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Claim this profileWorks217 from public data
- Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers3,852
This paper deploys a pure transformer to encode an image as a sequence of patches, termed SEgmentation TRansformer (SETR), and shows that SETR achieves new state of the art on ADE20K, Pascal Context, and competitive results on Cityscapes.
- Harmonious Attention Network for Person Re-identification1,463
A novel Harmonious Attention CNN (HA-CNN) model is formulated for joint learning of soft pixel attention and hard regional attention along with simultaneous optimisation of feature representations, dedicated to optimise person re-id in uncontrolled (misaligned) images.
- Multimodal Learning With Transformers: A Survey1,183
This paper presents a comprehensive survey of Transformer techniques oriented at multimodal data, and a summary of the common challenges and designs shared by the multi-modal Transformer models and applications.
- Person Re-identification by Video Ranking642
A novel model to automatically select the most discriminative video fragments from noisy image sequences of people where more reliable space-time features can be extracted, whilst simultaneously to learn a video ranking function for person re-id is presented.
- Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-identification637
This work introduces an Transferable Joint Attribute-Identity Deep Learning (TJ-AIDL) for simultaneously learning an attribute-semantic and identity-discriminative feature representation space transferrable to any new (unseen) target domain for re-id tasks without the need for collecting new labelled training data from the target domain.
- Distribution-Aware Coordinate Representation for Human Pose Estimation552
This work finds that the process of decoding the predicted heatmaps into the final joint coordinates in the original image space is surprisingly significant for the performance, and forms a novel Distribution-Aware coordinate Representation of Keypoints (DARK) method.
- Person Re-Identification by Deep Joint Learning of Multi-Loss Classification426
This work designs a novel CNN architecture for Jointly Learning Multi-Loss (JLML) of local and global discriminative feature optimisation subject concurrently to the same re-id labelled information.
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- Imbalanced Deep Learning by Minority Class Incremental Rectification373
These experimental results demonstrate the performance advantages and model scalability of the proposed batch-wise incremental minority class rectification model over the existing state-of-the-art models for addressing the problem of imbalanced data learning.
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- Person Re-Identification by Camera Correlation Aware Feature Augmentation338
This work forms a novel view-specific person re-identification framework from the feature augmentation point of view, called Camera coR relation Aware Feature augmenTation (CRAFT), and presents a domain-generic deep person appearance representation which is designed particularly to be towards view invariant for facilitating cross-view adaptation by CRAFT.
- Fast Human Pose Estimation299
This work investigates the under-studied but practically critical pose model efficiency problem, and presents a new Fast Pose Distillation (FPD) model learning strategy that trains a lightweight pose neural network architecture capable of executing rapidly with low computational cost.
- Incremental Few-Shot Object Detection269
OpeN-ended Centre nEt (ONCE), a detector designed for incrementally learning to detect novel class objects with few examples, is proposed by an elegant adaptation of the efficient CentreNet detector to the few-shot learning scenario, and meta-learning a class-wise code generator model for registering novel classes.
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- Unsupervised Person Re-identification by Deep Learning Tracklet Association256
A Tracklet Association Unsupervised Deep Learning (TAUDL) framework characterised by jointly learning per-camera (within-camera) tracklet association (labelling) and cross-camera tracklet correlation by maximising the discovery of most likely tracklet relationships across camera views.
- Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer243
This work proposes to simplify the meta-learning task by focusing solely on the simplest component – the classifier, whilst leaving the en-coder and decoder to pre-training, and introduces a Classifier Weight Transformer (CWT) designed to dynamically adapt the support-set trained classifier’s weights to each query image in an inductive way.
- Person Re-Identification by Discriminative Selection in Video Ranking231
This work presents a novel model to automatically select the most discriminative video fragments from noisy/incomplete image sequences of people from which reliable space-time and appearance features can be computed, whilst simultaneously learning a video ranking function for person ReID.
- Semi-Supervised and Unsupervised Deep Visual Learning: A Survey228
This survey reviews the recent advanced deep learning algorithms on semi- supervised learning and unsupervised learning for visual recognition from a unified perspective and proposes a unified taxonomy to offer a holistic understanding of the state-of-the-art in these areas.
- Class Rectification Hard Mining for Imbalanced Deep Learning227
This work develops an end-to-end deep learning framework capable of avoiding the dominant effect of majority classes by discovering sparsely sampled boundaries of minority classes by introducing a Class Rectification Loss (CRL) regularising algorithm.
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- Unsupervised Tracklet Person Re-Identification188
This work forms an Unsupervised Tracklet Association Learning (UTAL) framework that is capable of incrementally discovering and exploiting the underlying re-id discriminative information from automatically generated person tracklet data end-to-end.
- Semi-Supervised Learning under Class Distribution Mismatch182
This work addresses under-studied and realistic SSL problem by a novel algorithm named Uncertainty-Aware Self-Distillation (UASD), which produces soft targets that avoid catastrophic error propagation, and empower learning effectively from unconstrained unlabelled data with out-of-distribution (OOD) samples.
- Stochastic Classifiers for Unsupervised Domain Adaptation179
This paper introduces a novel method called STochastic clAssifieRs (STAR) for addressing the problem of misaligned local regions between source and target domain, which finds that using more classifiers leads to better performance, but also introduces more model parameters, therefore risking overfitting.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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