MAST with spiking neural networks achieves 93.14% mean accuracy detecting AI-generated videos from 10 unseen generators by exploiting smoother pixel residuals and compact semantic trajectories.
Mvitv2: Improved multiscale vision transformers for classification and detection
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ShellfishNet is a new benchmark of 8,691 images across 32 mollusc taxa for evaluating vision models on real-world underwater ecological monitoring tasks including robustness to degradation.
ConvFormer3D-TAP classifies six cine CMR views at 96% accuracy using 3D conv tokenization, multiscale attention, and uncertainty-aware multi-clip fusion on 150k sequences.
citing papers explorer
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Detecting AI-Generated Videos with Spiking Neural Networks
MAST with spiking neural networks achieves 93.14% mean accuracy detecting AI-generated videos from 10 unseen generators by exploiting smoother pixel residuals and compact semantic trajectories.
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ShellfishNet: A Domain-Specific Benchmark for Visual Recognition of Marine Molluscs
ShellfishNet is a new benchmark of 8,691 images across 32 mollusc taxa for evaluating vision models on real-world underwater ecological monitoring tasks including robustness to degradation.
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ConvFormer3D-TAP: Phase/Uncertainty-Aware Front-End Fusion for Cine CMR View Classification Pipelines
ConvFormer3D-TAP classifies six cine CMR views at 96% accuracy using 3D conv tokenization, multiscale attention, and uncertainty-aware multi-clip fusion on 150k sequences.