Attention-Guided Integration of CLIP and SAM for Precise Object Masking in Robotic Manipulation
This work addresses the specific problem of object masking for robotic manipulation in convenience stores, representing an incremental advancement in the field.
The paper tackled the problem of precise object masking for robotic manipulation in convenience stores by integrating CLIP and SAM with gradient-based attention mechanisms, achieving improved segmentation masks for robotic systems.
This paper introduces a novel pipeline to enhance the precision of object masking for robotic manipulation within the specific domain of masking products in convenience stores. The approach integrates two advanced AI models, CLIP and SAM, focusing on their synergistic combination and the effective use of multimodal data (image and text). Emphasis is placed on utilizing gradient-based attention mechanisms and customized datasets to fine-tune performance. While CLIP, SAM, and Grad- CAM are established components, their integration within this structured pipeline represents a significant contribution to the field. The resulting segmented masks, generated through this combined approach, can be effectively utilized as inputs for robotic systems, enabling more precise and adaptive object manipulation in the context of convenience store products.