In recent years the emergence of Spiking Neural Net- works (SNNs) has shown that these networks are a promis- ing alternative to traditional Artificial Neural Networks (ANNs) due to their low-power computing capabilities and noise robustness. Nevertheless, in recent approaches, they have either strayed away from spike time backpropaga- tion, used discrete time, single spike neurons, and/or limited themselves to shallow networks. These approaches limit the potential of SNNs by sacrificing significant aspects of what makes them special in order to have a functional network. It is for this reason that we believe that, the implemen- tation of residual connections, allowing a model that has multi-spiking neurons, precise time and spike time back- propagation is the path to allow these networks to truly shine. As it will solve one of this model’s most severe lim- itations which prevent it from being used to build deeper networks, the banishing of spikes at a deeper depth. Hence we aim to remedy this problem by feeding inputs from fur- ther up the network to revitalize the spike counts at deeper layers. In this paper, we explore the implementation of residual connections in precise time multi-spiking neural networks as a way of solving the disappearing spikes problem that inhibits spike time backpropagation. We explore two al- ternatives in the implementation of the residual connection and we analyze how they affect the accuracy of the network as the depth increases in both Multi-Layer Perceptrons and Convolutional Neural Networks. We also developed an ar- chitecture to allow for swapping of the fuse function in or- der to take full advantage of the flexibility that precise time provides us. Results show that the implemented residual connections allow for deeper training, which has the poten- tial to aid in the network’s performance, although in some cases some hurdles remain.

Event cameras are novel sensors whose high temporal resolution and bandwidth motivate their use for the optical flow estimation problem. However, the properties of event cameras also introduce a vulnerability to flickering. Flickering hurts the perceptibility of motion by overwhelming event data with unrelated information. The single existing event de-flicker method (EFR) is built for scenarios where the relative position of the camera and the flickering object is constant, which is uncommon in motion-heavy optical flow estimation scenarios. Our contribution is a new de-flickering method that incorporates spatial awareness of nearby pixels. We hypothesize this feature to increase robustness to movement, and thus to better improve optical flow accuracy. Compared to EFR our method falters at filtering intensely flickering surfaces, but better preserves the spatial coherence of edges. However, we observe that both de-flickering methods remove much geometric information, especially given slow motion or weak ambient illumination. Our benchmarking shows that neither our method nor EFR significantly affects optical flow estimation accuracy, despite reducing event counts by 50-65%. Overall, we conclude that the niche benefits of spatial filtering are nullified by the result that filtering hardly affects optical flow estimation.

Computer vision tasks have shown to benefit greatly from both developments in deep learning networks, and the emergence of event cameras. Deep networks can require a large amount of training data, which is not readily available for event cameras, specifically for optical flow estimation. The need for simulating this data in a realistic, physics-driven manner is therefore crucial. This paper compares the state of the art event camera simulators on different criteria, including event timestamp modeling, performance under low illumination, bandwidth simulation, computation speed and various types of noise simulation. We also summarize the shortcomings of some commonly used optical flow event datasets. For generating high-quality, realistic events, The V2E and DVS-Voltmeter simulators have shown to produce the most accurate data.

Optical flow estimation with event cameras encompasses two primary algorithm classes: model-based and learning-based methods. Model-based approaches, do not require any training data while learning-based approaches utilize datasets of events to train neural networks. To effectively apply these algorithms, it's essential to understand their respective strengths and weaknesses.
This study compares model-based and learning-based optical flow estimation methods using event cameras, aiming to provide guidance for real-world applications. We evaluated these methods on the MVSEC and DSEC datasets, focusing on their accuracy and runtime. Our findings indicate that model-based methods excel on the MVSEC dataset, characterized by small motions, while learning-based approaches perform better on the more dynamic DSEC dataset. To investigate potential overfitting of learning-based methods to DSEC, we retrained the IDNet and TMA models on the BlinkFlow dataset. The retrained models demonstrated competitive accuracy, surpassing model-based methods which indicates that learning-based models perform better on datasets like DSEC even when not able to overfit. Finally, our analysis on runtime showed that model-based methods achieve real-time performance on CPUs and learning-based methods require a GPU to run in real-time.

