Count objects in image with point prompts from object localization and CLIP to identify with PseCo
- morrislee
- Nov 22, 2023
- 1 min read
Count objects in image with point prompts from object localization and CLIP to identify with PseCo
Point, Segment and Count: A Generalized Framework for Object Counting
arXiv paper abstract https://arxiv.org/abs/2311.12386
arXiv PDF paper https://arxiv.org/pdf/2311.12386.pdf
Class-agnostic object counting aims to count all objects in an image with respect to example boxes or class names, a.k.a few-shot and zero-shot counting. Current state-of-the-art methods highly rely on density maps to predict object counts, which lacks model interpretability.
... propose a generalized framework for both few-shot and zero-shot object counting based on detection.
... framework combines the superior advantages of two foundation models without compromising their zero-shot capability: (i) SAM to segment all possible objects as mask proposals, and (ii) CLIP to classify proposals to obtain accurate object counts.
... framework, termed PseCo, follows three steps: point, segment, and count ... propose a class-agnostic object localization to provide but least point prompts for SAM, which ... reduces computation ... avoids missing small objects.
... propose ... object classification that leverages CLIP image/text embeddings as the classifier, following a hierarchical knowledge distillation to obtain discriminative classifications among ... mask proposals.
... demonstrate that PseCo achieves state-of-the-art performance in both few-shot/zero-shot object counting/detection ...
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