Author: Ioannis Mademlis;

GreekPolitics: Sentiment Analysis on Greek Politically Charged Tweets

The rapid growth of on-line social media platforms has rendered opinion mining/sentiment analysis a critical area of research. This paper focuses on analyzing Twitter posts (tweets), written in the Greek language and politically charged in content. This is a rather underexplored topic, due to the inadequacy of publicly available annotated datasets. Thus, we present and release GreekPolitics: a dataset of Greek tweets with politically charged content, annotated for four different sentiments: polarity, figurativeness, aggressiveness and bias. GreekPolitics has been evaluated comprehensively using state-of-the-art Deep Neural Networks (DNNs) and data augmentation methods. This paper details the dataset, the evaluation process and the experimental results.

Quantifying the knowledge in Deep Neural Networks: an overview

Deep Neural Networks (DNNs) have proven to be extremely effective at learning a wide range of tasks. Due to their complexity and frequently inexplicable internal state, DNNs are difficult to analyze: their black-box nature makes it challenging for humans to comprehend their internal behavior. Several attempts to interpret their operation have been made during the last decade, but analyzing deep neural models from the perspective of the knowledge encoded in their layers is a very promising research direction, which has barely been touched upon. Such a research approach could provide a more accurate insight into a DNN model, its internal state, learning progress, and knowledge
storage capabilities. The purpose of this survey is two-fold: a) to review the concept of DNN knowledge quantification and highlight it as an important near-future challenge, as well as b) to provide a brief account of the scant existing methods attempting to actually quantify DNN knowledge. Although a few such algorithms have been proposed, this is an emerging topic still under investigation.

Deep Reinforcement Learning with semi-expert distillation for autonomous UAV cinematography

Unmanned Aerial Vehicles (UAVs, or drones) have revolutionized modern media production. Being rapidly deployable “flying cameras”, they can easily capture aesthetically pleasing aerial footage of static or moving filming targets/subjects. Current approaches rely either on manual UAV/gimbal control by human experts or on a combination of complex computer vision algorithms and hardware configurations for automating the flight+flying process. This paper explores an efficient Deep Reinforcement Learning (DRL) alternative, which implicitly merges the target detection and path planning steps into a single algorithm. To achieve this, a baseline DRL approach is augmented with a novel policy distillation component, which transfers knowledge from a suitable, semi-expert Model Predictive Control (MPC) controller into the DRL agent. Thus, the latter is able to autonomously execute a specific UAV cinematography task with purely visual input. Unlike the MPC controller, the proposed DRL agent does not need to know the 3D world position of the filming target during inference. Experiments conducted in a photorealistic simulator showcase superior performance and training speed compared to the baseline agent while surpassing the MPC controller in terms of visual occlusion avoidance.

Temporal Normalization in Attentive Key-frame Extraction for Deep Neural Video Summarization

Attention-based neural architectures have consistently demonstrated superior performance over Long Short-Term Memory (LSTM) Deep Neural Networks (DNNs) in tasks such as key-frame extraction for video summarization. However, existing approaches mostly rely on rather shallow Transformer DNNs. This paper revisits the issue of model depth and proposes DATS: a deep attentive architecture for supervised video summarization that meaningfully exploits skip connections. Additionally, a novel per-layer temporal normalization algorithm is proposed that yields improved test accuracy. Finally, the model’s noisy output is rectified in an innovative post-processing step. Experiments conducted on two common, publicly available benchmark datasets showcase performance superior to competing state-of-the-art video summarization methods, both supervised and unsupervised.