Event cameras are bio-inspired sensors with high dynamic range, high temporal resolution, and low power consumption. These features enable precise motion detection even in challenging lighting conditions and fast-changing scenes, rendering them well-suited for optical flow estimation. However, event camera output is sparse and unstructured, making it challenging to process. Transformer architectures have shown to be effective in capturing long-term temporal dependencies and processing sparse input, hence they might be better suited to processing this output by leveraging the fine time granularity inherent to event camera data.
We introduce E-GMFlow, an approach for event-based optical flow inspired by the recent success in terms of accuracy of transformer-based models for frame-based optical flow. We explore the effect of temporal details on the accuracy of this transformer architecture by changing the number of temporal bins in which events are discretized. We observe that the increase in the number of temporal bins generally causes higher accuracy and comment on the limitations of this study.

Spiking Neural Networks (SNN) represent a distinct class of neural network models that incorporate an additional temporal dimension. Neurons within SNN operate according to the Leaky Integrate-and-Fire principle, governed by ordinary differential equations. Inter-layer neuronal communication occurs through spike propagation when membrane potentials reach a specified threshold. This unique mechanism renders conventional Artificial Neural Network (ANN) design principles and learning rules inapplicable to SNN. This study presents a theoretical investigation into the impact of time discretization on SNN performance in real-world tasks. We present a network architecture based on the Error Backpropagation Through Spikes model. This approach allows for the transformation of the original system of differential equations into a system of difference equations across multiple time steps. We evaluate our model's performance on the MNIST dataset and identify several phenomena that influence accuracy. Our analysis primarily focuses on gradient dynamics and spectral properties of the voltage signals. These findings contribute to a deeper understanding of SNN behavior and potential optimization strategies.

The promise of Artificial Neural Networks has lead to their immense usage intertwined with concerns over energy consumption. This has led to development of alternatives, such as Spiking Neural Networks (SNNs), which allows their implementation on neuromorphic hardware. In effect, the network must be time discretized. SNNs learn through backpropagation, similarly to ANNs, where two main methods exist: spike-based backpropagation and backpropagation through time. This study investigates and compares the effect of varying timesteps on the convergence rate of BATS, an example of spikebased backpropagation, and of SLAYER - an example of backpropagation through time on the NMNIST and MNIST dataset. The results conclude that BATS withstands growing timestep sizes. Although SLAYER maintains a good performance for small timestep sizes, it seems to self-destruct as they grow.

Spiking neural networks (SNNs) aim to utilize mechanisms from biological neurons to bridge the computational and efficiency gaps between the human brain and machine learning systems. The widely used Leaky-Integrate-and-Fire (LIF) neuron model accumulates input spikes into an exponentially decaying membrane potential and generates a spike when this potential exceeds a set threshold. A LIF neuron is characterized by learnable input weights and a manually selected membrane time constant 𝜏, which determines the decay rate of a neuron's membrane potential. Previous work introduced the Parametric LIF (PLIF) neuron model with a learnable 𝜏. However, the published experiments only featured a single 𝜏 per spiking layer. This leaves space for exploration of the effect of having a learnable 𝜏 for each neuron. The importance of 𝜏 is given by the trade-off it inherently introduces, prioritizing the neuron's capability to convey information about spatial or temporal features. This work examines the effect of introducing a learnable 𝜏 per neuron with a new initialization method and a new regularization term which incentivizes low variance in each PLIF layer. The experiments are done using the DVS128 Gesture dataset and compared to a baseline model from the original paper introducing the PLIF neuron model. Results are inconclusive but suggest that introducing 𝜏 per neuron does not have a significant effect on the accuracy of a spiking neural network. Moreover, the evolution of 𝜏 during training exhibits interesting behavior and leads to two new hypotheses.

The increasing computational costs of training deep learning models have drawn more and more attention towards more power-efficient alternatives such as spiking neural networks (SNNs). SNNs are an artificial neural network that mimics the brain’s way of processing information. These models can be even more power-efficient when run on specialized hardware like digital neuromorphic chips. These chips are designed to handle the unique processing needs of SNNs like sparse and event-driven computations. A lot of the state-of-the-art performance of SNNs in recent research has been achieved through supervised learning models that leverage intricate error backpropagation techniques. These models impose specific constraints on the network and rely on continuous time to facilitate the backpropagation process. However, this fix imposes a new challenge when converting these mechanisms on a neuromorphic chip. Because time is discrete on hardware numerical errors can be introduced as we can not calculate the infinitely precise value of variables depending on time. This work proposes a time discretization technique that allows for fast and stable backpropagation and analyses the effects of it on the efficiency of the SNN. Specifically, we look at sparsity, latency, and accuracy as the main factors that explain the power efficiency of the model. The experiments show that choosing a suitable time step size can improve sparsity while maintaining a high level of accuracy. However, too large values affect the network’s ability to learn.

