[CourseClub.NET] Coursera - Deep Learning in Computer Vision - 磁力狗

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文档个数：104个文档
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收录时间：2020-04-06
最近下载：2024-12-28
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文档列表

001.Introduction and digital images/004. Color models.mp4 60.5 MB
003.Image classification/010. AlexNet, VGG and Inception architectures.mp4 46.0 MB
003.Image classification/011. ResNet and beyond.mp4 45.3 MB
008.Object tracking/038. Examples of visual object tracking methods.mp4 45.0 MB
004.Content-based image retrieval/016. Employing indexing structures for efficient retrieval of semantic neighbors.mp4 39.1 MB
004.Content-based image retrieval/015. Computing semantic image embeddings using convolutional neural networks.mp4 37.3 MB
010.Image segmentation/048. Human pose estimation as image segmentation.mp4 35.0 MB
010.Image segmentation/047. Deep learning models for image segmentation.mp4 34.3 MB
003.Image classification/009. Recap Image classification.mp4 34.0 MB
002.Basic image processing/008. Edge detection.mp4 33.4 MB
004.Content-based image retrieval/014. Content-based image retrieval.mp4 33.1 MB
011.Style transfer and image generation/050. Generative adversarial networks.mp4 30.9 MB
009.Action recognition/042. Action classification.mp4 27.9 MB
008.Object tracking/040. Examples of multiple object tracking methods.mp4 27.6 MB
002.Basic image processing/007. Image convolution.mp4 27.3 MB
005.Keypoints regression/019. Facial keypoints regression.mp4 26.8 MB
003.Image classification/012. Fine-grained image recognition.mp4 26.6 MB
004.Content-based image retrieval/017. Face verification.mp4 26.4 MB
003.Image classification/013. Detection and classification of facial attributes.mp4 25.3 MB
005.Keypoints regression/020. CNN for keypoints regression.mp4 24.4 MB
011.Style transfer and image generation/051. Image transformation with neural networks.mp4 23.8 MB
011.Style transfer and image generation/049. Style transfer.mp4 23.8 MB
006.Sliding window detectors/021. Object detection problem.mp4 23.5 MB
009.Action recognition/044. Action localization.mp4 23.5 MB
001.Introduction and digital images/003. Structure of human eye and vision.mp4 23.3 MB
009.Action recognition/041. Introduction to action recognition.mp4 23.0 MB
004.Content-based image retrieval/018. The re-identification problem in computer vision.mp4 22.1 MB
002.Basic image processing/006. Contrast and brightness correction.mp4 20.6 MB
006.Sliding window detectors/025. Viola-Jones face detector.mp4 20.4 MB
008.Object tracking/036. Deep learning in optical flow estimation.mp4 19.9 MB
009.Action recognition/043. Action classification with convolutional neural networks.mp4 19.7 MB
008.Object tracking/037. Visual object tracking.mp4 19.7 MB
008.Object tracking/039. Multiple object tracking.mp4 19.0 MB
010.Image segmentation/046. Oversegmentation.mp4 18.7 MB
007.Modern detector architectures/028. From R-CNN to Fast R-CNN.mp4 18.7 MB
008.Object tracking/035. Optical flow.mp4 18.2 MB
010.Image segmentation/045. Image segmentation.mp4 16.8 MB
007.Modern detector architectures/029. Faster R-CNN.mp4 16.5 MB
001.Introduction and digital images/001. Short introduction to computer vision.mp4 16.1 MB
007.Modern detector architectures/031. Single shot detectors.mp4 15.2 MB
008.Object tracking/034. Introduction to video analysis.mp4 13.3 MB
006.Sliding window detectors/026. Attentional cascades and neural networks.mp4 12.8 MB
001.Introduction and digital images/002. Digital images.mp4 12.8 MB
006.Sliding window detectors/022. Sliding windows.mp4 12.3 MB
006.Sliding window detectors/024. Detector training.mp4 12.3 MB
002.Basic image processing/005. Image processing goals and tasks.mp4 11.3 MB
007.Modern detector architectures/027. Region-based convolutional neural network.mp4 11.2 MB
006.Sliding window detectors/023. HOG-based detector.mp4 9.6 MB
007.Modern detector architectures/030. Region-based fully-convolutional network.mp4 8.9 MB
