arXiv (All)
open access
DisParQ: Self-Supervised Part Concepts for Interpretable Vision Foundation Models
Concept-based vision models represent images through an intermediate layer of human-inspectable concepts, so what a model relies on can be traced to those concepts. However, those models are often limited to fixed categories or depend on language to define their concepts. We introduce DisParQ (Discr
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
- Prompt-Anchored Residual Adaptation for Biomedical Vision-Language Models#1118774
- REViT-v2: Hierarchical Windowed Roto-reflection Equivariant ViT for Equivariant Feature Extraction#1216960
- UnSAMv2: Self-Supervised Learning Enables Segment Anything at Any Granularity#1326164
- 0.5%>100%: Bidirectional Reciprocal Learning for Referring Image Segmentation#1061055
- Benchmarking Self-Supervised 3D Medical Vision Foundation Models and Transferability Estimation#1342787
Record · ID 1234169
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