T-CLIP: Enabling Thermal Perception for Contrastive Language-Image Pretraining
Researchers have developed T-CLIP, a new framework designed to bridge the gap in understanding thermal images within contrastive language-image pretraining models. This approach addresses challenges such as the scarcity of captioned thermal datasets and the difficulty LLMs face in interpreting thermal phenomena. T-CLIP utilizes a decoupled dual-LoRA system to independently process scene-level and object-level thermal information, leading to improved performance in cross-modal retrieval tasks and potential applications in thermal image generation. AI
IMPACT Enables vision-language models to interpret thermal data, potentially improving performance in low-light and adverse conditions.