Complex objects are usually with multiple labels, and can be represented by multiple modal representations, e.g., the complex articles contain text and image information as well as multiple annotations. Previous methods assume that the homogeneous multi-modal data are consistent, while in real applications, the raw data are disordered, e.g., the article constitutes with variable number of inconsistent text and image instances. Therefore, Multi-modal Multi-instance Multi-label (M3) learning provides a framework for handling such task and has exhibited excellent performance. However, M3 learning is facing two main challenges: 1) how to effectively utilize label correlation and 2) how to take advantage of multi-modal learning to process unlabeled instances. To solve these problems, we first propose a novel Multi-modal Multi-instance Multi-label Deep Network (M3DN), which considers M3 learning in an end-to-end multi-modal deep network and utilizes consistency principle among different modal bag-level predictions. Based on the M3DN, we learn the latent ground label metric with the optimal transport. Moreover, we introduce the extrinsic unlabeled multi-modal multi-instance data, and propose the M3DNS, which considers the instance-level auto-encoder for single modality and modified bag-level optimal transport to strengthen the consistency among modalities. Thereby M3DNS can better predict label and exploit label correlation simultaneously. Experiments on benchmark datasets and real world WKG Game-Hub dataset validate the effectiveness of the proposed methods.

Reinforcement learning has played an important role in decision making related applications, e.g., robotics motion, self-driving, recommendation, etc. The reward function, as a crucial component, affects the efficiency and effectiveness of reinforcement learning to a large extent. In this paper, we focus on the investigation of reinforcement learning with more than one auxiliary reward. It is found that different auxiliary rewards can boost up the learning rate and effectiveness in different stages, and consequently we propose the Automatic Successive Reinforcement Learning (ASR) for auxiliary rewards grading selection for efficient reinforcement learning by stages. Experiments and simulations have shown the superiority of our proposed ASR on a range of environments, including OpenAI classical control domains and video games; Freeway and Catcher.