Stanford 40 Actions
Stanford 40 Actions ---- A dataset for understanding human actions in still images Introduction The Stanford 40 Action Dataset contains images of humans performing 40 actions. In each image, we provide a bounding box of the person who is performing the action indicated by the filename of the image. There are 9532 images in total with 180-300 images per action class. Download Please download the da
Crowdsourcing Annotations for Visual Object Detection
Crowdsourcing Annotations for Visual Object Detection Hao Su, Jia Deng, Li Fei-Fei Computer Science Department, Stanford University Abstract A large number of images with ground truth object bounding boxes are critical for learning object detec- tors, which is a fundamental task in compute vision. In this paper, we study strategies to crowd-source bound- ing box annotations. The core challenge of
PSY250/CS431: High-Level Vision
Stanford University PSY250/CS431: High-Level Vision Behaviors, Neurons and Computational Models Spring 2013-2014
Stanford University CS 131 Computer Vision: Foundations and Applications
Stanford University CS 131 Computer Vision: Foundations and Applications Fall 2016-2017
PowerPoint Presentation
Struck: Structured Output Tracking with Kernels Sam Hare, Amir Saffari, And Philip H. S. Torr International Conference On Computer Vision (ICCV), 2011 Motivations Problem: tracking-by-detection Input: target Output: locations over times Performance summary MIL TLD Struck Y Wu, J Lim, MH Yang “Online Object Tracking: A Benchmark”, Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference
CS231n Caffe Tutorial Outline ● Caffe walkthrough ● Finetuning example ○ With demo! ● Python interface ○ With demo! Caffe Most important tip... Don’t be afraid to read the code! Caffe: Main classes ● Blob: Stores data and derivatives (heade
CS231n Caffe Tutorial Outline ● Caffe walkthrough ● Finetuning example ○ With demo! ● Python interface ○ With demo! Caffe Most important tip... Don’t be afraid to read the code! Caffe: Main classes ● Blob: Stores data and derivatives (header source) ● Layer: Transforms bottom blobs to top blobs (header + source) ● Net: Many layers; computes gradients via forward / backward (header source) ● Solve
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http://vision.stanford.edu/teaching/cs231b_spring1415/slides/ssearch_schuyler.pdf
Stanford University CS231n: Convolutional Neural Networks for Visual Recognition
http://vision.stanford.edu/teaching/cs231b_spring1415/slides/alexnet_tugce_kyunghee.pdf
