arXiv (All)
open access
Constructive Distortion: Improving MLLMs with Attention-Guided Image Warping
Multimodal large language models (MLLMs) often miss small details and spatial relations in cluttered scenes, leading to errors in fine-grained perceptual grounding. We introduce AttWarp, a lightweight method that allocates more resolution to query-relevant content while compressing less informative
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Resolution as a First-Class Decision: Task-Conditioned Routing for Efficient Multimodal Large Language Models#1129550
- OSPO: Object-Centric Self-Improving Preference Optimization for Text-to-Image Generation#1105911
- SpanCalib-VLM: Calibrated Hallucination Span Detection in Vision-Language Models#799221
- Walking the Embedding Space: Datastore Extraction from Multimodal RAG#1160925
- Walking the Embedding Space: Datastore Extraction from Multimodal RAG#1157142
Record · ID 1131419
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive