arXiv:2402.02555v2 Announce Type: replace-cross Abstract: In this work, we propose ESG, a pipeline for high-quality entity segmentation and grounding supported by a new dataset EntitySeg. At first, the proposed dataset naming EntitySeg contains images spanning various image domains and entities, along with plentiful high-resolution images and high-quality mask annotations for training and testing. Then, the ESG mainly consists of two modules: CropFormer for high-quality entity segmentation whereas GELLA for accurate noun extraction from sentences and semantic matching between language and visu
The continuous advancements in AI, particularly in computer vision and natural language processing, are leading to increasingly sophisticated models capable of detailed perception and understanding.
High-quality entity segmentation and grounding is crucial for developing more robust and generalizable AI models, enabling better interaction between language and visual data for autonomous systems and intelligent agents.
This development pushes the frontier of multimodal AI, enhancing the ability of machines to 'see' and 'understand' in a more granular and semantically rich way.
- · AI developers
- · Robotics industry
- · e-commerce platforms
- · Autonomous vehicle developers
- · Companies relying on less precise AI vision
- · Manual data annotation services (for certain tasks)
Improved object recognition and understanding across various AI applications.
Accelerated development of autonomous AI agents capable of complex decision-making based on visual and linguistic input.
New forms of human-computer interaction and a reduction in the need for human supervision in object-centric tasks.
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