Unverified50% confidenceFactExact time
SL-Ground proposes encoding each category description as a complete sentence-level representation, letting object queries score directly against the holistic representation rather than detouring through tokens.
1
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50%
Confidence
Long-term
Relevance
10/20/2026
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Related Claims
UnverifiedSL-Ground avoids the need for prompt chunking through whole-sentence encoding, simplifying the deployment process and reducing engineering complexity on long-tail categories.70% similarUnverifiedMainstream OVOD methods adopt a phrase grounding framework: the text description is broken into tokens, object queries are aligned with the tokens, and category scores are then recovered through token-to-category aggregation.66% similarUnverified按特性（Feature）分层完成是Spec Coding的实践方式，每个特性有明确规格边界，AI在边界内工作66% similarUnverifiedThe core idea of generative recommendation is to encode items into semantic IDs and then generate these IDs layer by layer in an autoregressive manner.65% similarUnverifiedSentence-level representations move compositional semantics to the encoding stage, and alignment between queries and descriptions occurs within the full semantic space, so the model granularity benefits the most.64% similar
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