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Found 8 papers, 3 papers with code
A Survey on Image Quality Assessment: Insights, Analysis, and Future Outlook
Image quality assessment (IQA) represents a pivotal challenge in image-focused technologies, significantly influencing the advancement trajectory of image processing and computer vision.
WeatherQA: Can Multimodal Language Models Reason about Severe Weather?
In this work, we introduce WeatherQA, the first multimodal dataset designed for machines to reason about complex combinations of weather parameters (a. k. a., ingredients) and predict severe weather in real-world scenarios.
Weather Prediction with Diffusion Guided by Realistic Forecast Processes
Weather forecasting remains a crucial yet challenging domain, where recently developed models based on deep learning (DL) have approached the performance of traditional numerical weather prediction (NWP) models.
From Classification to Clinical Insights: Towards Analyzing and Reasoning About Mobile and Behavioral Health Data With Large Language Models
Passively collected behavioral health data from ubiquitous sensors holds significant promise to provide mental health professionals insights from patient's daily lives; however, developing analysis tools to use this data in clinical practice requires addressing challenges of generalization across devices and weak or ambiguous correlations between the measured signals and an individual's mental health.
BigSmall: Efficient Multi-Task Learning for Disparate Spatial and Temporal Physiological Measurements
As an example, perception occurs at different scales both spatially and temporally, suggesting that the extraction of salient visual information may be made more effective by paying attention to specific features at varying scales.
Rate-Perception Optimized Preprocessing for Video Coding
In the past decades, lots of progress have been done in the video compression field including traditional video codec and learning-based video codec.
Automated Backend-Aware Post-Training Quantization
Quantization is a key technique to reduce the resource requirement and improve the performance of neural network deployment.
IA-MOT: Instance-Aware Multi-Object Tracking with Motion Consistency
Meanwhile, the spatial attention, which focuses on the foreground within the bounding boxes, is generated from the given instance masks and applied to the extracted embedding features.
