MAE-GEBD:Winning the CVPR'2023 LOVEU-GEBD Challenge

The Generic Event Boundary Detection (GEBD) task aims to build a model for segmenting videos into segments by detecting general event boundaries applicable to various classes. In this paper, based on last year's MAE-GEBD method, we have improved our model performance on the GEBD task by adjusting the data processing strategy and loss function. Based on last year's approach, we extended the application of pseudo-label to a larger dataset and made many experimental attempts. In addition, we applied focal loss to concentrate more on difficult samples and improved our model performance. Finally, we improved the segmentation alignment strategy used last year, and dynamically adjusted the segmentation alignment method according to the boundary density and duration of the video, so that our model can be more flexible and fully applicable in different situations. With our method, we achieve an F1 score of 86.03% on the Kinetics-GEBD test set, which is a 0.09% improvement in the F1 score compared to our 2022 Kinetics-GEBD method.Comment: Winner of CVPR2023 LOVEU GEBD Challeng

CVPR 2023 Text Guided Video Editing Competition

Humans watch more than a billion hours of video per day. Most of this video was edited manually, which is a tedious process. However, AI-enabled video-generation and video-editing is on the rise. Building on text-to-image models like Stable Diffusion and Imagen, generative AI has improved dramatically on video tasks. But it's hard to evaluate progress in these video tasks because there is no standard benchmark. So, we propose a new dataset for text-guided video editing (TGVE), and we run a competition at CVPR to evaluate models on our TGVE dataset. In this paper we present a retrospective on the competition and describe the winning method. The competition dataset is available at https://sites.google.com/view/loveucvpr23/track4.Comment: Project page: https://sites.google.com/view/loveucvpr23/track

High-efficiency electrocatalyst for N2 conversion to NH3 based on Au nanoparticles loaded on defective WO3x

In this work, Au nanoparticles (NPs) grown on defective WO3-x (Au/WO3-x) show superior electrocatalytic N2 reduction activity under ambient conditions in 0.1 M KOH. At -0.2 V versus the reversible hydrogen electrode (vs. RHE), the Au/WO3-x catalyst achieves a large NH3 yield of 23.15 μg h-1 mg-1 and a high faradaic efficiency (FE) of 14.72%. Further density functional theory (DFT) calculations indicate that electron transfer between the Au nanoparticles and defective WO3-x promotes the activation of N2 molecules effectively