Multi-modality (MM) image fusion aims to render fused images that maintain the merits of
different modalities, eg, functional highlight and detailed textures. To tackle the challenge in …

Previous works have shown that increasing the window size for Transformer-based image
super-resolution models (eg, SwinIR) can significantly improve the model performance but …

Video restoration aims to restore high-quality frames from low-quality frames. Different from
single image restoration, video restoration generally requires to utilize temporal information …

Video restoration aims at restoring multiple high-quality frames from multiple low-quality
frames. Existing video restoration methods generally fall into two extreme cases, ie, they …

This paper offers an insightful examination of how currently top-trending AI technologies, ie,
generative artificial intelligence (Generative AI) and large language models (LLMs), are …

Video anomaly detection (VAD) is a significant computer vision problem. Existing deep
neural network (DNN) based VAD methods mostly follow the route of frame reconstruction or …

Spatial-Temporal Video Super-Resolution (ST-VSR) aims to generate high-quality videos
with higher resolution (HR) and higher frame rate (HFR). Quite intuitively, pioneering two …

Guided depth map super-resolution (GDSR), as a hot topic in multi-modal image processing,
aims to upsample low-resolution (LR) depth maps with additional information involved in …

Vision-centric joint perception and prediction (PnP) has become an emerging trend in
autonomous driving research. It predicts the future states of the traffic participants in the …

As one of the core video semantic understanding tasks, Video Semantic Role Labeling
(VidSRL) aims to detect the salient events from given videos, by recognizing the predict …