Latest AI and machine learning research in care of terminally ill / palliative care for healthcare professionals.
Current end-to-end (E2E) and plug-and-play (PnP) image reconstruction algorithms approximate the maximum a posteriori (MAP) estimate but cannot offer sampling from the posterior distribution, like diffusion models. By contrast, it is challenging for diffusion models to be trained in an E2E fashion. This paper introduces a Deep End-to-End Posterior ENergy (DEEPEN) framework, which enables MAP est...
Modern scene text recognition systems often depend on large end-to-end architectures that require extensive training and are prohibitively expensive for real-time scenarios. In such cases, the deployment of heavy models becomes impractical due to constraints on memory, computational resources, and latency. To address these challenges, we propose a novel, training-free plug-and-play framework tha...
Scene graph (SG) representations can neatly and efficiently describe scene semantics, which has driven sustained intensive research in SG generation...
Creating expressive character animations is labor-intensive, requiring intricate manual adjustment of animators across space and time. Previous work...
Computed tomography (CT)-guided needle biopsies are critical for diagnosing a range of conditions, including lung cancer, but present challenges suc...
The computer vision community has developed numerous techniques for digitally restoring true scene information from single-view degraded photographs...
Recent video diffusion models have enhanced video editing, but it remains challenging to handle instructional editing and diverse tasks (e.g., addin...
Accurate transformation estimation between camera space and robot space is essential. Traditional methods using markers for hand-eye calibration req...
Electrocardiogram data, one of the most widely available biosignal data, has become increasingly valuable with the emergence of deep learning method...
Hydra-MDP++ introduces a novel teacher-student knowledge distillation framework with a multi-head decoder that learns from human demonstrations and ...
This paper investigates whether sequence models can learn to perform numerical algorithms, e.g. gradient descent, on the fundamental problem of leas...
Localization is one of the core parts of modern robotics. Classic localization methods typically follow the retrieve-then-register paradigm, achievi...
In this paper, we introduce a novel image-goal navigation approach, named RFSG. Our focus lies in leveraging the fine-grained connections between go...
We introduce MentalChat16K, an English benchmark dataset combining a synthetic mental health counseling dataset and a dataset of anonymized transcri...
Navigation and manipulation in open-world environments remain unsolved challenges in the Embodied AI. The high cost of commercial mobile manipulatio...
Developing robust and versatile deep-learning models is essential for enhancing diagnostic accuracy and guiding clinical interventions in medical im...
Advanced end-to-end autonomous driving systems predict other vehicles' motions and plan ego vehicle's trajectory. The world model that can foresee t...
Video body-swapping aims to replace the body in an existing video with a new body from arbitrary sources, which has garnered more attention in recen...
Existing learning-based video compression methods still face challenges related to inaccurate motion estimates and inadequate motion compensation st...
This paper proposes a task-oriented co-design framework that integrates communication, computing, and control to address the key challenges of bandw...