Search Results for author: Yixiong Zou

Found 18 papers, 9 papers with code

Lightweight Frequency Masker for Cross-Domain Few-Shot Semantic Segmentation

no code implementations29 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.

Cross-Domain Few-Shot Few-Shot Semantic Segmentation +2

MICM: Rethinking Unsupervised Pretraining for Enhanced Few-shot Learning

1 code implementation23 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.

Contrastive Learning Unsupervised Few-Shot Learning

Learning Unknowns from Unknowns: Diversified Negative Prototypes Generator for Few-Shot Open-Set Recognition

1 code implementation23 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.

Meta-Learning Open Set Learning

Compositional Few-Shot Class-Incremental Learning

1 code implementation27 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

A Survey on Self-Supervised Graph Foundation Models: Knowledge-Based Perspective

1 code implementation24 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.

Language Modelling Large Language Model +1

Flatten Long-Range Loss Landscapes for Cross-Domain Few-Shot Learning

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.

cross-domain few-shot learning

Masked Graph Autoencoder with Non-discrete Bandwidths

1 code implementation6 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.

Blocking Link Prediction +2

Decoupling Representation and Knowledge for Few-Shot Intent Classification and Slot Filling

no code implementations21 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.

intent-classification Intent Classification +4

ECEA: Extensible Co-Existing Attention for Few-Shot Object Detection

no code implementations15 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.

Few-Shot Object Detection Object +1

Decoupled Rationalization with Asymmetric Learning Rates: A Flexible Lipschitz Restraint

1 code implementation23 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.

CSGCL: Community-Strength-Enhanced Graph Contrastive Learning

1 code implementation8 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.

Attribute Contrastive Learning +3

Margin-Based Few-Shot Class-Incremental Learning with Class-Level Overfitting Mitigation

1 code implementation10 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

Annotation Efficient Person Re-Identification with Diverse Cluster-Based Pair Selection

no code implementations10 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.

Clustering Diversity +1

Annotation-Efficient Untrimmed Video Action Recognition

no code implementations30 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.

Action Recognition Contrastive Learning +3

Revisiting Mid-Level Patterns for Cross-Domain Few-Shot Recognition

no code implementations7 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.

cross-domain few-shot learning

Compositional Few-Shot Recognition with Primitive Discovery and Enhancing

no code implementations12 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.

Few-Shot Image Classification Few-Shot Learning +1

ODN: Opening the Deep Network for Open-set Action Recognition

no code implementations23 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.

Open Set Action Recognition Temporal Action Localization +1

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