Wei Wu

Chuanqi Cheng, Jian Guan, Wei Wu et al.

We explore multi-step reasoning in vision-language models (VLMs). The problem is challenging, as reasoning data consisting of multiple steps of visual and language processing are barely available. To overcome the challenge, we first introduce a least-to-most visual reasoning paradigm, which interleaves steps of decomposing a question into sub-questions and invoking external tools for resolving sub-questions. Based on the paradigm, we further propose a novel data synthesis approach that can automatically create questions and multi-step reasoning paths for an image in a bottom-up manner. Our approach divides the complex synthesis task into a few simple sub-tasks, and (almost entirely) relies on open-sourced models to accomplish the sub-tasks. Therefore, the entire synthesis process is reproducible and cost-efficient, and the synthesized data is quality guaranteed. With the approach, we construct $50$k visual reasoning examples. Then, we develop a visual reasoner through supervised fine-tuning, which is capable of generally enhancing the reasoning abilities of a wide range of existing VLMs in a plug-and-play fashion. Extensive experiments indicate that the visual reasoner can consistently and significantly improve four VLMs on four VQA benchmarks. Our code and dataset are available at https://github.com/steven-ccq/VisualReasoner.

Xingjian Ding, Jianxiong Guo, Deying Li et al.

The world-changing blockchain technique provides a novel method to establish a secure, trusted and decentralized system for solving the security and personal privacy problems in Industrial Internet of Things (IIoT) applications. The mining process in blockchain requires miners to solve a proof-of-work puzzle, which requires high computational power. However, the lightweight IIoT devices cannot directly participate in the mining process due to the limitation of power and computational resources. The edge computing service makes it possible for IIoT applications to build a blockchain network, in which IIoT devices purchase computational resources from edge servers and thus can offload their computational tasks. The amount of computational resource purchased by IIoT devices depends on how many profits they can get in the mining process, and will directly affect the security of the blockchain network. In this paper, we investigate the incentive mechanism for the blockchain platform to attract IIoT devices to purchase more computational power from edge servers to participate in the mining process, thereby building a more secure blockchain network. We model the interaction between the blockchain platform and IIoT devices as a two-stage Stackelberg game, where the blockchain platform act as the leader, and IIoT devices act as followers. We analyze the existence and uniqueness of the Stackelberg equilibrium, and propose an efficient algorithm to compute the Stackelberg equilibrium point. Furthermore, we evaluate the performance of our algorithm through extensive simulations, and analyze the strategies of blockchain platform and IIoT devices under different situations.