Ophthalmology

Robust Multimodal Learning for Ophthalmic Disease Grading via Disentangled Representation

This paper discusses how ophthalmologists often rely on multimodal data to improve diagnostic accuracy. However, complete multimodal data is rare in real-world applications due to a lack of medical equipment and concerns about data privacy. Traditional deep learning methods typically address these issues by learning representations in latent space. However, the paper highlights two key limitatio...

A Map-free Deep Learning-based Framework for Gate-to-Gate Monocular Visual Navigation aboard Miniaturized Aerial Vehicles

Palm-sized autonomous nano-drones, i.e., sub-50g in weight, recently entered the drone racing scenario, where they are tasked to avoid obstacles and navigate as fast as possible through gates. However, in contrast with their bigger counterparts, i.e., kg-scale drones, nano-drones expose three orders of magnitude less onboard memory and compute power, demanding more efficient and lightweight visi...

Unified Reward Model for Multimodal Understanding and Generation

Recent advances in human preference alignment have significantly enhanced multimodal generation and understanding. A key approach is training reward...

Inclusive STEAM Education: A Framework for Teaching Cod-2 ing and Robotics to Students with Visually Impairment Using 3 Advanced Computer Vision

STEAM education integrates Science, Technology, Engineering, Arts, and Mathematics to foster creativity and problem-solving. However, students with ...

Quantifying and Modeling Driving Styles in Trajectory Forecasting

Trajectory forecasting has become a popular deep learning task due to its relevance for scenario simulation for autonomous driving. Specifically, tr...

Fine-Tuning Florence2 for Enhanced Object Detection in Un-constructed Environments: Vision-Language Model Approach

Vision-Language Models (VLMs) have emerged as powerful tools in artificial intelli-gence, capable of integrating textual and visual data for a unifi...

CAUSAL3D: A Comprehensive Benchmark for Causal Learning from Visual Data

True intelligence hinges on the ability to uncover and leverage hidden causal relations. Despite significant progress in AI and computer vision (CV)...

Iris Style Transfer: Enhancing Iris Recognition with Style Features and Privacy Preservation through Neural Style Transfer

Iris texture is widely regarded as a gold standard biometric modality for authentication and identification. The demand for robust iris recognition ...

A Benchmark for Multi-Lingual Vision-Language Learning in Remote Sensing Image Captioning

Remote Sensing Image Captioning (RSIC) is a cross-modal field bridging vision and language, aimed at automatically generating natural language descr...

ViT-VS: On the Applicability of Pretrained Vision Transformer Features for Generalizable Visual Servoing

Visual servoing enables robots to precisely position their end-effector relative to a target object. While classical methods rely on hand-crafted fe...

Interpretable Transformation and Analysis of Timelines through Learning via Surprisability

The analysis of high-dimensional timeline data and the identification of outliers and anomalies is critical across diverse domains, including sensor...

An Egocentric Vision-Language Model based Portable Real-time Smart Assistant

We present Vinci, a vision-language system designed to provide real-time, comprehensive AI assistance on portable devices. At its core, Vinci levera...

Bridging the Vision-Brain Gap with an Uncertainty-Aware Blur Prior

Can our brain signals faithfully reflect the original visual stimuli, even including high-frequency details? Although human perceptual and cognitive...

RetinalGPT: A Retinal Clinical Preference Conversational Assistant Powered by Large Vision-Language Models

Recently, Multimodal Large Language Models (MLLMs) have gained significant attention for their remarkable ability to process and analyze non-textual...

AI-Driven Multi-Stage Computer Vision System for Defect Detection in Laser-Engraved Industrial Nameplates

Automated defect detection in industrial manufacturing is essential for maintaining product quality and minimizing production errors. In air disc br...

See What You Are Told: Visual Attention Sink in Large Multimodal Models

Large multimodal models (LMMs) "see" images by leveraging the attention mechanism between text and visual tokens in the transformer decoder. Ideally...

Label-Efficient LiDAR Semantic Segmentation with 2D-3D Vision Transformer Adapters

LiDAR semantic segmentation models are typically trained from random initialization as universal pre-training is hindered by the lack of large, dive...

BEVMOSNet: Multimodal Fusion for BEV Moving Object Segmentation

Accurate motion understanding of the dynamic objects within the scene in bird's-eye-view (BEV) is critical to ensure a reliable obstacle avoidance s...

Enhancing Abnormality Grounding for Vision Language Models with Knowledge Descriptions

Visual Language Models (VLMs) have demonstrated impressive capabilities in visual grounding tasks. However, their effectiveness in the medical domai...

DSPNet: Dual-vision Scene Perception for Robust 3D Question Answering

3D Question Answering (3D QA) requires the model to comprehensively understand its situated 3D scene described by the text, then reason about its su...