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

Detecting objects becomes difficult when we need to deal with large shapedeformation, occlusion and low resolution. We propose a novel approach to i)handle large deformations and partial occlusions in animals (as examples ofhighly deformable objects), ii) describe them in terms of body parts, and iii)detect them when their body parts are hard to detect (e.g., animals depicted atlow resolution). We represent the holistic object and body parts separately anduse a fully connected model to arrange templates for the holistic object andbody parts. Our model automatically decouples the holistic object or body partsfrom the model when they are hard to detect. This enables us to represent alarge number of holistic object and body part combinations to better deal withdifferent "detectability" patterns caused by deformations, occlusion and/or lowresolution.  We apply our method to the six animal categories in the PASCAL VOC datasetand show that our method significantly improves state-of-the-art (by 4.1% AP)and provides a richer representation for objects. During training we useannotations for body parts (e.g., head, torso, etc), making use of a newdataset of fully annotated object parts for PASCAL VOC 2010, which provides amask for each part.

Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts