Dan Jurafsky, Stanford University Monday, October 22, 2018, 10:30am Networking reception, 10:30-11:00am; Seminar 11:00-12:00pm Kilachand Center, 610 Commonwealth Avenue Boston, MA 02215 “Does This Vehicle Belong to You?” Processing the Language of Policing for Improving Police-Community Relations Abstract: Police body-worn cameras have the potential to play an important role in understanding and improving police-community relations. […]
BU postdoctoral associate Bryan Plummer introduces an approach to improving many image-language tasks where neural networks learn a set of models, each of which capture a different concept which is useful in the task.
This talk by Mike Jones, senior principal research scientist at Mitsubishi Electric Research Labs, will demonstrate how L2 distance is not the best basis of comparison to use in convolutional neural network (CNN) analysis for face verification and propose the hyperplane similarity as a more appropriate similarity function that is derived from the softmax loss function used to train the network.
This talk by Zhengming Ding, a graduate student at Northeastern University, outlines a proposal to build a large-scale face recognizer capable of fighting off the data imbalance difficulty that existing machine learning approaches experience in mimicking human visual intelligence. To seek a more effective general classifier, we develop a novel generative model attempting to synthesize meaningful data for one-shot classes by adapting the data variances from other normal classes.
This talk by Andrei Barbu, a research scientist at MIT, will discuss a program to unify research around a number of vision-language problems into a single mathematical framework culminating in a robotic platform that is able to follow natural language commands, store knowledge, and answer questions.
This talk by Guorong Li, an associate professor at the University of Chinese Academy of Sciences, will outline recent research in media analysis including learning label-specific features for multi-label classification, learning common space for cross-modal retrieval and car tracking in UAV video.
Craig Yu, an associate professor at UMass Boston, discusses recent progress in data-driven computational design, including automatic interior design, architectural design, zoomorphic furniture design, and the design of reconfigurable objects and dissections puzzles.
Mélina Skouras, a postdoctoral associate at MIT, presents novel algorithms based on physics-based simulation and inverse modeling that can be used to alleviate difficulties in digital manufacturing and allow the designer to easily create custom deformable objects.
Filip Malmberg, an associate professor with the Centre for Image Analysis at Sweden’s Uppsala University, provides an overview of ongoing research projects in medical image analysis at Uppsala University that offer a broad range of interesting research challenges, from solving large scale combinatorial optimization problems to developing efficient interfaces for complex 3D user interaction.
Jan Kautz, senior director of visual computing and machine learning research at NVIDIA, describes the company’s current research into monitoring car drivers using NVIDIA’s tracking technologies, including head pose, gaze tracking, and dynamic hand gesture recognition.