A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP
To address the limitations of existing open-vocabulary object recognition methods, including high system complexity, substantial training costs, and limited generalization capability, this paper proposes a novel Open-Vocabulary Object Recognition (OVOR) framework based on a streamlined two-stage strategy: object segmentation followed by recognition. The proposed framework eliminates the need for complex retraining procedures and labor-intensive annotation. After extracting object regions, object-level image embeddings and category-level text embeddings are generated using CLIP, enabling recognition over arbitrary vocabularies. To reduce dependence on CLIP and enhance encoding flexibility,...
| Authors | Wei Yu Chen, Ying Dai |
|---|---|
| Primary category | cs.CV |
| Published | 6 March 2026 |
| Last updated at source | 23 June 2026 |
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