no code implementations • 26 Mar 2024 • Chenhongyi Yang, Anastasia Tkach, Shreyas Hampali, Linguang Zhang, Elliot J. Crowley, Cem Keskin
We also show that our method can be seamlessly extended to monocular settings, which achieves state-of-the-art performance on the SceneEgo dataset, improving MPJPE by 25. 5mm (21% improvement) compared to the best existing method with only 60. 7% model parameters and 36. 4% FLOPs.
Ranked #1 on Egocentric Pose Estimation on UnrealEgo
1 code implementation • 26 Mar 2024 • Chenhongyi Yang, Zehui Chen, Miguel Espinosa, Linus Ericsson, Zhenyu Wang, Jiaming Liu, Elliot J. Crowley
In this paper, we further adapt the selective scanning process of Mamba to the visual domain, enhancing its ability to learn features from two-dimensional images by (i) a continuous 2D scanning process that improves spatial continuity by ensuring adjacency of tokens in the scanning sequence, and (ii) direction-aware updating which enables the model to discern the spatial relations of tokens by encoding directional information.
1 code implementation • 8 Jan 2024 • Chenhongyi Yang, Tianwei Lin, Lichao Huang, Elliot J. Crowley
In this work, we present WidthFormer, a novel transformer-based Bird's-Eye-View (BEV) 3D detection method tailored for real-time autonomous-driving applications.
2 code implementations • 13 Dec 2022 • Chenhongyi Yang, Jiarui Xu, Shalini De Mello, Elliot J. Crowley, Xiaolong Wang
In each GP Block, features are first grouped together by a fixed number of learnable group tokens; we then perform Group Propagation where global information is exchanged between the grouped features; finally, global information in the updated grouped features is returned back to the image features through a transformer decoder.
1 code implementation • 21 Nov 2022 • Chenhongyi Yang, Lichao Huang, Elliot J. Crowley
To overcome this challenge, we introduce Plug and Play Active Learning (PPAL), a simple and effective AL strategy for object detection.
no code implementations • ICCV 2023 • Jiahao Chang, Shuo Wang, HaiMing Xu, Zehui Chen, Chenhongyi Yang, Feng Zhao
Next, we propose a target-aware feature distillation to help the student model learn from the object-centric features of the teacher model.
1 code implementation • 10 Mar 2022 • Chenhongyi Yang, Mateusz Ochal, Amos Storkey, Elliot J. Crowley
Based on this, we propose Prediction-Guided Distillation (PGD), which focuses distillation on these key predictive regions of the teacher and yields considerable gains in performance over many existing KD baselines.
1 code implementation • 26 Nov 2021 • Chenhongyi Yang, Lichao Huang, Elliot J. Crowley
The goal of contrastive learning based pre-training is to leverage large quantities of unlabeled data to produce a model that can be readily adapted downstream.
2 code implementations • 7 Jul 2021 • Zehui Chen, Chenhongyi Yang, Qiaofei Li, Feng Zhao, Zheng-Jun Zha, Feng Wu
Extensive experiments on MS COCO benchmark show that our approach can lead to 2. 0 mAP, 2. 4 mAP and 2. 2 mAP absolute improvements on RetinaNet, FCOS, and ATSS baselines with negligible extra overhead.
1 code implementation • CVPR 2022 • Chenhongyi Yang, Zehao Huang, Naiyan Wang
On the popular COCO dataset, the proposed method improves the detection mAP by 1. 0 and mAP-small by 2. 0, and the high-resolution inference speed is improved to 3. 0x on average.
no code implementations • 18 Sep 2020 • Kaihong Wang, Chenhongyi Yang, Margrit Betke
Unsupervised domain adaptation for semantic segmentation has been intensively studied due to the low cost of the pixel-level annotation for synthetic data.
1 code implementation • ECCV 2020 • Chenhongyi Yang, Vitaly Ablavsky, Kaihong Wang, Qi Feng, Margrit Betke
While visual object detection with deep learning has received much attention in the past decade, cases when heavy intra-class occlusions occur have not been studied thoroughly.