arXiv Computer Vision
AcroMELD: Recovering Interactive PDF Forms with Structure-Aware Graph Set Transformers
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arXiv AI
Oct 16
Loud and Clear: Making Visual Evidence Count in Multimodal Document RAG
The paper introduces Loud and Clear (LoC), a method that enhances multimodal document retrieval-augmented generation by fully utilizing visual token capacity and refining retrieved pages to highlight evidence. LoC combines higher-resolution pages with OCR of query-relevant regions, achieving significant accuracy gains over eight prior methods on multiple benchmarks. The study demonstrates that the way retrieved pages are presented to the generator is a critical bottleneck beyond retrieval itself.
arXiv Computer Vision
Sep 14
Retrieved Images as Visual Thought: Training-Free Multimodal In-Context Learning for the Open-vs-Closed Gap
ReVisIT is a train‑free framework that turns retrieved image‑label pairs into units of visual thought, combining structured class definitions, multimodal retrieval, and alternating user/assistant injection before joint decoding. On several benchmarks—including Fast Open MiniImageNet, Bongard‑OpenWorld, and the newly released MAAC‑Bench—ReVisIT achieves performance comparable to or surpassing large, trained models while using far fewer parameters. The approach demonstrates that high‑quality retrieval and a simple turns layer can provide a universal performance boost across diverse multimodal tasks.
arXiv Computer Vision
Sep 21
MAGIC: Marginal-Guided Compression with Optimal Transport for Efficient Visual Document Retrieval
arXiv:2609.21018v1 Announce Type: new Abstract: Recent visual document retrieval (VDR) systems such as ColPali use multi-vector page embeddings, in which patch-level vectors enable fine-grained evide...
arXiv Computer Vision
Sep 23
Hi-OPD: Hierarchy-Aware Open-Prompt Detection for Remote Sensing Images
Hi-OPD introduces a hierarchy‑aware open‑prompt detector for remote sensing images, addressing inconsistencies that arise when flat open‑prompt training fails to maintain descendant retrieval under ancestor queries. Using a large RS153‑HierOPD dataset with 175,644 images and 3.48M boxes mapped to 153 atomic categories, Hi‑OPD employs hierarchy‑safe negative sampling, path multi‑positive supervision, and one‑way upward consistency to learn ancestor retrieval. The method achieves state‑of‑the‑art AP50 scores on DIOR and DOTA‑v2.0, significantly improves parent and grandparent AP50 metrics, and attains near‑perfect CAR50 across multiple sources.
arXiv AI
Aug 18
LongDocBench: Benchmarking TOC Hierarchy and Contextual Relationship Recovery in Long Documents
arXiv:2608. 15064v1 Announce Type: new Abstract: Parsing visual documents into machine-readable representations is fundamental to document intelligence.
arXiv AI
Oct 12
Locate, Judge, Read: Role-Specialized Tool Agents over a Knowledge-Point Index for Open-Domain Multimodal Document QA
KnowForge is a modular pipeline for open‑domain multimodal document question answering that separates retrieval, judging, and reading into distinct agents. It uses a hybrid‑retrieval knowledge‑point index, a Locator agent for searching, a Bridge Judge to enforce a second hop, a Deep Reader that handles text, images, and tables, and a synthesizer with format checking. On the M3DocVQA benchmark it achieves an F1 of 47.5, outperforming prior systems by 11 points, and shows strong gains on MultiHop‑RAG and MMLongBench‑Doc as well.