5 accepted papers
We introduce SPECTRE, a fully transformer-based foundation model for volumetric computed tomography (CT). Our Self-Supervised & Cross-Modal Pretraining for CT Representation Extraction (SPECTRE) approach utilizes scalable 3D Vision Transformer architectures and modern self-supervised and vision-lang
Weakly-supervised methods for video anomaly detection (VAD) are conventionally based merely on RGB spatio-temporal features, which continues to limit their reliability in real-world scenarios. This is due to the fact that RGB-features are not sufficiently distinctive in setting apart categories such…
Deep Neural Networks (DNNs) are accurate but compute-intensive, leading to substantial energy consumption during inference. Exploiting temporal redundancy through \Delta-\Sigma convolution in video processing has proven to greatly enhance computation efficiency. However, temporal \Delta-\Sigma DNNs…
Video Anomaly Detection (VAD) is a challenging task due to the variability of anomalous events and the limited availability of labeled data. Under the Weakly-Supervised VAD (WSVAD) paradigm, only video-level labels are provided during training, while predictions are made at the frame level. Although…
"Brain-inspired computer architecture facilitates low-power, low-latency deep neural network inference for embedded AI applications. The hardware performance crucially hinges on the quantity of non-zero activations (i.e., events) during inference. Thus, we propose a novel event suppression method, d…