41 accepted papers
In recent years, Recurrent Spiking Neural Networks (RSNNs) have shown promising potential in long-term temporal modeling. Many studies focus on improving neuron models and also integrate recurrent structures, leveraging their synergistic effects to improve the long-term temporal modeling capabilitie…
Novel view synthesis for dynamic scenes remains challenging due to complex motion variations. Recent methods represent dynamic and static regions with separate Gaussians to improve efficiency and accuracy, but inaccurate assignment of static and dynamic Gaussian primitives still limits performance.
Bottleneck states, which connect distinct regions of the state space, provide a principled and interpretable basis for constructing temporal abstractions in Hierarchical Reinforcement Learning (HRL). However, existing bottleneck identification methods primarily rely on topological analysis of the st…
The surrogate gradient (SG) method has shown significant promise in enhancing the performance of deep spiking neural networks (SNNs), but it also introduces vulnerabilities to adversarial attacks. Although spike coding strategies and neural dynamics parameters have been extensively studied for their
As the third generation of neural networks, Spiking Neural Networks (SNNs) have demonstrated remarkable potential across diverse applications owing to their unique temporal dynamics. In recent years, analyzing the robustness of SNNs from a temporal perspective has become an emerging research focus.
Spiking Neural Networks (SNNs) have attracted increasing attention for their biologically inspired temporal dynamics. As their applications expand, understanding their robustness has become an important research focus. However, little is known about how the intrinsic temporal properties of SNNs affe
Dynamic Vision Sensor (DVS) asynchronously records sparse events triggered by changes in pixel intensity, offering high temporal resolution and low latency. Existing frame-based methods process event data densely, violating its inherent sparsity and introducing computational redundancy. While asynch
With their high dynamic range and temporal resolution, event cameras are well-suited for object detection, especially under motion blur and extreme illumination. Recent state-of-the-art works for event-based object detection primarily focus on the high-level design of backbones. However, developing
Recent brain decoding studies have primarily emphasized the development of brain decoders, while largely neglecting the segmentation step. Existing methods typically adopt fixed-length segmentation, which might overlook subject- or task-level variability and disrupt temporal patterns within brain si
Perception under low illumination remains a major challenge for computer vision systems, as RGB sensors often fail to capture sufficient structural and color information in extremely dark environments. Event cameras, with their high dynamic range and temporal resolution, provide complementary cues t
Inspired by the human brain's ability to adapt to new tasks without erasing prior knowledge, we develop spiking neural networks (SNNs) with dynamic structures for Class Incremental Learning (CIL). Our analytical experiments reveal that limited datasets introduce biases in logits distributions among…
The advantages of high temporal resolution and high dynamic range provided by event cameras are particularly suitable for moving object detection, especially in scenarios with motion blur and extreme lighting conditions. Current popular methods predominantly focus on designing powerful network archi…
Spiking neural networks (SNNs) show great potential in mapless navigation tasks due to their low power consumption, but the continuous representation of spatial information poses a challenge to SNN training. Neuroscience findings reveal that spatial cognition cells encode spatial information through
Synthesizing novel space-time views from a monocular video is a highly ill-posed problem, and its effectiveness relies on accurately reconstructing motion and appearance of the dynamic scene.Frame-based methods for novel space-time view synthesis in dynamic scenes rely on simplistic motion assumptio…
The task of generating novel views in dynamic scenes plays a critical role in the 3D vision domain. Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (3DGS) have shown great promise in this domain but struggle with motion blur, which often arises in real-world scenarios due to camera or objec
High Dynamic Range (HDR) video reconstruction seeks to accurately restore the extensive dynamic range present in real-world scenes and is widely employed in downstream applications. Existing methods typically operate on one or a small number of consecutive frames, which often leads to inconsistent b…
We present EvHDR-NeRF to recover a High Dynamic Range (HDR) radiance field from event streams and a set of Low Dynamic Range (LDR) views with single exposures. Using the EvHDR-NeRF, we can generate both novel HDR views and novel LDR views under different exposures. The key to our method is to model…
In the domain of space-time video super-resolution, it is typically challenging to handle complex motions (including large and nonlinear motions) and varying illumination scenes due to the lack of inter-frame information. Leveraging the dense temporal information provided by event signals offers a p…
Spiking neural networks (SNNs) are widely applied in various fields due to their energy-efficient and fast-inference capabilities. Applying SNNs to reinforcement learning (RL) can significantly reduce the computational resource requirements for agents and improve the algorithm's performance under re…
Reinforcement Learning (RL) has shown promise in robotic navigation tasks, yet applying it to real-world environments remains challenging due to dynamic complexities and the need for dynamically feasible actions. We propose a hierarchical control framework based on Spiking Deep Reinforcement Learnin
