79 citations · 440 across the 39 of their papers we have counts for
13 papers · 1 filter
IC3: Image Captioning by Committee Consensus
David M. Chan, Austin Myers, Sudheendra Vijayanarasimhan +2
If you ask a human to describe an image, they might do so in a thousand different ways. Traditionally, image captioning models are trained to generate a single "best" (most like a…
LAVA: Language Audio Vision Alignment for Contrastive Video Pre-Training
Sumanth Gurram, Andy Fang, David Chan +1
Generating representations of video data is of key importance in advancing the field of machine perception. Most current techniques rely on hand-annotated data, which can be diffic…
An Embedding-Dynamic Approach to Self-supervised Learning
Suhong Moon, Domas Buracas, Seunghyun Park +2
A number of recent self-supervised learning methods have shown impressive performance on image classification and other tasks. A somewhat bewildering variety of techniques have bee…
What's in a Caption? Dataset-Specific Linguistic Diversity and Its Effect on Visual Description Models and Metrics
David M. Chan, Austin Myers, Sudheendra Vijayanarasimhan +3
While there have been significant gains in the field of automated video description, the generalization performance of automated description models to novel domains remains a major…
Misinformation Detection in Social Media Video Posts
Kehan Wang, David Chan, Seth Z. Zhao +2
With the growing adoption of short-form video by social media platforms, reducing the spread of misinformation through video posts has become a critical challenge for social media…
Active Learning for Video Description With Cluster-Regularized Ensemble Ranking
David M. Chan, Sudheendra Vijayanarasimhan, David A. Ross +1
Automatic video captioning aims to train models to generate text descriptions for all segments in a video, however, the most effective approaches require large amounts of manual an…
Grounding Human-to-Vehicle Advice for Self-driving Vehicles
Jinkyu Kim, Teruhisa Misu, Yi-Ting Chen +2
Recent success suggests that deep neural control networks are likely to be a key component of self-driving vehicles. These networks are trained on large datasets to imitate human a…
Periphery-Fovea Multi-Resolution Driving Model guided by Human Attention
Ye Xia, Jinkyu Kim, John Canny +2
Inspired by human vision, we propose a new periphery-fovea multi-resolution driving model that predicts vehicle speed from dash camera videos. The peripheral vision module of the m…
Diagnostic Visualization for Deep Neural Networks Using Stochastic Gradient Langevin Dynamics
Biye Jiang, David M. Chan, Tianhao Zhang +1
The internal states of most deep neural networks are difficult to interpret, which makes diagnosis and debugging during training challenging. Activation maximization methods are wi…
Label and Sample: Efficient Training of Vehicle Object Detector from Sparsely Labeled Data
Xinlei Pan, Sung-Li Chiang, John Canny
Self-driving vehicle vision systems must deal with an extremely broad and challenging set of scenes. They can potentially exploit an enormous amount of training data collected from…
Textual Explanations for Self-Driving Vehicles
Jinkyu Kim, Anna Rohrbach, Trevor Darrell +2
Deep neural perception and control networks have become key components of self-driving vehicles. User acceptance is likely to benefit from easy-to-interpret textual explanations wh…
Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention
Jinkyu Kim, John Canny
Deep neural perception and control networks are likely to be a key component of self-driving vehicles. These models need to be explainable - they should provide easy-to-interpret r…
General models for rational cameras and the case of two-slit projections
Matthew Trager, Bernd Sturmfels, John Canny +2
The rational camera model recently introduced in [19] provides a general methodology for studying abstract nonlinear imaging systems and their multi-view geometry. This paper build…