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Assessing artistic creativity has long been a challenge. Traditional tests are widely used but often require time-consuming manual scoring. Thus, researchers are exploring a new way, such as machine learning, for automated artistic creativity assessment. Recent research on visual artistic creativity assessment has demonstrated that machine learning methods are effective but constrained by their reliance on visual data alone.

Due to the page limitations of the main paper, we provide more visualizations and dataset test results here.

In this supplementary material of our work, we provide additional experimental evaluation of the proposed Reinforcement Learning (RL) framework. For this purpose, a simple virtual environment of a four-walled room is exploited.

This work introduces a novel augmentation method that increases the diversity of a train set to improve the generalization abilities of a 6D pose estimation network. For this purpose, a Neural Radiance Field is trained on synthetic images and exploited to generate an augmented set. Our method enriches the initial set by enabling the synthesis of images with (i) unseen viewpoints, (ii) rich illumination conditions through appearance extrapolation, and (iii) randomized textures.

Capture stages are high-end sources of state-of-the-art recordings for downstream applications in movies, games, and other media. One crucial step in almost all pipelines is the matting of images to isolate the captured performances from the background. While common matting algorithms deliver remarkable performance in other applications like teleconferencing and mobile entertainment, we found that they struggle significantly with the peculiarities of capture stage content.

For the few-shot segmentation (FSS) task, existing methods
attempt to capture the diversity of new classes by fully uti-
lizing the limited support images, such as cross-attention and
prototype matching. However, they often overlook the fact
that there is variability in different regions of the same ob-
ject, and intra-image similarity is higher than inter-image sim-
ilarity.To address these limitations, a Self-Guided Trans-
former (SGT) is proposed by leveraging intra-image similar-

Channel reconstruction transforms a subsampled mutispectral image into hyperspectral, offering hyperspectral imaging benefits without a dedicated camera. MST++ is a
state of the art channel reconstruction technique, but it faces memory limitations for high spatial resolution images. In this context, we introduce VITMST++, a novel architecture in-
corporating Vision Transformer embedding and compression, multi-resolution image context and a channel-weighted loss. Developed for the ICASSP 2024 Hyperspectral Skin Chal-

Strategic subsampling has become a focal point due to its effectiveness in compressing data, particularly in the Full Matrix Capture (FMC) approach in ultrasonic imaging. This paper introduces the Joint Deep Probabilistic Subsampling (J-DPS) method, which aims to learn optimal selection matrices simultaneously for transmitters, receivers, and Fourier coefficients. This task-based algorithm is realized by introducing a specialized measurement model and integrating a customized Complex Learned FISTA (CL-FISTA) network.

This paper considers resource allocation problems for integrated sensing and communications (ISAC) systems operating in dynamic shared spectrum scenarios. Specifically, the paper proposes a new Model-Based Online Learning (MBOL) method that accounts for partial observability caused by noisy observations. First, the approach converts the partially observable Markov decision process (POMDP) to the equivalent belief state Markov decision process (MDP). Then, the state prediction model is learned from the sensor observations.

Detecting microexpressions presents formidable challenges, primarily due to their fleeting nature and the limited diversity in existing datasets. Our studies find that these datasets exhibit a pronounced bias towards specific ethnicities and suffer from significant imbalances in terms of both class and gender representation among the samples. These disparities create fertile ground for various biases to permeate deep learning models, leading to skewed results and inadequate portrayal of specific demographic groups.