5 accepted papers
Leveraging Large Vision-Language Models like CLIP has recently set new benchmarks for No-Reference Image Quality Assessment (NR-IQA). However, the contrastive pretraining of CLIP inherently prioritizes semantic invariance, which often suppresses subtle perceptual signals, a phenomenon we term percep…
Accurate and efficient Video Quality Assessment (VQA) has long been a key research challenge. Current mainstream VQA methods typically improve performance by pretraining on large-scale classification datasets, followed by fine-tuning on VQA datasets. However, this strategy presents two significant c…
The rapid growth of long-duration, high-definition videos has made efficient video quality assessment (VQA) a critical challenge. Existing research typically tackles this problem through two main strategies: reducing model parameters and resampling inputs. However, light-weight Convolution Neural Ne…
The rapid advancement of drone technology has led to the widespread application of micro-object detection in Unmanned Aerial Vehicle (UAV) systems. However, with the constraint of real-time computation, critical challenges remain in addressing extreme scale variations, low-resolution signatures and
Although the current deep neural network based no-reference video quality assessment (NR-VQA) methods can effectively simulate the human visual system (HVS), their interpretability is getting worse. The current methods only extract the low-level features of space and time of the video and do not con…