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Machine Learning
Overview: In problems with a large number of labels, most multi-label and multi-class techniques incur a significant computational burden at test time. This is because, for each test instance, they need to systematically evaluate every label to decide whether it is relevant for the instance or not.
Overview: This research project is at the forefront of integrating advanced Large Language Models (LLMs) into the process of deriving actionable insights from vast and complex document repositories. This initiative focuses on creating a system that allows users to interact with the LLM, guiding its analysis and refining the results based on the user’s expertise and evolving needs.
Overview: We have developed a system for real-time scene understanding and reasoning across various domains such as safety, manufacturing, retail, healthcare, and personal assistance. This system continuously monitors and analyzes video, acoustics, and time series data related to human activities, aiming to provide a comprehensive understanding of ongoing situations. The project utilizes advanced AI models to process large volumes of data, generating actionable insights that help users grasp complex scenarios.
Overview: The Trustworthy Generative AI Project is focused on developing advanced multimodal generative models that can create and reason with content across text, images, reports, and 3D videos. These models are designed for applications in advertisement, entertainment, law enforcement, and healthcare.
We study the problem of subgroup discovery with Cox regression models and introduce a method for finding an interpretable subset of the data on which a Cox model is highly accurate. Our method relies on two technical innovations: the emph (Unknown sysvar: (expected prediction entropy)), a novel metric
Multi-camera tracking plays a pivotal role in various real-world applications. While end-to-end methods have gained significant interest in single-camera tracking, multi-camera tracking remains predominantly reliant on heuristic techniques. In response to this gap, this paper introduces Multi-Camera
We are thrilled to announce the launch of our latest research initiative, the Trustworthy Generative AI Project. This ambitious project is set to revolutionize how we interact with multimodal content by developing cutting-edge generative models capable of compositional generation and reasoning across
In this paper, we introduce efforts to apply large language models (LLMs) to the field of material development. NEC is advancing the development of a material development platform. By applying core technologies corresponding to two material development steps, namely investigation activities (Read paper/patent)
Human actions often involve complex interactions across several inter-related objects in the scene. However, existing approaches to fine-grained video understanding or visual relationship detection often rely on single object representation or pairwise object relationships. Furthermore, learning interactions
