no code implementations • 27 Mar 2024 • Adilbek Karmanov, Dayan Guan, Shijian Lu, Abdulmotaleb El Saddik, Eric Xing
TDA works with a lightweight key-value cache that maintains a dynamic queue with few-shot pseudo labels as values and the corresponding test-sample features as keys.
2 code implementations • 5 Dec 2023 • Rizhao Cai, Zirui Song, Dayan Guan, Zhenhao Chen, Xing Luo, Chenyu Yi, Alex Kot
Large Multimodal Models (LMMs) such as GPT-4V and LLaVA have shown remarkable capabilities in visual reasoning with common image styles.
Ranked #1000000000 on Visual Question Answering on MS COCO
no code implementations • 26 Sep 2023 • Eman Ali, Dayan Guan, Shijian Lu, Abdulmotaleb Elsaddik
NtUA works as a key-value cache that formulates visual features and predicted pseudo-labels of the few-shot unlabelled target samples as key-value pairs.
1 code implementation • CVPR 2023 • Aoran Xiao, Jiaxing Huang, Weihao Xuan, Ruijie Ren, Kangcheng Liu, Dayan Guan, Abdulmotaleb El Saddik, Shijian Lu, Eric Xing
In addition, we design a domain randomization technique that alternatively randomizes the geometry styles of point clouds and aggregates their embeddings, ultimately leading to a generalizable model that can improve 3DSS under various adverse weather effectively.
2 code implementations • 30 Jul 2022 • Aoran Xiao, Jiaxing Huang, Dayan Guan, Kaiwen Cui, Shijian Lu, Ling Shao
The first is scene-level swapping which exchanges point cloud sectors of two LiDAR scans that are cut along the azimuth axis.
1 code implementation • 6 Jul 2022 • Yun Xing, Dayan Guan, Jiaxing Huang, Shijian Lu
Specifically, we design cross-frame pseudo labelling to provide pseudo supervision from previous video frames while learning from the augmented current video frames.
1 code implementation • CVPR 2022 • Dayan Guan, Jiaxing Huang, Aoran Xiao, Shijian Lu
We build the balanced subclass distributions by clustering pixels of each original class into multiple subclasses of similar sizes, which provide class-balanced pseudo supervision to regularize the class-biased segmentation.
1 code implementation • 28 Feb 2022 • Aoran Xiao, Jiaxing Huang, Dayan Guan, Xiaoqin Zhang, Shijian Lu, Ling Shao
The convergence of point cloud and DNNs has led to many deep point cloud models, largely trained under the supervision of large-scale and densely-labelled point cloud data.
1 code implementation • NeurIPS 2021 • Jiaxing Huang, Dayan Guan, Aoran Xiao, Shijian Lu
To this end, we design an innovative historical contrastive learning (HCL) technique that exploits historical source hypothesis to make up for the absence of source data in UMA.
1 code implementation • ICCV 2021 • Dayan Guan, Jiaxing Huang, Aoran Xiao, Shijian Lu
This paper presents DA-VSN, a domain adaptive video segmentation network that addresses domain gaps in videos by temporal consistency regularization (TCR) for consecutive frames of target-domain videos.
1 code implementation • 12 Jul 2021 • Aoran Xiao, Jiaxing Huang, Dayan Guan, Fangneng Zhan, Shijian Lu
Extensive experiments show that SynLiDAR provides a high-quality data source for studying 3D transfer and the proposed PCT achieves superior point cloud translation consistently across the three setups.
1 code implementation • ICCV 2021 • Jiaxing Huang, Dayan Guan, Aoran Xiao, Shijian Lu
With FAA-generated samples, the training can continue the 'random walk' and drift into an area with a flat loss landscape, leading to more robust domain adaptation.
1 code implementation • CVPR 2022 • Jiaxing Huang, Dayan Guan, Aoran Xiao, Shijian Lu, Ling Shao
In this work, we explore the idea of instance contrastive learning in unsupervised domain adaptation (UDA) and propose a novel Category Contrast technique (CaCo) that introduces semantic priors on top of instance discrimination for visual UDA tasks.
no code implementations • 5 Jun 2021 • Jiaxing Huang, Dayan Guan, Aoran Xiao, Shijian Lu
We position the few labeled target samples as references that gauge the similarity between source and target features and guide adaptive inter-domain alignment for learning more similar source features.
no code implementations • 24 Mar 2021 • Jiaxing Huang, Dayan Guan, Shijian Lu, Aoran Xiao
Recent progresses in domain adaptive semantic segmentation demonstrate the effectiveness of adversarial learning (AL) in unsupervised domain adaptation.
1 code implementation • CVPR 2021 • Jiaxing Huang, Dayan Guan, Aoran Xiao, Shijian Lu
The inter-task regularization exploits the complementary nature of instance segmentation and semantic segmentation and uses it as a constraint for better feature alignment across domains.
Ranked #2 on Domain Adaptation on Panoptic SYNTHIA-to-Mapillary
1 code implementation • CVPR 2021 • Jiaxing Huang, Dayan Guan, Aoran Xiao, Shijian Lu
It has been studied widely by domain randomization that transfers source images to different styles in spatial space for learning domain-agnostic features.
1 code implementation • 1 Mar 2021 • Aoran Xiao, Xiaofei Yang, Shijian Lu, Dayan Guan, Jiaxing Huang
Specifically, we design a residual dense block with multiple receptive fields as a building block in the encoder which preserves detailed information in each modality and learns hierarchical modality-specific and fused features effectively.
Ranked #23 on 3D Semantic Segmentation on SemanticKITTI
3 code implementations • 27 Feb 2021 • Dayan Guan, Jiaxing Huang, Aoran Xiao, Shijian Lu, Yanpeng Cao
Specifically, we design an uncertainty metric that assesses the alignment of each sample and adjusts the strength of adversarial learning for well-aligned and poorly-aligned samples adaptively.
1 code implementation • ECCV 2020 • Jiaxing Huang, Shijian Lu, Dayan Guan, Xiaobing Zhang
Recent advances in unsupervised domain adaptation for semantic segmentation have shown great potentials to relieve the demand of expensive per-pixel annotations.
no code implementations • 7 Apr 2019 • Dayan Guan, Xing Luo, Yanpeng Cao, Jiangxin Yang, Yanlong Cao, George Vosselman, Michael Ying Yang
In this paper, we propose a novel unsupervised domain adaptation framework for multispectral pedestrian detection, by iteratively generating pseudo annotations and updating the parameters of our designed multispectral pedestrian detector on target domain.
no code implementations • 14 Feb 2019 • Yanpeng Cao, Dayan Guan, Yulun Wu, Jiangxin Yang, Yanlong Cao, Michael Ying Yang
Effective fusion of complementary information captured by multi-modal sensors (visible and infrared cameras) enables robust pedestrian detection under various surveillance situations (e. g. daytime and nighttime).
no code implementations • 27 Feb 2018 • Dayan Guan, Yanpeng Cao, Jun Liang, Yanlong Cao, Michael Ying Yang
Moreover, we utilized illumination information together with multispectral data to generate more accurate semantic segmentation which are used to boost pedestrian detection accuracy.