Aug 2026· International Conference on Machine Learning and Unmanned Systems· Vol 14307, pp. 143070I - 143070I-8· 0 citations· 13 references
Engineering
TL;DR
This study focuses on spatial-relation cases, where cross-modal alignment and relation drift are especially severe, and MuCRE-TextVQA combines USG, CFT, Hybrid Mamba2, and CRE, with CRE serving as the main relation-execution component.
Abstract
Text-based visual question answering (TextVQA) jointly interprets image content, scene text, and natural-language questions from both fixed-vocabulary and OCR-derived answer spaces. This study focuses on spatial-relation cases, where cross-modal alignment and relation drift are especially severe. MuCRE-TextVQA combines USG, CFT, Hybrid Mamba2, and CRE, with CRE serving as the main relation-execution component. With the updated results, MuCRE improves spatial-subset accuracy from 0.3774 to 0.3936 while reaching 0.4449 overall accuracy; CFT alone remains slightly higher in overall accuracy (0.4491). The claimed advantage is therefore relation-sensitive reasoning rather than uniformly best overall performance.
This work proposes a TextVQA framework that integrates external prior knowledge to support multimodal reasoning and demonstrates that this approach effectively leverages external knowledge to improve performance on knowledge-intensive questions.
This work introduces DocHop, a benchmark for integrated chart--context reasoning in document-style images and constructs DocHop via a stochastic logic-first generation pipeline with controllable reasoning depth and visual density, to enable systematic evaluation.
Zhuoran Yu, Le Thien Phuc Nguyen, Jaden Park et al.· 1 citation
In tri-modal visual question answering (VQA), auxiliary text is commonly used to complement visual and textual inputs, yet its reliability is often uncontrolled. While prior work studies modality conflicts in general, it remains unclear how different types of unreliable auxiliary text affect answer selection under fixe...
Tam Le Thi Thanh, Van Tran Hoang, Hong-Hanh Nguyen-Le et al.· 0 citations
Experiments with representative closed-source and open-source MLLMs show that OCR-grounded meta-reasoning remains far from saturated: models struggle with visible-rule application and layout-sensitive inference, while process-compliant rationales can accompany incorrect final answers under exact-match evaluation.
The resulting training-free pipeline, RECAST, consistently outperforms recent frame-selection baselines without modifying the ITM encoder, and preserves the per-frame matching cost of a standard single-query baseline.
S. Han, Thang Vu, Junyeong Kim· IEEE Access· 0 citations
Overall, the proposed KV-ER Hybrid representation improved retrieval and answer generation under the evaluated OCR–RAG configuration without modifying the OCR model, retriever, or generator.
Minchae Song· IEEE Access· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
