Detect all objects 21.3% better than SAM by expanding prompts for Grounding DINO with DiPEx
Detect all objects 21.3% better than SAM by expanding prompts for Grounding DINO with DiPEx
DiPEx: Dispersing Prompt Expansion for Class-Agnostic Object Detection
arXiv paper abstract https://arxiv.org/abs/2406.14924
arXiv PDF paper https://arxiv.org/pdf/2406.14924
Class-agnostic object detection (OD) ... investigate using vision-language models (VLMs) to enhance object detection via a self-supervised prompt learning strategy.
... propose a Dispersing Prompt Expansion (DiPEx) ... expand a set of distinct, non-overlapping hyperspherical prompts to enhance recall rates
... DiPEx initiates the process by self-training generic parent prompts and selecting the one with the highest semantic uncertainty for further expansion ... child prompts ... inherit semantics from their parent prompts while capturing more fine-grained semantics.
... apply dispersion losses to ensure high inter-class discrepancy among child prompts while preserving semantic consistency between parent-child prompt pairs.
To prevent excessive growth of the prompt sets, ... utilize the maximum angular coverage (MAC) of the semantic space as a criterion for early termination.
... DiPEx ... achieving a 21.3% AP improvement over SAM ...
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