Compressive Learning (CL) is a framework that solves computer vision tasks directly from a small amount of acquired signals via compressive sensing (CS). Existing CL works have achieved accuracy comparable to classical image-domain methods, showcasing innovative potential [1]. However, they attempt to cover the incomplete information by applying a function to enhance the features of acquired signals. Moreover, they do not consider the possibility of producing uncertain predictions by inferring from insufficient information. In this paper, we propose a multi-functional compressive sensing sampling-based trustworthy compressive learning, dubbed MCSTCL as shown in Figure 1. First CS-sampling is performed and obtain measurement vectors. Then, we rearrange each element in a image structure following convolution operations algorithm, expecting it to enhance performance in subsequent tasks. The resulting image structure can be regarded as a feature map, enabling an efficient process that simultaneously performs signal acquisition, compression, and information extraction during the CS sampling process. Additionally, we propose a new loss function based on evidential deep learning (EDL) to appropriately quantify uncertainty and allocate more evidence to the target class. Experimental results show that MCSTCL can achieve an image classification accuracy of 96.83% at a low sampling rate of 6% while reducing the model size by 85.76% compared with state-of-the-art work on practical datasets such as the UC Merced Land Use Dataset.

In this paper, we propose a novel bidirectional learned animation codec that generates natural facial videos by using past and future keyframes. First, we introduce a compact auxiliary stream for non-keyframes, which is enhanced by adaptively selecting one of two keyframes (past and future) in the BRG-ASE process. This stream improves video quality with a slight increase in bitrate. Then, we animate the adaptively selected keyframe and reconstruct the target frame using both the animated keyframe and the auxiliary frame in the BRG-VRec process. In our bidirectional frame reconstruction method, the future keyframe is used as the past keyframe in the next group of pictures. Therefore, it is temporarily stored in the decoder.