Unsupervised orientation learning using autoencoders

We present a method to learn the orientation of symmetric objects in real-world images in an unsupervised way. Our method explicitly maps in-plane relative rotations to the latent space of an autoencoder, by rotating both in the image domain and latent domain. This is achieved by adding a proposed crossing loss to a standard autoencoder training framework which enforces consistency between the image domain and latent domain rotations. This relative representation of rotation is made absolute, by using the symmetry of the observed object, resulting in an unsupervised method to learn the orientation. Furthermore, orientation is disentangled in latent space from other descriptive factors. We apply this method on two real-world datasets: aerial images of planes in the DOTA dataset and images of densely packed honeybees. We empirically show this method can learn orientation using no annotations with high accuracy compared to the same models trained with annotations

Variational Inference for SDEs Driven by Fractional Noise

We present a novel variational framework for performing inference in (neural) stochastic differential equations (SDEs) driven by Markov-approximate fractional Brownian motion (fBM). SDEs offer a versatile tool for modeling real-world continuous-time dynamic systems with inherent noise and randomness. Combining SDEs with the powerful inference capabilities of variational methods, enables the learning of representative function distributions through stochastic gradient descent. However, conventional SDEs typically assume the underlying noise to follow a Brownian motion (BM), which hinders their ability to capture long-term dependencies. In contrast, fractional Brownian motion (fBM) extends BM to encompass non-Markovian dynamics, but existing methods for inferring fBM parameters are either computationally demanding or statistically inefficient. In this paper, building upon the Markov approximation of fBM, we derive the evidence lower bound essential for efficient variational inference of posterior path measures, drawing from the well-established field of stochastic analysis. Additionally, we provide a closed-form expression to determine optimal approximation coefficients. Furthermore, we propose the use of neural networks to learn the drift, diffusion and control terms within our variational posterior, leading to the variational training of neural-SDEs. In this framework, we also optimize the Hurst index, governing the nature of our fractional noise. Beyond validation on synthetic data, we contribute a novel architecture for variational latent video prediction,-an approach that, to the best of our knowledge, enables the first variational neural-SDE application to video perception.Comment: 24 pages, under revie

Evaluation of skin prick location on the forearm using a novel skin prick automated test device

BackgroundThe skin prick test (SPT) is the gold standard for identifying allergic sensitization in individuals suspected of having an inhalant allergy. Recently, it was demonstrated that SPT using a novel skin prick automated test (SPAT) device showed increased reproducibility and tolerability compared to the conventional SPT, among other benefits.ObjectiveThis study aimed to evaluate prick location bias using the novel SPAT device.MethodsA total of 118 volunteers were enrolled in this study and underwent SPATs with histamine (nine pricks) and glycerol control (one prick) solutions on the volar side of their forearms. Imaging of the skin reactions was performed using the SPAT device, and the physician determined the longest wheal diameter by visually inspecting the images using a web interface. Prick location bias was assessed along the medial vs. lateral and proximal vs. distal axes of the forearm.ResultsIn total, 944 histamine pricks were analyzed. Four medial and four lateral histamine pricks were grouped, and wheal sizes were compared. The longest wheal diameters were not significantly different between the medial and lateral prick locations (p = 0.41). Furthermore, the pricks were grouped by two based on their position on the proximal–distal axis of the forearm. No significant difference was observed among the four groups of analyzed prick locations (p = 0.73).ConclusionThe prick location on the volar side of the forearm did not influence wheal size in SPAT-pricked individuals

CenDerNet : center and curvature representations for render-and-compare 6D pose estimation

We introduce CenDerNet, a framework for 6D pose estimation from multi-view images based on center and curvature representations. Finding precise poses for reflective, textureless objects is a key challenge for industrial robotics. Our approach consists of three stages: First, a fully convolutional neural network predicts center and curvature heatmaps for each view; Second, center heatmaps are used to detect object instances and find their 3D centers; Third, 6D object poses are estimated using 3D centers and curvature heatmaps. By jointly optimizing poses across views using a render-and-compare approach, our method naturally handles occlusions and object symmetries. We show that CenDerNet outperforms previous methods on two industry-relevant datasets: DIMO and T-LESS