The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
A Deep Network Solution for Attention and Aesthetics Aware Photo Cropping
Abstract
We study the problem of photo cropping, which aims to find a cropping window of an input image to preserve as much as possible its important parts while being aesthetically pleasant. Seeking a deep learning-based solution, we design a neural network that has two branches for attention box prediction (ABP) and aesthetics assessment (AA), respectively. Given the input image, the ABP network predicts an attention bounding box as an initial minimum cropping window, around which a set of cropping candidates are generated with little loss of important information. Then, the AA network is employed to select the final cropping window with the best aesthetic quality among the candidates. The two sub-networks are designed to share the same full-image convolutional feature map, and thus are computationally efficient. By leveraging attention prediction and aesthetics assessment, the cropping model produces high-quality cropping results, even with the limited availability of training data for photo cropping. The experimental results on benchmark datasets clearly validate the effectiveness of the proposed approach. In addition, our approach runs at 5 fps, outperforming most previous solutions. The code and results are available at: https://github.com/shenjianbing/DeepCropping.
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MobileSal: Extremely Efficient RGB-D Salient Object Detection
Wu, Y.-H., Liu, Y., Xu, J., Bian, J.-W., Gu, Y.-C., & Cheng, M.-M. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3134684 · Cited by 176
DTR-HAR: deep temporal residual representation for human activity recognition
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Metadata
- Authors
- Wang, W., Shen, J., & Ling, H.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2019-07-01
- Volume
- 41
- Issue
- 7
- Pages
- 1531-1544
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W2804743778
- Cited by
- 348
- References
- 69
- FWCI
- 26.54
- Type
- journal article
- Language
- en
- Open access
- closed
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