A Simple Baseline for Audio-Visual Scene-Aware Dialog Schwartz, Idan; Schwing, Alexander G.; Hazan, Tamir
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),
2019-June
Conference Proceeding
Odprti dostop
The recently proposed audio-visual scene-aware dialog task paves the way to a more data-driven way of learning virtual assistants, smart speakers and car navigation systems. However, very little is ...known to date about how to effectively extract meaningful information from a plethora of sensors that pound the computational engine of those devices. Therefore, in this paper, we provide and carefully analyze a simple baseline for audio-visual scene-aware dialog which is trained end-to-end. Our method differentiates in a data-driven manner useful signals from distracting ones using an attention mechanism. We evaluate the proposed approach on the recently introduced and challenging audio-visual scene-aware dataset, and demonstrate the key features that permit to outperform the current state-of-the-art by more than 20% on CIDEr.
The midsession reversal paradigm confronts an animal with a two-choice discrimination task where the reward contingencies are reversed at the midpoint of the session. Species react to the reversal ...with either
win-stay/lose-shift
, using local information of reinforcement, or
reversal estimation
, using global information, e.g. time, to estimate the point of reversal. Besides pigeons, only mammalian species were tested in this paradigm so far and analyses were conducted on pooled data, not considering possible individually different responses. We tested twelve kea parrots with a 40-trial midsession reversal test and additional shifted reversal tests with a variable point of reversal. Birds were tested in two groups on a touchscreen, with the discrimination task having either only visual or additional spatial information. We used Generalized Linear Mixed Models to control for individual differences when analysing the data. Our results demonstrate that kea can use win-stay/lose-shift independently of local information. The predictors group, session, and trial number as well as their interactions had a significant influence on the response. Furthermore, we discovered notable individual differences not only between birds but also between sessions of individual birds, including the ability to quite accurately estimate the reversal position in alternation to win-stay/lose-shift. Our findings of the kea’s quick and flexible responses contribute to the knowledge of diversity in avian cognitive abilities and emphasize the need to consider individuality as well as the limitation of pooling the data when analysing midsession reversal data.
3D Spatial Recognition without Spatially Labeled 3D Ren, Zhongzheng; Misra, Ishan; Schwing, Alexander G. ...
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),
2021-June
Conference Proceeding
Odprti dostop
We introduce WyPR, a Weakly-supervised framework for Point cloud Recognition, requiring only scene-level class tags as supervision. WyPR jointly addresses three core 3D recognition tasks: point-level ...semantic segmentation, 3D proposal generation, and 3D object detection, coupling their predictions through self and cross-task consistency losses. We show that in conjunction with standard multiple-instance learning objectives, WyPR can detect and segment objects in point cloud data without access to any spatial labels at training time. We demonstrate its efficacy using the ScanNet and S3DIS datasets, outperforming prior state of the art on weakly-supervised segmentation by more than 6% mIoU. In addition, we set up the first benchmark for weakly-supervised 3D object detection on both datasets, where WyPR outperforms standard approaches and establishes strong baselines for future work.
The southern Gulf of Mexico (sGoM) is home to an extensive oil recovery and development infrastructure. In addition, the basin harbors sites of submarine hydrocarbon seepage and receives terrestrial ...inputs from bordering rivers. We used stable carbon, nitrogen, and radiocarbon analyses of bulk sediment organic matter to define the current baseline isoscapes of surface sediments in the sGoM and determined which factors might influence them. These baseline surface isoscapes will be useful for accessing future environmental impacts. We also examined the region for influence of hydrocarbon deposition in the sedimentary record that might be associated with hydrocarbon recovery, spillage and seepage, as was found in the northern Gulf of Mexico (nGoM) following the Deepwater Horizon (DWH) oil spill in 2010. In 1979, the sGoM experienced a major oil spill, Ixtoc 1. Surface sediment δ13C values ranged from -22.4‰ to -19.9‰, while Δ14C values ranged from -337.1‰ to -69.2‰. Sediment δ15N values ranged from 2.8‰ to 7.2‰, while the %C on a carbonate-free basis ranged in value of 0.65% to 3.89% and %N ranged in value of 0.09% to 0.49%. Spatial trends for δ13C and Δ14C were driven by water depth and distance from the coastline, while spatial trends for δ15N were driven by location (latitude and longitude). Location and distance from the coastline were significantly correlated with %C and %N. At depth in two of twenty (10%) core profiles, we found negative δ13C and Δ14C excursions from baseline values in bulk sedimentary organic material, consistent with either oil-residue deposition or terrestrial inputs, but likely the latter. We then used 210Pb dating on those two profiles to determine the time in which the excursion-containing horizons were deposited. Despite the large spill in 1979, no evidence of hydrocarbon residue remained in the sediments from this specific time period.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Despite the promising performance of conventional fully supervised algorithms, semantic segmentation has remained an important, yet challenging task. Due to the limited availability of complete ...annotations, it is of great interest to design solutions for semantic segmentation that take into account weakly labeled data, which is readily available at a much larger scale. Contrasting the common theme to develop a different algorithm for each type of weak annotation, in this work, we propose a unified approach that incorporates various forms of weak supervision - image level tags, bounding boxes, and partial labels - to produce a pixel-wise labeling. We conduct a rigorous evaluation on the challenging Siftflow dataset for various weakly labeled settings, and show that our approach outperforms the state-of-the-art by 12% on per-class accuracy, while maintaining comparable per-pixel accuracy.
