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Found 10 papers, 2 papers with code
Automated Visual Monitoring of Nocturnal Insects with Light-based Camera Traps
We used this dataset to develop and evaluate a two-stage pipeline for insect detection and moth species classification in previous work.
Deep Learning Pipeline for Automated Visual Moth Monitoring: Insect Localization and Species Classification
Biodiversity monitoring is crucial for tracking and counteracting adverse trends in population fluctuations.
Automatic Plant Cover Estimation with Convolutional Neural Networks
Monitoring the responses of plants to environmental changes is essential for plant biodiversity research.
Towards Learning an Unbiased Classifier from Biased Data via Conditional Adversarial Debiasing
Often, the bias of a classifier is a direct consequence of a bias in the training dataset, frequently caused by the co-occurrence of relevant features and irrelevant ones.
End-to-end Learning of a Fisher Vector Encoding for Part Features in Fine-grained Recognition
We assume that part-based methods suffer from a missing representation of local features, which is invariant to the order of parts and can handle a varying number of visible parts appropriately.
Ranked #10 on
Fine-Grained Image Classification
on NABirds
Classification-Specific Parts for Improving Fine-Grained Visual Categorization
Fine-grained visual categorization is a classification task for distinguishing categories with high intra-class and small inter-class variance.
Ranked #14 on
Fine-Grained Image Classification
on NABirds
Maximally Divergent Intervals for Anomaly Detection
We present new methods for batch anomaly detection in multivariate time series.
Active Learning and Discovery of Object Categories in the Presence of Unnameable Instances
In active learning, all categories occurring in collected data are usually assumed to be known in advance and experts should be able to label every requested instance.
Seeing through bag-of-visual-word glasses: towards understanding quantization effects in feature extraction methods
To answer this question, we present an in-depth analysis of the effect of local feature quantization on human recognition performance.
Kernel Null Space Methods for Novelty Detection
In contrast to modeling the support of each known class individually, our approach makes use of a projection in a joint subspace where training samples of all known classes have zero intra-class variance.
