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.
citation dossier
Xception: Deep learning with depthwise separable convolutions.CoRR, abs/1610.02357
5Pith papers citing it
5reference links
cs.CVtop field · 4 papers
UNVERDICTEDtop verdict bucket · 4 papers
why this work matters in Pith
Pith has found this work in 5 reviewed papers. Its strongest current cluster is cs.CV (4 papers). The largest review-status bucket among citing papers is UNVERDICTED (4 papers). For highly cited works, this page shows a dossier first and a bounded explorer second; it never tries to render every citing paper at once.
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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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
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.
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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.