arXiv (OAI Expanded)
open access
Talk in Pieces, See in Whole: Disentangled and Hierarchical Representation Learning in Language-based Object Detection
Vision-language models (VLMs) have advanced multimodal perception, demonstrated by open-vocabulary object detection with simple language queries. State-of-the-art VLMs still struggle to handle complex queries involving descriptive attributes and relational clauses. To address this problem, we propos
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
- Talk in Pieces, See in Whole: Disentangled and Hierarchical Representation Learning in Language-based Object Detection#798646
- SalQ-VLM: Fine-Grained Saliency-Guided Quantization for Vision-Language Models#1049655
- NeuronEye: Query-Guided Visual Concept Activation for Vision-Language Reasoning#1142394
- NeuronEye: Query-Guided Visual Concept Activation for Vision-Language Reasoning#1122325
- VocaDet: Sample-Driven Open-Vocabulary Object Detection and Segmentation via Visual Tokenization and Vector Database Retrieval#353101
Record · ID 797232
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