29 research outputs found
Bayesian Optimal Active Search and Surveying
We consider two active binary-classification problems with atypical
objectives. In the first, active search, our goal is to actively uncover as
many members of a given class as possible. In the second, active surveying, our
goal is to actively query points to ultimately predict the proportion of a
given class. Numerous real-world problems can be framed in these terms, and in
either case typical model-based concerns such as generalization error are only
of secondary importance.
We approach these problems via Bayesian decision theory; after choosing
natural utility functions, we derive the optimal policies. We provide three
contributions. In addition to introducing the active surveying problem, we
extend previous work on active search in two ways. First, we prove a novel
theoretical result, that less-myopic approximations to the optimal policy can
outperform more-myopic approximations by any arbitrary degree. We then derive
bounds that for certain models allow us to reduce (in practice dramatically)
the exponential search space required by a naive implementation of the optimal
policy, enabling further lookahead while still ensuring that optimal decisions
are always made.Comment: Appears in Proceedings of the 29th International Conference on
Machine Learning (ICML 2012
UnLoc: A Unified Framework for Video Localization Tasks
While large-scale image-text pretrained models such as CLIP have been used
for multiple video-level tasks on trimmed videos, their use for temporal
localization in untrimmed videos is still a relatively unexplored task. We
design a new approach for this called UnLoc, which uses pretrained image and
text towers, and feeds tokens to a video-text fusion model. The output of the
fusion module are then used to construct a feature pyramid in which each level
connects to a head to predict a per-frame relevancy score and start/end time
displacements. Unlike previous works, our architecture enables Moment
Retrieval, Temporal Localization, and Action Segmentation with a single stage
model, without the need for action proposals, motion based pretrained features
or representation masking. Unlike specialized models, we achieve state of the
art results on all three different localization tasks with a unified approach.
Code will be available at: \url{https://github.com/google-research/scenic}.Comment: ICCV 202
Supervised Descent Method
<p>In this dissertation, we focus on solving Nonlinear Least Squares problems using a supervised approach. In particular, we developed a Supervised Descent Method (SDM), performed thorough theoretical analysis, and demonstrated its effectiveness on optimizing analytic functions, and four other real-world applications: Inverse Kinematics, Rigid Tracking, Face Alignment (frontal and multi-view), and 3D Object Pose Estimation. In Rigid Tracking, SDM was able to take advantage of more robust features, such as, HoG and SIFT. Those non-differentiable image features were out of consideration of previous work because they relied on gradient-based methods for optimization. In Inverse Kinematics where we minimize a non-convex function, SDM achieved significantly better convergence than gradient-based approaches. In Face Alignment, SDM achieved state-of-the-arts results. Moreover, it was extremely computationally efficient, which makes it applicable for many mobile applications. In addition, we provided a unified view of several popular methods including SDM on sequential prediction, and reformulated them as a sequence of function compositions. Finally, we suggested some future research directions on SDM and sequential prediction.</p