arXiv Computer Vision
What Remains OOD? OODConceptBench for Open-World Concept Recognition in Vision-Language Models
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OODBench: Out-of-Distribution Benchmark for Large Vision-Language Models
arXiv:2602. 18094v2 Announce Type: replace-cross Abstract: Existing Visual-Language Models (VLMs) have achieved significant progress by being trained on massive-scale datasets, typically under the assumption that data are independent and identically distributed (IID).
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VisualWorldBench: A Fine-Grained Multi-Task Benchmark for Evaluating Visual World Knowledge in MLLMs
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SetCon: Towards Open-Ended Referring Segmentation via Set-Level Concept Prediction
SetCon reformulates open-ended referring segmentation as set-level concept prediction, using LVLM-generated natural-language concepts instead of segmentation-specific tokens. It employs a hierarchical semantic decomposition that first predicts a shared set-level concept and then refines it into fine-grained concept groups aligned with target subsets. The method, supported by a two-stage annotation pipeline adding hierarchical semantic supervision, achieves state-of-the-art results on multiple image and video benchmarks, with performance gains increasing as the number of referred targets grows.
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Low-cost concept-based localized explanations: How far can we get with training-free approaches?
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Hugging Face Trending Papers
Jul 27
Multimodal Semantic-Probabilistic Objectness for Open World Object Detection
Open-world object detection (OWOD) requires a detector to recognize known categories, discover unnamed objects from unseen categories, and incrementally learn newly annotated classes. PROB improves unknown discovery by modeling class-agnostic probabilistic objectness in the decoder-query space.