Zero-Shot Recognition using Dual Visual-Semantic Mapping Paths

Zero-shot recognition aims to accurately recognize objects of unseen classes by using a shared visual-semantic mapping between the image feature space and the semantic embedding space. This mapping is learned on training data of seen classes and is expected to have transfer ability to unseen classes. In this paper, we tackle this problem by exploiting the intrinsic relationship between the semantic space manifold and the transfer ability of visual-semantic mapping. We formalize their connection and cast zero-shot recognition as a joint optimization problem. Motivated by this, we propose a novel framework for zero-shot recognition, which contains dual visual-semantic mapping paths. Our analysis shows this framework can not only apply prior semantic knowledge to infer underlying semantic manifold in the image feature space, but also generate optimized semantic embedding space, which can enhance the transfer ability of the visual-semantic mapping to unseen classes. The proposed method is evaluated for zero-shot recognition on four benchmark datasets, achieving outstanding results.Comment: Accepted as a full paper in IEEE Computer Vision and Pattern Recognition (CVPR) 201

Relational Knowledge Transfer for Zero-Shot Learning

General zero-shot learning (ZSL) approaches exploit transfer learning via semantic knowledge space. In this paper, we reveal a novel relational knowledge transfer (RKT) mechanism for ZSL, which is simple, generic and effective. RKT resolves the inherent semantic shift problem existing in ZSL through restoring the missing manifold structure of unseen categories via optimizing semantic mapping. It extracts the relational knowledge from data manifold structure in semantic knowledge space based on sparse coding theory. The extracted knowledge is then transferred backwards to generate virtual data for unseen categories in the feature space. On the one hand, the generalizing ability of the semantic mapping function can be enhanced with the added data. On the other hand, the mapping function for unseen categories can be learned directly from only these generated data, achieving inspiring performance. Incorporated with RKT, even simple baseline methods can achieve good results. Extensive experiments on three challenging datasets show prominent performance obtained by RKT, and we obtain 82.43% accuracy on the Animals with Attributes dataset

Nitrogen fixation island and rhizosphere competence traits in the genome of root-associated Pseudomonas stutzeri A1501.

International audienceThe capacity to fix nitrogen is widely distributed in phyla of Bacteria and Archaea but has long been considered to be absent from the Pseudomonas genus. We report here the complete genome sequencing of nitrogen-fixing root-associated Pseudomonas stutzeri A1501. The genome consists of a single circular chromosome with 4,567,418 bp. Comparative genomics revealed that, among 4,146 protein-encoding genes, 1,977 have orthologs in each of the five other Pseudomonas representative species sequenced to date. The genome contains genes involved in broad utilization of carbon sources, nitrogen fixation, denitrification, degradation of aromatic compounds, biosynthesis of polyhydroxybutyrate, multiple pathways of protection against environmental stress, and other functions that presumably give A1501 an advantage in root colonization. Genetic information on synthesis, maturation, and functioning of nitrogenase is clustered in a 49-kb island, suggesting that this property was acquired by lateral gene transfer. New genes required for the nitrogen fixation process have been identified within the nif island. The genome sequence offers the genetic basis for further study of the evolution of the nitrogen fixation property and identification of rhizosphere competence traits required in the interaction with host plants; moreover, it opens up new perspectives for wider application of root-associated diazotrophs in sustainable agriculture