7 citations · 12 across the 26 of their papers we have counts for
28 papers
Free-Lunch Augmentation by Revisiting Diffusion-Based Data Generation for Cross-Domain Few-Shot Object Detection
Zijian Zhuang, Yixiong Zou, Yuhua Li +1
Cross-Domain Few-Shot Object Detection (CDFSOD) aims to transfer knowledge from data-rich upstream generic domains to downstream expert domains using scarce training data, where th…
Position Is All You Need: A Free Lunch Token Compression Strategy for MLLM-based Referring Expression Segmentation
Yuhan Liu, Yixiong Zou, Yuhua Li +1
Referring Expression Segmentation (RES) aims to generate pixel-wise segmentation masks from complex and implicit textual queries. While recent advances in Multimodal Large Language…
Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning
Haichen Zhou, Yazhe Lyu, Yixiong Zou +2
Few-shot class-incremental learning (FSCIL) aims to incrementally learn novel classes with only a few samples while avoiding forgetting base classes. However, current methods show…
Improving CLIP Adaptation by Breaking Tail Alignment for Source-Free Cross-Domain Few-Shot Learning
Shuai Yi, Yixiong Zou, Yuhua Li +1
Vision-Language Models (VLMs) such as CLIP demonstrate strong zero-shot generalization, but their performance significantly degrades in cross-domain scenarios with scarce target-do…
Addressing Exacerbated Attention Sink for Source-Free Cross-Domain Few-Shot Learning
Shuai Yi, Yixiong Zou, Yuhua Li +1
Vision-language models (VLMs) like CLIP have shown impressive generalization capabilities, yet their potential for Cross-Domain Few-Shot Learning (CDFSL) remains underexplored, whe…
Reviving In-domain Fine-tuning Methods for Source-Free Cross-domain Few-shot Learning
Yaze Zhao, Yicong Liu, Yixiong Zou +2
Cross-Domain Few-Shot Learning (CDFSL) aims to adapt large-scale pretrained models to specialized target domains with limited samples, yet the few-shot fine-tuning of vision-langua…
BetaEdit: Null-Space Constrained Sequential Model Editing
Bingqing Liu, Wei Liu, Yuhua Li
Null-space-based methods have garnered considerable attention in model editing by constraining updates to the null space of the pre-existing knowledge representation, thereby prese…
Interpretable Cross-Domain Few-Shot Learning with Rectified Target-Domain Local Alignment
Yaze Zhao, Yixiong Zou, Yuhua Li +1
Cross-Domain Few-Shot Learning (CDFSL) adapts models trained with large-scale general data (source domain) to downstream target domains with only scarce training data, where the re…
Remedying Target-Domain Astigmatism for Cross-Domain Few-Shot Object Detection
Yongwei Jiang, Yixiong Zou, Yuhua Li +1
Cross-domain few-shot object detection (CD-FSOD) aims to adapt pretrained detectors from a source domain to target domains with limited annotations, suffering from severe domain sh…
Mind the Discriminability Trap in Source-Free Cross-domain Few-shot Learning
Zhenyu Zhang, Yixiong Zou, Yuhua Li +2
Source-Free Cross-Domain Few-Shot Learning (SF-CDFSL) focuses on fine-tuning with limited training data from target domains (e.g., medical or satellite images), where Vision-Langua…
Reclaiming Lost Text Layers for Source-Free Cross-Domain Few-Shot Learning
Zhenyu Zhang, Guangyao Chen, Yixiong Zou +2
Source-Free Cross-Domain Few-Shot Learning (SF-CDFSL) focuses on fine-tuning with limited training data from target domains (e.g., medical or satellite images), where CLIP has rece…
Decoupling Template Bias in CLIP: Harnessing Empty Prompts for Enhanced Few-Shot Learning
Zhenyu Zhang, Guangyao Chen, Yixiong Zou +2
The Contrastive Language-Image Pre-Training (CLIP) model excels in few-shot learning by aligning visual and textual representations. Our study shows that template-sample similarity…
Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning
Yongwei Jiang, Yixiong Zou, Yuhua Li +1
Few-Shot Class-Incremental Learning (FSCIL) faces dual challenges of data scarcity and incremental learning in real-world scenarios. While pool-based prompting methods have demonst…
Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation
Jintao Tong, Ran Ma, Yixiong Zou +3
Cross-domain few-shot segmentation (CD-FSS) is proposed to pre-train the model on a source-domain dataset with sufficient samples, and then transfer the model to target-domain data…
Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning
Shuai Yi, Yixiong Zou, Yuhua Li +1
Vision Transformer (ViT) has achieved remarkable success due to its large-scale pretraining on general domains, but it still faces challenges when applying it to downstream distant…
Random Registers for Cross-Domain Few-Shot Learning
Shuai Yi, Yixiong Zou, Yuhua Li +1
Cross-domain few-shot learning (CDFSL) aims to transfer knowledge from a data-sufficient source domain to data-scarce target domains. Although Vision Transformer (ViT) has shown su…
Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation
Jintao Tong, Yixiong Zou, Guangyao Chen +2
Cross-Domain Few-Shot Segmentation (CD-FSS) aims to transfer knowledge from a source-domain dataset to unseen target-domain datasets with limited annotations. Current methods typic…
FlowCut: Rethinking Redundancy via Information Flow for Efficient Vision-Language Models
Jintao Tong, Wenwei Jin, Pengda Qin +5
Large vision-language models (LVLMs) excel at multimodal understanding but suffer from high computational costs due to redundant vision tokens. Existing pruning methods typically r…
The Devil is in Low-Level Features for Cross-Domain Few-Shot Segmentation
Yuhan Liu, Yixiong Zou, Yuhua Li +1
Cross-Domain Few-Shot Segmentation (CDFSS) is proposed to transfer the pixel-level segmentation capabilities learned from large-scale source-domain datasets to downstream target-do…
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…
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…
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…