11 accepted papers
Precise orientation of oocyte is a prerequisite for many cell manipulations, especially Intracytoplasmic Sperm Injection (ICSI), to prevent damage to the meiotic spindle. However, automating this process presents significant challenges due to the visual ambiguity of the polar body and the complex fr
Retina-like event cameras and brain-inspired Spiking Neural Networks (SNNs) demonstrate exceptional energy efficiency through bio-inspired sensing and computation. While SNNs are naturally well-suited to the asynchronous nature of event data, their practical applications face the following challenge…
Large multimodal models (LMMs) have shown promising performance for various document recognition tasks. However, LMMs adopt implicit modeling, and the parameters lack interpretability. Inspired by recent advances in human memory and learning research, we propose an explicit multiscale prototype memo
Spiking Neural Networks (SNNs), with their event-driven and biologically inspired mechanisms, are well-suited for energy-efficient neuromorphic hardware. Neural coding, which is critical to SNNs, determines how information is represented via spikes. While Time-to-First-Spike (TTFS) coding uses a sin…
Bridging high-level semantic understanding with low-level physical control remains a persistent challenge in embodied intelligence, stemming from the fundamental spatiotemporal scale mismatch between cognition and action. Existing generative policies typically adopt a "Generation-from-Noise" paradig…
Spiking Neural Networks (SNNs) offer a promising energy-efficient computing paradigm owing to their event-driven properties and biologically inspired dynamics. Among various encoding schemes, Time-to-First-Spike (TTFS) is particularly notable for its extreme sparsity, utilizing a single spike per ne
Spiking Neural Networks (SNNs) are considered to have enormous potential in the future development of Artificial Intelligence due to their brain-inspired and energy-efficient properties. Compared to vanilla Spatial-Temporal Back-propagation (STBP) training methods, online training can effectively av
Cell penetration and intracellular injection are indispensable procedures in many cell surgery tasks. Because of the complex layered structure of oocytes, conventional piezo-assisted penetration often causes unavoidable cellular damage. Moreover, the small volume of single cells and the nonlinear dy
Deep spiking neural networks (SNNs) hold immense promise for low-power event-driven computing, but their direct training via backpropagation through time (BPTT) incurs prohibitive memory cost, which limits their scalability. Existing memory-saving approaches, such as online learning, BPTT-to-BP, and…
Object detection plays a crucial role in many security-sensitive applications, such as autonomous driving and video surveillance. However, several recent studies have shown that object detectors can be easily fooled by physically realizable attacks, e.g., adversarial patches and recent adversarial t…
Some electric-powered wheelchairs are recently redefined as personal mobility devices. Their users are not only elderly or handicapped people, but also passengers with large baggage or pedestrians going from station to destination, i.e., last-mile transport. Consequently, people with different opera…