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Given the prevalence of cancer and leishmaniasis worldwide, the presence of these two pathologies in the same tissue sample may be merely fortuitous. The clinical outcome of both ...diseases is under the control of innate and adaptive immunity, and in both cases these progressive diseases are characterized by an impaired host Th1 response. As a consequence, the Th2 cytokine microenvironment occurring in progressive leishmaniasis may potentially promote tumor cell proliferation and vice versa. On the other hand, clinical aspects of subclinical cutaneous or visceral leishmaniasis sometimes closely resemble those observed in various neoplasms thus leading to misdiagnosis. In this review, we present recent findings on the association between leishmaniasis and malignant disorders. Our review includes HIV positive, HIV negative subjects and patients whose HIV status has not been established. Leishmaniasis mimicking a malignant disorder was confirmed and extended to unreported neoplastic disorders including squamous cell carcinoma, T-cell and B-cell lymphoma, oral and intranasal tumors and granulomas. Thus, leishmaniasis should be considered in the differential diagnosis and course of various cancers in Leishmania endemic areas or in patients with travel history to these areas. We also listed recent reports showing that Leishmania can promote cancer development in immunocompromised as well as in immunocompetent patients. The potential mechanisms supporting this promoting effect are discussed.
Using detailed simulations of calorimeter showers as training data, we investigate the use of deep learning algorithms for the simulation and reconstruction of single isolated particles produced in ...high-energy physics collisions. We train neural networks on single-particle shower data at the calorimeter-cell level, and show significant improvements for simulation and reconstruction when using these networks compared to methods which rely on currently-used state-of-the-art algorithms. We define two models: an end-to-end reconstruction network which performs simultaneous particle identification and energy regression of particles when given calorimeter shower data, and a generative network which can provide reasonable modeling of calorimeter showers for different particle types at specified angles and energies. We investigate the optimization of our models with hyperparameter scans. Furthermore, we demonstrate the applicability of the reconstruction model to shower inputs from other detector geometries, specifically ATLAS-like and CMS-like geometries. These networks can serve as fast and computationally light methods for particle shower simulation and reconstruction for current and future experiments at particle colliders.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Dynamic Bayesian Networks for Student Modeling Kaser, Tanja; Klingler, Severin; Schwing, Alexander G. ...
IEEE transactions on learning technologies,
10/2017, Letnik:
10, Številka:
4
Journal Article
Recenzirano
Intelligent tutoring systems adapt the curriculum to the needs of the individual student. Therefore, an accurate representation and prediction of student knowledge is essential. Bayesian Knowledge ...Tracing (BKT) is a popular approach for student modeling. The structure of BKT models, however, makes it impossible to represent the hierarchy and relationships between the different skills of a learning domain. Dynamic Bayesian networks (DBN) on the other hand are able to represent multiple skills jointly within one model. In this work, we suggest the use of DBNs for student modeling. We introduce a constrained optimization algorithm for parameter learning of such models. We extensively evaluate and interpret the prediction accuracy of our approach on five large-scale data sets of different learning domains such as mathematics, spelling learning, and physics. We furthermore provide comparisons to previous student modeling approaches and analyze the influence of the different student modeling techniques on instructional policies. We demonstrate that our approach outperforms previous techniques in prediction accuracy on unseen data across all learning domains and yields meaningful instructional policies.
Human conversation is a complex mechanism with subtle nuances. It is hence an ambitious goal to develop artificial intelligence agents that can participate fluently in a conversation. While we are ...still far from achieving this goal, recent progress in visual question answering, image captioning, and visual question generation shows that dialog systems may be realizable in the not too distant future. To this end, a novel dataset was introduced recently and encouraging results were demonstrated, particularly for question answering. In this paper, we demonstrate a simple symmetric discriminative baseline, that can be applied to both predicting an answer as well as predicting a question. We show that this method performs on par with the state of the art, even memory net based methods. In addition, for the first time on the visual dialog dataset, we assess the performance of a system asking questions, and demonstrate how visual dialog can be generated from discriminative question generation and question answering.
Temperatures of crystallization for all or portions of three thin granitic pegmatite dikes in southern California are derived from feldspar solvus thermometry, with supporting data from the K/Cs ...ratio of K-feldspar, the extent of Al/Si order in K-feldspar, and the texture of granophyre found along the margins of dikes. Although K-feldspars become perthitic and increasingly ordered toward the centers of dikes, their ratio of K/Cs falls from margin to core along trajectories that reflect fractional crystallization from silicate melt without subsequent interaction with an aqueous solution in an open system. A few sporadic samples that record loss of Cs, and consequent rise in K/Cs, validate the test of fidelity that the perthites generally retain their igneous compositions. Feldspar solvus thermometry from these three dikes indicates that their pegmatite-forming melts crystallized at ~ 375–475 °C. Those low temperatures are consistent with the occurrence of granophyric plagioclase–quartz intergrowths along the borders of pegmatites, thick and thin, that arise from thermal quenching of their melts against much cooler host rocks, and hence at much shallower depths than the igneous sources of the pegmatite-forming melts. The temperature profiles are nearly isothermal across the pegmatites, but where variation exists, apparent temperatures are highest along their margins or in their central domains (cores). Plagioclase shows normal fractionation of decreasing An content from margins to center, which mimics the line of descent with cooling down the solidus and solvus surfaces. However, the fractionation trends in the feldspars are attributable to their isothermal crystallization far from the equilibrium of the liquidus at a highly undercooled state, not to crystallization upon cooling on the solidus surface.
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