Search Results for author:
Found 17 papers, 7 papers with code
DeepLab2: A TensorFlow Library for Deep Labeling
DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a state-of-the-art and easy-to-use TensorFlow codebase for general dense pixel prediction problems in computer vision.
Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation
We view this work as a notable step towards building a simple procedure to harness unlabeled video sequences and extra images to surpass state-of-the-art performance on core computer vision tasks.
Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation
In this work, we introduce Panoptic-DeepLab, a simple, strong, and fast system for panoptic segmentation, aiming to establish a solid baseline for bottom-up methods that can achieve comparable performance of two-stage methods while yielding fast inference speed.
Ranked #6 on
Panoptic Segmentation
on Cityscapes test
(using extra training data)
SegSort: Segmentation by Discriminative Sorting of Segments
The proposed SegSort further produces an interpretable result, as each choice of label can be easily understood from the retrieved nearest segments.
Ranked #10 on
Unsupervised Semantic Segmentation
on PASCAL VOC 2012 val
(using extra training data)
Panoptic-DeepLab
The semantic segmentation branch is the same as the typical design of any semantic segmentation model (e. g., DeepLab), while the instance segmentation branch is class-agnostic, involving a simple instance center regression.
DeeperLab: Single-Shot Image Parser
We present a single-shot, bottom-up approach for whole image parsing.
Ranked #32 on
Panoptic Segmentation
on Cityscapes val
Searching for Efficient Multi-Scale Architectures for Dense Image Prediction
Recent progress has demonstrated that such meta-learning methods may exceed scalable human-invented architectures on image classification tasks.
Ranked #1 on
Human Part Segmentation
on PASCAL-Person-Part
Tensorize, Factorize and Regularize: Robust Visual Relationship Learning
Visual relationships provide higher-level information of objects and their relations in an image â this enables a semantic understanding of the scene and helps downstream applications.
A Deterministic Nonsmooth Frank Wolfe Algorithm with Coreset Guarantees
We present a new Frank-Wolfe (FW) type algorithm that is applicable to minimization problems with a nonsmooth convex objective.
Spatially Adaptive Computation Time for Residual Networks
This paper proposes a deep learning architecture based on Residual Network that dynamically adjusts the number of executed layers for the regions of the image.
Coupled Harmonic Bases for Longitudinal Characterization of Brain Networks
There is a great deal of interest in using large scale brain imaging studies to understand how brain connectivity evolves over time for an individual and how it varies over different levels/quantiles of cognitive function.
A Projection Free Method for Generalized Eigenvalue Problem With a Nonsmooth Regularizer
Eigenvalue problems are ubiquitous in computer vision, covering a very broad spectrum of applications ranging from estimation problems in multi-view geometry to image segmentation.
Efficient non-greedy optimization of decision trees
In this paper, we present an algorithm for optimizing the split functions at all levels of the tree jointly with the leaf parameters, based on a global objective.
CO2 Forest: Improved Random Forest by Continuous Optimization of Oblique Splits
We develop a convex-concave upper bound on the classification loss for a one-level decision tree, and optimize the bound by stochastic gradient descent at each internal node of the tree.
Memory Bounded Deep Convolutional Networks
In this work, we investigate the use of sparsity-inducing regularizers during training of Convolution Neural Networks (CNNs).
Multivariate General Linear Models (MGLM) on Riemannian Manifolds with Applications to Statistical Analysis of Diffusion Weighted Images
Linear regression is a parametric model which is ubiquitous in scientific analysis.
Incorporating User Interaction and Topological Constraints within Contour Completion via Discrete Calculus
We study the problem of interactive segmentation and contour completion for multiple objects.
