Learning from mixed datasets: A monotonic image quality assessment model

Abstract

Deep learning based image quality assessment models usually learn to predict image quality from a single dataset, leading the model to overfit specific scenes. To account for this, mixed datasets training can be an effective way to enhance the generalization capability of the model. However, it is nontrivial to combine different image quality assessment datasets, as their quality evaluation criteria, score ranges, view conditions, as well as subjects are usually not shared during the image quality annotation. Instead of aligning the annotations, this paper proposes a monotonic neural network for image quality assessment model learning with different datasets combined. In particular, this model consists of a dataset‐shared quality regressor and several dataset‐specific quality transformers. The quality regressor aims to obtain the perceptual quality of each image of each dataset and the quality transformer maps the perceptual quality to the corresponding annotation monotonically. The experimental results verify the effectiveness of the proposed learning strategy and the code is available at https://github.com/fzp0424/MonotonicIQA.

Lightweight air-to-air unmanned aerial vehicle target detection model

“… Graph Neural Network (GNN) Approaches: Applications of GNN in Target Detection: Certain studies explore the application of Graph Neural Networks (GNNs) in target detection, leveraging the capture of relational information within graph structures to enhance detection performance 50 . Transformer-Based Approaches: Target Detection Based on Transformers: Recently, some endeavors have sought to apply Transformer architectures to the field of target detection, incorporating self-attention mechanisms to capture global and local relationships 51 , 52 . …”

“…Transformer-Based Approaches: Target Detection Based on Transformers: Recently, some endeavors have sought to apply Transformer architectures to the field of target detection, incorporating self-attention mechanisms to capture global and local relationships 51 , 52 .…”

“…Subsequently, generative classifiers are deployed to assess whether these moving objects correspond to UAV targets. This method emphasizes the analysis of trajectories, velocities, and motion patterns of moving objects to distinguish UAVs from other mobile entities [40][41][42] 51,52 .…”