4 accepted papers
The scientific reasoning ability of large language models (LLMs) has recently attracted significant attention. Time series, as a fundamental modality in scientific data, presents unique challenges that are often overlooked in current multimodal LLMs, which either encode numerical sequences as text o…
Adversarial examples are one critical security threat to various visual applications, where injected human-imperceptible perturbations confuse the output. Generating transferable adversarial examples in the black-box setting is crucial but challenging in practice. Existing input-diversity-based meth…
Split learning is a promising paradigm for distributed learning at resource-constrained devices, where the learning model is split to be trained at the participants collaboratively. Unlike federated learning that shares the entire gradients among participants, split learning only exchanges the inter…
Facial expression recognition (FER) based on the convolutional neural network (CNN) in the wild have numerous challenges. For instance, the complexity of the network model makes FER tasks difficult to deploy on portable devices. Some approaches design lightweight networks to reduce the model size, w…