4 accepted papers
Gaussian splatting (GS) struggles with degraded rendering quality on low-cost devices. To address this issue, we present edge collaborative GS (ECO-GS), where each user can switch between a local small GS model to guarantee timeliness and a remote large GS model to guarantee fidelity. However, decid…
Large-scale pre-training has been shown to benefit speech translation tasks. However, existing multimodal pre-training efforts rely on parallel corpora for semantic alignment, potentially limiting performance to the scale of available data and causing data imbalance. Hence, we propose an adversarial…
Significant progress has recently been made in combining Speech Foundation Models (SFMs) and Large Language Models (LLMs) into a unified model to tackle Speech-to-Text Translation (ST) tasks. However, fine-tuning LLMs to adapt to specific downstream tasks requires substantial resources, which is oft…
End-to-end paradigm has aroused more and more interests and attention for improving speech-to-text translation (ST) recently. Existing end-to-end models mainly attributes and attempts to address the problem of modeling burden and data scarcity, while always fail to maintain both cross-modal and cros…