Event cameras are cameras that capture events asynchronously based on changes in lighting. They offer multiple benifits, but pose challenges in computer vision due to their asynchronous nature and hard to capture ground truth values to compare against. This paper shows the effects training of a state of the art unsupervised learning algorithm Taming Contrast Maximisation for predicting optical flow on a new dataset BlinkFlow which promises improvements in performance of supervised algorithms. This paper aims to see if these improved performances also happen for unsupervised models. Results of this research were inconclusive for the effectiveness of training unsupervised models, but it was shown that pretrained models on DSEC and MVSEC datasets did not perform well on this new dataset.

Spiking neural networks have gained traction as both a tool for neuroscience research and a new frontier in machine learning. A plethora of neuroscience literature exists exploring the realistic simulation of neurons, with complex models re- quiring the formulation and integration of ordinary differential equations. Overcoming this challenge has led to the exploration of various numerical integration techniques with the goal of highly stable and accurate simulations. In contrast, training spiking neural networks is often done with simple leaky integrate-and-fire models and rudimentary integration methods such as the forward-Euler method. In this research we explore how more complex numerical integration methods, borrowed from neuroscience research, affect the training of networks based on current-based leaky integrate- and-fire neurons. We derive equations required for the integration process and suggest the use of spike time interpolation. Furthermore, we pro- vide insights into applying backpropagation on numerically integrated networks and highlight possible pitfalls of the process. We conclude that numerically integrated networks can achieve training accuracies close to their theoretical limits, with good convergence and training time characteristics. Specifically, high order integrations achieve robust and computationally viable training. Additionally, we explore the effects of spike time interpolation on network accuracy and use our findings to pro- vide insights into the role of different integration parameters on the effective training of spiking neural networks.

Insects such as Diptera are capable of highly complex aerial maneuvers and rapid responses to environmental stimuli, making them a subject of interest for studies in flight dynamics and motor control. To accurately quantify these movements, high-speed cameras are employed, capturing footage at 5000 frames per second. This results in a vast amount of data, necessitating the development of automated analysis models. In this work, we present a segmentation model specifically designed for the wings of Diptera species, aiming to track their wingbeats with high precision. The model’s performance was evaluated on species included in the training set as well as on species only included in the testset, allowing us to assess its generalization capabilities. Our results show that the model achieves a 95th percentile error of 7.8 pixels, while operating at a speed 2500 times faster than manual human analysis. This significant improvement in speed highlights the potential for our model to facilitate large-scale, high-speed analysis of insect flight, with implications for both biological research and bio-inspired engineering applications.

Live-cell imaging captures dynamic cellular behaviors and aims to maximize both spatial and temporal resolution while minimizing sample damage, enabling advancements in fundamental cell biology. However, spatial resolution is limited by the diffraction barrier of optical lenses, which prevents the visualization of many subcellular structures. Single-molecule localization microscopy (SMLM) overcomes this barrier, achieving resolutions as fine as 10 nm, but it typically requires millions of frames and higher illumination, which reduces temporal resolution and can damage the sample. Super-resolution Optical Fluctuation Imaging (SOFI) operates at lower illumination levels and requires hundreds of frames, leveraging the statistical relationships of blinking fluorophores to achieve n-fold spatial resolution based on the nth-order SOFI calculation. Despite its benefits, SOFI still demands too many frames and involves extensive post-processing, making it impractical for real-time live-cell imaging. Without real-time imaging, researchers are unable to make immediate decisions, ultimately costing valuable time for the researchers.

