Using frequency information to improve accuracy of object detectors

This research paper analyses the effect that using frequency information can have on object detectors. The latter are complex networks that learn information about objects from images and are then able to predict the location of these objects in new, unseen images. There are, however, certain datasets that are hard to learn on, partly because...

Injecting prior frequency information in DETR for wheat head detection

Wheat is among the most important grains worldwide. For the assessment of wheat fields, image detection of spikes atop the plant containing grain is used. Previous work in deep learning for precision agriculture employs the already established object detectors, Faster R-CNN and YOLO, adapted for the given context. However, these models suffer...

Classifying Continuous Labels: A Simple Tweak to Make Regression Robust

Regression is difficult because of noise, imbalanced data sampling, missing data, etc. We propose a method by classifying the continuous regression labels to tackle regression robustness problems. We analyze if our method can help regression, given that the class information is already included in the regression labels. We start by extensively...

Segmenting actions by aligning video frames to learned prototypes

Video temporal action localization is the task of identifying and localizing specific actions or activities within a video stream. Instead of only classifying which actions occur in the video stream, we aim to detect when an action begins and ends. In this work, we focus on solving this task without any supervision. Existing unsupervised methods...

Feasibility study of "Invariant Information Clustering for Unsupervised Image Segmentation"

In this paper we analyze the performance of a novel clustering objective that optimizes a neural network to predict segmentation. We challenge the reported results by replicating the original experiments and conducting additional tests to gain an insight into the algorithm. We analyzed the efficiency of the clustering objective on a different...

Activity Progress Prediction: Is there progress in video progress prediction methods?

In this paper, we investigate the behaviour of current progress prediction methods on the currently used benchmark datasets. We show that the progress prediction methods can fail to extract useful information from visual data on these datasets. Moreover, when the methods fail to extract visual information, memory-based methods adopt a frame...

Hand-tremor Frequency Estimation in Videos

We focus on the problem of estimating human hand-tremor frequency from input RGB video data. Estimating tremors from video is important for non-invasive monitoring, analyzing and diagnosing patients suffering from motor-disorders such as Parkinson’s disease. We consider two approaches for hand-tremor frequency estimation: (a) a Lagrangian...

Frequency learning for structured CNN filters with Gaussian fractional derivatives

A structured CNN filter basis allows incorporating priors about natural image statistics and thus require less training examples to learn, saving valuable annotation time. Here, we build on the Gaussian derivative CNN filter basis that learn both the orientation and scale of the filters. However, this Gaussian filter basis definition depends on...