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Found 15 papers, 6 papers with code
A simple, strong baseline for building damage detection on the xBD dataset
We construct a strong baseline method for building damage detection by starting with the highly-engineered winning solution of the xView2 competition, and gradually stripping away components.
Probabilistic 3d regression with projected huber distribution
We also show that the mode of the predicted distribution outperform our regression baselines.
Contrastive pretraining for semantic segmentation is robust to noisy positive pairs
Domain-specific variants of contrastive learning can construct positive pairs from two distinct in-domain images, while traditional methods just augment the same image twice.
Are All Linear Regions Created Equal?
The number of linear regions has been studied as a proxy of complexity for ReLU networks.
Probabilistic Regression with Huber Distributions
In this paper we describe a probabilistic method for estimating the position of an object along with its covariance matrix using neural networks.
Large-Scale Zero-Shot Image Classification from Rich and Diverse Textual Descriptions
We study the impact of using rich and diverse textual descriptions of classes for zero-shot learning (ZSL) on ImageNet.
Explanation-based Weakly-supervised Learning of Visual Relations with Graph Networks
Visual relationship detection is fundamental for holistic image understanding.
Face Attribute Prediction Using Off-the-Shelf CNN Features
Predicting attributes from face images in the wild is a challenging computer vision problem.
Leveraging Mid-Level Deep Representations For Predicting Face Attributes in the Wild
Predicting facial attributes from faces in the wild is very challenging due to pose and lighting variations in the real world.
Visual Instance Retrieval with Deep Convolutional Networks
This paper provides an extensive study on the availability of image representations based on convolutional networks (ConvNets) for the task of visual instance retrieval.
Persistent Evidence of Local Image Properties in Generic ConvNets
Supervised training of a convolutional network for object classification should make explicit any information related to the class of objects and disregard any auxiliary information associated with the capture of the image or the variation within the object class.
Factors of Transferability for a Generic ConvNet Representation
In the common scenario, a ConvNet is trained on a large labeled dataset (source) and the feed-forward units activation of the trained network, at a certain layer of the network, is used as a generic representation of an input image for a task with relatively smaller training set (target).
One Millisecond Face Alignment with an Ensemble of Regression Trees
This paper addresses the problem of Face Alignment for a single image.
Ranked #5 on
Face Alignment
on AFLW2000
CNN Features off-the-shelf: an Astounding Baseline for Recognition
We report on a series of experiments conducted for different recognition tasks using the publicly available code and model of the \overfeat network which was trained to perform object classification on ILSVRC13.
3D Pictorial Structures for Multiple View Articulated Pose Estimation
We consider the problem of automatically estimating the 3D pose of humans from images, taken from multiple calibrated views.
