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CREDiT reveals how counterfactual reasoning can disentangle causal evidence from confounding factors, leading to a dramatic boost in VideoQA accuracy.
Achieve comparable or better performance with significantly reduced model size and computational cost by replacing standard convolutional kernels with structured, kernelized higher-order layers.
