Tensor Decomposition and Language Description for Open-Vocabulary Object Detection

Qiuyu Liang; Yongqiang Zhang

AAAI 2026technical0 citations

Tensor Decomposition and Language Description for Open-Vocabulary Object Detection

Abstract

Open-vocabulary object detection (OVOD) aims at detecting and recognizing objects beyond a fixed set of classes. Although region-word alignment and knowledge distillation have been explored for training a strong open-vocabulary detector, our analysis reveals three main issues (inaccurate alignment, redundant distillation, and low-quality class embedding) that limit OVOD

BibTeX

@inproceedings{aaai2026_tensordecomposit,
  title = {Tensor Decomposition and Language Description for Open-Vocabulary Object Detection},
  author = {Qiuyu Liang and Yongqiang Zhang},
  booktitle = {AAAI 2026},
  year = {2026}
}