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The video game industry has seen rapid growth over the last decade. Thousands of video games are released and played by millions of people every year, creating a large community of players. Steam is a leading gaming platform and social networking site, which allows its users to purchase and store games. A by-product of Steam is a large database of information about games, players, and gaming behavior. In this paper, we take recent video games released on Steam and aim to discover the relation between game popularity and a game's features that can be acquired through Steam. We approach this task by predicting the popularity of Steam games in the early stages after their release and we use a Bayesian approach to understand the influence of a game's price, size, supported languages, release date, and genres on its player count. We implement several models and discover that a genre-based hierarchical approach achieves the best performance. We further analyze the model and interpret its coefficients, which indicate that games released at the beginning of the month and games of certain genres correlate with game popularity.

Few-shot semantic segmentation, which aims at learning new categories from only a few training examples, has progressed substantially in the last decade. The progress was in part driven by datasets derived from the existing datasets for semantic segmentation. However, these datasets have several drawbacks in the context of the few-shot performance evaluation. PASCAL-5 has a low number of classes and objects well separated from the background, COCO-20 has too diverse classes, and FSS-1000 contains objects that are trivial to segment so that our Zero-Shot Segmentation Baseline (ZSSB) model achieves a high mean mIoU of 81.1%. Therefore we construct a new dataset LVIS-1025 from the general semantic segmentation dataset LVIS by applying new criteria for measuring object predictability and expressiveness. We evaluate three state-of-the-art methods (PANet, PPNet, and ASGNet) on this dataset and show that the ranks change compared to those obtained on existing public datasets. ASGNet on the standard datasets outperforms PANet and PPNet by a large margin, but on LVIS-1025 performs worse, indicating that ASGNet is prone to segmenting the most salient object in the image. We believe that future models developed on LVIS-1025 will have better generalization capabilities and will not that heavily rely on the always-present assumption. Semantična segmentacija z malo učnimi primeri, katere cilj je naučiti se novih kategorij z le nekaj učnimi primeri, je v zadnjem desetletju močno napredovala. Napredek so deloma začrtale podatkovne množice, ki izhajajo iz obstoječih podatkovnih množic za semantično segmentacijo. Te podatkovne množice imajo v okviru evalvacije z malo učnimi primeri več pomanjkljivosti, PASCAL-5 ima majhno število razredov in nekatere predmete dobro ločene od ozadja, COCO-20 ima preveč raznolike razrede, FSS-1000 pa vsebuje predmete, ki jih je trivialno segmentirati, tako da naš model ZSSB, ki ne uporablja učne slike, doseže visok povprečni mIoU 81,1%. Zaradi teh pomanjkljivosti zgradimo novo podatkovno množico LVIS-1025, ki jo dobimo iz podatkovne množice LVIS z uporabo novih meril za merjenje predvidljivosti in izraznosti objektov. Na LVIS-1025 evalviramo tri najsodobnejše metode (PANet, PPNet in ASGNet) in pokažemo, da se vrstni red uspešnosti spremeni v primerjavi s pridobljenim na obstoječih podatkovnih množicah. ASGNet na standardnih podatkovnih množicah močno preseže PANet in PPNet, vendar je na LVIS-1025 slabši, kar nakazuje, da je ASGNet nagnjen k segmentiranju najbolj izraznega objekta na sliki. Verjamemo, da bodo prihodnji modeli razviti na LVIS-1025 zmožni boljšega posploševanja in se ne bodo tako močno opirali na predpostavko, da je ciljni predmet vedno prisoten na sliki.

Konvolucijske nevronske mreže dosegajo izjemne rezultate na področju računalniškega vida. Osrednja operacija teh mrež je konvolucija z jedrom majhne in nespremenljive velikosti. V praksi je zato standardni prijem za povečavo dovzetnega polja združevanje sosednjih slikovnih točk, kar pa za mnoge probleme v računalniškem vidu nima zadovoljive izhodne resolucije. Problem naslavlja t. i. dilatacija, ki enote iz konvolucijskega jedra razširi na širše območje in s tem poveča dovzetno polje. Velikost razširitve je ročno nastavljena in tekom učenja ni spremenljiva, kar lahko predstavlja težavo, saj v splošnem njene optimalne vrednosti ne poznamo. Učljivo velikost dovzetnega polja ima nedavno predlagana metoda, pri kateri je konvolucijsko jedro sestavljeno iz premičnih združevalnih enot (angl. displaced aggregation units, DAU). Vsako jedro ima svoj nabor parametrov, svojo velikost dovzetnega polja. V tej diplomski nalogi naslavljamo vprašanje, ali je mogoče reducirati prostostne stopnje modela brez izgube natančnosti. Predlagamo tri načine reduciranja prostostnih stopenj z deljenjem odmikov na vhodih in izhodih. Implementiramo prehod naprej in vzvratni prehod za te tri različice, jih vgradimo v arhitekture konvolucijskih nevronskih mrež različnih velikosti in evalviramo na problemu klasifikacije slik v 10 razredov. Vse različice imajo za več kot 50% manj parametrov kot originalni sloj DAU. Eksperimentalni rezultati kažejo, da ima model, ki ima odmike neodvisne od izhoda, znatno manjšo računsko zahtevnost kot originalni sloj DAU, pri tem da klasifikacijska točnost pade za manj kot 2%. Convolutional neural networks have demonstrated excellent performance at computer vision tasks. The central operation of these networks is a convolution with a small, fixed size kernel. In practice, therefore, the standard approach for increasing the receptive field is to combine adjacent pixels, which for many computer vision tasks does not have a sufficient output resolution. The problem is addressed by the so-called dilation, which extends the units from the convolution kernel to a wider area, thereby increasing the receptive field. The size of the kernel is manually set and is not variable during learning, which can be a problem, as we generally do not know its optimal value. To solve this problem, a method has recently been proposed in which the convolution kernel consists of displaced aggregation units (DAU). Each kernel has its own set of parameters, its own size of receptive field. In this thesis we address the question of whether it is possible to reduce model degree of freedom without loss of its accuracy. We propose three ways to reduce degrees of freedom by sharing displacements at the inputs and outputs. We implement a forward and backward pass for these three versions, embed them in architectures of convolutional neural networks of different sizes and evaluate on the problem of classifying images into 10 classes. All versions have more than 50% fewer parameters than the original DAU layer. The experimental results show that the model, which has output-independent displacements, has a significantly lower computational complexity than the original DAU layer, with the classification accuracy lower by less than 2%.