The details of multiple datasets for OIQA task are presented in Table A. For the dataset that contains scanpath coordinates, we can directly sample viewport sequences from it and use our network to predict the quality scores. However, it is challenging and costly to record user scanpath data for every ODI in realistic scenarios. The scanpath information is likely unavailable when evaluating the quality of a panorama. Therefore, we propose a generalized Recursive Probability Sampling (RPS) method to generate multiple pseudo viewport sequences for the panorama, which assists the network to predict an accurate quality score in a way that is similar to the observer's actual scoring process. In JUFE and JXUFE, each ODI consists of 300 viewport coordinates, recorded using a head-mounted display (HMD).

But, our current design has a limitation: the transition direction and distance are fixed, resulting in equally spaced distances between viewports.

Document retrieval plays an essential role in web search applications and various downstream knowledge-intensive tasks by identifying relevant documents to satisfy users' queries.

Deep neural networks often work well when they are over-parameterized and trained withamassiveamount ofnoiseandregularization, suchasweight decay and dropout. Although dropout is widely used as a regularization technique for fully connected layers, it is often less effective for convolutional layers.