Search Results for author: Guoliang Kang

Found 28 papers, 17 papers with code

Content-Consistent Matching for Domain Adaptive Semantic Segmentation

1 code implementation ECCV 2020 Guangrui Li, Guoliang Kang, Wu Liu, Yunchao Wei, Yi Yang

The target of CCM is to acquire those synthetic images that share similar distribution with the real ones in the target domain, so that the domain gap can be naturally alleviated by employing the content-consistent synthetic images for training.

Domain Adaptation Semantic Segmentation +1

Mining Open Semantics from CLIP: A Relation Transition Perspective for Few-Shot Learning

no code implementations17 Jun 2024 Cilin Yan, Haochen Wang, XiaoLong Jiang, Yao Hu, Xu Tang, Guoliang Kang, Efstratios Gavves

Specifically, we adopt a transformer module which takes the visual feature as "Query", the text features of the anchors as "Key" and the similarity matrix between the text features of anchor and target classes as "Value".

Few-Shot Learning Relation

Learn to Rectify the Bias of CLIP for Unsupervised Semantic Segmentation

1 code implementation CVPR 2024 Jingyun Wang, Guoliang Kang

In this paper we propose to explicitly model and rectify the bias existing in CLIP to facilitate the unsupervised semantic segmentation.

Segmentation Unsupervised Semantic Segmentation

Tuning-Free Inversion-Enhanced Control for Consistent Image Editing

no code implementations22 Dec 2023 Xiaoyue Duan, Shuhao Cui, Guoliang Kang, Baochang Zhang, Zhengcong Fei, Mingyuan Fan, Junshi Huang

Consistent editing of real images is a challenging task, as it requires performing non-rigid edits (e. g., changing postures) to the main objects in the input image without changing their identity or attributes.


LatentWarp: Consistent Diffusion Latents for Zero-Shot Video-to-Video Translation

no code implementations1 Nov 2023 Yuxiang Bao, Di Qiu, Guoliang Kang, Baochang Zhang, Bo Jin, Kaiye Wang, Pengfei Yan

As a result, the corresponding regions across the adjacent frames can share closely-related query tokens and attention outputs, which can further improve latent-level consistency to enhance visual temporal coherence of generated videos.

Denoising Optical Flow Estimation +1

AttriCLIP: A Non-Incremental Learner for Incremental Knowledge Learning

no code implementations CVPR 2023 Runqi Wang, Xiaoyue Duan, Guoliang Kang, Jianzhuang Liu, Shaohui Lin, Songcen Xu, Jinhu Lv, Baochang Zhang

Text consists of a category name and a fixed number of learnable parameters which are selected from our designed attribute word bank and serve as attributes.

Attribute Continual Learning +1

PiClick: Picking the desired mask from multiple candidates in click-based interactive segmentation

1 code implementation23 Apr 2023 Cilin Yan, Haochen Wang, Jie Liu, XiaoLong Jiang, Yao Hu, Xu Tang, Guoliang Kang, Efstratios Gavves

Click-based interactive segmentation aims to generate target masks via human clicking, which facilitates efficient pixel-level annotation and image editing.

Interactive Segmentation Segmentation

Adversarially Masking Synthetic To Mimic Real: Adaptive Noise Injection for Point Cloud Segmentation Adaptation

no code implementations CVPR 2023 Guangrui Li, Guoliang Kang, Xiaohan Wang, Yunchao Wei, Yi Yang

With the help of adversarial training, the masking module can learn to generate source masks to mimic the pattern of irregular target noise, thereby narrowing the domain gap.

Point Cloud Segmentation Semantic Segmentation

Rethinking the Number of Shots in Robust Model-Agnostic Meta-Learning

no code implementations28 Nov 2022 Xiaoyue Duan, Guoliang Kang, Runqi Wang, Shumin Han, Song Xue, Tian Wang, Baochang Zhang

Based on this observation, we propose a simple strategy, i. e., increasing the number of training shots, to mitigate the loss of intrinsic dimension caused by robustness-promoting regularization.


VAQF: Fully Automatic Software-Hardware Co-Design Framework for Low-Bit Vision Transformer

no code implementations17 Jan 2022 Mengshu Sun, Haoyu Ma, Guoliang Kang, Yifan Jiang, Tianlong Chen, Xiaolong Ma, Zhangyang Wang, Yanzhi Wang

To the best of our knowledge, this is the first time quantization has been incorporated into ViT acceleration on FPGAs with the help of a fully automatic framework to guide the quantization strategy on the software side and the accelerator implementations on the hardware side given the target frame rate.


