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- Title
Task-specific image summaries using semantic information and self-supervision.
- Authors
Sharma, Deepak Kumar; Singh, Anurag; Sharma, Sudhir Kumar; Srivastava, Gautam; Lin, Jerry Chun-Wei
- Abstract
Large annotated datasets are needed for successful Deep Learning methodologies to achieve human-level performance. These needs restrict the impact of Deep Learning and build the necessity to create smaller and richer representative datasets that can offer a potential solution to this problem. In this paper, we propose task-specific image corpus summarization using semantic information and self-supervision. Our methodology makes use of GAN for the generation of features and leverages rotational invariance for employing self-supervision. All these objectives are facilitated on features from Resnet34. A summary can be obtained efficiently by using k-means clustering on the semantic embedding space and then selecting examples nearest to centroids. In comparison to end-to-end trained models, the proposed model does not require retraining to obtain summaries of different lengths. We also test our model by extensive qualitative and quantitative experiments.
- Subjects
DEEP learning; K-means clustering; GENERATIVE adversarial networks
- Publication
Soft Computing - A Fusion of Foundations, Methodologies & Applications, 2022, Vol 26, Issue 16, p7581
- ISSN
1432-7643
- Publication type
Article
- DOI
10.1007/s00500-021-06603-6