8 research outputs found
Unsupervised Domain Adaptation using Graph Transduction Games
Unsupervised domain adaptation (UDA) amounts to assigning class labels to the
unlabeled instances of a dataset from a target domain, using labeled instances
of a dataset from a related source domain. In this paper, we propose to cast
this problem in a game-theoretic setting as a non-cooperative game and
introduce a fully automatized iterative algorithm for UDA based on graph
transduction games (GTG). The main advantages of this approach are its
principled foundation, guaranteed termination of the iterative algorithms to a
Nash equilibrium (which corresponds to a consistent labeling condition) and
soft labels quantifying the uncertainty of the label assignment process. We
also investigate the beneficial effect of using pseudo-labels from linear
classifiers to initialize the iterative process. The performance of the
resulting methods is assessed on publicly available object recognition
benchmark datasets involving both shallow and deep features. Results of
experiments demonstrate the suitability of the proposed game-theoretic approach
for solving UDA tasks.Comment: Oral IJCNN 201
Machine Learning Algorithm for the Scansion of Old Saxon Poetry
Several scholars designed tools to perform the automatic scansion of poetry in many languages, but none of these tools
deal with Old Saxon or Old English. This project aims to be a first attempt to create a tool for these languages. We
implemented a Bidirectional Long Short-Term Memory (BiLSTM) model to perform the automatic scansion of Old Saxon
and Old English poems. Since this model uses supervised learning, we manually annotated the Heliand manuscript, and
we used the resulting corpus as labeled dataset to train the model. The evaluation of the performance of the algorithm
reached a 97% for the accuracy and a 99% of weighted average for precision, recall and F1 Score. In addition, we tested
the model with some verses from the Old Saxon Genesis and some from The Battle of Brunanburh, and we observed that
the model predicted almost all Old Saxon metrical patterns correctly misclassified the majority of the Old English input
verses
Multi-Phase Relaxation Labeling for Square Jigsaw Puzzle Solving
We present a novel method for solving square jigsaw puzzles based on global
optimization. The method is fully automatic, assumes no prior information, and
can handle puzzles with known or unknown piece orientation. At the core of the
optimization process is nonlinear relaxation labeling, a well-founded approach
for deducing global solutions from local constraints, but unlike the classical
scheme here we propose a multi-phase approach that guarantees convergence to
feasible puzzle solutions. Next to the algorithmic novelty, we also present a
new compatibility function for the quantification of the affinity between
adjacent puzzle pieces. Competitive results and the advantage of the
multi-phase approach are demonstrated on standard datasets.Comment: 10 pages, 7 figures. Published in Proceedings of the 18th
International Joint Conference on Computer Vision, Imaging and Computer
Graphics Theory and Applications - Volume 4 VISAPP: VISAPP, 785-795, 202
The Group Loss++: A deeper look into group loss for deep metric learning
Deep metric learning has yielded impressive results in tasks such as clustering and image retrieval by leveraging neural networks to obtain highly discriminative feature embeddings, which can be used to group samples into different classes. Much research has been devoted to the design of smart loss functions or data mining strategies for training such networks. Most methods consider only pairs or triplets of samples within a mini-batch to compute the loss function, which is commonly based on the distance between embeddings. We propose Group Loss, a loss function based on a differentiable label-propagation method that enforces embedding similarity across all samples of a group while promoting, at the same time, low-density regions amongst data points belonging to different groups. Guided by the smoothness assumption that '`similar objects should belong to the same group'', the proposed loss trains the neural network for a classification task, enforcing a consistent labelling amongst samples within a class. We design a set of inference strategies tailored towards our algorithm, named Group Loss++ that further improve the results of our model. We show state-of-the-art results on clustering and image retrieval on four retrieval datasets, and present competitive results on two person re-identification datasets, providing a unified framework for retrieval and re-identification
The Group Loss++: A deeper look into group loss for deep metric learning
Deep metric learning has yielded impressive results in tasks such as
clustering and image retrieval by leveraging neural networks to obtain highly
discriminative feature embeddings, which can be used to group samples into
different classes. Much research has been devoted to the design of smart loss
functions or data mining strategies for training such networks. Most methods
consider only pairs or triplets of samples within a mini-batch to compute the
loss function, which is commonly based on the distance between embeddings. We
propose Group Loss, a loss function based on a differentiable label-propagation
method that enforces embedding similarity across all samples of a group while
promoting, at the same time, low-density regions amongst data points belonging
to different groups. Guided by the smoothness assumption that "similar objects
should belong to the same group", the proposed loss trains the neural network
for a classification task, enforcing a consistent labelling amongst samples
within a class. We design a set of inference strategies tailored towards our
algorithm, named Group Loss++ that further improve the results of our model. We
show state-of-the-art results on clustering and image retrieval on four
retrieval datasets, and present competitive results on two person
re-identification datasets, providing a unified framework for retrieval and
re-identification.Comment: Accepted to IEEE Transactions on Pattern Analysis and Machine
Intelligence (tPAMI), 2022. Includes supplementary materia
A Computer Vision System for Monitoring Ice-Cream Freezers
In this paper, we describe a computer vision system aimed at monitoring the evolution of the content of a commercial ice-cream freezer. In particular, the system is able to detect the volume occupied by ice-creams in a basket and to track ice-cream sales. To this end, three modules have been developed performing the detection of the baskets and the products inside them, along with the tracking of the interactions with the freezer to take/drop products. The system comprises four cameras connected to an embedded mini-computer able to communicate with a telemetry system that sends information about the freezer context. Our proposed methods achieve promising results for the basket detection and the product tracking (accuracy around 70-80%) and good results in the volume estimation
Exploiting Context in Handwriting Recognition Using Trainable Relaxation Labeling
Handwriting Text Recognition (HTR) is a fast-moving research topic in computer vision and machine learning domains. Many models have been introduced over the years, one of the most well-established ones being the Convolutional Recurrent Neural Network (CRNN), which combines convolutional feature extraction with recurrent processing of the visual embeddings. Such a model, however, presents some limitations such as a limited capability to account for contextual information. To counter this problem, we propose a new learning module built on top of the convolutional part of a classical CRNN model, derived from the relaxation labeling processes, which is able to exploit the global context reducing the local ambiguities and increasing the global consistency of the prediction. Experiments performed on three well-known handwritten recognition datasets demonstrate that the relaxation labeling procedures improve the overall transcription accuracy at both character and word levels