Most machine-learning methods focus on classifying instances whose classes have already
been seen in training. In practice, many applications require classifying instances whose …

Cross-modal retrieval aims to enable flexible retrieval experience across different modalities
(eg, texts vs. images). The core of cross-modal retrieval research is to learn a common …

Hashing based methods have attracted considerable attention for efficient cross-modal
retrieval on large-scale multimedia data. The core problem of cross-modal hashing is how to …

This paper studies the problem of zero-short sketch-based image retrieval (ZS-SBIR),
however with two significant differentiators to prior art (i) we tackle all variants (inter …

Hashing methods have been extensively applied to efficient multimedia data indexing and
retrieval on account of the explosion of multimedia data. Cross-modal hashing usually …

Cross-modal hashing has sparked much attention in large-scale information retrieval for its
storage and query efficiency. Despite the great success achieved by supervised …

Existing semantic segmentation models heavily rely on dense pixel-wise annotations. To
reduce the annotation pressure, we focus on a challenging task named zero-shot semantic …

Recent years have witnessed the wide application of hashing for large-scale image retrieval,
because of its high computation efficiency and low storage cost. Particularly, benefiting from …

Zero-shot learning (ZSL) and cold-start recommendation (CSR) are two challenging
problems in computer vision and recommender system, respectively. In general, they are …

Zero-shot sketch-based image retrieval (SBIR) is an emerging task in computer vision,
allowing to retrieve natural images relevant to sketch queries that might not been seen in the …