PPT-Sparsity and Saliency

Xiaodi Hou KLab Computation and Neural Systems California Institute of Technology for the Crash Course on Visual Saliency Modeling Behavioral Findings and Computational Models CVPR 2013 Schedule. Presenter: Wei Wang. Institute of Digital Media, . PKU . Outline. Introduction to visual attention. The . computational . models of visual attention. The state-of-the-art models . of visual attention. Felix Lee. HPC I. 28.10.2013. Real-time Live Panoramic Video. OMNICAM. real-time acquisition. warping & stitching. blending and rendering of 6 HD video streams. one panoramic video of . 7k x 2k . Sparsity. Authors:. Junzhou. Huang, Tong Zhang, . Dimitris. Metaxas. 1. Zhennan Yan. Introduction. Fixed set of . p. basis vectors where for each . j. . --> . Given a random observation , which depends on an underlying coefficient vector .. Stas. . Goferman. Lihi. . Zelnik. -Manor. Ayellet. Tal. What is saliency?. …. Please describe this picture. Picture description. Man in a flower field. In the fields. Spring blossom. Please describe this picture. : Advanced . Computer. Graphics. Perception. in 3D . Computer Graphics. Motivation. Çok. rendering . yavaş. . olur. ! (told by?). Take perception into account. Don’t waste resources. Render less. Felix Lee. HPC I. 28.10.2013. Real-time Live Panoramic Video. OMNICAM. real-time acquisition. warping & stitching. blending and rendering of 6 HD video streams. one panoramic video of . 7k x 2k . . Junzhou. Huang . Xiaolei. Huang . Dimitris. Metaxas . Rutgers University Lehigh University Rutgers University. Outline. Problem: Applications where the useful information is very less compared with the given data . Sabareesh Ganapathy. Manav Garg. Prasanna. . Venkatesh. Srinivasan. Convolutional Neural Network. State of the art in Image classification. Terminology – Feature Maps, Weights. Layers - Convolution, . . Junzhou. Huang . Xiaolei. Huang . Dimitris. Metaxas . Rutgers University Lehigh University Rutgers University. Outline. Problem: Applications where the useful information is very less compared with the given data . : Advanced . Computer. Graphics. Perception. in 3D . Computer Graphics. Motivation. Çok. rendering . yavaş. . olur. ! (told by?). Take perception into account. Don’t waste resources. Render less. sparse acoustic modeling for speech separation. Afsaneh . Asaei. Joint work with: . Mohammad . Golbabaee. ,. Herve. Bourlard, . Volkan. . Cevher. φ. 21. φ. 52. s. 1. s. 2. s. 3. . s. 4. s. 5. x. Donghun. Yeo. CV Lab.. Contents. Definition of Saliency Detection. Saliency Detection via Dense and Sparse Reconstruction. Saliency Detection via Absorbing Markov Chain. Definition of Saliency Detection. SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks. 9 authors @ NVIDIA, MIT, Berkeley, Stanford. ISCA . 2017. Convolution operation. Reuse. Memory: size vs. access energy. Dataflow decides reuse. one. ?. Chao Zhang . NAOC . . CSIRO. What is machine learning?. https. ://www.youtube.com/watch?v=ukzFI9rgwfU. Machine learning (ML) is the scientific study of algorithms and statistical models that computer .