[NeurIPS 2021Outstanding Paper] MAUVE: Measuring Gap Between Neural and Human Text using Divergence Frontiers

[NeurIPS 2021Outstanding Paper] MAUVE: Measuring Gap Between Neural and Human Text using Divergence Frontiers

Dec 08, 2021
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If you have any copyright issues on video, please send us an email at khawar512@gmail.com Top CV and PR Conferences: Publication h5-index h5-median 1. IEEE/CVF Conference on Computer Vision and Pattern Recognition 356 583 2. European Conference on Computer Vision 197 342 3. IEEE/CVF International Conference on Computer Vision 184 311 4. IEEE Transactions on Pattern Analysis and Machine Intelligence 149 275 5. IEEE Transactions on Image Processing 123 187 6. Pattern Recognition 99 141 7. IEEE/CVF Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 89 154 8. Medical Image Analysis 76 149 9. International Journal of Computer Vision 72 173 10. British Machine Vision Conference (BMVC) 66 102 11. Pattern Recognition Letters 66 93 12. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 62 121 13. IEEE International Conference on Image Processing (ICIP) 60 89 14. IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) 57 83 15. Computer Vision and Image Understanding 52 91 16. Journal of Visual Communication and Image Representation 47 64 17. International Conference on 3D Vision (3DV) 44 89 18. International Conference on Pattern Recognition 43 78 19. Asian Conference on Computer Vision (ACCV) 43 69 20. IEEE International Conference on Automatic Face & Gesture Recognition 42 66 Top Papers at CVPR Deep Residual Learning for Image Recognition. Densely Connected Convolutional Networks. You Only Look Once: Unified, Real-Time Object Detection. Rethinking the Inception Architecture for Computer Vision. Image-to-Image Translation with Conditional Adversarial Networks. YOLO9000: Better, Faster, Stronger. Feature Pyramid Networks for Object Detection. Squeeze-and-Excitation Networks Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. Xception: Deep Learning with Depthwise Separable Convolutions. MobileNetV2: Inverted Residuals and Linear Bottlenecks The Cityscapes Dataset for Semantic Urban Scene Understanding. Pyramid Scene Parsing Network. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Aggregated Residual Transformations for Deep Neural Networks. Learning Deep Features for Discriminative Localization. Accurate Image Super-Resolution Using Very Deep Convolutional Networks. Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields. Non-local Neural Networks Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset.

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