Task Independent Knowledge Makes for Transferable Representations for Generalized Zero Shot Learning
Generalized Zero Shot Learning (GZSL) targets recognizing new categories by learning transferable image representations. Existing methods find that, by aligning image representations with corresponding semantic labels, the semanticaligned representations can be transferred to unseen categories. However, supervised by only seen category labels, the learned semantic knowledge is highly task specific, which makes image representations biased towards seen categories. In this paper, we propose a novel Dual Contrastive Embedding Network (DCEN) that simultaneously learns taskspecific and task independent knowledge via semantic alignment and instance discrimination. First, DCEN leverages task labels to cluster representations of the same semantic category by cross modal contrastive learning and exploring semantic visual complementarity. Besides task specific knowledge, DCEN then introduces task independent knowledge by attracting representations of different views of the same image and repelling representations of different images. Compared to high level seen category supervision, this instance discrimination supervision encourages DCEN to capture low level visual knowledge, which is less biased toward seen categories and alleviates the representation bias. Consequently, the task specific and task independent knowledge jointly make for transferable representations of DCEN, which obtains averaged 4.1% improvement on four public benchmarks.
AAAI