1 code implementation • 30 Apr 2024 • Zhanwei Zhang, Zishuo Hua, Minghao Chen, Wei Lu, Binbin Lin, Deng Cai, Wenxiao Wang
Finally, to ensure the optimal granularity of key steps, we design a selectable granularity strategy that caters to each predicted trajectory.
no code implementations • 30 Apr 2024 • Zhanwei Zhang, Minghao Chen, Shuai Xiao, Liang Peng, Hengjia Li, Binbin Lin, Ping Li, Wenxiao Wang, Boxi Wu, Deng Cai
Specifically, in the selection process, to improve the reliability of pseudo boxes, we propose a complementary augmentation strategy.
no code implementations • 29 Apr 2024 • Minghao Chen, Iro Laina, Andrea Vedaldi
However, this is often slow as it requires do update a computationally expensive 3D representations such as a neural radiance field, and to do so by using contradictory guidance from a 2D model which is inherently not multi-view consistent.
1 code implementation • 20 Dec 2023 • Yuqi Lin, Minghao Chen, Kaipeng Zhang, Hengjia Li, Mingming Li, Zheng Yang, Dongqin Lv, Binbin Lin, Haifeng Liu, Deng Cai
As a result, we dissect the preservation of patch-wise spatial information in CLIP and proposed a local-to-global framework to obtain image tags.
no code implementations • 20 Dec 2023 • Minghao Chen
The proposed approach involves continual learning of features extracted from spectra for the classification of underwater acoustic signals.
no code implementations • 14 Dec 2023 • Minghao Chen, Junyu Xie, Iro Laina, Andrea Vedaldi
In particular, we hypothesise that editing can be greatly simplified by first encoding 3D objects in a suitable latent space.
no code implementations • 1 Aug 2023 • Minghao Chen, Zepeng Gao, Shuai Zhao, Qibo Qiu, Wenxiao Wang, Binbin Lin, Xiaofei He
Unsupervised domain adaptation (UDA) methods facilitate the transfer of models to target domains without labels.
1 code implementation • CVPR 2023 • Honghui Yang, Wenxiao Wang, Minghao Chen, Binbin Lin, Tong He, Hua Chen, Xiaofei He, Wanli Ouyang
The key to associating the two different representations is our introduced input-dependent Query Initialization module, which could efficiently generate reference points and content queries.
1 code implementation • 6 Apr 2023 • Minghao Chen, Iro Laina, Andrea Vedaldi
We thoroughly evaluate our approach on three benchmarks and provide several qualitative examples and a comparative analysis of the two strategies that demonstrate the superiority of backward guidance compared to forward guidance, as well as prior work.
1 code implementation • CVPR 2023 • Yuqi Lin, Minghao Chen, Wenxiao Wang, Boxi Wu, Ke Li, Binbin Lin, Haifeng Liu, Xiaofei He
To efficiently generate high-quality segmentation masks from CLIP, we propose a novel WSSS framework called CLIP-ES.
Ranked #12 on Weakly-Supervised Semantic Segmentation on COCO 2014 val
no code implementations • 6 Dec 2022 • Minghao Chen, Renbo Tu, Chenxi Huang, Yuqi Lin, Boxi Wu, Deng Cai
In this paper, we introduce a new framework of contrastive action representation learning (CARL) to learn frame-wise action representation in a self-supervised or weakly-supervised manner, especially for long videos.
2 code implementations • 4 Aug 2022 • Bolin Ni, Houwen Peng, Minghao Chen, Songyang Zhang, Gaofeng Meng, Jianlong Fu, Shiming Xiang, Haibin Ling
Extensive experiments demonstrate that our approach is effective and can be generalized to different video recognition scenarios.
Ranked #8 on Zero-Shot Action Recognition on Kinetics
1 code implementation • CVPR 2022 • Minghao Chen, Fangyun Wei, Chong Li, Deng Cai
In this paper, we introduce a novel contrastive action representation learning (CARL) framework to learn frame-wise action representations, especially for long videos, in a self-supervised manner.
2 code implementations • NeurIPS 2021 • Minghao Chen, Kan Wu, Bolin Ni, Houwen Peng, Bei Liu, Jianlong Fu, Hongyang Chao, Haibin Ling
Vision Transformer has shown great visual representation power in substantial vision tasks such as recognition and detection, and thus been attracting fast-growing efforts on manually designing more effective architectures.
