OV-DINO: Unified Open-Vocabulary Detection with Language-Aware Selective Fusion

• URL: http://arxiv.org/abs/2407.07844v2
• Date: Mon, 22 Jul 2024 03:26:21 GMT
• Title: OV-DINO: Unified Open-Vocabulary Detection with Language-Aware Selective Fusion
• Authors: Hao Wang, Pengzhen Ren, Zequn Jie, Xiao Dong, Chengjian Feng, Yinlong Qian, Lin Ma, Dongmei Jiang, Yaowei Wang, Xiangyuan Lan, Xiaodan Liang,
• Abstract summary: We propose a novel unified open-vocabulary detection method called OV-DINO. It is pre-trained on diverse large-scale datasets with language-aware selective fusion in a unified framework. We evaluate the performance of the proposed OV-DINO on popular open-vocabulary detection benchmarks.
• Score: 88.59397418187226
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Open-vocabulary detection is a challenging task due to the requirement of detecting objects based on class names, including those not encountered during training. Existing methods have shown strong zero-shot detection capabilities through pre-training and pseudo-labeling on diverse large-scale datasets. However, these approaches encounter two main challenges: (i) how to effectively eliminate data noise from pseudo-labeling, and (ii) how to efficiently leverage the language-aware capability for region-level cross-modality fusion and alignment. To address these challenges, we propose a novel unified open-vocabulary detection method called OV-DINO, which is pre-trained on diverse large-scale datasets with language-aware selective fusion in a unified framework. Specifically, we introduce a Unified Data Integration (UniDI) pipeline to enable end-to-end training and eliminate noise from pseudo-label generation by unifying different data sources into detection-centric data format. In addition, we propose a Language-Aware Selective Fusion (LASF) module to enhance the cross-modality alignment through a language-aware query selection and fusion process. We evaluate the performance of the proposed OV-DINO on popular open-vocabulary detection benchmarks, achieving state-of-the-art results with an AP of 50.6% on the COCO benchmark and 40.1% on the LVIS benchmark in a zero-shot manner, demonstrating its strong generalization ability. Furthermore, the fine-tuned OV-DINO on COCO achieves 58.4% AP, outperforming many existing methods with the same backbone. The code for OV-DINO is available at https://github.com/wanghao9610/OV-DINO.

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