Image captioning is an important problem in artificial intelligence, related to both computer vision and natural language processing. There are two main problems in existing methods: in the training phase, it is difficult to find which parts of the captions are more essential to the image; in the caption generation phase, the objects or the scenes are sometimes misrecognized. In this paper, we consider the training images as the references and propose a Reference based Long Short Term Memory (R-LSTM) model, aiming to solve these two problems in one goal. When training the model, we assign different weights to different words, which enables the network to better learn the key information of the captions. When generating a caption, the consensus score is utilized to exploit the reference information of neighbor images, which might fix the misrecognition and make the descriptions more natural-sounding. The proposed R-LSTM model outperforms the state-of-the-art approaches on the benchmark dataset MS COCO and obtains top 2 position on 11 of the 14 metrics on the online test server.

As an interesting and emerging topic, zero-shot recognition (ZSR) makes it possible to train a recognition model by specifying the category's attributes when there are no labeled exemplars available. The fundamental idea for ZSR is to transfer knowledge from the abundant labeled data in different but related source classes via the class attributes. Conventional ZSR approaches adopt a two-step strategy in test stage, where the samples are projected into the attribute space in the first step, and then the recognition is carried out based on considering the relationship between samples and classes in the attribute space. Due to this intermediate transformation, information loss is unavoidable, thus degrading the performance of the overall system. Rather than following this two-step strategy, in this paper, we propose a novel one-step approach that is able to perform ZSR in the original feature space by using directly trained classifiers. To tackle the problem that no labeled samples of target classes are available, we propose to assign pseudo labels to samples based on the reliability and diversity, which in turn will be used to train the classifiers. Moreover, we adopt a robust SVM that accounts for the unreliability of pseudo labels. Extensive experiments on four datasets demonstrate consistent performance gains of our approach over the state-of-the-art two-step ZSR approaches.

How to save labeling efforts for training supervised classifiers is an important research topic in machine learning community. Active learning (AL) and transfer learning (TL) are two useful tools to achieve this goal, and their combination, i.e., transfer active learning (T-AL) has also attracted considerable research interest. However, existing T-AL approaches consider to transfer knowledge from a source/auxiliary domain which has the same class labels as the target domain, but ignore the relationship among classes. In this paper, we investigate a more practical setting where the classes in source domain are related/similar to but different from the target domain classes. Specifically, we propose a novel cross-class T-AL approach to simultaneously transfer knowledge from source domain and actively annotate the most informative samples in target domain so that we can train satisfactory classifiers with as few labeled samples as possible. In particular, based on the class-class similarity and sample-sample similarity, we adopt a similarity propagation to find the source domain samples that can well capture the characteristics of a target class and then transfer the similar samples as the (pseudo) labeled data for the target class. In turn, the labeled and transferred samples are used to train classifiers and actively select new samples for annotation. Extensive experiments on three datasets demonstrate that the proposed approach outperforms significantly the state-of-the-art related approaches.