1.1k citations · 4.4k across the 93 of their papers we have counts for
19 papers · 1 filter
Margin-Based Transfer Bounds for Meta Learning with Deep Feature Embedding
Jiechao Guan, Zhiwu Lu, Tao Xiang +1
By transferring knowledge learned from seen/previous tasks, meta learning aims to generalize well to unseen/future tasks. Existing meta-learning approaches have shown promising emp…
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
Sixiao Zheng, Jiachen Lu, Hengshuang Zhao +8
Most recent semantic segmentation methods adopt a fully-convolutional network (FCN) with an encoder-decoder architecture. The encoder progressively reduces the spatial resolution a…
Boundary-sensitive Pre-training for Temporal Localization in Videos
Mengmeng Xu, Juan-Manuel Perez-Rua, Victor Escorcia +5
Many video analysis tasks require temporal localization thus detection of content changes. However, most existing models developed for these tasks are pre-trained on general video…
Cross-Modal Hierarchical Modelling for Fine-Grained Sketch Based Image Retrieval
Aneeshan Sain, Ayan Kumar Bhunia, Yongxin Yang +2
Sketch as an image search query is an ideal alternative to text in capturing the fine-grained visual details. Prior successes on fine-grained sketch-based image retrieval (FG-SBIR)…
BézierSketch: A generative model for scalable vector sketches
Ayan Das, Yongxin Yang, Timothy Hospedales +2
The study of neural generative models of human sketches is a fascinating contemporary modeling problem due to the links between sketch image generation and the human drawing proces…
On Learning Semantic Representations for Million-Scale Free-Hand Sketches
Peng Xu, Yongye Huang, Tongtong Yuan +4
In this paper, we study learning semantic representations for million-scale free-hand sketches. This is highly challenging due to the domain-unique traits of sketches, e.g., divers…
Egocentric Action Recognition by Video Attention and Temporal Context
Juan-Manuel Perez-Rua, Antoine Toisoul, Brais Martinez +4
We present the submission of Samsung AI Centre Cambridge to the CVPR2020 EPIC-Kitchens Action Recognition Challenge. In this challenge, action recognition is posed as the problem o…
Long-Term Cloth-Changing Person Re-identification
Xuelin Qian, Wenxuan Wang, Li Zhang +5
Person re-identification (Re-ID) aims to match a target person across camera views at different locations and times. Existing Re-ID studies focus on the short-term cloth-consistent…
Knowing What, Where and When to Look: Efficient Video Action Modeling with Attention
Juan-Manuel Perez-Rua, Brais Martinez, Xiatian Zhu +3
Attentive video modeling is essential for action recognition in unconstrained videos due to their rich yet redundant information over space and time. However, introducing attention…
Domain-Adaptive Few-Shot Learning
An Zhao, Mingyu Ding, Zhiwu Lu +5
Existing few-shot learning (FSL) methods make the implicit assumption that the few target class samples are from the same domain as the source class samples. However, in practice t…
Deep Domain-Adversarial Image Generation for Domain Generalisation
Kaiyang Zhou, Yongxin Yang, Timothy Hospedales +1
Machine learning models typically suffer from the domain shift problem when trained on a source dataset and evaluated on a target dataset of different distribution. To overcome thi…
Incremental Few-Shot Object Detection
Juan-Manuel Perez-Rua, Xiatian Zhu, Timothy Hospedales +1
Most existing object detection methods rely on the availability of abundant labelled training samples per class and offline model training in a batch mode. These requirements subst…
AdarGCN: Adaptive Aggregation GCN for Few-Shot Learning
Jianhong Zhang, Manli Zhang, Zhiwu Lu +2
Existing few-shot learning (FSL) methods assume that there exist sufficient training samples from source classes for knowledge transfer to target classes with few training samples.…
Domain Adaptive Ensemble Learning
Kaiyang Zhou, Yongxin Yang, Yu Qiao +1
The problem of generalizing deep neural networks from multiple source domains to a target one is studied under two settings: When unlabeled target data is available, it is a multi-…
Fine-Grained Instance-Level Sketch-Based Video Retrieval
Peng Xu, Kun Liu, Tao Xiang +4
Existing sketch-analysis work studies sketches depicting static objects or scenes. In this work, we propose a novel cross-modal retrieval problem of fine-grained instance-level ske…
Sketch Less for More: On-the-Fly Fine-Grained Sketch Based Image Retrieval
Ayan Kumar Bhunia, Yongxin Yang, Timothy M. Hospedales +2
Fine-grained sketch-based image retrieval (FG-SBIR) addresses the problem of retrieving a particular photo instance given a user's query sketch. Its widespread applicability is how…
Meta-Learning across Meta-Tasks for Few-Shot Learning
Nanyi Fei, Zhiwu Lu, Yizhao Gao +3
Existing meta-learning based few-shot learning (FSL) methods typically adopt an episodic training strategy whereby each episode contains a meta-task. Across episodes, these tasks a…
Few-Shot Learning as Domain Adaptation: Algorithm and Analysis
Jiechao Guan, Zhiwu Lu, Tao Xiang +1
To recognize the unseen classes with only few samples, few-shot learning (FSL) uses prior knowledge learned from the seen classes. A major challenge for FSL is that the distributio…
Deep Learning for Person Re-identification: A Survey and Outlook
Mang Ye, Jianbing Shen, Gaojie Lin +3
Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-overlapping cameras. With the advancement of deep neural networks and increasing demand…