This article presents a new control framework using vision language models (VLMs) for multiple tasks and robots. The authors combine the vision-language CLIP model with randomized control. This framework aims…
Continue readingVision-Language Models like CLIP are becoming prevalent due to their excellent generalization abilities. However, adapting them for downstream tasks remains challenging. One method involves learning prompts using visual information, but…
Continue readingThe paper tackles the challenge of generalization in neural networks to new domains not seen during training. The authors introduce a simple framework for generalizing semantic segmentation networks by using…
Continue readingThe article investigates the basic language capabilities of pre-trained multimodal models, questioning their understanding of image-text interaction. It introduces the BLA Benchmark, a tool designed to evaluate these models based…
Continue readingThe article discusses the use of pretrained Vision-Language Models (VLMs) as zero-shot reward models (RMs) for Reinforcement Learning (RL). The authors propose a method, called VLM-RMs, that uses these models…
Continue readingThe paper focuses on the Contrastive Language-Image Pre-training (CLIP) technique used in computer vision. The authors argue that the success of CLIP lies in its data, not its architecture or…
Continue readingOpen-vocabulary segmentation is a pivotal advancement in computer vision, allowing for the segmentation and recognition of objects across a vast set of categories. Traditional methods often employ a two-stage framework…
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