GenIR: Generative Visual Feedback for Mental Image Retrieval

• URL: http://arxiv.org/abs/2506.06220v1
• Date: Fri, 06 Jun 2025 16:28:03 GMT
• Title: GenIR: Generative Visual Feedback for Mental Image Retrieval
• Authors: Diji Yang, Minghao Liu, Chung-Hsiang Lo, Yi Zhang, James Davis,
• Abstract summary: We study the task of Mental Image Retrieval (MIR)<n>MIR targets the realistic yet underexplored setting where users refine their search for a mentally envisioned image through multi-round interactions with an image search engine.<n>We propose GenIR, a generative multi-round retrieval paradigm leveraging diffusion-based image generation to explicitly reify the AI system's understanding at each round.
• Score: 6.813922846074993
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-language models (VLMs) have shown strong performance on text-to-image retrieval benchmarks. However, bridging this success to real-world applications remains a challenge. In practice, human search behavior is rarely a one-shot action. Instead, it is often a multi-round process guided by clues in mind, that is, a mental image ranging from vague recollections to vivid mental representations of the target image. Motivated by this gap, we study the task of Mental Image Retrieval (MIR), which targets the realistic yet underexplored setting where users refine their search for a mentally envisioned image through multi-round interactions with an image search engine. Central to successful interactive retrieval is the capability of machines to provide users with clear, actionable feedback; however, existing methods rely on indirect or abstract verbal feedback, which can be ambiguous, misleading, or ineffective for users to refine the query. To overcome this, we propose GenIR, a generative multi-round retrieval paradigm leveraging diffusion-based image generation to explicitly reify the AI system's understanding at each round. These synthetic visual representations provide clear, interpretable feedback, enabling users to refine their queries intuitively and effectively. We further introduce a fully automated pipeline to generate a high-quality multi-round MIR dataset. Experimental results demonstrate that GenIR significantly outperforms existing interactive methods in the MIR scenario. This work establishes a new task with a dataset and an effective generative retrieval method, providing a foundation for future research in this direction.

Related papers

• A Picture is Worth a Thousand Prompts? Efficacy of Iterative Human-Driven Prompt Refinement in Image Regeneration Tasks [1.8563642867160601]
  The creation of AI-generated images often involves refining the input prompt iteratively to achieve desired visual outcomes.<n>This study focuses on the relatively underexplored concept of image regeneration using AI.<n>We present a structured user study evaluating how iterative prompt refinement affects the similarity of regenerated images relative to their targets.
  arXiv  Detail & Related papers  (2025-04-29T01:21:16Z)
• ImageScope: Unifying Language-Guided Image Retrieval via Large Multimodal Model Collective Reasoning [62.61187785810336]
  ImageScope is a training-free, three-stage framework that unifies language-guided image retrieval tasks.<n>In the first stage, we improve the robustness of the framework by synthesizing search intents across varying levels of semantic granularity.<n>In the second and third stages, we reflect on retrieval results by verifying predicate propositions locally, and performing pairwise evaluations globally.
  arXiv  Detail & Related papers  (2025-03-13T08:43:24Z)
• ChatReID: Open-ended Interactive Person Retrieval via Hierarchical Progressive Tuning for Vision Language Models [49.09606704563898]
  Person re-identification is a crucial task in computer vision, aiming to recognize individuals across non-overlapping camera views.<n>We propose a novel framework ChatReID, that shifts the focus towards a text-side-dominated retrieval paradigm, enabling flexible and interactive re-identification.<n>We introduce a hierarchical progressive tuning strategy, which endows Re-ID ability through three stages of tuning, i.e., from person attribute understanding to fine-grained image retrieval and to multi-modal task reasoning.
  arXiv  Detail & Related papers  (2025-02-27T10:34:14Z)
• Towards Text-Image Interleaved Retrieval [49.96332254241075]
  We introduce the text-image interleaved retrieval (TIIR) task, where the query and document are interleaved text-image sequences.<n>We construct a TIIR benchmark based on naturally interleaved wikiHow tutorials, where a specific pipeline is designed to generate interleaved queries.<n>We propose a novel Matryoshka Multimodal Embedder (MME), which compresses the number of visual tokens at different granularity.
  arXiv  Detail & Related papers  (2025-02-18T12:00:47Z)
• Spatio-Temporal Context Prompting for Zero-Shot Action Detection [13.22912547389941]
  We propose a method which can effectively leverage the rich knowledge of visual-language models to perform Person-Context Interaction.<n>To address the challenge of recognizing distinct actions by multiple people at the same timestamp, we design the Interest Token Spotting mechanism.<n>Our method achieves superior results compared to previous approaches and can be further extended to multi-action videos.
  arXiv  Detail & Related papers  (2024-08-28T17:59:05Z)
• TIGeR: Unifying Text-to-Image Generation and Retrieval with Large Multimodal Models [96.72318842152148]
  We propose a unified framework for text-to-image generation and retrieval with one single Large Multimodal Model (LMM)<n> Specifically, we first explore the intrinsic discriminative abilities of LMMs and introduce an efficient generative retrieval method for text-to-image retrieval in a training-free manner.<n>We then propose an autonomous decision mechanism to choose the best-matched one between generated and retrieved images as the response to the text prompt.
  arXiv  Detail & Related papers  (2024-06-09T15:00:28Z)
• Enhancing Interactive Image Retrieval With Query Rewriting Using Large Language Models and Vision Language Models [17.171715290673678]
  We propose an interactive image retrieval system capable of refining queries based on user relevance feedback. This system incorporates a vision language model (VLM) based image captioner to enhance the quality of text-based queries. To evaluate our system, we curate a new dataset by adapting the MSR-VTT video retrieval dataset to the image retrieval task.
  arXiv  Detail & Related papers  (2024-04-29T14:46:35Z)
• Advancing Image Retrieval with Few-Shot Learning and Relevance Feedback [5.770351255180495]
  Image Retrieval with Relevance Feedback (IRRF) involves iterative human interaction during the retrieval process. We propose a new scheme based on a hyper-network, that is tailored to the task and facilitates swift adjustment to user feedback. We show that our method can attain SoTA results in few-shot one-class classification and reach comparable results in binary classification task of few-shot open-set recognition.
  arXiv  Detail & Related papers  (2023-12-18T10:20:28Z)
• Object-Centric Open-Vocabulary Image-Retrieval with Aggregated Features [11.112981323262337]
  We present a simple yet effective approach to object-centric open-vocabulary image retrieval.<n>Our approach aggregates dense embeddings extracted from CLIP into a compact representation.<n>We show the effectiveness of our scheme to the task by achieving significantly better results than global feature approaches on three datasets.
  arXiv  Detail & Related papers  (2023-09-26T15:13:09Z)
• Improving Human-Object Interaction Detection via Virtual Image Learning [68.56682347374422]
  Human-Object Interaction (HOI) detection aims to understand the interactions between humans and objects. In this paper, we propose to alleviate the impact of such an unbalanced distribution via Virtual Image Leaning (VIL) A novel label-to-image approach, Multiple Steps Image Creation (MUSIC), is proposed to create a high-quality dataset that has a consistent distribution with real images.
  arXiv  Detail & Related papers  (2023-08-04T10:28:48Z)
• Tasks Integrated Networks: Joint Detection and Retrieval for Image Search [99.49021025124405]
  In many real-world searching scenarios (e.g., video surveillance), the objects are seldom accurately detected or annotated. We first introduce an end-to-end Integrated Net (I-Net), which has three merits. We further propose an improved I-Net, called DC-I-Net, which makes two new contributions.
  arXiv  Detail & Related papers  (2020-09-03T03:57:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.