PIXL Lunch Talk Speaker: *Alex Berg* Date: Monday, March 06, 2017 Time: 12:30-1:30PM (lunch is served) Location: CS 402 *Title**: *Rethinking object detection in computer vision *Abstract: *Object detection is one of the core problems in computer vision and is a lens through which to view the field. It brings together machine learning (classification, regression) with the variation in appearance of objects and scenes with pose and articulation (lighting, geometry) , and the difficulty of what to recognize for what purpose (semantics) all in a setting where computational complexity is not something to talk about in abstract terms, but matters every millisecond for inference and where it can take exaflops to train a model (computation). I will talk about our ongoing work attacking all fronts of the detection problem. One is the speed-accuracy trade-off, which determines the settings where it is reasonably possible to use detection. Our work on single shot detection (SSD) is currently the leading approach [1,2]. Another direction is moving beyond detecting the presence and location of an object to detecting 3D pose. We are working on both learning deep-network models of how visual appearance changes with pose and object [3], as well as integrating pose estimation as a first class element in detection [4]. One place where pose is especially important is for object detection in the world around us, e.g in robotics, as opposed to on isolated internet images without context. I call this setting "situated recognition". A key illustration that this setting is under addressed is the lack of work in computer vision on the problem of active vision, where perception is integrated in a loop with sensor platform motion, a key challenge in robotics. I will present our work on a new approach to collecting datasets for training and evaluating situated recognition, allowing computer vision researchers to study active vision, for instance training networks using reinforcement learning on a densely sampled data of real RGBD imagery without the difficulty of operating a robot in the training loop. This is a counterpoint to recent work using simulation and CG for such reinforcement learning, where our use of real images allows studying and evaluating real-world perception. I will also briefly mention our lower-level work on computation for computer vision and deep learning algorithms and building tools for implementation on GPUS and fPGAs, as well as other ongoing projects. Collaborators for major parts of this talk UNC Students- Wei Liu, Cheng-Yang Fu, Phil Ammirato, Ric Poirson, Eunbyung Park Outside academic collaborator- Prof. Jana Kosecka (George Mason University) Adobe: Duygu Ceylan, Jimei Yang, Ersin Yumer; Google: Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed Amazon: Ananth Ranga, Ambrish Tyagi [1] SSD: Single Shot MultiBox Detector Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg ECCV 2016 https://arxiv.org/pdf/1512.02325.pdf [2] DSSD : Deconvolutional Single Shot Detector Cheng-Yang Fu, Wei Liu, Ananth Ranga, Ambrish Tyagi, and Alexander C. Berg arXiv preprint arXiv:1701.06659 https://arxiv.org/pdf/1701.06659.pdf [3] Transformation-Grounded Image Generation Network for Novel 3D View Synthesis Eunbyung Park, Jimei Yang, Ersin Yumer, Duygu Ceylan, Alexander C. Berg To appear CVPR 2017 [4] Fast Single Shot Detection and Pose Estimation Patrick Poirson, Philip Ammirato, Cheng-Yang Fu, Wei Liu, Jana Kosecka, Alexander C. Berg 3DV 2016 https://arxiv.org/pdf/1609.05590 [5] A Dataset for Developing and Benchmarking Active Vision Phil Ammirato,Patrick Poirson, Eunbyung Park, Jana Kosecka, and Alexander C. Berg to appear ICRA 2017 *Bio: *Alex Berg's research concerns computational visual recognition. His work addresses aspects of computer, human, and robot vision. He has worked on general object recognition in images, action recognition in video, human pose identification in images, image parsing, face recognition, image search, and large-scale machine learning. He co-organizes the ImageNet Large Scale Visual Recognition Challenge, and organized the first Large-Scale Learning for Vision workshop. He is currently an associate professor in computer science at UNC Chapel Hill. Prior to that he was on the faculty at Stony Brook University, a research scientist at Columbia University, and research scientist at Yahoo! Research. His PhD at U.C. Berkeley developed a novel approach to deformable template matching. He earned a BA and MA in Mathematics from Johns Hopkins University and learned to race sailboats at SSA in Annapolis. In 2013, his work received the Marr prize. https://scholar.google.com/citations?user=jjEht8wAAAAJ&pagesize=100 http://acberg.com/