PPT-HOGgles Visualizing Object Detection Features C. Vondrick

HOGgles Visualizing Object Detection Features C Vondrick A Khosla T Malisiewicz A Torralba ICCV 2013 presented by Ezgi Mercan Object Detection Failures Why do our detectors think water looks like a car. mitedu Abstract We introduce algorithms to visualize feature spaces used by object detectors The tools in this paper allow a human to put on HOG goggles and perceive the visual world as a HOG based object detector sees it We found that these visualiz mitedu Abstract We introduce algorithms to visualize feature spaces used by object detectors The tools in this paper allow a human to put on HOG goggles and perceive the visual world as a HOG based object detector sees it We found that these visualiz : Visualizing Object Detection Features . (to be appeared in ICCV 2013). Carl . Vondrick. . Aditya. . Khosla. Tomasz . Malisiewicz. and Antonio . Torralba. ,MIT . Presented By: Yonatan . Dishon. Discriminative part-based models. Many slides based on . P. . . Felzenszwalb. Challenge: Generic object detection. Pedestrian detection. Features: Histograms of oriented gradients (HOG). Partition image into 8x8 pixel blocks and compute histogram of gradient orientations in each block. Before deep . convnets. Using deep . convnets. PASCAL VOC. Beyond sliding windows: Region proposals. Advantages:. Cuts . down on number of regions detector must . evaluate. Allows detector to use more powerful features and classifiers. On Semantic Perception, Mapping and Exploration (SPME). Karlsruhe, Germany ,2013. Semantic Parsing for Priming Object Detection in RGB-D Scenes. Cesar Cadena and Jana Kosecka. Motivation. 5/5/2013. Long-term robotic operation. Facebook AI Research. Wenchi. Ma. Data: 11/04/2016. More information from object detection. More information from object detection. More information from object detection. Object Detection for now with Deep Learning. Oscar . Danielsson. (osda02@csc.kth.se). Stefan . Carlsson. (. stefanc@csc.kth.se. ). Josephine Sullivan (. sullivan@csc.kth.se. ). DICTA08. The Problem. Object categories are often modeled by collections (bag-of-features) or constellations (pictorial structures) of local features . Before deep . convnets. Using deep . convnets. PASCAL VOC. Beyond sliding windows: Region proposals. Advantages:. Cuts . down on number of regions detector must . evaluate. Allows detector to use more powerful features and classifiers. (Paul Viola , Michael Jones . ). Bibek. Jang . Karki. . Outline. Integral Image. Representation of image in summation format. AdaBoost. Ranking of features. Combining best features to form strong classifiers. Yongxi. . Lu. w. ith Tara . Javidi. Electrical and Computer Engineering. University of California, San . Diego. 1. Object Detection. Given. A set of categories of interest (car, pedestrian, etc.). A color image. 2019.3.15. HOI. 问题. 定义. HOI—Human-Object. . Interaction. HOI-. D. et. 问题. 定义. HOI—Human-Object. . Interaction. 主语. ->Human. 宾语. ->Object. 谓语. ->. . Action. State-of-the-art face detection demo. (Courtesy . Boris . Babenko. ). Face detection and recognition. Detection. Recognition. “Sally”. Face detection. Where are the faces? . Face Detection. What kind of features?. Xindian. Long. 2018.09. Outline. Introduction. Object Detection Concept and the YOLO Algorithm. Object Detection Example (CAS Action). Facial Keypoint Detection Example (. DLPy. ). Why SAS Deep Learning .