arXiv Computer Vision
Generative Retrieval for Unsupervised Text-Based Person Search
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
Aug 25
Training-Free Pseudo-Fusion for Composed Image Retrieval with Diffusion Models and Multimodal Large Language Models
arXiv:2608.23102v1 Announce Type: new Abstract: Composed Image Retrieval (CIR) is an emerging paradigm in content-based image retrieval that enables users to formulate compositional queries by combin...
Hugging Face Trending Papers
Jul 6
DiCE-CIR: Direct Composition Learning for Efficient Zero-Shot Composed Image Retrieval
Zero-shot composed image retrieval (ZS-CIR) aims to retrieve a target image from a multimodal query consisting of a reference image and an edit text describing the desired modification. Recent ZS-CIR studies have relied on projection-based methods that map a reference image into pseudo-word tokens in the text embedding space.
arXiv Machine Learning
Jul 21
The Many Senses of Visual Similarity: A Text-Prompted Image Perceptual Metric
arXiv:2607. 18237v1 Announce Type: cross Abstract: Human visual similarity judgments are context-dependent.
Hugging Face Trending Papers
Jul 30
FiRE: Enhancing MLLMs with Fine-Grained Context Learning for Complex Image Retrieval
Due to their strong generalizable multimodal processing and reasoning capabilities, Multimodal Large Language Models (MLLMs) have demonstrated significant potential as universal image retrievers, effectively addressing diverse real-world image retrieval tasks. Nevertheless, pioneering studies, while promising, overlook the potential of fine-grained context modeling and disentangled fine-tuning objectives in enhancing MLLMs' retrieval performance, particularly for complex tasks such as long-text-to-image retrieval, visual dialog retrieval, and composed image retrieval (CIR).
arXiv Machine Learning
Sep 22
Beyond Raw Context Transfer: Representation-based Federated Retrieval-Augmented Generation
arXiv:2609.22162v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) improves the factuality of large language models (LLMs) and vision-language models (VLMs) by grounding generatio...
Hugging Face Trending Papers
Jun 1
Towards Resolving Optimization Conflicts Between Image- and Text-Based Person Re-Identification
The joint optimization of image-based (I2I) and text-based (T2I) person re-identification (ReID) is hindered by modality discrepancies and conflicting training objectives, leading to suboptimal shared representations. While I2I ReID focuses on identity-level invariance across images of the same person, T2I ReID is driven by instance-specific textual descriptions tied to unique visual traits.