  Detecting objects becomes difficult when we need to deal with large shape
deformation, occlusion and low resolution. We propose a novel approach to i)
handle large deformations and partial occlusions in animals (as examples of
highly 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 at
low resolution). We represent the holistic object and body parts separately and
use a fully connected model to arrange templates for the holistic object and
body parts. Our model automatically decouples the holistic object or body parts
from the model when they are hard to detect. This enables us to represent a
large number of holistic object and body part combinations to better deal with
different "detectability" patterns caused by deformations, occlusion and/or low
resolution.
  We apply our method to the six animal categories in the PASCAL VOC dataset
and show that our method significantly improves state-of-the-art (by 4.1% AP)
and provides a richer representation for objects. During training we use
annotations for body parts (e.g., head, torso, etc), making use of a new
dataset of fully annotated object parts for PASCAL VOC 2010, which provides a
mask for each part.
