Attention-Guided Integration of CLIP and SAM for Precise Object Masking in Robotic Manipulation
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.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
GeoLanG: Geometry-Aware Language-Guided Grasping with Unified RGB-D Multimodal Learning
Language-guided grasping has emerged as a promising paradigm for enabling robots to identify and manipulate target objects through natural language instructions, yet it remains highly challenging in cluttered or occluded…
Correlation-Guided Query-Dependency Calibration for Video Temporal Grounding
Temporal Grounding is to identify specific moments or highlights from a video corresponding to textual descriptions. Typical approaches in temporal grounding treat all video clips equally during the encoding process rega…
Highlight DetectionMoment RetrievalNatural Language Moment RetrievalRepresentation Learning+1Tune-An-Ellipse: CLIP Has Potential to Find What You Want
Visual prompting of large vision language models such as CLIP exhibits intriguing zero-shot capabilities. A manually drawn red circle commonly used for highlighting can guide CLIP's attention to the surrounding regio…
ObjectReferring ExpressionReferring Expression ComprehensionVisual PromptingText-Guided Attention is All You Need for Zero-Shot Robustness in Vision-Language Models
Due to the impressive zero-shot capabilities, pre-trained vision-language models (e.g. CLIP), have attracted widespread attention and adoption across various domains. Nonetheless, CLIP has been observed to be susceptible…
Adversarial RobustnessAllDVLTA-VQA: Decoupled Vision-Language Modeling with Text-Guided Adaptation for Blind Video Quality Assessment
Inspired by the dual-stream theory of the human visual system (HVS) - where the ventral stream is responsible for object recognition and detail analysis, while the dorsal stream focuses on spatial relationships and motio…
Language ModelingLanguage ModellingObject RecognitionVideo Quality Assessment+1