Graph neural networks-enhanced relation prediction for ecotoxicology (GRAPE).

Exposure to toxic chemicals threatens species and ecosystems. This study introduces a novel approach using Graph Neural Networks (GNNs) to integrate aquatic toxicity data, providing an alternative to complement traditional in vivo ecotoxicity testing. This study pioneers the application of GNN in ecotoxicology by formulating the problem as a relation prediction task.

Exploiting Field Dependencies for Learning on Categorical Data.

Traditional approaches for learning on categorical data underexploit the dependencies between columns (a.k.a.

Accurate 3-DoF Camera Geo-Localization via Ground-to-Satellite Image Matching.

We address the problem of ground-to-satellite image geo-localization, that is, estimating the camera latitude, longitude and orientation (azimuth angle) by matching a query image captured at the ground level against a large-scale database with geotagged satellite images. Our prior arts treat the above task as pure image retrieval by selecting the most similar satellite reference image matching the ground-level query image. However, such an approach often produces coarse location estimates because the geotag of the retrieved satellite image only corresponds to the image center while the ground camera can be located at any point within the image.

Power Normalizations in Fine-Grained Image, Few-Shot Image and Graph Classification.

Power Normalizations (PN) are useful non-linear operators which tackle feature imbalances in classification problems. We study PNs in the deep learning setup via a novel PN layer pooling feature maps. Our layer combines the feature vectors and their respective spatial locations in the feature maps produced by the last convolutional layer of CNN into a positive definite matrix with second-order statistics to which PN operators are applied, forming so-called Second-order Pooling (SOP).

Tensor Representations for Action Recognition.

Human actions in video sequences are characterized by the complex interplay between spatial features and their temporal dynamics. In this paper, we propose novel tensor representations for compactly capturing such higher-order relationships between visual features for the task of action recognition. We propose two tensor-based feature representations, viz.

A Comparative Review of Recent Kinect-Based Action Recognition Algorithms.

Video-based human action recognition is currently one of the most active research areas in computer vision. Various research studies indicate that the performance of action recognition is highly dependent on the type of features being extracted and how the actions are represented. Since the release of the Kinect camera, a large number of Kinect-based human action recognition techniques have been proposed in the literature.

Higher-Order Occurrence Pooling for Bags-of-Words: Visual Concept Detection.

In object recognition, the Bag-of-Words model assumes: i) extraction of local descriptors from images, ii) embedding the descriptors by a coder to a given visual vocabulary space which results in mid-level features, iii) extracting statistics from mid-level features with a pooling operator that aggregates occurrences of visual words in images into signatures, which we refer to as First-order Occurrence Pooling. This paper investigates higher-order pooling that aggregates over co-occurrences of visual words. We derive Bag-of-Words with Higher-order Occurrence Pooling based on linearisation of Minor Polynomial Kernel, and extend this model to work with various pooling operators.