VLMs exhibit affirmation bias that varies by language, with a new multilingual benchmark showing CLIP at or below chance on non-Latin scripts, MultiCLIP most uniform, and SpaceVLM corrections effective unevenly across typologies.
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VisualBERT is a Transformer model that implicitly aligns text and image regions through self-attention and achieves competitive or superior results on VQA, VCR, NLVR2, and Flickr30K after pre-training on captions.
citing papers explorer
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Disparities In Negation Understanding Across Languages In Vision-Language Models
VLMs exhibit affirmation bias that varies by language, with a new multilingual benchmark showing CLIP at or below chance on non-Latin scripts, MultiCLIP most uniform, and SpaceVLM corrections effective unevenly across typologies.
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VisualBERT: A Simple and Performant Baseline for Vision and Language
VisualBERT is a Transformer model that implicitly aligns text and image regions through self-attention and achieves competitive or superior results on VQA, VCR, NLVR2, and Flickr30K after pre-training on captions.