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Found 22 papers, 9 papers with code
Data-Driven Detection of General Chiasmi Using Lexical and Semantic Features
In contrast, we propose an approach targeting the more general and challenging case A B B’ A’, where the words A, A’ and B, B’ constituting the chiasmus do not need to be identical but just related in meaning.
Image Classification With Small Datasets: Overview and Benchmark
However, as research in this scope is still in its infancy, two key ingredients are missing for ensuring reliable and truthful progress: a systematic and extensive overview of the state of the art, and a common benchmark to allow for objective comparisons between published methods.
Domain Adaptation and Active Learning for Fine-Grained Recognition in the Field of Biodiversity
We discovered that domain adaptation works very well for fine-grained recognition and that the normalization methods have a great influence on the results.
A Strong Baseline for the VIPriors Data-Efficient Image Classification Challenge
Learning from limited amounts of data is the hallmark of intelligence, requiring strong generalization and abstraction skills.
Anomaly Attribution of Multivariate Time Series using Counterfactual Reasoning
There are numerous methods for detecting anomalies in time series, but that is only the first step to understanding them.
Tune It or Don't Use It: Benchmarking Data-Efficient Image Classification
Data-efficient image classification using deep neural networks in settings, where only small amounts of labeled data are available, has been an active research area in the recent past.
WikiChurches: A Fine-Grained Dataset of Architectural Styles with Real-World Challenges
We introduce a novel dataset for architectural style classification, consisting of 9, 485 images of church buildings.
Self-Supervised Learning from Semantically Imprecise Data
Learning from imprecise labels such as "animal" or "bird", but making precise predictions like "snow bunting" at inference time is an important capability for any classifier when expertly labeled training data is scarce.
Content-based Image Retrieval and the Semantic Gap in the Deep Learning Era
Content-based image retrieval has seen astonishing progress over the past decade, especially for the task of retrieving images of the same object that is depicted in the query image.
Finding Relevant Flood Images on Twitter using Content-based Filters
The analysis of natural disasters such as floods in a timely manner often suffers from limited data due to coarsely distributed sensors or sensor failures.
Making Every Label Count: Handling Semantic Imprecision by Integrating Domain Knowledge
For example, a non-breeding snow bunting is labeled as a bird.
Enhancing Flood Impact Analysis using Interactive Retrieval of Social Media Images
The analysis of natural disasters such as floods in a timely manner often suffers from limited data due to a coarse distribution of sensors or sensor failures.
Do We Train on Test Data? Purging CIFAR of Near-Duplicates
However, we find that 3. 3% and 10% of the images from the test sets of these datasets have duplicates in the training set.
Deep Learning on Small Datasets without Pre-Training using Cosine Loss
The categorical cross-entropy loss after softmax activation is the method of choice for classification.
Towards Automatic Identification of Elephants in the Wild
Identifying animals from a large group of possible individuals is very important for biodiversity monitoring and especially for collecting data on a small number of particularly interesting individuals, as these have to be identified first before this can be done.
Hierarchy-based Image Embeddings for Semantic Image Retrieval
Such an embedding does not only improve image retrieval results, but could also facilitate integrating semantics for other tasks, e. g., novelty detection or few-shot learning.
Information-Theoretic Active Learning for Content-Based Image Retrieval
We propose Information-Theoretic Active Learning (ITAL), a novel batch-mode active learning method for binary classification, and apply it for acquiring meaningful user feedback in the context of content-based image retrieval.
Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection
Automatic detection of anomalies in space- and time-varying measurements is an important tool in several fields, e. g., fraud detection, climate analysis, or healthcare monitoring.
Automatic Query Image Disambiguation for Content-Based Image Retrieval
Query images presented to content-based image retrieval systems often have various different interpretations, making it difficult to identify the search objective pursued by the user.
Fast Learning and Prediction for Object Detection using Whitened CNN Features
We combine features extracted from pre-trained convolutional neural networks (CNNs) with the fast, linear Exemplar-LDA classifier to get the advantages of both: the high detection performance of CNNs, automatic feature engineering, fast model learning from few training samples and efficient sliding-window detection.
Maximally Divergent Intervals for Anomaly Detection
We present new methods for batch anomaly detection in multivariate time series.
ARTOS -- Adaptive Real-Time Object Detection System
ARTOS is all about creating, tuning, and applying object detection models with just a few clicks.
