Latest AI and machine learning research in ophthalmology for healthcare professionals.
Direct Preference Optimization (DPO) has been demonstrated to be highly effective in mitigating hallucinations in Large Vision Language Models (LVLMs) by aligning their outputs more closely with human preferences. Despite the recent progress, existing methods suffer from two drawbacks: 1) Lack of scalable token-level rewards; and 2) Neglect of visual-anchored tokens. To this end, we propose a no...
Generation with source attribution is important for enhancing the verifiability of retrieval-augmented generation (RAG) systems. However, existing approaches in RAG primarily link generated content to document-level references, making it challenging for users to locate evidence among multiple content-rich retrieved documents. To address this challenge, we propose Retrieval-Augmented Generation w...
Generating a bird's eye view of road users is beneficial for a variety of applications, including navigation, detecting agent conflicts, and measuri...
Considering the difficulty of interpreting generative model output, there is significant current research focused on determining meaningful evaluati...
Current ophthalmology clinical workflows are plagued by over-referrals, long waits, and complex and heterogeneous medical records. Large language mo...
In this work, we propose Visual-Predictive Instruction Tuning (VPiT) - a simple and effective extension to visual instruction tuning that enables a ...
The visual understanding are often approached from 3 granular levels: image, patch and pixel. Visual Tokenization, trained by self-supervised recons...
Camera-based Bird's Eye View (BEV) perception models receive increasing attention for their crucial role in autonomous driving, a domain where conce...
Many surveillance cameras switch between daytime and nighttime modes based on illuminance levels. During the day, the camera records ordinary RGB im...
Recently, semantic communications have drawn great attention as the groundbreaking concept surpasses the limited capacity of Shannon's theory. Speci...
Existing cross-modal retrieval methods typically rely on large-scale vision-language pair data. This makes it challenging to efficiently develop a c...
Image to image matching has been well studied in the computer vision community. Previous studies mainly focus on training a deep metric learning mod...
Scaling the input image resolution is essential for enhancing the performance of Vision Language Models (VLMs), particularly in text-rich image unde...
Recent works on accelerating Vision-Language Models show that strong performance can be maintained across a variety of vision-language tasks despite...
Deep learning has enabled the development of highly robust foundation models for various pathological tasks across diverse diseases and patient coho...
The advent of multimodal learning has brought a significant improvement in document AI. Documents are now treated as multimodal entities, incorporat...
Instance segmentation algorithms in remote sensing are typically based on conventional methods, limiting their application to seen scenarios and clo...
Fundus imaging is a critical tool in ophthalmology, with different imaging modalities offering unique advantages. For instance, fundus fluorescein a...
Strongly lensed quasars provide valuable insights into the rate of cosmic expansion, the distribution of dark matter in foreground deflectors, and t...
In the evolving landscape of transportation systems, integrating Large Language Models (LLMs) offers a promising frontier for advancing intelligent ...