[talks] Talk by Alex Berg on Monday March 6
funk at CS.Princeton.EDU
Tue Feb 28 18:43:40 EST 2017
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 , as well as integrating pose estimation as a first class
element in detection .
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,
 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
 DSSD : Deconvolutional Single Shot Detector Cheng-Yang Fu, Wei Liu,
Ananth Ranga, Ambrish Tyagi, and Alexander C. Berg arXiv preprint
 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
 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
 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.
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