Learning from Counting: Leveraging Temporal Classification for Weakly Supervised Object Localization and Detection

• URL: http://arxiv.org/abs/2103.04009v1
• Date: Sat, 6 Mar 2021 02:18:03 GMT
• Title: Learning from Counting: Leveraging Temporal Classification for Weakly Supervised Object Localization and Detection
• Authors: Chia-Yu Hsu and Wenwen Li
• Abstract summary: We introduce scan-order techniques to serialize 2D images into 1D sequence data. We then leverage a combined LSTM (Long, Short-Term Memory) and CTC network to achieve object localization.
• Score: 4.971083368517706
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper reports a new solution of leveraging temporal classification to support weakly supervised object detection (WSOD). Specifically, we introduce raster scan-order techniques to serialize 2D images into 1D sequence data, and then leverage a combined LSTM (Long, Short-Term Memory) and CTC (Connectionist Temporal Classification) network to achieve object localization based on a total count (of interested objects). We term our proposed network LSTM-CCTC (Count-based CTC). This "learning from counting" strategy differs from existing WSOD methods in that our approach automatically identifies critical points on or near a target object. This strategy significantly reduces the need of generating a large number of candidate proposals for object localiza- tion. Experiments show that our method yields state-of-the-art performance based on an evaluation on PASCAL VOC datasets.

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