The booming development of deep learning applications and services heavily relies on
large deep learning models and massive data in the cloud. However, cloud-based deep …

Abstract We present Ego-Exo4D a diverse large-scale multimodal multiview video dataset
and benchmark challenge. Ego-Exo4D centers around simultaneously-captured egocentric …

Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …

Current benchmarks for facial expression recognition (FER) mainly focus on static images,
while there are limited datasets for FER in videos. It is still ambiguous to evaluate whether …

Abstract Empowered by Large Language Models (LLMs), recent advancements in Video-
based LLMs (VideoLLMs) have driven progress in various video understanding tasks. These …

Adaptive optimization methods for deep learning adjust the inference task to the current
circumstances at runtime to improve the resource footprint while maintaining the model's …

Both accuracy and efficiency are crucial for image captioning in real-world scenarios.
Although Transformer-based models have gained significant improved captioning …

Mirrors generally lack a consistent visual appearance, making mirror detection very
challenging. Although recent works that are based on exploiting contextual contrasts and …

Recent works have shown that the computational efficiency of video recognition can be
significantly improved by reducing the spatial redundancy. As a representative work, the …

Recent advancements in deploying deep neural networks (DNNs) on resource-constrained
devices have generated interest in input-adaptive dynamic neural networks (DyNNs) …