Object Detection via a Multi-region and Semantic Segmentation-Aware CNN Model
Open accessAbstract
We propose an object detection system that relies on a multi-region deep convolutional neural network (CNN) that also encodes semantic segmentation-aware features. The resulting CNN-based representation aims at capturing a diverse set of discriminative appearance factors and exhibits localization sensitivity that is essential for accurate object localization. We exploit the above properties of our recognition module by integrating it on an iterative localization mechanism that alternates between scoring a box proposal and refining its location with a deep CNN regression model. Thanks to the efficient use of our modules, we detect objects with very high localization accuracy. On the detection challenges of PASCAL VOC2007 and PASCAL VOC2012 we achieve mAP of 78.2% and 73.9% correspondingly, surpassing any other published work by a significant margin.
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The simplest way to improve detection with segmentation is to think of the segmentation network as a fixed feature extractor and integrate it into a detector as auxiliary features.
“To improve detection with segmentation, the simplest way is to think of the segmentation network as a fixed feature extractor and to integrate it into a detector as auxiliary features [83, 192, 193].”
From Object Detection in 20 Years: A Survey (2023) · cites this paper as [83]
Our method does not exploit many popular improvements, such as iterative regression, hard negative mining, context modeling, and stronger data augmentation.
“Moreover, our method does not exploit many popular improvements, such as iterative regression [9], hard negative mining [35], context modeling [16], stronger data augmentation [22], etc .”
From Feature Pyramid Networks for Object Detection (2017) · cites this paper as [9]
Connections among words can be discerned without reliance on dictionaries or parsers.
“Through this methodology, connections among words can be discerned sans reliance on dictionaries or parsers (Gidaris and Komodakis 2015).”
From Large language models (LLMs): survey, technical frameworks, and future challenges (2024) · cites this paper as Gidaris and Komodakis 2015
The paper’s object-representation discussion (p. 1) concerns image features and object detection, not connections among words. Searches for word connections and for using neither dictionaries nor parsers found no passage addressing the claim. (LLM, conf=0.78)
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- Authors
- Gidaris, S., & Komodakis, N.
- Affiliations
- École nationale des ponts et chaussées (FR)
- ORCID
- Gidaris, Spyros
- Venue
- 2015 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 1134-1142
- OpenAlex
- W1932624639
- Cited by
- 801
- References
- 35
- Citations by year
- 2026: 12 · 2025: 33 · 2024: 42 · 2023: 54 · 2022: 63 · 2021: 107 · 2020: 121 · 2019: 126 · 2018: 103 · 2017: 88 · 2016: 45 · 2015: 7
- FWCI
- 33.22
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (publishedVersion)
- HAL (submittedVersion, other-oa)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Agence Nationale de la Recherche (ANR-13-CORD-0003)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- object detection, iterative localization, appearance features, VOC2007
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