Hierarchical Scheduling for Multi-Vector Image Retrieval

• URL: http://arxiv.org/abs/2510.08976v1
• Date: Fri, 10 Oct 2025 03:36:18 GMT
• Title: Hierarchical Scheduling for Multi-Vector Image Retrieval
• Authors: Maoliang Li, Ke Li, Yaoyang Liu, Jiayu Chen, Zihao Zheng, Yinjun Wu, Xiang Chen,
• Abstract summary: HiMIR is an efficient scheduling framework for image retrieval.<n>We introduce a novel hierarchical paradigm, employing multiple intermediate granularities for varying image objects to enhance alignment.<n>Our empirical study shows that, HiMIR not only achieves substantial accuracy improvements but also reduces computation by up to 3.5 times over the existing MVR system.
• Score: 17.023146933530484
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To effectively leverage user-specific data, retrieval augmented generation (RAG) is employed in multimodal large language model (MLLM) applications. However, conventional retrieval approaches often suffer from limited retrieval accuracy. Recent advances in multi-vector retrieval (MVR) improve accuracy by decomposing queries and matching against segmented images. They still suffer from sub-optimal accuracy and efficiency, overlooking alignment between the query and varying image objects and redundant fine-grained image segments. In this work, we present an efficient scheduling framework for image retrieval - HiMIR. First, we introduce a novel hierarchical paradigm, employing multiple intermediate granularities for varying image objects to enhance alignment. Second, we minimize redundancy in retrieval by leveraging cross-hierarchy similarity consistency and hierarchy sparsity to minimize unnecessary matching computation. Furthermore, we configure parameters for each dataset automatically for practicality across diverse scenarios. Our empirical study shows that, HiMIR not only achieves substantial accuracy improvements but also reduces computation by up to 3.5 times over the existing MVR system.

Related papers

• Pixel-Grounded Retrieval for Knowledgeable Large Multimodal Models [58.46663983451155]
  PixSearch is an end-to-end Segmenting Large Multimodal Model (LMM) that unifies region-level perception and retrieval-augmented reasoning.<n>During encoding, PixSearch emits search> tokens to trigger retrieval, selects query modalities (text, image, or region), and generates pixel-level masks that directly serve as visual queries.<n>On egocentric and entity-centric VQA benchmarks, PixSearch substantially improves factual consistency and generalization.
  arXiv  Detail & Related papers  (2026-01-27T00:46:08Z)
• CSMCIR: CoT-Enhanced Symmetric Alignment with Memory Bank for Composed Image Retrieval [54.15776146365823]
  Composed Image Retrieval (CIR) enables users to search for target images using both a reference image and manipulation text.<n>We propose CSMCIR, a unified representation framework that achieves efficient query-target alignment through three synergistic components.
  arXiv  Detail & Related papers  (2026-01-07T09:21:38Z)
• Generalized Contrastive Learning for Universal Multimodal Retrieval [53.70202081784898]
  Cross-modal retrieval models (e.g., CLIP) show degraded performances with retrieving keys composed of fused image-text modality.<n>This paper proposes Generalized Contrastive Learning (GCL), a novel loss formulation that improves multimodal retrieval performance without the need for new dataset curation.
  arXiv  Detail & Related papers  (2025-09-30T01:25:04Z)
• Mask-aware Text-to-Image Retrieval: Referring Expression Segmentation Meets Cross-modal Retrieval [13.296362770269452]
  Mask-aware TIR (MaTIR) aims to find relevant images based on a textual query.<n>We propose a two-stage framework, comprising a first stage for segmentation-aware image retrieval and a second stage for reranking and object grounding.<n>We evaluate our approach on COCO and D$3$ datasets, demonstrating significant improvements in both retrieval accuracy and segmentation quality over previous methods.
  arXiv  Detail & Related papers  (2025-06-28T12:19:49Z)
• QuARI: Query Adaptive Retrieval Improvement [10.896025071832055]
  We show that a linear transformation of VLM features trained for instance retrieval can improve performance by emphasizing subspaces that relate to the domain of interest.<n>Because this transformation is linear, it can be applied with minimal computational cost to millions of image embeddings.<n>Results show that this method consistently outperforms state-of-the-art alternatives, including those that require many orders of magnitude more at query time.
  arXiv  Detail & Related papers  (2025-05-27T18:21:48Z)
• Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning [62.640169289390535]
  SPLIT-RAG is a multi-agent RAG framework that addresses the limitations with question-driven semantic graph partitioning and collaborative subgraph retrieval.<n>The innovative framework first create Semantic Partitioning of Linked Information, then use the Type-Specialized knowledge base to achieve Multi-Agent RAG.<n>The attribute-aware graph segmentation manages to divide knowledge graphs into semantically coherent subgraphs, ensuring subgraphs align with different query types.<n>A hierarchical merging module resolves inconsistencies across subgraph-derived answers through logical verifications.
  arXiv  Detail & Related papers  (2025-05-20T06:44:34Z)
• Marmot: Object-Level Self-Correction via Multi-Agent Reasoning [55.74860093731475]
  Marmot is a novel and generalizable framework that leverages Multi-Agent Reasoning for Multi-Object Self-Correcting.<n>Marmot significantly improves accuracy in object counting, attribute assignment, and spatial relationships for image generation tasks.
  arXiv  Detail & Related papers  (2025-04-10T16:54:28Z)
• 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)
• Image2Sentence based Asymmetrical Zero-shot Composed Image Retrieval [92.13664084464514]
  The task of composed image retrieval (CIR) aims to retrieve images based on the query image and the text describing the users' intent. Existing methods have made great progress with the advanced large vision-language (VL) model in CIR task, however, they generally suffer from two main issues: lack of labeled triplets for model training and difficulty of deployment on resource-restricted environments. We propose Image2Sentence based Asymmetric zero-shot composed image retrieval (ISA), which takes advantage of the VL model and only relies on unlabeled images for composition learning.
  arXiv  Detail & Related papers  (2024-03-03T07:58:03Z)
• Data Roaming and Quality Assessment for Composed Image Retrieval [25.452015862927766]
  Composed Image Retrieval (CoIR) involves queries that combine image and text modalities, allowing users to express their intent more effectively. We introduce the Large Scale Composed Image Retrieval (LaSCo) dataset, a new CoIR dataset which is ten times larger than existing ones. We also introduce a new CoIR baseline, the Cross-Attention driven Shift (CASE)
  arXiv  Detail & Related papers  (2023-03-16T16:02:24Z)

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.