no code implementations • 29 Oct 2024 • Jintao Tong, Yixiong Zou, Yuhua Li, Ruixuan Li
Cross-domain few-shot segmentation (CD-FSS) is proposed to first pre-train the model on a large-scale source-domain dataset, and then transfer the model to data-scarce target-domain datasets for pixel-level segmentation.
1 code implementation • 23 Aug 2024 • Zhenyu Zhang, Guangyao Chen, Yixiong Zou, Zhimeng Huang, Yuhua Li, Ruixuan Li
Humans exhibit a remarkable ability to learn quickly from a limited number of labeled samples, a capability that starkly contrasts with that of current machine learning systems.
1 code implementation • 23 Aug 2024 • Zhenyu Zhang, Guangyao Chen, Yixiong Zou, Yuhua Li, Ruixuan Li
Few-shot open-set recognition (FSOR) is a challenging task that requires a model to recognize known classes and identify unknown classes with limited labeled data.
1 code implementation • 27 May 2024 • Yixiong Zou, Shanghang Zhang, Haichen Zhou, Yuhua Li, Ruixuan Li
Few-shot class-incremental learning (FSCIL) is proposed to continually learn from novel classes with only a few samples after the (pre-)training on base classes with sufficient data.
class-incremental learning Few-Shot Class-Incremental Learning +1
no code implementations • 8 May 2024 • Haichen Zhou, Yixiong Zou, Ruixuan Li, Yuhua Li, Kui Xiao
We first interpret the confusion as the collision between the novel-class and the base-class region in the feature space.
class-incremental learning Few-Shot Class-Incremental Learning +1
1 code implementation • 24 Mar 2024 • Ziwen Zhao, Yixin Su, Yuhua Li, Yixiong Zou, Ruixuan Li, Rui Zhang
As the ultimate goal of GFMs is to learn generalized graph knowledge, we provide a comprehensive survey of self-supervised GFMs from a novel knowledge-based perspective.
1 code implementation • CVPR 2024 • Yixiong Zou, Yicong Liu, Yiman Hu, Yuhua Li, Ruixuan Li
To enhance the transferability and facilitate fine-tuning, we introduce a simple yet effective approach to achieve long-range flattening of the minima in the loss landscape.
1 code implementation • 6 Feb 2024 • Ziwen Zhao, Yuhua Li, Yixiong Zou, Jiliang Tang, Ruixuan Li
Inspired by these understandings, we explore non-discrete edge masks, which are sampled from a continuous and dispersive probability distribution instead of the discrete Bernoulli distribution.
no code implementations • 21 Dec 2023 • Jie Han, Yixiong Zou, Haozhao Wang, Jun Wang, Wei Liu, Yao Wu, Tao Zhang, Ruixuan Li
Therefore, current works first train a model on source domains with sufficiently labeled data, and then transfer the model to target domains where only rarely labeled data is available.
no code implementations • 15 Sep 2023 • Zhimeng Xin, Tianxu Wu, Shiming Chen, Yixiong Zou, Ling Shao, Xinge You
Extensive experiments on the PASCAL VOC and COCO datasets show that our ECEA module can assist the few-shot detector to completely predict the object despite some regions failing to appear in the training samples and achieve the new state of the art compared with existing FSOD methods.
1 code implementation • 23 May 2023 • Wei Liu, Jun Wang, Haozhao Wang, Ruixuan Li, Yang Qiu, Yuankai Zhang, Jie Han, Yixiong Zou
However, such a cooperative game may incur the degeneration problem where the predictor overfits to the uninformative pieces generated by a not yet well-trained generator and in turn, leads the generator to converge to a sub-optimal model that tends to select senseless pieces.
1 code implementation • 8 May 2023 • Han Chen, Ziwen Zhao, Yuhua Li, Yixiong Zou, Ruixuan Li, Rui Zhang
Graph Contrastive Learning (GCL) is an effective way to learn generalized graph representations in a self-supervised manner, and has grown rapidly in recent years.
1 code implementation • 10 Oct 2022 • Yixiong Zou, Shanghang Zhang, Yuhua Li, Ruixuan Li
Few-shot class-incremental learning (FSCIL) is designed to incrementally recognize novel classes with only few training samples after the (pre-)training on base classes with sufficient samples, which focuses on both base-class performance and novel-class generalization.
class-incremental learning Few-Shot Class-Incremental Learning +1
no code implementations • 10 Mar 2022 • Lantian Xue, Yixiong Zou, Peixi Peng, Yonghong Tian, Tiejun Huang
To solve this problem, we propose the Annotation Efficient Person Re-Identification method to select image pairs from an alternative pair set according to the fallibility and diversity of pairs, and train the Re-ID model based on the annotation.
no code implementations • 30 Nov 2020 • Yixiong Zou, Shanghang Zhang, Guangyao Chen, Yonghong Tian, Kurt Keutzer, José M. F. Moura
In this paper, we target a new problem, Annotation-Efficient Video Recognition, to reduce the requirement of annotations for both large amount of samples and the action location.
no code implementations • 7 Aug 2020 • Yixiong Zou, Shanghang Zhang, JianPeng Yu, Yonghong Tian, José M. F. Moura
To solve this problem, cross-domain FSL (CDFSL) is proposed very recently to transfer knowledge from general-domain base classes to special-domain novel classes.
no code implementations • 12 May 2020 • Yixiong Zou, Shanghang Zhang, Ke Chen, Yonghong Tian, Yao-Wei Wang, José M. F. Moura
Inspired by such capability of humans, to imitate humans' ability of learning visual primitives and composing primitives to recognize novel classes, we propose an approach to FSL to learn a feature representation composed of important primitives, which is jointly trained with two parts, i. e. primitive discovery and primitive enhancing.
no code implementations • 23 Jan 2019 • Yu Shu, Yemin Shi, Yao-Wei Wang, Yixiong Zou, Qingsheng Yuan, Yonghong Tian
Most of the existing action recognition works hold the \textit{closed-set} assumption that all action categories are known beforehand while deep networks can be well trained for these categories.