arXiv Computer Vision
CBX-Bench: A Human-Aligned MLLM Council for Benchmarking Concept Bottleneck Model Explanations
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Explaining is Harder Than Predicting Alone: Evaluating Concept-based Explanations of MLLMs as ICL Visual Classifiers
arXiv:2605. 28215v2 Announce Type: replace Abstract: In-context learning (ICL) enables multimodal large language models (MLLMs) to classify images from a few labelled examples.
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Rule2Text: A Framework for Generating and Evaluating Natural Language Explanations of Knowledge Graph Rules
arXiv:2508. 10971v2 Announce Type: replace-cross Abstract: Knowledge graphs (KGs) can be enhanced through rule mining; however, the resulting logical rules are often difficult for humans to interpret due to their inherent complexity and the idiosyncratic labeling conventions of individual KGs.
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Defake-o3: From Speculative Rationales to Verifiable Evidence for Explainable AIGI Detection
arXiv:2608. 16259v1 Announce Type: cross Abstract: The rapid progress of image generation models calls for AI-generated image (AIGI) detectors that are not only accurate but also explainable and reliable.
arXiv Machine Learning
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Clarity: The Flexibility-Interpretability Trade-Off in Sparsity-aware Concept Bottleneck Models
arXiv:2601. 21944v3 Announce Type: replace Abstract: The widespread adoption of deep learning models in computer vision has intensified concerns about interpretability.
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NoteVQA: Benchmarking VLMs on Real-Life Questions from Human Communities
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arXiv AI
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