MobileNets introduce depthwise separable convolutions plus width and resolution multipliers to produce efficient CNNs that trade off latency and accuracy for mobile and embedded vision applications.
Xception: Deep learning with depthwise separable convolutions.CoRR, abs/1610.02357
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
representative citing papers
ANL uses diffusion noise prediction and attention to regularize deepfake detectors for better generalization to unseen synthesis methods without added inference cost.
DeepLabv3 improves semantic segmentation by capturing multi-scale context with cascaded or parallel atrous convolutions and adding global context to ASPP, achieving better results on PASCAL VOC 2012 without DenseCRF post-processing.
Pith review generated a malformed one-line summary.
DYMAPIA builds dynamic anomaly masks from Fourier spectra, texture, edges, and optical flow to guide a lightweight DistXCNet classifier, reporting over 99% accuracy and F1 on FF++, Celeb-DF, and VDFD.
citing papers explorer
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Deepfake Detection Generalization with Diffusion Noise
ANL uses diffusion noise prediction and attention to regularize deepfake detectors for better generalization to unseen synthesis methods without added inference cost.
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Rethinking Atrous Convolution for Semantic Image Segmentation
DeepLabv3 improves semantic segmentation by capturing multi-scale context with cascaded or parallel atrous convolutions and adding global context to ASPP, achieving better results on PASCAL VOC 2012 without DenseCRF post-processing.
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Attention Is All You Need
Pith review generated a malformed one-line summary.
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DYMAPIA: A Multi-Domain Framework for Detecting AI-based Video Manipulation
DYMAPIA builds dynamic anomaly masks from Fourier spectra, texture, edges, and optical flow to guide a lightweight DistXCNet classifier, reporting over 99% accuracy and F1 on FF++, Celeb-DF, and VDFD.