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Found 20 papers, 4 papers with code
Self-Supervised Image Representation Learning: Transcending Masking with Paired Image Overlay
Self-supervised learning has become a popular approach in recent years for its ability to learn meaningful representations without the need for data annotation.
ARAH: Animatable Volume Rendering of Articulated Human SDFs
We demonstrate that our proposed pipeline can generate clothed avatars with high-quality pose-dependent geometry and appearance from a sparse set of multi-view RGB videos.
Compositional Human-Scene Interaction Synthesis with Semantic Control
Furthermore, inspired by the compositional nature of interactions that humans can simultaneously interact with multiple objects, we define interaction semantics as the composition of varying numbers of atomic action-object pairs.
Investor Behavior Modeling by Analyzing Financial Advisor Notes: A Machine Learning Perspective
With the help of natural language processing (NLP) we analyze an unstructured (textual) dataset of financial advisors' summary notes, taken after every investor conversation, to gain first ever insights into advisor-investor interactions.
MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images
In contrast, we propose an approach that can quickly generate realistic clothed human avatars, represented as controllable neural SDFs, given only monocular depth images.
Locally Aware Piecewise Transformation Fields for 3D Human Mesh Registration
We combine PTF with multi-class occupancy networks, obtaining a novel learning-based framework that learns to simultaneously predict shape and per-point correspondences between the posed space and the canonical space for clothed human.
Extend the FFmpeg Framework to Analyze Media Content
This paper introduces a new set of video analytics plugins developed for the FFmpeg framework.
End-to-end Training of CNN-CRF via Differentiable Dual-Decomposition
These algorithms can efficiently and exactly solve sub-problems and directly optimize a convex upper bound of the real problem, providing optimality certificates on the way.
Accelerating Column Generation via Flexible Dual Optimal Inequalities with Application to Entity Resolution
We tackle optimization of weighted set packing by relaxing integrality in our ILP formulation.
Movement science needs different pose tracking algorithms
Over the last decade, computer science has made progress towards extracting body pose from single camera photographs or videos.
Accelerating Dynamic Programs via Nested Benders Decomposition with Application to Multi-Person Pose Estimation
We present a novel approach to solve dynamic programs (DP), which are frequent in computer vision, on tree-structured graphs with exponential node state space.
Accelerating Message Passing for MAP with Benders Decomposition
To circumvent the difficulty of enumerating the vector product of state spaces of hyper-variables we introduce a novel Benders decomposition approach.
Efficient Multi-Person Pose Estimation with Provable Guarantees
We test our approach on the MPII-Multiperson dataset, showing that our approach obtains comparable results with the state-of-the-art algorithm for joint node labeling and grouping problems, and that NBD achieves considerable speed-ups relative to a naive dynamic programming approach.
Efficient Column Generation for Cell Detection and Segmentation
To solve this integer program, we propose a column generation formulation where the pricing program is solved via exact optimization of very small scale integer programs.
Multi-Person Pose Estimation via Column Generation
We give a novel integer program formulation of the multi-person pose estimation problem, in which variables correspond to assignments of parts in the image to poses in a two-tier, hierarchical way.
Exploiting skeletal structure in computer vision annotation with Benders decomposition
Many annotation problems in computer vision can be phrased as integer linear programs (ILPs).
Efficient Pose and Cell Segmentation using Column Generation
We study the problems of multi-person pose segmentation in natural images and instance segmentation in biological images with crowded cells.
Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions
In this learning framework, we evaluate two different approaches to finding an optimal set of tracks under a quadratic model objective, one based on an LP relaxation and the other based on novel greedy variants of dynamic programming that handle pairwise interactions.
Tracking Objects with Higher Order Interactions using Delayed Column Generation
We study the problem of multi-target tracking and data association in video.
Learning Multi-target Tracking with Quadratic Object Interactions
We describe a model for multi-target tracking based on associating collections of candidate detections across frames of a video.
