Video frame interpolation (VFI) that leverages the bio-inspired event cameras as guidance has recently shown better performance and memory efficiency than the frame-based methods, thanks to the event cameras’ advantages, such as high temporal resolution. A hurdle for event-based VFI is how to effectively deal with non-linear motion, caused by the dynamic changes in motion direction and speed within the scene. Existing methods either use events to estimate sparse optical flow or fuse events with image features to estimate dense optical flow. Unfortunately, motion errors often degrade the VFI quality as the continuous motion cues from events do not align with the dense spatial information of images in the temporal dimension. In this paper, we find that object motion is continuous in space, tracking local regions over continuous time enables more accurate identification of spatiotemporal feature correlations. In light of this, we propose a novel continuous point tracking-based VFI framework, named TimeTracker. Specifically, we first design a Scene-Aware Region Segmentation (SARS) module to divide the scene into similar patches. Then, a Continuous Trajectory guided Motion Estimation (CTME) module is proposed to track the continuous motion trajectory of each patch through events. Finally, intermediate frames at any given time are generated through global motion optimization and frame refinement. Moreover, we collect a real-world dataset that features fast non-linear motion. Extensive experiments show that our method outperforms prior arts in both motion estimation and frame interpolation quality.

Large language models (LLMs) excel at providing general and comprehensive health advice in single-turn dialogues. However, the limited information in single-turn conversations provided by users results in generated advice lacking personalization and specificity. In real-world medical consultations, doctors typically gain a comprehensive understanding of a patient’s condition through a series of iterative inquiries, enabling them to subsequently offer effective and personalized advice. To enhance capabilities similar to those of doctors, existing approaches often learn by increasing multi-turn medical dialogue corpora. In this study, we consider capturing the transitions of medical terms in each turn crucial, as they aid in understanding the flow of the conversation and enhance the accuracy of generating medical term information in the next turn. Therefore, we propose a Term-aware Agent (T-Agent) and develop a corresponding term extraction tool and term prediction model. T-Agent explicitly models the flow of term information in the dialogue by invoking the term extraction tool and the term prediction model. To better learn the term prediction task, we adopt a two-stage training approach. In the first stage, we conduct mixed training on a single large model, simultaneously learning term prediction and the ability of T-Agent to invoke term tools for dialogue. This mixed training in the first stage allows the large model to initially adapt to the term prediction task. In the second stage, we independently train the term prediction model and T-Agent on this basis, enhancing their expertise and performance in their respective tasks. We validated the effectiveness of the proposed method on two Chinese multi-turn medical dialogue datasets, demonstrating significant performance improvements, particularly in the accuracy of term information within dialogues.