English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper reports a new solution of leveraging temporal classification tosupport weakly supervised object detection (WSOD). Specifically, we introduceraster scan-order techniques to serialize 2D images into 1D sequence data, andthen leverage a combined LSTM (Long, Short-Term Memory) and CTC (ConnectionistTemporal Classification) network to achieve object localization based on atotal count (of interested objects). We term our proposed network LSTM-CCTC(Count-based CTC). This "learning from counting" strategy differs from existingWSOD methods in that our approach automatically identifies critical points onor near a target object. This strategy significantly reduces the need ofgenerating a large number of candidate proposals for object localization.Experiments show that our method yields state-of-the-art performance based onan evaluation on PASCAL VOC datasets.

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