang Gong, Xudong Xie, Jianming Hu, Kin-Man Lam, Yisheng Zhong

Crucially, this model does not require pairwise labelled training data (i. e. unsupervised) therefore readily scalable to large scale camera networks of arbitrary camera pairs without the need for exhaustive data annotation for every camera pair.

Dynamic Time Warping Person Re-Identification +1

Deep Learning Prototype Domains for Person Re-Identification

no code implementations17 Oct 2016 Arne Schumann, Shaogang Gong, Tobias Schuchert

Person re-identification (re-id) is the task of matching multiple occurrences of the same person from different cameras, poses, lighting conditions, and a multitude of other factors which alter the visual appearance.

Person Re-Identification Unsupervised Domain Adaptation

Multi-Task Curriculum Transfer Deep Learning of Clothing Attributes

no code implementations12 Oct 2016 Qi Dong, Shaogang Gong, Xiatian Zhu

Recognising detailed clothing characteristics (fine-grained attributes) in unconstrained images of people in-the-wild is a challenging task for computer vision, especially when there is only limited training data from the wild whilst most data available for model learning are captured in well-controlled environments using fashion models (well lit, no background clutter, frontal view, high-resolution).

Computer Vision Transfer Learning

Unsupervised Cross-Dataset Transfer Learning for Person Re-Identification

no code implementations CVPR 2016 Peixi Peng, Tao Xiang, Yao-Wei Wang, Massimiliano Pontil, Shaogang Gong, Tiejun Huang, Yonghong Tian

Most existing person re-identification (Re-ID) approaches follow a supervised learning framework, in which a large number of labelled matching pairs are required for training.

Dictionary Learning Person Re-Identification +1

Person Re-Identification by Discriminative Selection in Video Ranking

no code implementations23 Jan 2016 Taiqing Wang, Shaogang Gong, Xiatian Zhu, Shengjin Wang

Current person re-identification (ReID) methods typically rely on single-frame imagery features, whilst ignoring space-time information from image sequences often available in the practical surveillance scenarios.

Gait Recognition Person Re-Identification

Unsupervised Domain Adaptation for Zero-Shot Learning

no code implementations ICCV 2015 Elyor Kodirov, Tao Xiang, Zhen-Yong Fu, Shaogang Gong

Zero-shot learning (ZSL) can be considered as a special case of transfer learning where the source and target domains have different tasks/label spaces and the target domain is unlabelled, providing little guidance for the knowledge transfer.

Action Recognition Transfer Learning +1

Partial Person Re-Identification

no code implementations ICCV 2015 Wei-Shi Zheng, Xiang Li, Tao Xiang, Shengcai Liao, Jian-Huang Lai, Shaogang Gong

We address a new partial person re-identification (re-id) problem, where only a partial observation of a person is available for matching across different non-overlapping camera views.

Person Re-Identification

Multi-Scale Learning for Low-Resolution Person Re-Identification

no code implementations ICCV 2015 Xiang Li, Wei-Shi Zheng, Xiaojuan Wang, Tao Xiang, Shaogang Gong

In real world person re-identification (re-id), images of people captured at very different resolutions from different locations need be matched.

Person Re-Identification

Transductive Zero-Shot Action Recognition by Word-Vector Embedding

no code implementations13 Nov 2015 Xun Xu, Timothy Hospedales, Shaogang Gong

This is a more challenging problem than existing ZSL of still images and/or attributes, because the mapping between video spacetime features of actions and the semantic space is more complex and harder to learn for the purpose of generalising over any cross-category domain shift.

Action Recognition Zero-Shot Action Recognition

Free-hand Sketch Synthesis with Deformable Stroke Models

no code implementations9 Oct 2015 Yi Li, Yi-Zhe Song, Timothy Hospedales, Shaogang Gong

We present a generative model which can automatically summarize the stroke composition of free-hand sketches of a given category.

Discovery of Shared Semantic Spaces for Multi-Scene Video Query and Summarization

no code implementations27 Jul 2015 Xun Xu, Timothy Hospedales, Shaogang Gong

The growing rate of public space CCTV installations has generated a need for automated methods for exploiting video surveillance data including scene understanding, query, behaviour annotation and summarization.

Scene Understanding Semantic Similarity +2

Zero-Shot Object Recognition by Semantic Manifold Distance

no code implementations CVPR 2015 Zhenyong Fu, Tao Xiang, Elyor Kodirov, Shaogang Gong

The semantic manifold structure is used to redefine the distance metric in the semantic embedding space for more effective ZSL.

Object Recognition Transfer Learning +1

Transductive Multi-label Zero-shot Learning

no code implementations26 Mar 2015 Yanwei Fu, Yongxin Yang, Tim Hospedales, Tao Xiang, Shaogang Gong

Zero-shot learning has received increasing interest as a means to alleviate the often prohibitive expense of annotating training data for large scale recognition problems.

Multi-label zero-shot learning

Semantic Embedding Space for Zero-Shot Action Recognition

no code implementations5 Feb 2015 Xun Xu, Timothy Hospedales, Shaogang Gong

In this framework a mapping is constructed between visual features and a human interpretable semantic description of each category, allowing categories to be recognised in the absence of any training data.

Action Recognition Data Augmentation +1

Robust Subjective Visual Property Prediction from Crowdsourced Pairwise Labels

no code implementations25 Jan 2015 Yanwei Fu, Timothy M. Hospedales, Tao Xiang, Jiechao Xiong, Shaogang Gong, Yizhou Wang, Yuan YAO

In this paper, we propose a more principled way to identify annotation outliers by formulating the subjective visual property prediction task as a unified robust learning to rank problem, tackling both the outlier detection and learning to rank jointly.

Learning-To-Rank Outlier Detection

Transductive Multi-view Zero-Shot Learning

no code implementations19 Jan 2015 Yanwei Fu, Timothy M. Hospedales, Tao Xiang, Shaogang Gong

A projection from a low-level feature space to the semantic representation space is learned from the auxiliary dataset and is applied without adaptation to the target dataset.

Transfer Learning Zero-Shot Learning

Learning from Multiple Sources for Video Summarisation

no code implementations13 Jan 2015 Xiatian Zhu, Chen Change Loy, Shaogang Gong

Many visual surveillance tasks, e. g. video summarisation, is conventionally accomplished through analysing imagerybased features.

Video Understanding

Semantic Graph for Zero-Shot Learning

no code implementations16 Jun 2014 Zhen-Yong Fu, Tao Xiang, Shaogang Gong

Specifically, in contrast to previous work which ignores the semantic relationships between seen classes and focus merely on those between seen and unseen classes, in this paper a novel approach based on a semantic graph is proposed to represent the relationships between all the seen and unseen class in a semantic word space.

Transfer Learning Zero-Shot Learning

Constructing Robust Affinity Graphs for Spectral Clustering

no code implementations CVPR 2014 Xiatian Zhu, Chen Change Loy, Shaogang Gong

Spectral clustering requires robust and meaningful affinity graphs as input in order to form clusters with desired structures that can well support human intuition.

Cumulative Attribute Space for Age and Crowd Density Estimation

no code implementations CVPR 2013 Ke Chen, Shaogang Gong, Tao Xiang, Chen Change Loy

A number of computer vision problems such as human age estimation, crowd density estimation and body/face pose (view angle) estimation can be formulated as a regression problem by learning a mapping function between a high dimensional vector-formed feature input and a scalarvalued output.

Age Estimation Computer Vision +2

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