From Traditional to Modern : Domain Adaptation for Action Classification in Short Social Video Clips

Short internet video clips like vines present a significantly wild distribution compared to traditional video datasets. In this paper, we focus on the problem of unsupervised action classification in wild vines using traditional labeled datasets. To this end, we use a data augmentation based simple domain adaptation strategy. We utilise semantic word2vec space as a common subspace to embed video features from both, labeled source domain and unlablled target domain. Our method incrementally augments the labeled source with target samples and iteratively modifies the embedding function to bring the source and target distributions together. Additionally, we utilise a multi-modal representation that incorporates noisy semantic information available in form of hash-tags. We show the effectiveness of this simple adaptation technique on a test set of vines and achieve notable improvements in performance.Comment: 9 pages, GCPR, 201

Fusion of Single View Soft k-NN Classifiers for Multicamera Human Action Recognition

Proceedings of: 5th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2010). San Sebastián, Spain, June 23-25, 2010This paper presents two different classifier fusion algorithms applied in the domain of Human Action Recognition from video. A set of cameras observes a person performing an action from a predefined set. For each camera view a 2D descriptor is computed and a posterior on the performed activity is obtained using a soft classifier. These posteriors are combined using voting and a bayesian network to obtain a single belief measure to use for the final decision on the performed action. Experiments are conducted with different low level frame descriptors on the IXMAS dataset, achieving results comparable to state of the art 3D proposals, but only performing 2D processing.This work was supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/TIC-1485) and DPS2008-07029-C02-02Publicad

Gender equality in STEM programs: a proposal to analyse the situation of a university about the gender gap

[EN]According to the Global Gender Gap Report 2020, most of the countries have achieved gender parity in educational attainment. Furthermore, Latin America and Europe have more women than men enrolled in tertiary education. The problem arises when those numbers are analysed by degree studies. There is a gender gap in science, technology, engineering and mathematics (STEM), with a low number of women enrolled in those programs and even lower numbers of graduates. The universities have a key role to steer new conceptions and understanding of the females in STEM . The higher education institutions have to define measures and policies to reduce the gender gap in the careers of the future. This work aims to provide a proposal to analyse the gender equality gap in STEM as a first step to define gender equality action plans focused on processes of attraction, access and retention and guidance in STEM programs. The proposal was applied in ten Latin American universities from Chile, Colombia, Costa Rica, Ecuador and Mexico, and five European universities from Finland, Ireland, Italy, Spain, United Kingdom

Study of phase transformations by x-ray diffraction and energy dispersive spectrometry microanalysis in welded joints AA5083-H116 with GMAW-P process and shielded gas mixture 80AR19HE1O2

En este trabajo se estudian las transformaciones de fase en las regiones de soldadura de un solo pase de la aleación AA5083-H116 con proceso GMAW-P automatizado, con gas de protección 80Ar19He1O2, material de aporte ER5183 y con diferentes aportes térmicos. La metodología incluyó un análisis previo de resultados de microscopia óptica que permitió identificar cambios morfológicos en la estructura de las regiones en estudio. Se usó simulación termodinámica computacional mediante el método Calphad para obtener los campos de estabilidad de las fases en aleaciones Al-Mg. A través de microscopía electrónica de barrido y espectrometría por dispersión de energías de rayos X, se identificaron, tanto la morfología, como la composición química de los precipitados en las regiones de soldadura. Finalmente, se utilizó difracción de rayos X, permitiendo obtener difractogramas de las regiones bajo diferentes condiciones de soldadura. Los resultados muestran que, con los diferentes aportes térmicos usados, predomina la matriz FCC rica en Al con Mg en solución sólida, mientras variaron las proporciones de precipitados intermedios de segunda fase FeAl6, FeAl y MnAl12 en las regiones de soldadura.In this work we studied the phase transformations in a single pass welding joint of AA5083-H116 alloy using GMAWP automated process, 80Ar19He1O2 shielding gas mixture, ER5183 filler metal and different heat inputs. The methodology included a preliminary analysis using optical microscopy, which identified morphological changes in microstructure of studied welding regions. A series of computational thermodynamic simulations, based on Calphad method, were used to calculate the phase stability fields in Al-Mg alloys. Scanning electron microscopy and energy dispersive spectrometry techniques were used to identify the morphology and the chemical composition of those precipitates found in welded regions. Finally, X-ray diffraction was used to obtain diffraction patterns of the regions under different welding conditions. Under different heat input values, our results showed that Al-rich FCC matrix with Mg in solid solution predominates in the microstructure, whereas, the proportions of secondary phases (FeAl6, FeAl and MnAl12) changed in some regions of the welded joint

