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 localization. Experiments show that our method yields state-of-the-art performance based on an evaluation on PASCAL VOC datasets.
Original language | English (US) |
---|---|
State | Published - 2020 |
Event | 31st British Machine Vision Conference, BMVC 2020 - Virtual, Online Duration: Sep 7 2020 → Sep 10 2020 |
Conference
Conference | 31st British Machine Vision Conference, BMVC 2020 |
---|---|
City | Virtual, Online |
Period | 9/7/20 → 9/10/20 |
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Vision and Pattern Recognition