1 code implementation • 14 May 2024 • Yulin Wang, Yang Yue, Rui Lu, Yizeng Han, Shiji Song, Gao Huang
These patterns, when observed through frequency and spatial domains, incorporate lower-frequency components, and the natural image contents without distortion or data augmentation.
1 code implementation • 11 Mar 2024 • Chaoqun Du, Yulin Wang, Shiji Song, Gao Huang
To overcome this obstacle, we propose a novel probabilistic contrastive (ProCo) learning algorithm that estimates the data distribution of the samples from each class in the feature space, and samples contrastive pairs accordingly.
Ranked #10 on Long-tail Learning on iNaturalist 2018
no code implementations • 6 Nov 2023 • Dawei Li, Yaxuan Li, Dheeraj Mekala, Shuyao Li, Yulin Wang, Xueqi Wang, William Hogan, Jingbo Shang
DAIL leverages the intuition that large language models are more familiar with the content generated by themselves.
1 code implementation • 1 Sep 2023 • Yifan Pu, Yizeng Han, Yulin Wang, Junlan Feng, Chao Deng, Gao Huang
Since images belonging to the same meta-category usually share similar visual appearances, mining discriminative visual cues is the key to distinguishing fine-grained categories.
no code implementations • 27 Aug 2023 • Yulin Wang, Yizeng Han, Chaofei Wang, Shiji Song, Qi Tian, Gao Huang
Over the past decade, deep learning models have exhibited considerable advancements, reaching or even exceeding human-level performance in a range of visual perception tasks.
1 code implementation • ICCV 2023 • Yizeng Han, Dongchen Han, Zeyu Liu, Yulin Wang, Xuran Pan, Yifan Pu, Chao Deng, Junlan Feng, Shiji Song, Gao Huang
Early exits are placed exclusively within the classification branch, thus eliminating the need for linear separability in low-level features.
1 code implementation • ICCV 2023 • Yifan Pu, Yiru Wang, Zhuofan Xia, Yizeng Han, Yulin Wang, Weihao Gan, Zidong Wang, Shiji Song, Gao Huang
In our ARC module, the convolution kernels rotate adaptively to extract object features with varying orientations in different images, and an efficient conditional computation mechanism is introduced to accommodate the large orientation variations of objects within an image.
Ranked #3 on Object Detection In Aerial Images on DOTA (using extra training data)
1 code implementation • ICCV 2023 • Wenxuan Ma, Shuang Li, Jinming Zhang, Chi Harold Liu, Jingxuan Kang, Yulin Wang, Gao Huang
To address this issue, this paper presents a novel approach that seeks to leverage linguistic knowledge for data-efficient visual learning.
3 code implementations • ICCV 2023 • Zanlin Ni, Yulin Wang, Jiangwei Yu, Haojun Jiang, Yue Cao, Gao Huang
In this paper, we present Deep Incubation, a novel approach that enables the efficient and effective training of large models by dividing them into smaller sub-modules that can be trained separately and assembled seamlessly.
1 code implementation • ICCV 2023 • Yulin Wang, Yang Yue, Rui Lu, Tianjiao Liu, Zhao Zhong, Shiji Song, Gao Huang
It is also effective for self-supervised learning (e. g., MAE).
no code implementations • 27 Sep 2022 • Yulin Wang, Yang Yue, Xinhong Xu, Ali Hassani, Victor Kulikov, Nikita Orlov, Shiji Song, Humphrey Shi, Gao Huang
Recent research has revealed that reducing the temporal and spatial redundancy are both effective approaches towards efficient video recognition, e. g., allocating the majority of computation to a task-relevant subset of frames or the most valuable image regions of each frame.
1 code implementation • 2 Aug 2022 • Wenxuan Ma, Jinming Zhang, Shuang Li, Chi Harold Liu, Yulin Wang, Wei Li
To alleviate these issues, we propose to simultaneously conduct feature alignment in two individual spaces focusing on different domains, and create for each space a domain-oriented classifier tailored specifically for that domain.
1 code implementation • 9 Jan 2022 • Gao Huang, Yulin Wang, Kangchen Lv, Haojun Jiang, Wenhui Huang, Pengfei Qi, Shiji Song
Spatial redundancy widely exists in visual recognition tasks, i. e., discriminative features in an image or video frame usually correspond to only a subset of pixels, while the remaining regions are irrelevant to the task at hand.
