  Zero-shot object detection (ZSD), the task that extends conventional
detection models to detecting objects from unseen categories, has emerged as a
new challenge in computer vision. Most existing approaches tackle the ZSD task
with a strict mapping-transfer strategy, which may lead to suboptimal ZSD
results: 1) the learning process of those models ignores the available unseen
class information, and thus can be easily biased towards the seen categories;
2) the original visual feature space is not well-structured and lack of
discriminative information. To address these issues, we develop a novel
Semantics-Guided Contrastive Network for ZSD, named ContrastZSD, a detection
framework that first brings contrastive learning mechanism into the realm of
zero-shot detection. Particularly, ContrastZSD incorporates two
semantics-guided contrastive learning subnets that contrast between
region-category and region-region pairs respectively. The pairwise contrastive
tasks take advantage of additional supervision signals derived from both ground
truth label and pre-defined class similarity distribution. Under the guidance
of those explicit semantic supervision, the model can learn more knowledge
about unseen categories to avoid the bias problem to seen concepts, while
optimizing the data structure of visual features to be more discriminative for
better visual-semantic alignment. Extensive experiments are conducted on two
popular benchmarks for ZSD, i.e., PASCAL VOC and MS COCO. Results show that our
method outperforms the previous state-of-the-art on both ZSD and generalized
ZSD tasks.
