Sufang Li

Guangyang Han, Sufang Li, Runmin Wang et al.

Crowdsourcing is an online outsourcing mode which can solve the current machine learning algorithm's urge need for massive labeled data. Requester posts tasks on crowdsourcing platforms, which employ online workers over the Internet to complete tasks, then aggregate and return results to requester. How to model the interaction between different types of workers and tasks is a hot spot. In this paper, we try to model workers as four types based on their ability: expert, normal worker, sloppy worker and spammer, and divide tasks into hard, medium and easy task according to their difficulty. We believe that even experts struggle with difficult tasks while sloppy workers can get easy tasks right, and spammers always give out wrong answers deliberately. So, good examination tasks should have moderate degree of difficulty and discriminability to score workers more objectively. Thus, we first score workers' ability mainly on the medium difficult tasks, then reducing the weight of answers from sloppy workers and modifying the answers from spammers when inferring the tasks' ground truth. A probability graph model is adopted to simulate the task execution process, and an iterative method is adopted to calculate and update the ground truth, the ability of workers and the difficulty of the task successively. We verify the rightness and effectiveness of our algorithm both in simulated and real crowdsourcing scenes.

Pengfei Hao, Shuaibo Li, Hongqiu Wang et al.

In recent years, significant progress has been made in the field of surgical scene understanding, particularly in the task of Visual Question Localized-Answering in robotic surgery (Surgical-VQLA). However, existing Surgical-VQLA models lack deep reasoning capabilities and interpretability in surgical scenes, which limits their reliability and potential for development in clinical applications. To address this issue, inspired by the development of Reasoning Multimodal Large Language Models (MLLMs), we first build the Surgery-R1-54k dataset, including paired data for Visual-QA, Grounding-QA, and Chain-of-Thought (CoT). Then, we propose the first Reasoning MLLM for Surgical-VQLA (Surgery-R1). In our Surgery-R1, we design a two-stage fine-tuning mechanism to enable the basic MLLM with complex reasoning abilities by utilizing supervised fine-tuning (SFT) and reinforcement fine-tuning (RFT). Furthermore, for an efficient and high-quality rule-based reward system in our RFT, we design a Multimodal Coherence reward mechanism to mitigate positional illusions that may arise in surgical scenarios. Experiment results demonstrate that Surgery-R1 outperforms other existing state-of-the-art (SOTA) models in the Surgical-VQLA task and widely-used MLLMs, while also validating its reasoning capabilities and the effectiveness of our approach. The code and dataset will be organized in https://github.com/FiFi-HAO467/Surgery-R1.