The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
Detection of Co-salient Objects by Looking Deep and Wide
Open accessAbstract
In this paper, we propose a unified co-salient object detection framework by introducing two novel insights: (1) looking deep to transfer higher-level representations by using the convolutional neural network with additional adaptive layers could better reflect the sematic properties of the co-salient objects; (2) looking wide to take advantage of the visually similar neighbors from other image groups could effectively suppress the influence of the common background regions. The wide and deep information are explored for the object proposal windows extracted in each image. The window-level co-saliency scores are calculated by integrating the intra-image contrast, the intra-group consistency, and the inter-group separability via a principled Bayesian formulation and are then converted to the superpixel-level co-saliency maps through a foreground region agreement strategy. Comprehensive experiments on two existing and one newly established datasets have demonstrated the consistent performance gain of the proposed approach.
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Frequency-tuned salient region detection
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“This kind of representation has been demonstrated in previous works (Girshick et al. 2014; Oquab et al. 2014) to have the capability to capture useful abstract concepts in image classification and segmentation and thus can help us to effectively model the concept-level properties, such as the…”
BackgroundOverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
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Remote Sensing Image Scene Classification: Benchmark and State of the Art
Cheng, G., Han, J., & Lu, X. (2017) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2675998 · Cited by 2,926
Self-citationSalient Object Detection via Integrity Learning
Zhuge, M., Fan, D.-P., Liu, N., Zhang, D., Xu, D., & Shao, L. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3179526 · Cited by 403
Self-citationWeakly Supervised Object Localization and Detection: A Survey
Zhang, D., Han, J., Cheng, G., & Yang, M.-H. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3074313 · Cited by 277
Self-citationRobust Object Co-Segmentation Using Background Prior
Han, J., Quan, R., Zhang, D., & Nie, F. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2781424 · Cited by 169
Self-citationLearning to Hash With Optimized Anchor Embedding for Scalable Retrieval
Guo, Y., Ding, G., Liu, L., Han, J., & Shao, L. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2652730 · Cited by 108
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Metadata
- Authors
- Zhang, D., Han, J., Li, C., Wang, J., & Li, X.
- Venue
- International Journal of Computer Vision
- Published
- 2016-04-27
- Volume
- 120
- Issue
- 2
- Pages
- 215-232
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2342491128
- Cited by
- 328
- References
- 54
- FWCI
- 44.73
- Type
- journal article
- Language
- en
- Open access
- green
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- Zhang, D., Han, J., Li, C., Wang, J., & Li, X. (2016). Detection of Co-salient Objects by Looking Deep and Wide. International Journal of Computer Vision. https://doi.org/10.1007/s11263-016-0907-4
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