with a hybrid residual and dense connection structure are suggested by [PERSON] et al. to improve super-resolution performance. They show that the proposed methods could significantly reduce both the inference speed and the memory required to store parameters and intermediate feature maps, while maintaining similar image quality compared to the previous methods. [PERSON] et al. propose an efficient scene classification algorithm for three different classes by detecting objects in the scene [11]. The authors utilize a pre-trained semantic segmentation model to extract objects from an image. After that, they construct a weighting matrix to better determine the scene class. Finally, this classifies an image into one of three scene classes (i.e., indoor, nature, city) using the designed weighting matrix. This technique can be utilized for semantic searches in multimedia databases. Lastly, an estimation method for human height is proposed by [PERSON] et al. using color and depth information [12]. They use color images for deep learning by mask R-CNN to detect a human body and a human head separately. If color images are not available for extracting the human body region due to a low light environment, then the human body region is extracted by comparison with the current frame in the depth video. For speech, sound, and text processing, [PERSON] et al. improve the raw-signal-input network from other research using deeper network architectures [13]. They also propose a network architecture that can combine different kinds of network feeds with different features. In the experiment, the proposed scheme achieves an accuracy of 73.55% in the open audio dataset, \"Dataset for Environmental Sound Classification 50\" (ESC50). A multi-scale discriminator that discriminates between real and generated speech at various sampling rates is devised by [PERSON] et al. to stabilize GAN training [14]. In this paper, the proposed structure is compared with conventional GAN-based speech enhancement algorithms using the VoiceBank-DEMAND dataset. They show that the proposed approach can make the training faster and more stable. To translate the speech, a multimodal unsupervised scheme is proposed by [PERSON] and [PERSON] [15]. They make a variational autoencoder (VAE)-based speech conversion network by decomposing the spectral features of the speech into a speaker-invariant content factor and a speaker-specific style factor to estimate diverse and robust speech styles. This approach can help second language (L2) speech education. To develop a 3D avatar-based sign language learning system, [PERSON] et al. suggest a system that converts the input speech/text into corresponding sign movements for Indian Sign Language (ISL) [16]. The translation module achieves a 10.50 SER (sign error rate) score in the actual test. Two papers concern content analysis and information mining. The first one, by [PERSON] and [PERSON], regards a context-based structure mining pipeline [17]. The proposed scheme not only attempts to enrich the content, but also simultaneously splits it into shots and logical story units (LSU). They demonstrate quantitatively that the pipeline outperforms existing state-of-the-art methods for shot boundary detection, scene detection, and re-identification tasks. The other paper outlines a framework which can learn the multimodal joint representation of pins, including text representation, image representation, and multimodal fusion [18]. In this work, the authors combine image representations and text representations in a multimodal form. It is shown that the proposed multimodal joint representation outperforms unimodal representation in different recommendation tasks. For ECG signal processing, [PERSON] and [PERSON] propose a 1D convolutional neural network (CNN) that exploits a novel analysis of the correlation between the two leads of the noisy electrocardiogram (ECG) to classify heartbeats [19]. This approach is one-dimensional, enabling complex structures while maintaining reasonable computational complexity.