Search Results for author:
Found 13 papers, 2 papers with code
UniTSyn: A Large-Scale Dataset Capable of Enhancing the Prowess of Large Language Models for Program Testing
The remarkable capability of large language models (LLMs) in generating high-quality code has drawn increasing attention in the software testing community.
Generative Video Diffusion for Unseen Cross-Domain Video Moment Retrieval
We address two problems in video editing for optimising unseen domain VMR: (1) generation of high-quality simulation videos of different moments with subtle distinctions, (2) selection of simulation videos that complement existing source training videos without introducing harmful noise or unnecessary repetitions.
Code Representation Pre-training with Complements from Program Executions
The test cases are obtained with the assistance of a customized fuzzer and are only required during pre-training.
Towards Generalisable Video Moment Retrieval: Visual-Dynamic Injection to Image-Text Pre-Training
The correlation between the vision and text is essential for video moment retrieval (VMR), however, existing methods heavily rely on separate pre-training feature extractors for visual and textual understanding.
Video Activity Localisation with Uncertainties in Temporal Boundary
Such uncertainties in temporal labelling are currently ignored in model training, resulting in learning mis-matched video-text correlation with poor generalisation in test.
Feature-Distribution Perturbation and Calibration for Generalized Person ReID
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.
Local-Global Associative Frame Assemble in Video Re-ID
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.
Cross-Sentence Temporal and Semantic Relations in Video Activity Localisation
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
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.
Unsupervised Transfer Learning with Self-Supervised Remedy
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.
Deep Semantic Clustering by Partition Confidence Maximisation
In this work, we propose to solve this problem by learning the most confident clustering solution from all the possible separations, based on the observation that assigning samples from the same semantic categories into different clusters will reduce both the intra-cluster compactness and inter-cluster diversity, i. e. lower partition confidence.
Batch Face Alignment using a Low-rank GAN
This paper studies the problem of aligning a set of face images of the same individual into a normalized image while removing the outliers like partial occlusion, extreme facial expression as well as significant illumination variation.
Unsupervised Deep Learning by Neighbourhood Discovery
Deep convolutional neural networks (CNNs) have demonstrated remarkable success in computer vision by supervisedly learning strong visual feature representations.
