arXiv Computer Vision
SOCO: Benchmarking Semantic Object Correspondence in Vision Foundation Models
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arXiv AI
5d ago
CFM: Language-aligned Concept Foundation Model for Vision
The paper introduces CFM, a language‑aligned concept foundation model for vision that generates fine‑grained, human‑interpretable concepts with spatial grounding. By pairing CFM with a strong semantic foundation model, it provides explanations for downstream tasks such as classification, segmentation, and captioning. The authors also analyze local co‑occurrence of concepts to define relationships, improving concept naming and yielding richer explanations while maintaining competitive performance.
arXiv AI
Aug 11
Vision-Language Grounding as Bidirectional Concept Correspondence
arXiv:2608. 07886v1 Announce Type: cross Abstract: Vision-language grounding connects language to visual content, yet most existing formulations reduce grounding to a unidirectional localization problem: given a prespecified text phrase or category name, identify the corresponding image region.
arXiv AI
Jul 31
TraceCLIP: Recovering Local Semantics from Patch-to-CLS Contributions
arXiv:2607. 26107v1 Announce Type: cross Abstract: Dense vision-language understanding, including object localization, region recognition, and open-vocabulary semantic segmentation, requires associating language concepts with spatially grounded visual regions.
arXiv Machine Learning
Aug 12
Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives
arXiv:2608. 11093v1 Announce Type: new Abstract: Cross-view feature matching aims to establish reliable correspondences across images with large viewpoint variations.
arXiv AI
Jun 24
Evaluating the Interpretability of Sparse Autoencoders with Concept Annotations
arXiv:2606. 24716v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) are increasingly used to extract interpretable concepts from vision and vision language models, yet existing evaluation methods largely rely on proxy metrics or qualitative inspection rather than measuring semantic correspondence.
arXiv Machine Learning
Jun 9
Zero-Shot Semantic Re-Identification for Autonomous Driving: A VLM Baseline Study
arXiv:2606. 09362v1 Announce Type: cross Abstract: Re-Identification (ReID) in autonomous driving is typically formulated as a visual matching problem, where observations of vehicles, pedestrians, and cyclists are associated across time, frames, or camera views using learned appearance embeddings, often complemented by motion, geometric, or multimodal cues.