1 code implementation • CVPR 2022 • Yulin Wang, Yang Yue, Yuanze Lin, Haojun Jiang, Zihang Lai, Victor Kulikov, Nikita Orlov, Humphrey Shi, Gao Huang
Recent works have shown that the computational efficiency of video recognition can be significantly improved by reducing the spatial redundancy.
no code implementations • 25 Nov 2021 • Wenxuan Ma, Jinming Zhang, Shuang Li, Chi Harold Liu, Yulin Wang, Wei Li
Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain.
2 code implementations • NeurIPS 2021 • Yulin Wang, Rui Huang, Shiji Song, Zeyi Huang, Gao Huang
Inspired by this phenomenon, we propose a Dynamic Transformer to automatically configure a proper number of tokens for each input image.
Ranked #29 on Image Classification on CIFAR-100 (using extra training data)
1 code implementation • ICCV 2021 • Yulin Wang, Zhaoxi Chen, Haojun Jiang, Shiji Song, Yizeng Han, Gao Huang
In this paper, we explore the spatial redundancy in video recognition with the aim to improve the computational efficiency.
1 code implementation • CVPR 2021 • Le Yang, Haojun Jiang, Ruojin Cai, Yulin Wang, Shiji Song, Gao Huang, Qi Tian
Reusing features in deep networks through dense connectivity is an effective way to achieve high computational efficiency.
1 code implementation • CVPR 2021 • Shuang Li, Mixue Xie, Kaixiong Gong, Chi Harold Liu, Yulin Wang, Wei Li
To remedy this, we propose a Transferable Semantic Augmentation (TSA) approach to enhance the classifier adaptation ability through implicitly generating source features towards target semantics.
1 code implementation • CVPR 2021 • Shuang Li, Kaixiong Gong, Chi Harold Liu, Yulin Wang, Feng Qiao, Xinjing Cheng
Real-world training data usually exhibits long-tailed distribution, where several majority classes have a significantly larger number of samples than the remaining minority classes.
Ranked #2 on Long-tail Learning on CIFAR-100-LT (ρ=200)
no code implementations • 9 Feb 2021 • Yizeng Han, Gao Huang, Shiji Song, Le Yang, Honghui Wang, Yulin Wang
Dynamic neural network is an emerging research topic in deep learning.
1 code implementation • 26 Jan 2021 • Yulin Wang, Zanlin Ni, Shiji Song, Le Yang, Gao Huang
Due to the need to store the intermediate activations for back-propagation, end-to-end (E2E) training of deep networks usually suffers from high GPUs memory footprint.
no code implementations • ICLR 2021 • Yulin Wang, Zanlin Ni, Shiji Song, Le Yang, Gao Huang
As InfoPro loss is difficult to compute in its original form, we derive a feasible upper bound as a surrogate optimization objective, yielding a simple but effective algorithm.
1 code implementation • NeurIPS 2020 • Yulin Wang, Kangchen Lv, Rui Huang, Shiji Song, Le Yang, Gao Huang
The accuracy of deep convolutional neural networks (CNNs) generally improves when fueled with high resolution images.
1 code implementation • 21 Jul 2020 • Yulin Wang, Gao Huang, Shiji Song, Xuran Pan, Yitong Xia, Cheng Wu
The proposed method is inspired by the intriguing property that deep networks are effective in learning linearized features, i. e., certain directions in the deep feature space correspond to meaningful semantic transformations, e. g., changing the background or view angle of an object.
no code implementations • 5 Jul 2020 • Yulin Wang, Jiayi Guo, Shiji Song, Gao Huang
In this paper, we propose a novel meta-learning based SSL algorithm (Meta-Semi) that requires tuning only one additional hyper-parameter, compared with a standard supervised deep learning algorithm, to achieve competitive performance under various conditions of SSL.
1 code implementation • NeurIPS 2019 • Yulin Wang, Xuran Pan, Shiji Song, Hong Zhang, Cheng Wu, Gao Huang
Our work is motivated by the intriguing property that deep networks are surprisingly good at linearizing features, such that certain directions in the deep feature space correspond to meaningful semantic transformations, e. g., adding sunglasses or changing backgrounds.