24 citations · 44 across the 35 of their papers we have counts for
13 papers · 1 filter
Shallow to Deep: Aligning Token Pruning with Stage-wise Roles in LVLMs
Shuo Zhang, Jintao Tong, Yixiong Zou +2
Large Vision-Language Models (LVLMs) incur high computational costs from redundant visual tokens. Although training-free attention-based multi-layer pruning in the vision encoder s…
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…
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…
SwimBird: Eliciting Switchable Reasoning Mode in Hybrid Autoregressive MLLMs
Jintao Tong, Shilin Yan, Hongwei Xue +5
Multimodal Large Language Models (MLLMs) have made remarkable progress in multimodal perception and reasoning by bridging vision and language. However, most existing MLLMs perform…
QKVQA: Question-Focused Filtering for Knowledge-based VQA
Wei Ye, Yixin Su, Yueguo Chen +4
Visual Question Answering (VQA) is the task of answering questions based on image content. Building upon this, Knowledge-Based VQA (KB-VQA) requires models to answer questions that…