Summarization of Videos with the Signature Transform

Abstract

This manuscript presents a new benchmark for assessing the quality of visual summaries without the need for human annotators. It is based on the Signature Transform, specifically focusing on the RMSE and the MAE Signature and Log-Signature metrics, and builds upon the assumption that uniform random sampling can offer accurate summarization capabilities. We provide a new dataset comprising videos from Youtube and their corresponding automatic audio transcriptions. Firstly, we introduce a preliminary baseline for automatic video summarization, which has at its core a Vision Transformer, an image–text model pre-trained with Contrastive Language–Image Pre-training (CLIP), as well as a module of object detection. Following that, we propose an accurate technique grounded in the harmonic components captured by the Signature Transform, which delivers compelling accuracy. The analytical measures are extensively evaluated, and we conclude that they strongly correlate with the notion of a good summary.

Signature and Log-Signature for the Study of Empirical Distributions Generated with GANs

“…The Signature [11,14,[77][78][79][80] of an input data stream encodes the order in which data arrive without being concerned with the precise timing of its arrival. This property, known as invariance to time reparameterizations [81], makes it an ideal candidate for measuring GAN-generated distributions against an original data stream.…”

“…RMSE and MAE, when understood through the element-wise mean, can be considered as score functions built upon the Signature Transform, capable of measuring the quality of the generated distribution. This perspective on these measures is important for future applications, as it allows for the possibility of generalizing them to other tasks [11] or even applying them to other transforms. RMSE and MAE Signature and Log-Signature can serve multiple purposes, such as comparing models, monitoring performance during training across several epochs, and analytically detecting overfitting, as demonstrated in Table 3, whereas all these measures capture information about the visual cues present in the distributions, RMSE and MAE Signature, as well as MAE Log-Signature, prove to be more accurate in tracking the convergence of the GAN training procedure.…”

“…In this paper, we present a novel approach to study empirical distributions generated with GANs, leveraging the well-established Signature Transform and Log-Signature as powerful mathematical tools [10][11][12][13]. Our work is the first to introduce the use of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) Signature, along with Log-Signature, as alternatives for measuring GAN convergence.…”

Siamese Visual Tracking with Spatial-Channel Attention and Ranking Head Network

“…Object tracking is important work in the field of computer vision [1,2]. With the advancements of deep learning, object tracking has a wide range of applications in human-computer interaction [3], intelligent driving [4], video surveillance [5], virtual reality [6], and other fields [7,8]. Although object tracking has made significant progress at present, it still faces challenges from two aspects: (1) the target itself, such as deformation, scale variation, etc; (2) the external environment, such as occlusion, low resolution, illumination variation, etc.…”