arXiv AI
DocIntent: Answerability-Guided Agentic Restoration for Real-World Document Visual Question Answering
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
Hugging Face Trending Papers
Aug 4
Failure-Informed Image Self-Augmentation for Multimodal Large Language Model Self-Improvement
Multimodal large language models (MLLMs) have achieved remarkable performance across vision-language tasks, but their progress depends heavily on large-scale, high-quality multimodal data that are costly to annotate. Self-augmentation offers a promising alternative by enabling models to expand their own training data without external supervision.
arXiv AI
Aug 5
Failure-Informed Image Self-Augmentation for Multimodal Large Language Model Self-Improvement
arXiv:2608. 03733v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved remarkable performance across vision-language tasks, but their progress depends heavily on large-scale, high-quality multimodal data that are costly to annotate.
arXiv Computer Vision
Oct 1
EvoIR-Agent: Self-Evolving Image Restoration Agentic System via Experience-Driven Learning
arXiv:2605.22208v3 Announce Type: replace Abstract: Multimodal Large Language Model (MLLM)-driven image restoration agent demonstrates effectiveness in degradation coupling scenarios by flexibly sele...
arXiv AI
Sep 1
WeAgent-MMSearch: Native Text-Vision Interaction for Multimodal Search Agents
WeAgent-MMSearch introduces a multimodal search agent that preserves retrieved images as persistent references, enabling the model to inspect, process, and cite them throughout a search trajectory. The system includes a harness (WeAgent-Harness), a post‑training method (FA‑GSPO) that recovers salvageable rollouts, and a new benchmark (VisTarget‑Bench) to evaluate image‑retrieval versus visual‑perception failures. Evaluation shows that agentic post‑training boosts performance by 19.22 points, allowing the model to outperform similarly sized open‑source models and compete with much larger ones.
arXiv Computer Vision
Sep 1
ClearText-Video: A Large-Scale Text-Centric Video Dataset Bridging Video Restoration and Scene-Text Enhancement
ClearText-Video (CTVid) is a large-scale, scene-text-aware benchmark that examines text-centric video understanding under varying quality conditions. It comprises 4,639 real-world egocentric videos, over 550,000 frames, 1.6 million human-verified scene-text annotations, and more than 220,000 spatial/temporal question–answer pairs in Chinese and English. For each high-quality video, CTVid provides matched degraded- and restored-quality variants, enabling studies of Text-Centric Video Restoration and Multi-Quality VideoQA, and revealing that visual enhancement does not always improve textual fidelity or downstream reasoning.
arXiv AI
Jun 29
ProMSA:Progressive Multimodal Search Agents for Knowledge-Based Visual Question Answering
arXiv:2606. 27974v1 Announce Type: cross Abstract: Knowledge-based Visual Question Answering (KB-VQA) requires models to combine image understanding with external knowledge.