This work proposes a cross-attention-based aggregation method in which the query projections come from the node, while the key projections come from its neighbors, and introduces a novel architecture called AttentionViG that uses the proposed cross-attention aggregation scheme to conduct non-local message passing.
H. Gedik, Andrew Martin, Mustafa Munir et al.· arXiv.org· 2 citations
No-reference video quality assessment (NR VQA) has recently seen promising progress with deep learning. However, video data is inherently large, and processing them with deep models incurs high computational cost. This challenge is particularly acute in VQA, where preserving original-resolution cues and dense temporal...
H. Gedik, Shashank Gupta, Alan C. Bovik· 0 citations
ReLIQS is a CLIP-based multiscale patch-driven architecture that is resolution-agnostic, preserves original-resolution quality cues, learns from multiple subjective studies, and remains computationally efficient and budget-adaptive.
H. Gedik, Shashank Gupta, Alan C. Bovik· 0 citations
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