Summary CoDet Co-Occurrence Guided Region-Word Alignment for Open-Vocabulary Object Detection arxiv.org
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CoDet enhances object detection by aligning co-occurring objects, surpassing other methods, and achieving top-notch performance through the use of region correspondences and text guidance.
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Key Points
- CoDet is a novel approach to open-vocabulary object detection that addresses the challenge of deriving reliable region-word alignment from image-text pairs.
- CoDet reformulates region-word alignment as a co-occurring object discovery problem by grouping images that mention a shared concept in their captions and leveraging visual similarities.
- CoDet outperforms existing methods in open-vocabulary detection, achieving superior performances and scalability.
- CoDet avoids the reliance on a pre-aligned vision-language space and solely relies on the vision space to discover region-word correspondences.
- CoDet introduces text guidance into similarity estimation between region proposals to make it concept-aware and more accurately reflect the closeness of objects concerning the shared semantic concept.
- Experimental results demonstrate the effectiveness of CoDet in achieving superior performances in novel object detection on OV-LVIS and OV-COCO benchmark datasets.
- CoDet presents a novel approach to open-vocabulary object detection by reformulating region-word alignment and leveraging co-occurrence information.
Summaries
26 word summary
CoDet improves object detection by aligning co-occurring objects and outperforms existing methods in open-vocabulary detection. It effectively leverages region correspondences and text guidance for state-of-the-art performance.
80 word summary
CoDet is a novel approach to open-vocabulary object detection that improves region-word alignment by discovering and aligning co-occurring objects. It achieves superior performances, outperforming existing methods in open-vocabulary detection. CoDet focuses on the open-vocabulary setting and leverages region correspondences across images to accurately reflect the closeness of objects. Experimental results show CoDet's effectiveness in achieving superior performances, particularly on novel categories. Overall, CoDet effectively leverages cross-image region correspondences and text guidance to discover co-occurring objects for alignment, achieving state-of-the-art performance.
172 word summary
CoDet is a novel approach to open-vocabulary object detection that improves region-word alignment by discovering and aligning co-occurring objects. It achieves superior performances and scalability, outperforming existing methods in open-vocabulary detection. CoDet achieves 37.0 AP on novel categories and 44.7 AP on all categories in the OV-LVIS benchmark, surpassing the previous state-of-the-art by 4.2 AP and 9.8 AP, respectively.
CoDet focuses on the open-vocabulary setting and leverages region correspondences across images to discover and align co-occurring objects. It introduces text guidance into similarity estimation to accurately reflect the closeness of objects concerning the shared semantic concept.
Experimental results show CoDet's effectiveness in achieving superior performances in novel object detection on various benchmark datasets. It significantly improves performance, particularly on novel categories, and the prototype-based strategy for co-occurring object discovery yields better results.
Overall, CoDet effectively leverages cross-image region correspondences and text guidance to discover co-occurring objects for alignment, achieving state-of-the-art performance on various benchmark datasets. It presents a novel approach to open-vocabulary object detection and has potential for combining with vision-language models.
417 word summary
CoDet is a novel approach to open-vocabulary object detection that reformulates region-word alignment as a co-occurring object discovery problem. It groups images that mention a shared concept in their captions and uses visual similarities to discover and align co-occurring objects with the shared concept. CoDet outperforms existing methods in open-vocabulary detection, achieving superior performances and scalability. It achieves 37.0 AP on novel categories and 44.7 AP on all categories in the OV-LVIS benchmark, surpassing the previous state-of-the-art by 4.2 AP and 9.8 AP, respectively.
Object detection traditionally has a fixed vocabulary, but CoDet focuses on the open-vocabulary setting, training the detector to recognize objects of arbitrary categories. Existing methods rely on vision-language models (VLMs) for region-word alignments, but these models have limitations in alignment accuracy for novel concepts. CoDet proposes a new approach that leverages region correspondences across images for co-occurring concept discovery and alignment. It constructs semantic groups by sampling images that mention a shared concept in their captions, inferring the existence of a common object corresponding to the shared concept across images.
CoDet avoids reliance on a pre-aligned vision-language space and solely relies on the vision space to discover region-word correspondences. It introduces text guidance into similarity estimation between region proposals to make it concept-aware and more accurately reflect the closeness of objects concerning the shared semantic concept. Experimental results demonstrate the effectiveness of CoDet in achieving superior performances in novel object detection on OV-LVIS and OV-COCO benchmarks.
The authors evaluate CoDet's performance on different benchmark datasets and compare it with existing methods, showing its superior results in terms of average precision (AP) on COCO and Objects365 datasets. They investigate different alignment strategies and find that alignments based on region-region similarity produce more accurate pseudo labels. Text guidance significantly improves the performance of CoDet, particularly on novel categories. The prototype-based strategy for co-occurring object discovery achieves better results.
Increasing the group size in co-occurring concept discovery reduces ambiguity when there are multiple co-occurring concepts in a group. However, preferences for concept group size differ between human-curated caption data and web-crawled image-text pairs. Overall, CoDet effectively leverages cross-image region correspondences and text guidance to discover co-occurring objects for alignment, achieving state-of-the-art performance on various benchmark datasets.
In conclusion, CoDet presents a novel approach to open-vocabulary object detection by reformulating region-word alignment as a co-occurring object discovery problem. It outperforms existing methods and demonstrates scalability with visual backbones. The authors highlight the potential of combining CoDet with previous efforts in aligning regions and words with vision-language models.
