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We study how well different types of approaches generalise in the task of 3D hand pose estimation under single hand scenarios and handobject interaction. We show that the accuracy of state-of-the-art methods can drop, and that they fail mostly on poses absent from the training set. Unfortunately, since the space of hand poses is highly dimensional, it is inherently not feasible to cover the whole space densely, despite recent efforts in collecting large-scale training datasets. This sampling problem is even more severe when hands are interacting with objects and/or inputs are RGB rather than depth images, as RGB images also vary with lighting conditions and colors. To address these issues, we designed a public challenge (HANDS'19) to evaluate the abilities of current 3D hand pose estimators (HPEs) to interpolate and extrapolate the poses of a training set. More exactly, HANDS'19 is designed (a) to evaluate the influence of both depth and color modalities on 3D hand pose estimation, under the presence or absence of objects; (b) to assess the generalisation abilities w.r.t. four main axes: shapes, articulations, viewpoints, and objects; (c) to explore the use of a synthetic hand models to fill the gaps of current datasets. Through the challenge, the overall accuracy has dramatically improved over the baseline, especially on extrapolation tasks, from 27mm to 13mm mean joint error. Our analyses highlight the impacts of: Data pre-processing, ensemble approaches, the use of a parametric 3D hand model (MANO), and different HPE methods/backbones.

Abstract. This paper presents a quantitative performance analysis of two different approaches to the lemmatization of the Czech text data. The first one is based on manually prepared dictionary of lemmas and set of derivation rules while the second one is based on automatic inference of the dictionary and the rules from training data. The comparison is done by evaluating the mean Generalized Average Precision (mGAP) measure of the lemmatized documents and search queries in the set of information retrieval (IR) experiments. Such method is suitable for efficient and rather reliable comparison of the lemmatization performance since a correct lemmatization has proven to be crucial for IR effectiveness in highly inflected languages. Moreover, the proposed indirect comparison of the lemmatizers circumvents the need for manually lemmatized test data which are hard to obtain and also face the problem of incompatible sets of lemmas across different systems.

Abstract. This paper deals with the automatic construction of a lemmatizer from a Full Form -Lemma (FFL) training dictionary and with lemmatization of new, in the FFL dictionary unseen, i.e. out-ofvocabulary (OOV) words. Three methods of lemmatization of three kinds of OOV words (missing full forms, unknown words, and compound words) are introduced. These methods were tested on Czech test data. The best result (recall: 99.3 % and precision: 75.1 %) has been achieved by a combination of these methods. The lexicon-free lemmatizer based on the method of lemmatization of unknown words (lemmatization patterns method) is introduced too.

Abstract. This paper deals with lemmatization technique and its using for the phonetic transcription of exceptional words. The lemmatizer is based on language morphology and uses the lexicon of word basic forms and inversion of the derivation rules to acquire the lemmatization rules which are essential for finding the word bases. We have described the lemmatization algorithm and necessary modifications of the lemmatizer to transcribe exceptional words. The main goal of the designed system is memory saving of the exceptional lexicon. The experimental results have shown that we can save from 18.3% (English) to 98.4% (Finnish) of size of the full lexicon. Hence, this system is suitable for high inflectional and agglutinative languages.