To address this limitation, we introduce a supervised deep learning model designed to accelerate second-order SOFI. Our model reconstructs super-resolved second-order SOFI images using just 20 frames, compared to the hundreds typically required, while maintaining a 2-fold improvement in spatial resolution and showing minimal background artifacts. We demonstrate that after being trained on real fixed-cell (static) mitochondria data, the model is able to reconstruct super-resolved images in a dynamic environment by moving the microscope stage. The model achieves real-time temporal resolutions of up to 4.85 fps, unlocking new possibilities for real-time studies of live-cell dynamics.

Designing processors for implantable closed-loop neuromodulation systems presents a formidable challenge owing to the constrained operational environment, which requires low latency and high energy efficacy. Previous benchmarks have provided limited insights into power consumption and latency. However, this study introduces algorithmic metrics that capture the potential and limitations of neural decoders for closed-loop intra-cortical brain-computer interfaces in the context of energy and hardware constraints. This study benchmarks common decoding methods for predicting a primate’s finger kinematics from the motor cortex and explores their suitability for low latency and high energy efficient neural decoding. The study found that ANN-based decoders provide superior decoding accuracy, requiring high latency and many operations to effectively decode neural signals. Spiking neural networks have emerged as a solution, bridging this gap by achieving competitive decoding performance within sub-10ms while utilizing a fraction of computational resources. These distinctive advantages of neuromorphic spiking neural networks make them highly suitable for the challenging closed-loop neural modulation environment. Their capacity to balance decoding accuracy and operational efficiency offers immense potential in reshaping the landscape of neural decoders, fostering greater understanding, and opening new frontiers in closed-loop intra-cortical human-machine interaction.

Tracker-level fusion (TLF) is recognized as an effective approach to comprehensively improve visual object tracking performance by combining the capabilities of multiple baseline trackers. Although there is considerable interest in TLF, there are still challenges related to insufficient understanding, high cost, and unstable performance that make studying TLF difficult. In this thesis, I begin with an explicit summary of the overall pipeline of TLF which has significant guidance for TLF study. Additionally, I conduct a deep analysis of the positive and negative effects of baseline trackers to fully understand their influence on TLF. For visual object tracking, I propose three TLF frameworks based on three Distributed Kalman filters which are optimized for different scenarios and enable the fusion of different baseline trackers to enhance tracking performance. My TLF frameworks fuse the tracking results of different baseline trackers based on the principle of minimal trace to produce fusion results.Additionally, they exhibit superior and stable performance with general baseline tracker requirements, while also being simple, online, and real-time.Furthermore, the proposed frameworks can benefit from state-of-the-art baseline trackers over time, which will further improve their tracking performance. The proposed analysis and frameworks are studied extensively on three challenging benchmarks: generic tracking OTB2015, short-term tracking GOT-10k, and long-term tracking LaSOT. At over 240 FPS, the state-of-the-art success AUC score of 72.7% is achieved on OTB2015.

Most recent works on optical flow use convex upsampling as the last step to obtain high-resolution flow. In this work, we show and discuss several issues and limitations of this currently widely adopted convex upsampling approach. We propose a series of changes, inspired by the observation that convex upsampling as currently implemented performs badly in high-detail areas. We identify three possible causes; wrong training data, the non-existence of a convex combination, and the inability of the convex upsampler to find the correct convex combination.

We propose several ideas in an attempt to resolve current issues. First, we propose to decouple the weights for the final convex upsampler, making it easier to find the correct convex combination. For the same reason, we also provide extra contextual features to the convex upsampler. Then, we increase the convex mask size by using an attention-based alternative convex upsampler; Transformers for Convex Upsampling. This upsampler is based on the observation that convex upsampling can be reformulated as attention, and we propose to use local attention masks as a drop-in replacement for convex masks in order to increase the mask size. We provide empirical evidence that a larger mask size increases the likelihood of the existence of the convex combination. Lastly, we propose an alternative training scheme to remove bilinear interpolation artifacts from the model output.

We investigate whether an increase in accuracy can be achieved while leaving the low-resolution flow prediction architecture unchanged. Due to that, our proposed ideas could theoretically be applied to almost every current state-of-the-art optical flow architecture. On the FlyingChairs + FlyingThings3D training setting we reduce the Sintel Clean training end-point-error of GMA from 1.31 to 1.18, which is a 10% decrease caused solely by changes regarding the convex upsampler.