007.Modern detector architectures/032. Speed vs. accuracy tradeoff.mp4 7.4 MB
007.Modern detector architectures/033. Fun with pedestrian detectors.mp4 6.1 MB
001.Introduction and digital images/004. Color models.srt 22.0 kB
008.Object tracking/038. Examples of visual object tracking methods.srt 21.3 kB
003.Image classification/010. AlexNet, VGG and Inception architectures.srt 14.5 kB
003.Image classification/011. ResNet and beyond.srt 12.6 kB
008.Object tracking/040. Examples of multiple object tracking methods.srt 12.3 kB
009.Action recognition/042. Action classification.srt 12.2 kB
002.Basic image processing/008. Edge detection.srt 12.0 kB
004.Content-based image retrieval/016. Employing indexing structures for efficient retrieval of semantic neighbors.srt 11.9 kB
004.Content-based image retrieval/015. Computing semantic image embeddings using convolutional neural networks.srt 11.3 kB
010.Image segmentation/048. Human pose estimation as image segmentation.srt 11.1 kB
003.Image classification/009. Recap Image classification.srt 11.1 kB
010.Image segmentation/047. Deep learning models for image segmentation.srt 10.3 kB
009.Action recognition/044. Action localization.srt 10.3 kB
011.Style transfer and image generation/050. Generative adversarial networks.srt 10.1 kB
006.Sliding window detectors/021. Object detection problem.srt 10.0 kB
002.Basic image processing/007. Image convolution.srt 9.8 kB
009.Action recognition/041. Introduction to action recognition.srt 9.5 kB
004.Content-based image retrieval/014. Content-based image retrieval.srt 9.5 kB
008.Object tracking/036. Deep learning in optical flow estimation.srt 8.8 kB
001.Introduction and digital images/003. Structure of human eye and vision.srt 8.6 kB
009.Action recognition/043. Action classification with convolutional neural networks.srt 8.4 kB
008.Object tracking/039. Multiple object tracking.srt 8.3 kB
003.Image classification/013. Detection and classification of facial attributes.srt 8.3 kB
008.Object tracking/037. Visual object tracking.srt 8.2 kB
006.Sliding window detectors/025. Viola-Jones face detector.srt 8.1 kB
004.Content-based image retrieval/017. Face verification.srt 8.1 kB
008.Object tracking/035. Optical flow.srt 8.0 kB
005.Keypoints regression/019. Facial keypoints regression.srt 7.7 kB
003.Image classification/012. Fine-grained image recognition.srt 7.6 kB
005.Keypoints regression/020. CNN for keypoints regression.srt 7.3 kB
001.Introduction and digital images/001. Short introduction to computer vision.srt 7.3 kB
002.Basic image processing/006. Contrast and brightness correction.srt 7.1 kB
007.Modern detector architectures/028. From R-CNN to Fast R-CNN.srt 7.1 kB
004.Content-based image retrieval/018. The re-identification problem in computer vision.srt 7.0 kB
011.Style transfer and image generation/049. Style transfer.srt 6.8 kB
007.Modern detector architectures/029. Faster R-CNN.srt 5.8 kB
011.Style transfer and image generation/051. Image transformation with neural networks.srt 5.8 kB
010.Image segmentation/046. Oversegmentation.srt 5.5 kB
008.Object tracking/034. Introduction to video analysis.srt 5.3 kB
001.Introduction and digital images/002. Digital images.srt 5.2 kB
010.Image segmentation/045. Image segmentation.srt 4.9 kB
006.Sliding window detectors/026. Attentional cascades and neural networks.srt 4.9 kB
006.Sliding window detectors/022. Sliding windows.srt 4.8 kB
006.Sliding window detectors/024. Detector training.srt 4.5 kB
007.Modern detector architectures/027. Region-based convolutional neural network.srt 4.5 kB
002.Basic image processing/005. Image processing goals and tasks.srt 3.7 kB
006.Sliding window detectors/023. HOG-based detector.srt 3.5 kB
007.Modern detector architectures/030. Region-based fully-convolutional network.srt 3.2 kB
007.Modern detector architectures/031. Single shot detectors.srt 2.6 kB
007.Modern detector architectures/032. Speed vs. accuracy tradeoff.srt 2.6 kB
007.Modern detector architectures/033. Fun with pedestrian detectors.srt 1.6 kB
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