Latest AI and machine learning research in care of terminally ill / palliative care for healthcare professionals.
In this report, we present Qwen2.5-Omni, an end-to-end multimodal model designed to perceive diverse modalities, including text, images, audio, and video, while simultaneously generating text and natural speech responses in a streaming manner. To enable the streaming of multimodal information inputs, both audio and visual encoders utilize a block-wise processing approach. To synchronize the time...
End-to-end (E2E) autonomous driving methods still struggle to make correct decisions in interactive closed-loop evaluation due to limited causal reasoning capability. Current methods attempt to leverage the powerful understanding and reasoning abilities of Vision-Language Models (VLMs) to resolve this dilemma. However, the problem is still open that few VLMs for E2E methods perform well in the c...
Achieving fine-grained controllability in human image synthesis is a long-standing challenge in computer vision. Existing methods primarily focus on...
Feature Coding for Machines (FCM) aims to compress intermediate features effectively for remote intelligent analytics, which is crucial for future i...
Encryption is crucial for securing sensitive data during transmission over networks. Various encryption techniques exist, such as AES, DES, and RC4,...
UAV has been widely used in various fields. However, most of the existing object detectors used in drones are not end-to-end and require the design ...
Validating key claims in scientific literature, particularly in biomedical research, is essential for ensuring accuracy and advancing knowledge. Thi...
Implicit neural representations (INRs) such as NeRF and SIREN encode a signal in neural network parameters and show excellent results for signal rec...
The real-time assessment of complex motor skills presents a challenge in fields such as surgical training and rehabilitation. Recent advancements in...
Current end-to-end (E2E) and plug-and-play (PnP) image reconstruction algorithms approximate the maximum a posteriori (MAP) estimate but cannot offe...
Modern scene text recognition systems often depend on large end-to-end architectures that require extensive training and are prohibitively expensive...
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...
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...