992 word summary
CoDet is a novel approach to open-vocabulary object detection that addresses the challenge of deriving reliable region-word alignment from image-text pairs. Unlike existing methods that rely on pre-trained or self-trained vision-language models for alignment, CoDet reformulates region-word alignment as a co-occurring object discovery problem. The key idea is to group images that mention a shared concept in their captions and leverage visual similarities to discover and align co-occurring objects with the shared concept.
CoDet outperforms existing methods in open-vocabulary detection, achieving superior performances and scalability. For example, by scaling up the visual backbone, CoDet achieves 37.0 AP on novel categories and 44.7 AP on all categories in the OV-LVIS benchmark, surpassing the previous state-of-the-art by 4.2 AP and 9.8 AP, respectively. The code for CoDet is available on GitHub.
Object detection is a fundamental vision task, but traditional detectors are mostly limited to a fixed vocabulary defined by training data. To address this limitation, CoDet focuses on the open-vocabulary setting of object detection, where the detector is trained to recognize objects of arbitrary categories. Recent advancements in vision-language pretraining on web-scale image-text pairs have inspired the adaptation of this paradigm to object detection. However, human-annotated region-text pairs are limited and difficult to scale, leading to the need for methods that can mine additional region-text pairs from image-text pairs.
Existing methods typically rely on vision-language models (VLMs) to determine region-word alignments. However, the quality of generated pseudo region-text pairs is subject to limitations of VLMs. VLMs pre-trained with image-level supervision are largely unaware of localization quality, while detector-based VLMs mitigate this issue but still face limitations in alignment accuracy for novel concepts. Additionally, obtaining high-quality region-word pairs requires a VLM with object-level vision-language knowledge, but training such a VLM depends on a large number of aligned region-word pairs.
CoDet proposes a new approach by leveraging region correspondences across images for co-occurring concept discovery and alignment. The method constructs semantic groups by sampling images that mention a shared concept in their captions. By grouping images, CoDet infers the existence of a common object corresponding to the shared concept across images. It then uses cross-image region similarity to identify regions potentially containing the common object and constructs a prototype from them. The prototype and the shared concept form a natural region-text pair, which is used to supervise the training of an open-vocabulary object detector.
CoDet avoids the reliance on a pre-aligned vision-language space and solely relies on the vision space to discover region-word correspondences. However, there could be multiple co-occurring concepts in the same group, and the same concept may exhibit high intra-category variation in appearance. To address this issue, CoDet introduces text guidance into similarity estimation between region proposals to make it concept-aware and more accurately reflect the closeness of objects concerning the shared semantic concept.
The main contributions of CoDet include introducing a novel perspective in discovering region-word correspondences from image-text pairs, proposing an open-vocabulary detection framework that learns object-level vision-language alignment directly from web-crawled image-text pairs, consistently outperforming existing methods on the challenging OV-LVIS benchmark, and exhibiting strong scalability with visual backbones.
Experimental results demonstrate the effectiveness of CoDet. It achieves superior performances in novel object detection on OV-LVIS, outperforming other state-of-the-art methods. CoDet also performs well on OV-COCO, achieving the second best performance among existing methods. The scalability of CoDet is validated by testing with more powerful visual backbones, which leads to significant performance gains. Ablation studies show that CoDet works well on web-crawled data, which is a more practical setting and can easily be scaled up.
In conclusion, CoDet presents a novel approach to open-vocabulary object detection by reformulating
The paper presents CoDet, a method for open-vocabulary object detection that leverages co-occurrence information to discover region-word alignments. The authors conduct experiments to demonstrate the effectiveness of CoDet in various scenarios.
In the first set of experiments, the authors evaluate CoDet's performance on different benchmark datasets. They compare CoDet with several existing methods and show that CoDet achieves superior results in terms of average precision (AP). CoDet outperforms the second-best method by 2.0% and 2.1% AP on COCO and Objects365 datasets, respectively. This demonstrates the generalization capability of CoDet across different image domains and vocabularies.
Next, the authors investigate different alignment strategies for discovering reliable region-word alignments. They compare strategies based on region-word similarity, hand-crafted prior, and region-region similarity. The results show that alignments based on region-region similarity produce more accurate pseudo labels compared to the other two strategies. Additionally, the authors find that their method can benefit from self-training, which leads to steadily increasing pseudo-label quality. However, this pattern is not observed in similarly self-trained models relying on region-word similarity for alignment.
The authors provide visualizations of pseudo bounding box labels generated by different alignment strategies. The visualizations show that strategies based on region-region similarity produce more accurate and reliable results compared to the other strategies.
In an ablation study, the authors analyze the impact of text guidance and the strategy for co-occurring object discovery on the performance of CoDet. They find that text guidance significantly improves the performance of CoDet, particularly on novel categories. They also compare a prototype-based strategy with a heuristic strategy for co-occurring object discovery and show that the prototype-based strategy achieves better results.
The authors further analyze the impact of the size of the concept group on the performance of CoDet. They find that increasing the group size can effectively reduce ambiguity when there are multiple co-occurring concepts in a group. However, they observe different preferences for concept group size on human-curated caption data and web-crawled image-text pairs.
In conclusion, the authors make the first attempt to explore co-occurrence information for open-vocabulary object detection. They propose CoDet, which effectively leverages cross-image region correspondences and text guidance to discover co-occurring objects for alignment. The experiments demonstrate the state-of-the-art performance of CoDet on various benchmark datasets. The authors also highlight the potential of combining their method with previous efforts in aligning regions and words with vision-language models.