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Found 3 papers, 1 papers with code
EyeFormer: Predicting Personalized Scanpaths with Transformer-Guided Reinforcement Learning
From a visual perception perspective, modern graphical user interfaces (GUIs) comprise a complex graphics-rich two-dimensional visuospatial arrangement of text, images, and interactive objects such as buttons and menus.
PiTL: Cross-modal Retrieval with Weakly-supervised Vision-language Pre-training via Prompting
To further reduce the amount of supervision, we propose Prompts-in-The-Loop (PiTL) that prompts knowledge from large language models (LLMs) to describe images.
CLIP4IDC: CLIP for Image Difference Captioning
Different from directly fine-tuning CLIP to generate sentences, we introduce an adaptation training process to adapt CLIP's visual encoder to capture and align differences in image pairs based on the textual descriptions.
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