PRISM benchmark perturbs Crello layouts into 110K samples isolating design principle violations, reveals limited sensitivity in several multimodal models, and proposes a multi-scale framework combining scorers, instruction-tuned VLMs, and prompt methods for interpretable design assessment.
The all- seeing project: Towards panoptic visual recognition and understanding of the open world
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Modality representations share dominant semantic geometry but have an anisotropic residual gap; AnisoAlign corrects source representations boundedly using target geometry for unpaired alignment.
VINA trains a single detector on images plus video frames using a cross-modal supervised contrastive objective, yielding bidirectional gains and SOTA results on 14 image, video, and in-the-wild benchmarks.
DeepSeek-VL2 is a series of MoE vision-language models using dynamic tiling and latent attention that reach competitive or state-of-the-art results on VQA, OCR, document understanding and grounding with 1.0B to 4.5B activated parameters.
InternVL scales a vision model to 6B parameters and aligns it with LLMs using web data to achieve state-of-the-art results on 32 visual-linguistic benchmarks.
citing papers explorer
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Through the PRISM: Principle-Aware, Interpretable, and Multi-Scale Evaluation of Visual Designs
PRISM benchmark perturbs Crello layouts into 110K samples isolating design principle violations, reveals limited sensitivity in several multimodal models, and proposes a multi-scale framework combining scorers, instruction-tuned VLMs, and prompt methods for interpretable design assessment.
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Anisotropic Modality Align
Modality representations share dominant semantic geometry but have an anisotropic residual gap; AnisoAlign corrects source representations boundedly using target geometry for unpaired alignment.
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Video as Natural Augmentation: Towards Unified AI-Generated Image and Video Detection
VINA trains a single detector on images plus video frames using a cross-modal supervised contrastive objective, yielding bidirectional gains and SOTA results on 14 image, video, and in-the-wild benchmarks.
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DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding
DeepSeek-VL2 is a series of MoE vision-language models using dynamic tiling and latent attention that reach competitive or state-of-the-art results on VQA, OCR, document understanding and grounding with 1.0B to 4.5B activated parameters.
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InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
InternVL scales a vision model to 6B parameters and aligns it with LLMs using web data to achieve state-of-the-art results on 32 visual-linguistic benchmarks.