24 citations · 44 across the 35 of their papers we have counts for
12 papers · 1 filter
Reconstruction Target Matters in Masked Image Modeling for Cross-Domain Few-Shot Learning
Ran Ma, Yixiong Zou, Yuhua Li +1
Cross-Domain Few-Shot Learning (CDFSL) requires the model to transfer knowledge from the data-abundant source domain to data-scarce target domains for fast adaptation, where the la…
Lightweight Frequency Masker for Cross-Domain Few-Shot Semantic Segmentation
Jintao Tong, Yixiong Zou, Yuhua Li +1
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-domai…
Mixed-Precision Embeddings for Large-Scale Recommendation Models
Shiwei Li, Zhuoqi Hu, Xing Tang +6
Embedding techniques have become essential components of large databases in the deep learning era. By encoding discrete entities, such as words, items, or graph nodes, into continu…
Masked Random Noise for Communication Efficient Federated Learning
Shiwei Li, Yingyi Cheng, Haozhao Wang +7
Federated learning is a promising distributed training paradigm that effectively safeguards data privacy. However, it may involve significant communication costs, which hinders tra…
MICM: Rethinking Unsupervised Pretraining for Enhanced Few-shot Learning
Zhenyu Zhang, Guangyao Chen, Yixiong Zou +3
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. Un…
Learning Unknowns from Unknowns: Diversified Negative Prototypes Generator for Few-Shot Open-Set Recognition
Zhenyu Zhang, Guangyao Chen, Yixiong Zou +2
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. Existing approac…
Embedding Compression in Recommender Systems: A Survey
Shiwei Li, Huifeng Guo, Xing Tang +4
To alleviate the problem of information explosion, recommender systems are widely deployed to provide personalized information filtering services. Usually, embedding tables are emp…
Compositional Few-Shot Class-Incremental Learning
Yixiong Zou, Shanghang Zhang, Haichen Zhou +2
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 dat…
Delve into Base-Novel Confusion: Redundancy Exploration for Few-Shot Class-Incremental Learning
Haichen Zhou, Yixiong Zou, Ruixuan Li +2
Few-shot class-incremental learning (FSCIL) aims to acquire knowledge from novel classes with limited samples while retaining information about base classes. Existing methods addre…
Flatten Long-Range Loss Landscapes for Cross-Domain Few-Shot Learning
Yixiong Zou, Yicong Liu, Yiman Hu +2
Cross-domain few-shot learning (CDFSL) aims to acquire knowledge from limited training data in the target domain by leveraging prior knowledge transferred from source domains with…
A Survey on Self-Supervised Graph Foundation Models: Knowledge-Based Perspective
Ziwen Zhao, Yixin Su, Yuhua Li +3
Graph self-supervised learning (SSL) is now a go-to method for pre-training graph foundation models (GFMs). There is a wide variety of knowledge patterns embedded in the graph data…
Masked Graph Autoencoder with Non-discrete Bandwidths
Ziwen Zhao, Yuhua Li, Yixiong Zou +2
Masked graph autoencoders have emerged as a powerful graph self-supervised learning method that has yet to be fully explored. In this paper, we unveil that the existing discrete ed…