2 research outputs found
Complementary Attributes: A New Clue to Zero-Shot Learning
Zero-shot learning (ZSL) aims to recognize unseen objects using disjoint seen
objects via sharing attributes. The generalization performance of ZSL is
governed by the attributes, which transfer semantic information from seen
classes to unseen classes. To take full advantage of the knowledge transferred
by attributes, in this paper, we introduce the notion of complementary
attributes (CA), as a supplement to the original attributes, to enhance the
semantic representation ability. Theoretical analyses demonstrate that
complementary attributes can improve the PAC-style generalization bound of
original ZSL model. Since the proposed CA focuses on enhancing the semantic
representation, CA can be easily applied to any existing attribute-based ZSL
methods, including the label-embedding strategy based ZSL (LEZSL) and the
probability-prediction strategy based ZSL (PPZSL). In PPZSL, there is a strong
assumption that all the attributes are independent of each other, which is
arguably unrealistic in practice. To solve this problem, a novel rank
aggregation framework is proposed to circumvent the assumption. Extensive
experiments on five ZSL benchmark datasets and the large-scale ImageNet dataset
demonstrate that the proposed complementary attributes and rank aggregation can
significantly and robustly improve existing ZSL methods and achieve the
state-of-the-art performance.Comment: Accepted by IEEE TRANSACTIONS ON CYBERNETIC
PSC-Net: Learning Part Spatial Co-occurrence for Occluded Pedestrian Detection
Detecting pedestrians, especially under heavy occlusions, is a challenging
computer vision problem with numerous real-world applications. This paper
introduces a novel approach, termed as PSC-Net, for occluded pedestrian
detection. The proposed PSC-Net contains a dedicated module that is designed to
explicitly capture both inter and intra-part co-occurrence information of
different pedestrian body parts through a Graph Convolutional Network (GCN).
Both inter and intra-part co-occurrence information contribute towards
improving the feature representation for handling varying level of occlusions,
ranging from partial to severe occlusions. Our PSC-Net exploits the topological
structure of pedestrian and does not require part-based annotations or
additional visible bounding-box (VBB) information to learn part spatial
co-occurrence. Comprehensive experiments are performed on two challenging
datasets: CityPersons and Caltech datasets. The proposed PSC-Net achieves
state-of-the-art detection performance on both. On the heavy occluded
(\textbf{HO}) set of CityPerosns test set, our PSC-Net obtains an absolute gain
of 4.0\% in terms of log-average miss rate over the state-of-the-art with same
backbone, input scale and without using additional VBB supervision. Further,
PSC-Net improves the state-of-the-art from 37.9 to 34.8 in terms of log-average
miss rate on Caltech (\textbf{HO}) test set