Domain Consensus Clustering for Universal Domain Adaptation

1 code implementation CVPR 2021 Guangrui Li, Guoliang Kang, Yi Zhu, Yunchao Wei, Yi Yang

To better exploit the intrinsic structure of the target domain, we propose Domain Consensus Clustering (DCC), which exploits the domain consensus knowledge to discover discriminative clusters on both common samples and private ones.

Clustering domain classification +3

Training-free Monocular 3D Event Detection System for Traffic Surveillance

no code implementations1 Feb 2020 Lijun Yu, Peng Chen, Wenhe Liu, Guoliang Kang, Alexander G. Hauptmann

To deal with the aforementioned problems, in this paper, we propose a training-free monocular 3D event detection system for traffic surveillance.

Event Detection

Attract or Distract: Exploit the Margin of Open Set

1 code implementation ICCV 2019 Qianyu Feng, Guoliang Kang, Hehe Fan, Yi Yang

In this paper, we exploit the semantic structure of open set data from two aspects: 1) Semantic Categorical Alignment, which aims to achieve good separability of target known classes by categorically aligning the centroid of target with the source.

Domain Adaptation

Multi-Interest Network with Dynamic Routing for Recommendation at Tmall

5 code implementations17 Apr 2019 Chao Li, Zhiyuan Liu, Mengmeng Wu, Yuchi Xu, Pipei Huang, Huan Zhao, Guoliang Kang, Qiwei Chen, Wei Li, Dik Lun Lee

Industrial recommender systems usually consist of the matching stage and the ranking stage, in order to handle the billion-scale of users and items.

Clustering Information Retrieval +1

Shakeout: A New Approach to Regularized Deep Neural Network Training

1 code implementation13 Apr 2019 Guoliang Kang, Jun Li, DaCheng Tao

Dropout has played an essential role in many successful deep neural networks, by inducing regularization in the model training.

Model Compression

Contrastive Adaptation Network for Unsupervised Domain Adaptation

2 code implementations CVPR 2019 Guoliang Kang, Lu Jiang, Yi Yang, Alexander G. Hauptmann

Unsupervised Domain Adaptation (UDA) makes predictions for the target domain data while manual annotations are only available in the source domain.

Unsupervised Domain Adaptation

Asymptotic Soft Filter Pruning for Deep Convolutional Neural Networks

2 code implementations22 Aug 2018 Yang He, Xuanyi Dong, Guoliang Kang, Yanwei Fu, Chenggang Yan, Yi Yang

With asymptotic pruning, the information of the training set would be gradually concentrated in the remaining filters, so the subsequent training and pruning process would be stable.

Image Classification

Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

6 code implementations21 Aug 2018 Yang He, Guoliang Kang, Xuanyi Dong, Yanwei Fu, Yi Yang

Therefore, the network trained by our method has a larger model capacity to learn from the training data.

Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification

2 code implementations CVPR 2018 Weijian Deng, Liang Zheng, Qixiang Ye, Guoliang Kang, Yi Yang, Jianbin Jiao

To this end, we propose to preserve two types of unsupervised similarities, 1) self-similarity of an image before and after translation, and 2) domain-dissimilarity of a translated source image and a target image.

Generative Adversarial Network Person Re-Identification +2

EraseReLU: A Simple Way to Ease the Training of Deep Convolution Neural Networks

no code implementations22 Sep 2017 Xuanyi Dong, Guoliang Kang, Kun Zhan, Yi Yang

For most state-of-the-art architectures, Rectified Linear Unit (ReLU) becomes a standard component accompanied with each layer.

Blocking Image Classification

Random Erasing Data Augmentation

18 code implementations16 Aug 2017 Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, Yi Yang

In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN).

General Classification Image Augmentation +4

PatchShuffle Regularization

no code implementations22 Jul 2017 Guoliang Kang, Xuanyi Dong, Liang Zheng, Yi Yang

This paper focuses on regularizing the training of the convolutional neural network (CNN).

General Classification

Cannot find the paper you are looking for? You can Submit a new open access paper.