Globally Continuous and Non-Markovian Crowd Activity Analysis from Videos

Automatically recognizing activities in video is a classic problem in vision and helps to understand behaviors, describe scenes and detect anomalies. We propose an unsupervised method for such purposes. Given video data, we discover recurring activity patterns that appear, peak, wane and disappear over time. By using non-parametric Bayesian methods, we learn coupled spatial and temporal patterns with minimum prior knowledge. To model the temporal changes of patterns, previous works compute Markovian progressions or locally continuous motifs whereas we model time in a globally continuous and non-Markovian way. Visually, the patterns depict flows of major activities. Temporally, each pattern has its own unique appearance-disappearance cycles. To compute compact pattern representations, we also propose a hybrid sampling method. By combining these patterns with detailed environment information, we interpret the semantics of activities and report anomalies. Also, our method fits data better and detects anomalies that were difficult to detect previously

Predicting Actions from Static Scenes

International audienceHuman actions naturally co-occur with scenes. In this work we aim to discover action-scene correlation for a large number of scene categories and to use such correlation for action prediction. Towards this goal, we collect a new SUN Action dataset with manual annotations of typical human actions for 397 scenes. We next discover action-scene associations and demonstrate that scene categories can be well identified from their associated actions. Using discovered associations, we address a new task of predicting human actions for images of static scenes. We evaluate prediction of 23 and 38 action classes for images of indoor and outdoor scenes respectively and show promising results. We also propose a new application of geo-localized action prediction and demonstrate ability of our method to automatically answer queries such as "Where is a good place for a picnic?" or "Can I cycle along this path?"

Sympathy for the Details: Dense Trajectories and Hybrid Classification Architectures for Action Recognition

Action recognition in videos is a challenging task due to the complexity of the spatio-temporal patterns to model and the difficulty to acquire and learn on large quantities of video data. Deep learning, although a breakthrough for Image classification and showing promise for videos, has still not clearly superseded action recognition methods using hand-crafted features, even when training on massive datasets. In this paper, we introduce hybrid video classification architectures based on carefully designed unsupervised representations of hand-crafted spatio-temporal features classified by supervised deep networks. As we show in our experiments on five popular benchmarks for action recognition, our hybrid model combines the best of both worlds: it is data efficient (trained on 150 to 10000 short clips) and yet improves significantly on the state of the art, including recent deep models trained on millions of manually labelled images and videos

Action Recognition with a Bio--Inspired Feedforward Motion Processing Model: The Richness of Center-Surround Interactions

International audienceHere we show that reproducing the functional properties of MT cells with various center--surround interactions enriches motion representation and improves the action recognition performance. To do so, we propose a simplified bio--inspired model of the motion pathway in primates: It is a feedforward model restricted to V1-MT cortical layers, cortical cells cover the visual space with a foveated structure, and more importantly, we reproduce some of the richness of center-surround interactions of MT cells. Interestingly, as observed in neurophysiology, our MT cells not only behave like simple velocity detectors, but also respond to several kinds of motion contrasts. Results show that this diversity of motion representation at the MT level is a major advantage for an action recognition task. Defining motion maps as our feature vectors, we used a standard classification method on the Weizmann database: We obtained an average recognition rate of 98.9%, which is superior to the recent results by Jhuang et al. (2007). These promising results encourage us to further develop bio--inspired models incorporating other brain mechanisms and cortical layers in order to deal with more complex videos