no code implementations • 4 Feb 2024 • Yifeng He, Jiabo Huang, Yuyang Rong, Yiwen Guo, Ethan Wang, Hao Chen
The remarkable capability of large language models (LLMs) in generating high-quality code has drawn increasing attention in the software testing community.
no code implementations • 24 Jan 2024 • Dezhao Luo, Shaogang Gong, Jiabo Huang, Hailin Jin, Yang Liu
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.
no code implementations • 4 Sep 2023 • Jiabo Huang, Jianyu Zhao, Yuyang Rong, Yiwen Guo, Yifeng He, Hao Chen
The test cases are obtained with the assistance of a customized fuzzer and are only required during pre-training.
no code implementations • CVPR 2023 • Dezhao Luo, Jiabo Huang, Shaogang Gong, Hailin Jin, Yang Liu
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.
no code implementations • 26 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.
no code implementations • 23 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.
no code implementations • 22 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.
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.
no code implementations • 3 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.
no code implementations • 8 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.
1 code implementation • CVPR 2020 • Jiabo Huang, Shaogang Gong, Xiatian Zhu
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.
no code implementations • 21 Oct 2019 • Jiabo Huang, Xiaohua Xie, Wei-Shi Zheng
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.
1 code implementation • 25 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.