Transparency and specularity are challenging phenomena that modern depth perception systems have to deal with in order to be used in practice. A promising family of depth estimation methods is Multi-View Stereo (MVS), which combines multiple RGB images to predict depth, thus circumventing the need for costly specialized hardware. Although promising, finding pixel-to-pixel mappings between images is a challenging task, clouded by ambiguity. In order to determine the current ability to deal with such ambiguity, we introduce ToteMVS: a multi-view, multi-material synthetic dataset with diffuse, specular and transparent objects. Recent works in computer vision have effectively replaced Convolutional Neural Networks (CNNs) with the emerging Vision Transformer (ViT) architecture, but it remains unclear whether ViTs outperform CNNs in handling reflective and transparent materials. In our study, we use ToteMVS to compare ViT- and CNN-based architectures on the ability to extract useful features for depth estimation on diffuse, specular, and transparent objects. Our results show that, in contrast with the current trend of using ViTs over CNNs, the ViT-based model does not have a special capability for dealing with these challenging materials in the context of MVS. Our evaluation data, including related code, can be found on our \href{https://github.com/pietertolsma/ToteMVS/}{GitHub}.

An event-based camera enables capturing a video at a high temporal resolution, high dynamical range, reduced power consumption and minimal data bandwidth while the camera has minimal physical dimensions compared to a frame-based camera with the same vision properties. The limiting factor, however, of an event-based camera is the spatial resolution which ranges between 40 × 40 and 1280 × 960. To counter this deficiency, a method is researched to super resolve event-based vision in order to enhance spatial resolution. A selection of different neural network types and configurations are researched in a step-by-step fashion. Subsequent experiments tested the selected networks on their ability to process event-based data and extract features from it. Followed by experiments that exploited the limitations of the networks to super resolve at different ratios, lengths of eventstreams and more complex event-based data. Results of various experiments showed that a network configuration that utilizes a transformer architecture was best able to super resolve event-based vision. This type of network leverages the ability to extract features based on dependencies between events which aligns with the characteristics of event- based vision. Based on the obtained results from the exper- iments, a pipeline is proposed to super resolve event-based vision and consists of a combination of a transformer network, multilayer perceptrons and a k-nearest-neighbor algorithm. Using this pipeline, eventstreams can be super resolved in the spatial resolution at a scaling ratio of 4. Visually, these super resolved eventstreams resemble more detailed and enhanced version to the low-resolution input. This proposed pipeline can be considered as a starting point in further research toward the super-resolution of event-based data and thereby contributes to the extension of application possibilities of event-based vision.

Traditionally, convolutional neural networks are feedforward networks with a deep and complex hierarchy. Conversely, the human brain has a relatively shallow hierarchy with recurrent connections. Replicating this recurrence may allow for shallower and easier to understand computer vision models that may possess characteristics usually attributed to the brain. One of the characteristics we examine in this paper is the effect of recurrence on the robustness of the model against noise. Additionally, we vary the type of noise in order to observe what behaviour is universal and what behaviour is specific to the noise. However, it is crucial to make the distinction that recurrence in the brain is different from that in classical recurrent models. The key difference lies in how information passes through the layers at every step. This gap has been addressed by CORnet, a family of models that use a more biologically compatible hierarchy of recurrence.

In this paper, we take the idea of the biologically compatible recurrent hierarchy and look at the effects of recurrence when applied to a baseline down-scaled Resnet model. We show different settings where recurrence results in an increase in adversarial robustness, and settings where recurrence has the opposite effect. To offer an additional perspective of robustness, we also show how recurrence makes the feature maps of a model more resilient to perturbations. Following these observations, we conclude that biological recurrence allows the model to average out white noise spatio-temporally and we use the observations to support our hypothesis.

Color information has been shown to provide useful information during image classification. Yet current popular deep convolutional neural networks use 2-dimensional convolutional layers. The first 2-dimensional convolutional layer in the network combines the color channels of the input images, which produces feature maps per channel with only spatial dimensions, height and width, getting rid of the color dimension. In this work we introduce Full Color Deep networks which use 3-dimensional convolutions to retain the color dimension beyond the first layer. The 3D kernels convolve over the color and spatial dimensions. The network can extract features from all three dimensions in all layers which are subsequently used by the classifier. We show that the Full Color Deep networks perform at least as well as the current CNNs but outperform them in learning color information and using that information in other downstream tasks.