Event-based object detection has attracted increasing attention for its high temporal resolution, wide dynamic range, and asynchronous address-event representation. Leveraging these advantages, spiking neural networks (SNNs) have emerged as a promising approach, offering low energy consumption and r…
Spiking Neural Networks (SNNs) are considered promising energy-efficient models due to their dynamic capability to process spatial-temporal spike information. Existing work has demonstrated that SNNs exhibit temporal heterogeneity, which leads to diverse outputs of SNNs at different time steps and h…
Spiking neural networks (SNNs) are increasingly applied to event-based data generated by event cameras due to their asynchronous and sparse properties. Event cameras can inherently respond to the changes in the scene, which is a quite desirable property for action recognition tasks. However, existin…
Developing an efficient collision avoidance policy with Spiking Reinforcement Learning for dynamic, decision-making agents remains challenging. Moreover, the implementation of energy-efficient collision avoidance is important for mobile robots that operate with limited on-board computing resources.…
The next generation of machine intelligence requires the capability of continual learning to acquire new knowledge without forgetting the old one while conserving limited computing resources. Spiking neural networks (SNNs), compared to artificial neural networks (ANNs), have more characteristics th…
Currently, researchers think that the inherent robustness of spiking neural networks (SNNs) stems from their biologically plausible spiking neurons, and are dedicated to developing more bio-inspired models to defend attacks. However, most work relies solely on experimental analysis and lacks theoret…
Existing approaches usually perform spatiotemporal representation in the spatial and temporal dimensions, respectively, which isolates the spatial and temporal natures of the target and leads to sub-optimal embeddings. Neuroscience research has shown that the mammalian brain entorhinal-hippocampal s…
In recent years, spiking neural networks (SNNs) have been used in reinforcement learning (RL) due to their low power consumption and event-driven features. However, spiking reinforcement learning (SRL), which suffers from fixed coding methods, still faces the problems of high latency and poor versat…
Spiking neural networks (SNNs) with biologically inspired spatio-temporal dynamics demonstrate superior energy efficiency on neuromorphic architectures. Error backpropagation in SNNs is prohibited by the all-or-none nature of spikes. The existing solution circumvents this problem by a relaxation on…
Spiking neural networks (SNNs) are well known as the brain-inspired models with high computing efficiency, due to a key component that they utilize spikes as information units, close to the biological neural systems. Although spiking based models are energy efficient by taking advantage of discrete…
Spiking neural networks (SNNs) have manifested remarkable advantages in power consumption and event-driven property during the inference process. To take full advantage of low power consumption and improve the efficiency of these models further, the pruning methods have been explored to find sparse…
Spiking neural networks (SNNs) serve as one type of efficient model to process spatio-temporal patterns in time series, such as the Address-Event Representation data collected from Dynamic Vision Sensor (DVS). Although convolutional SNNs have achieved remarkable performance on these AER datasets, be…
Spiking neural networks (SNNs) have increasingly drawn massive research attention due to biological interpretability and efficient computation. Recent achievements are devoted to utilizing the surrogate gradient (SG) method to avoid the dilemma of non-differentiability of spiking activity to directl…
Our study focuses on mapless navigation in robotics, which involves navigating without an established obstacle map of the environment. Spiking Neural Networks (SNNs) have recently been applied to this task using Deep Reinforcement Learning (DRL), but face challenges in dynamic and partially observab…
Developing computational models of neural response is crucial for understanding sensory processing and neural computations. Current state-of-the-art neural network methods use temporal filters to handle temporal dependencies, resulting in an **unrealistic and inflexible processing paradigm**. Meanwh…
Human brain can effectively integrate visual and auditory information. Dynamic Vision Sensor (DVS) and Dynamic Audio Sensor (DAS) are event-based sensors imitating the mechanism of human retina and cochlea. Since the sensors record the visual and auditory input as asynchronous discrete events, they…
Stereo matching is an indispensable function that enables machine vision system to obtain depth information of its environment. However, most of existing algorithms rely on conventional camera, which follows the frame-based scheme and has several shortcomings: low dynamic range, low temporal resolut…
Spiking neural networks (SNNs) are bio-inspired neural networks with asynchronous discrete and sparse characteristics, which have increasingly manifested their superiority in low energy consumption. Recent research is devoted to utilizing spatio-temporal information to directly train SNNs by backpro…
Event-based cameras have attracted increasing attention due to their advantages of biologically inspired paradigm and low power consumption. Since event-based cameras record the visual input as asynchronous discrete events, they are inherently suitable to cooperate with the spiking neural network (S…
Image-based methods for indoor lighting estimation suffer from the problem of intensity-distance ambiguity. This paper introduces a novel setup to help alleviate the ambiguity based on the event camera. We further demonstrate that estimating the distance of a light source becomes a well-posed proble…
The extraordinary performance of the brain on various cognitive tasks motivates the design of a biologically plausible system for the challenging task of environmental sound recognition. In this paper, we propose a novel approach based on multi-spike learning and key-point encoding. Our encoding ext…