4 papers
Fine-tuning Pre-trained Vision-Language Models in a Human-Annotation-Free Manner
Qian-Wei Wang, Guanghao Meng, Ren Cai +2
Large-scale vision-language models (VLMs) such as CLIP exhibit strong zero-shot generalization, but adapting them to downstream tasks typically requires costly labeled data. Existi…
Heterogeneous Uncertainty-Guided Composed Image Retrieval with Fine-Grained Probabilistic Learning
Haomiao Tang, Jinpeng Wang, Minyi Zhao +4
Composed Image Retrieval (CIR) enables image search by combining a reference image with modification text. Intrinsic noise in CIR triplets incurs intrinsic uncertainty and threaten…
Enhancing Retrieval Augmentation via Adversarial Collaboration
Letian Zhang, Guanghao Meng, Xudong Ren +2
Retrieval-augmented Generation (RAG) is a prevalent approach for domain-specific LLMs, yet it is often plagued by "Retrieval Hallucinations"--a phenomenon where fine-tuned models f…
EvdCLIP: Improving Vision-Language Retrieval with Entity Visual Descriptions from Large Language Models
GuangHao Meng, Sunan He, Jinpeng Wang +7
Vision-language retrieval (VLR) has attracted significant attention in both academia and industry, which involves using text (or images) as queries to retrieve corresponding images…