1 code implementation • ICCV 2021 • Kan Wu, Houwen Peng, Minghao Chen, Jianlong Fu, Hongyang Chao
We then propose new relative position encoding methods dedicated to 2D images, called image RPE (iRPE).
Ranked #140 on Object Detection on COCO minival
2 code implementations • ICCV 2021 • Minghao Chen, Houwen Peng, Jianlong Fu, Haibin Ling
Specifically, the performance of these subnets with weights inherited from the supernet is comparable to those retrained from scratch.
Ranked #1 on Fine-Grained Image Classification on Oxford 102 Flowers (Top 1 Accuracy metric)
1 code implementation • 15 Jun 2021 • Chulin Xie, Minghao Chen, Pin-Yu Chen, Bo Li
Our method exploits clipping and smoothing on model parameters to control the global model smoothness, which yields a sample-wise robustness certification on backdoors with limited magnitude.
1 code implementation • ICCV 2021 • Hao Fang, Daoxin Zhang, Yi Zhang, Minghao Chen, Jiawei Li, Yao Hu, Deng Cai, Xiaofei He
In this paper, we study the Salient Object Ranking (SOR) task, which manages to assign a ranking order of each detected object according to its visual saliency.
1 code implementation • CVPR 2021 • Minghao Chen, Houwen Peng, Jianlong Fu, Haibin Ling
In this paper, we propose a one-shot neural ensemble architecture search (NEAS) solution that addresses the two challenges.
1 code implementation • 18 Mar 2021 • Zili Liu, Guodong Xu, Honghui Yang, Minghao Chen, Kuoliang Wu, Zheng Yang, Haifeng Liu, Deng Cai
In this work, we propose a suppress-and-refine framework to remove these handcrafted components.
no code implementations • 29 Jan 2021 • Hao Feng, Minghao Chen, Jinming Hu, Dong Shen, Haifeng Liu, Deng Cai
In this paper, to complement these low recall neighbor pseudo labels, we propose a joint learning framework to learn better feature embeddings via high precision neighbor pseudo labels and high recall group pseudo labels.
3 code implementations • 21 Nov 2020 • Hao-Zhe Feng, Kezhi Kong, Minghao Chen, Tianye Zhang, Minfeng Zhu, Wei Chen
Semi-supervised variational autoencoders (VAEs) have obtained strong results, but have also encountered the challenge that good ELBO values do not always imply accurate inference results.
1 code implementation • 19 Nov 2020 • Hao-Zhe Feng, Zhaoyang You, Minghao Chen, Tianye Zhang, Minfeng Zhu, Fei Wu, Chao Wu, Wei Chen
(2) A dynamic weighting strategy named Consensus Focus to identify both the malicious and irrelevant domains.
Knowledge Distillation Multi-Source Unsupervised Domain Adaptation +2
1 code implementation • 15 Oct 2020 • Jia Guo, Minghao Chen, Yao Hu, Chen Zhu, Xiaofei He, Deng Cai
We investigate this problem by study the gap of confidence between teacher and student.
no code implementations • 10 Oct 2020 • Wenxiao Wang, Minghao Chen, Shuai Zhao, Long Chen, Jinming Hu, Haifeng Liu, Deng Cai, Xiaofei He, Wei Liu
Specifically, it first casts the relationships between a certain model's accuracy and depth/width/resolution into a polynomial regression and then maximizes the polynomial to acquire the optimal values for the three dimensions.
1 code implementation • 4 Jan 2020 • Minghao Chen, Shuai Zhao, Haifeng Liu, Deng Cai
In order to combine the strengths of these two methods, we propose a novel method called Adversarial-Learned Loss for Domain Adaptation (ALDA).
no code implementations • 21 Dec 2019 • Wenxiao Wang, Shuai Zhao, Minghao Chen, Jinming Hu, Deng Cai, Haifeng Liu
The dominant pruning methods, filter-level pruning methods, evaluate their performance through the reduction ratio of computations and deem that a higher reduction ratio of computations is equivalent to a higher acceleration ratio in terms of inference time.
1 code implementation • ICCV 2019 • Minghao Chen, Hongyang Xue, Deng Cai
However, when applying the entropy minimization to UDA for semantic segmentation, the gradient of the entropy is biased towards samples that are easy to transfer.
no code implementations • 24 Oct 2018 • Qing Lyu, Minghao Chen, Xiang Chen
With our adapted synthetic data for training the semantic segmentation, we achieve an improvement of 6:59% when applied to real images, superior to alternative methods.