95 citations · 210 across the 26 of their papers we have counts for
26 papers
DSBD: Dual-Aligned Structural Basis Distillation for Graph Domain Adaptation
Yingxu Wang, Kunyu Zhang, Jiaxin Huang +4
Graph domain adaptation (GDA) aims to transfer knowledge from a labeled source graph to an unlabeled target graph under distribution shifts. However, existing methods are largely f…
A Survey on Vision Autoregressive Model
Kai Jiang, Jiaxing Huang
Autoregressive models have demonstrated great performance in natural language processing (NLP) with impressive scalability, adaptability and generalizability. Inspired by their not…
Open-Vocabulary Object Detection via Language Hierarchy
Jiaxing Huang, Jingyi Zhang, Kai Jiang +1
Recent studies on generalizable object detection have attracted increasing attention with additional weak supervision from large-scale datasets with image-level labels. However, we…
Historical Test-time Prompt Tuning for Vision Foundation Models
Jingyi Zhang, Jiaxing Huang, Xiaoqin Zhang +2
Test-time prompt tuning, which learns prompts online with unlabelled test samples during the inference stage, has demonstrated great potential by learning effective prompts on-the-…
LongHalQA: Long-Context Hallucination Evaluation for MultiModal Large Language Models
Han Qiu, Jiaxing Huang, Peng Gao +4
Hallucination, a phenomenon where multimodal large language models~(MLLMs) tend to generate textual responses that are plausible but unaligned with the image, has become one major…
Foundation Models for Remote Sensing and Earth Observation: A Survey
Aoran Xiao, Weihao Xuan, Junjue Wang +4
Remote Sensing (RS) is a crucial technology for observing, monitoring, and interpreting our planet, with broad applications across geoscience, economics, humanitarian fields, etc.…
A Survey on Evaluation of Multimodal Large Language Models
Jiaxing Huang, Jingyi Zhang
Multimodal Large Language Models (MLLMs) mimic human perception and reasoning system by integrating powerful Large Language Models (LLMs) with various modality encoders (e.g., visi…
Masked AutoDecoder is Effective Multi-Task Vision Generalist
Han Qiu, Jiaxing Huang, Peng Gao +3
Inspired by the success of general-purpose models in NLP, recent studies attempt to unify different vision tasks in the same sequence format and employ autoregressive Transformers…
LLMs Meet VLMs: Boost Open Vocabulary Object Detection with Fine-grained Descriptors
Sheng Jin, Xueying Jiang, Jiaxing Huang +2
Inspired by the outstanding zero-shot capability of vision language models (VLMs) in image classification tasks, open-vocabulary object detection has attracted increasing interest…
Learning to Prompt Segment Anything Models
Jiaxing Huang, Kai Jiang, Jingyi Zhang +4
Segment Anything Models (SAMs) like SEEM and SAM have demonstrated great potential in learning to segment anything. The core design of SAMs lies with Promptable Segmentation, which…
DA-BEV: Unsupervised Domain Adaptation for Bird's Eye View Perception
Kai Jiang, Jiaxing Huang, Weiying Xie +3
Camera-only Bird's Eye View (BEV) has demonstrated great potential in environment perception in a 3D space. However, most existing studies were conducted under a supervised setup w…
Domain Adaptation for Large-Vocabulary Object Detectors
Kai Jiang, Jiaxing Huang, Weiying Xie +4
Large-vocabulary object detectors (LVDs) aim to detect objects of many categories, which learn super objectness features and can locate objects accurately while applied to various…
Visual Instruction Tuning towards General-Purpose Multimodal Model: A Survey
Jiaxing Huang, Jingyi Zhang, Kai Jiang +2
Traditional computer vision generally solves each single task independently by a dedicated model with the task instruction implicitly designed in the model architecture, arising tw…
PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds
Aoran Xiao, Jiaxing Huang, Dayan Guan +3
LiDAR point clouds, which are usually scanned by rotating LiDAR sensors continuously, capture precise geometry of the surrounding environment and are crucial to many autonomous det…
Domain Adaptive Video Segmentation via Temporal Pseudo Supervision
Yun Xing, Dayan Guan, Jiaxing Huang +1
Video semantic segmentation has achieved great progress under the supervision of large amounts of labelled training data. However, domain adaptive video segmentation, which can mit…
Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data
Jiaxing Huang, Dayan Guan, Aoran Xiao +1
Unsupervised domain adaptation aims to align a labeled source domain and an unlabeled target domain, but it requires to access the source data which often raises concerns in data p…
GenCo: Generative Co-training for Generative Adversarial Networks with Limited Data
Kaiwen Cui, Jiaxing Huang, Zhipeng Luo +3
Training effective Generative Adversarial Networks (GANs) requires large amounts of training data, without which the trained models are usually sub-optimal with discriminator over-…
Domain Adaptive Video Segmentation via Temporal Consistency Regularization
Dayan Guan, Jiaxing Huang, Aoran Xiao +1
Video semantic segmentation is an essential task for the analysis and understanding of videos. Recent efforts largely focus on supervised video segmentation by learning from fully…
FBC-GAN: Diverse and Flexible Image Synthesis via Foreground-Background Composition
Kaiwen Cui, Gongjie Zhang, Fangneng Zhan +2
Generative Adversarial Networks (GANs) have become the de-facto standard in image synthesis. However, without considering the foreground-background decomposition, existing GANs ten…
Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentation
Aoran Xiao, Jiaxing Huang, Dayan Guan +2
Knowledge transfer from synthetic to real data has been widely studied to mitigate data annotation constraints in various computer vision tasks such as semantic segmentation. Howev…
Semi-Supervised Domain Adaptation via Adaptive and Progressive Feature Alignment
Jiaxing Huang, Dayan Guan, Aoran Xiao +1
Contemporary domain adaptive semantic segmentation aims to address data annotation challenges by assuming that target domains are completely unannotated. However, annotating a few…
Spectral Unsupervised Domain Adaptation for Visual Recognition
Jingyi Zhang, Jiaxing Huang, Zichen Tian +1
Though unsupervised domain adaptation (UDA) has achieved very impressive progress recently, it remains a great challenge due to missing target annotations and the rich discrepancy…
Category Contrast for Unsupervised Domain Adaptation in Visual Tasks
Jiaxing Huang, Dayan Guan, Aoran Xiao +2
Instance contrast for unsupervised representation learning has achieved great success in recent years. In this work, we explore the idea of instance contrastive learning in unsuper…
RDA: Robust Domain Adaptation via Fourier Adversarial Attacking
Jiaxing Huang, Dayan Guan, Aoran Xiao +1
Unsupervised domain adaptation (UDA) involves a supervised loss in a labeled source domain and an unsupervised loss in an unlabeled target domain, which often faces more severe ove…