1 code implementation • 30 Nov 2023 • Ju He, Qihang Yu, Inkyu Shin, Xueqing Deng, Xiaohui Shen, Alan Yuille, Liang-Chieh Chen
To alleviate the issue, we propose to adapt the trajectory attention for both the dense pixel features and object queries, aiming to improve the short-term and long-term tracking results, respectively.
Ranked #1 on Video Panoptic Segmentation on VIPSeg
no code implementations • 30 Nov 2023 • Jiawei Peng, Ju He, Prakhar Kaushik, Zihao Xiao, Jiteng Mu, Alan Yuille
We then benchmark Syn-to-Real animal part segmentation from SAP to PartImageNet, namely SynRealPart, with existing semantic segmentation domain adaptation methods and further improve them as our second contribution.
1 code implementation • NeurIPS 2023 • Qihang Yu, Ju He, Xueqing Deng, Xiaohui Shen, Liang-Chieh Chen
The proposed FC-CLIP, benefits from the following observations: the frozen CLIP backbone maintains the ability of open-vocabulary classification and can also serve as a strong mask generator, and the convolutional CLIP generalizes well to a larger input resolution than the one used during contrastive image-text pretraining.
Ranked #1 on Open Vocabulary Semantic Segmentation on Cityscapes
Open Vocabulary Panoptic Segmentation Open Vocabulary Semantic Segmentation +1
1 code implementation • CVPR 2023 • Ju He, Jieneng Chen, Ming-Xian Lin, Qihang Yu, Alan Yuille
Compositor achieves state-of-the-art performance on PartImageNet and Pascal-Part by outperforming previous methods by around 0. 9% and 1. 3% on PartImageNet, 0. 4% and 1. 7% on Pascal-Part in terms of part and object mIoU and demonstrates better robustness against occlusion by around 4. 4% and 7. 1% on part and object respectively.
1 code implementation • 2 Dec 2021 • Ju He, Shuo Yang, Shaokang Yang, Adam Kortylewski, Xiaoding Yuan, Jie-Neng Chen, Shuai Liu, Cheng Yang, Qihang Yu, Alan Yuille
To help address this problem, we propose PartImageNet, a large, high-quality dataset with part segmentation annotations.
no code implementations • 29 Nov 2021 • Bingchen Zhao, Shaozuo Yu, Wufei Ma, Mingxin Yu, Shenxiao Mei, Angtian Wang, Ju He, Alan Yuille, Adam Kortylewski
One reason is that existing robustness benchmarks are limited, as they either rely on synthetic data or ignore the effects of individual nuisance factors.
1 code implementation • CVPR 2022 • Junfei Xiao, Longlong Jing, Lin Zhang, Ju He, Qi She, Zongwei Zhou, Alan Yuille, Yingwei Li
Our method achieves the state-of-the-art performance on three video action recognition benchmarks (i. e., Kinetics-400, UCF-101, and HMDB-51) under several typical semi-supervised settings (i. e., different ratios of labeled data).
2 code implementations • CVPR 2022 • Jie-Neng Chen, Shuyang Sun, Ju He, Philip Torr, Alan Yuille, Song Bai
The confidence of the label will be larger if the corresponding input image is weighted higher by the attention map.
1 code implementation • 1 Jun 2021 • Ju He, Adam Kortylewski, Shaokang Yang, Shuai Liu, Cheng Yang, Changhu Wang, Alan Yuille
In particular, we decouple the training of the representation and the classifier, and systematically investigate the effects of different data re-sampling techniques when training the whole network including a classifier as well as fine-tuning the feature extractor only.
2 code implementations • 14 Mar 2021 • Ju He, Jie-Neng Chen, Shuai Liu, Adam Kortylewski, Cheng Yang, Yutong Bai, Changhu Wang
Fine-grained visual classification (FGVC) which aims at recognizing objects from subcategories is a very challenging task due to the inherently subtle inter-class differences.
Ranked #4 on Fine-Grained Image Classification on CUB-200-2011
no code implementations • 28 Jan 2021 • Ju He, Adam Kortylewski, Alan Yuille
In particular, during meta-learning, we train a knowledge base that consists of a dictionary of component representations and a dictionary of component activation maps that encode common spatial activation patterns of components.
no code implementations • 26 Jan 2021 • Ju He, Enyu Zhou, Liusheng Sun, Fei Lei, Chenyang Liu, Wenxiu Sun
Though synthetic dataset is proposed to fill the gaps of large data demand, the fine-tuning on real dataset is still needed due to the domain variances between synthetic data and real data.
1 code implementation • CVPR 2020 • Adam Kortylewski, Ju He, Qing Liu, Alan Yuille
Inspired by the success of compositional models at classifying partially occluded objects, we propose to integrate compositional models and DCNNs into a unified deep model with innate robustness to partial occlusion.
no code implementations • 28 Nov 2018 • Wei Hu, Gusi Te, Ju He, Dong Chen, Zongming Guo
Face anti-spoofing plays a crucial role in protecting face recognition systems from various attacks.