arXiv Computer Vision
MMArt: A Multi-Perspective Multimodal Dataset for Visual Art Understanding
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Domain-specific language alignment of multimodal embeddings towards explainable predictions
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arXiv AI
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MUSE: Benchmarking Large Vision-Language Models on Multi-Modal Understanding in Situated Education
MUSE is a new benchmark designed to evaluate large vision‑language models on artistic image understanding within situated educational contexts. It separates image annotation from question generation, offering twelve tasks that cover visual perception, semantic and affective interpretation, cultural understanding, and compositional reasoning across diverse artistic images from Singaporean, Southeast Asian, and Western traditions. The benchmark reveals significant gaps in model performance, especially in affective interpretation and compositional reasoning, and highlights common failure modes for trustworthy educational multimodal systems.
arXiv Computer Vision
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ArtSeek: Deep artwork understanding via multimodal in-context reasoning and late interaction retrieval
arXiv:2507.21917v2 Announce Type: replace Abstract: Analyzing digitized artworks requires both visual interpretation and deep artistic, contextual, and historical knowledge. We introduce ArtSeek, a m...
arXiv AI
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Lens: Bringing the Right Semantic Perspective into Focus for Training-Free Multimodal Representation Learning
The paper introduces Lens, a training‑free framework that aligns multimodal representations with the semantic perspective required by downstream tasks. Lens uses a task‑specific readout phrase to anchor the perspective and then aggregates token states after the full input, ensuring the extracted representation reflects task‑conditioned evidence integration rather than generic salient content. The method achieves a Precision@1 of 63.9 across 36 MMEB datasets, outperforming the nearest training‑free baseline by 10.2 points.
arXiv AI
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ENTRAP-VL: A Taxonomic Probe for Dual Contextual Entrainment in Vision-Language Models
arXiv:2607. 20092v1 Announce Type: cross Abstract: Contextual entrainment is the tendency of a model to let auxiliary context in its input pull its output, independently of whether that context is relevant, true, or even meaningful.
arXiv AI
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HCSU: A Dataset and Benchmark for Fine-Grained Historical Calligraphy Style Understanding
arXiv:2607. 04147v1 Announce Type: cross Abstract: Automated fine-grained perception of calligraphy styles--a task vital to cultural heritage preservation--remains a critical challenge for Large Vision-Language Models (LVLMs), largely constrained by existing datasets that suffer from modal mixture and flattened labels.