Simple image-level classification improves open-vocabulary object detection
Open-Vocabulary Object Detection (OVOD) aims to detect novel objects beyond a given set of base categories on which the detection model is trained. Recent OVOD methods focus on adapting the image-level pre-trained vision-language models (VLMs), such as CLIP, to a region-level object detection task v...
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sg-smu-ink.sis_research-97472024-05-03T07:49:19Z Simple image-level classification improves open-vocabulary object detection FANG, Ruohuan PANG, Guansong BAI, Xiao Open-Vocabulary Object Detection (OVOD) aims to detect novel objects beyond a given set of base categories on which the detection model is trained. Recent OVOD methods focus on adapting the image-level pre-trained vision-language models (VLMs), such as CLIP, to a region-level object detection task via, eg., region-level knowledge distillation, regional prompt learning, or region-text pre-training, to expand the detection vocabulary. These methods have demonstrated remarkable performance in recognizing regional visual concepts, but they are weak in exploiting the VLMs' powerful global scene understanding ability learned from the billion-scale image-level text descriptions. This limits their capability in detecting hard objects of small, blurred, or occluded appearance from novel/base categories, whose detection heavily relies on contextual information. To address this, we propose a novel approach, namely Simple Image-level Classification for Context-Aware Detection Scoring (SIC-CADS), to leverage the superior global knowledge yielded from CLIP for complementing the current OVOD models from a global perspective. The core of SIC-CADS is a multi-modal multi-label recognition (MLR) module that learns the object co-occurrence-based contextual information from CLIP to recognize all possible object categories in the scene. These image-level MLR scores can then be utilized to refine the instance-level detection scores of the current OVOD models in detecting those hard objects. This is verified by extensive empirical results on two popular benchmarks, OV-LVIS and OV-COCO, which show that SIC-CADS achieves significant and consistent improvement when combined with different types of OVOD models. Further, SIC-CADS also improves the cross-dataset generalization ability on Objects365 and OpenImages. 2024-02-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/8744 info:doi/10.1609/aaai.v38i2.27939 https://ink.library.smu.edu.sg/context/sis_research/article/9747/viewcontent/27939_Article_Text_31993_1_2_20240324.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Open-Vocabulary Object Detection (OVOD) Detection model Novel objects Databases and Information Systems Graphics and Human Computer Interfaces |
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Open-Vocabulary Object Detection (OVOD) Detection model Novel objects Databases and Information Systems Graphics and Human Computer Interfaces FANG, Ruohuan PANG, Guansong BAI, Xiao Simple image-level classification improves open-vocabulary object detection |
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Open-Vocabulary Object Detection (OVOD) aims to detect novel objects beyond a given set of base categories on which the detection model is trained. Recent OVOD methods focus on adapting the image-level pre-trained vision-language models (VLMs), such as CLIP, to a region-level object detection task via, eg., region-level knowledge distillation, regional prompt learning, or region-text pre-training, to expand the detection vocabulary. These methods have demonstrated remarkable performance in recognizing regional visual concepts, but they are weak in exploiting the VLMs' powerful global scene understanding ability learned from the billion-scale image-level text descriptions. This limits their capability in detecting hard objects of small, blurred, or occluded appearance from novel/base categories, whose detection heavily relies on contextual information. To address this, we propose a novel approach, namely Simple Image-level Classification for Context-Aware Detection Scoring (SIC-CADS), to leverage the superior global knowledge yielded from CLIP for complementing the current OVOD models from a global perspective. The core of SIC-CADS is a multi-modal multi-label recognition (MLR) module that learns the object co-occurrence-based contextual information from CLIP to recognize all possible object categories in the scene. These image-level MLR scores can then be utilized to refine the instance-level detection scores of the current OVOD models in detecting those hard objects. This is verified by extensive empirical results on two popular benchmarks, OV-LVIS and OV-COCO, which show that SIC-CADS achieves significant and consistent improvement when combined with different types of OVOD models. Further, SIC-CADS also improves the cross-dataset generalization ability on Objects365 and OpenImages. |
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text |
author |
FANG, Ruohuan PANG, Guansong BAI, Xiao |
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FANG, Ruohuan PANG, Guansong BAI, Xiao |
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FANG, Ruohuan |
title |
Simple image-level classification improves open-vocabulary object detection |
title_short |
Simple image-level classification improves open-vocabulary object detection |
title_full |
Simple image-level classification improves open-vocabulary object detection |
title_fullStr |
Simple image-level classification improves open-vocabulary object detection |
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Simple image-level classification improves open-vocabulary object detection |
title_sort |
simple image-level classification improves open-vocabulary object detection |
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Institutional Knowledge at Singapore Management University |
publishDate |
2024 |
url |
https://ink.library.smu.edu.sg/sis_research/8744 https://ink.library.smu.edu.sg/context/sis_research/article/9747/viewcontent/27939_Article_Text_31993_1_2_20240324.pdf |
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