7,554 research outputs found

    VIENA2: A Driving Anticipation Dataset

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    Action anticipation is critical in scenarios where one needs to react before the action is finalized. This is, for instance, the case in automated driving, where a car needs to, e.g., avoid hitting pedestrians and respect traffic lights. While solutions have been proposed to tackle subsets of the driving anticipation tasks, by making use of diverse, task-specific sensors, there is no single dataset or framework that addresses them all in a consistent manner. In this paper, we therefore introduce a new, large-scale dataset, called VIENA2, covering 5 generic driving scenarios, with a total of 25 distinct action classes. It contains more than 15K full HD, 5s long videos acquired in various driving conditions, weathers, daytimes and environments, complemented with a common and realistic set of sensor measurements. This amounts to more than 2.25M frames, each annotated with an action label, corresponding to 600 samples per action class. We discuss our data acquisition strategy and the statistics of our dataset, and benchmark state-of-the-art action anticipation techniques, including a new multi-modal LSTM architecture with an effective loss function for action anticipation in driving scenarios.Comment: Accepted in ACCV 201

    Carbonyl sulfide, dimethyl sulfide and carbon disulfide in the Pearl River Delta of southern China: Impact of anthropogenic and biogenic sources

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    Reduced sulfur compounds (RSCs) such as carbonyl sulfide (OCS), dimethyl sulfide (DMS) and carbon disulfide (CS2) impact radiative forcing, ozone depletion, and acid rain. Although Asia is a large source of these compounds, until now a long-term study of their emission patterns has not been carried out. Here we analyze 16 months of RSC data measured at a polluted rural/coastal site in the greater Pearl River Delta (PRD) of southern China. A total of 188 canister air samples were collected from August 2001 to December 2002. The OCS and CS2 mixing ratios within these samples were higher in autumn/winter and lower in summer due to the influence of Asian monsoon circulations. Comparatively low DMS values observed in this coastal region suggest a relatively low biological productivity during summer months. The springtime OCS levels in the study region (574 ± 40 pptv) were 25% higher than those on other East Asia coasts such Japan, whereas the springtime CS2 and DMS mixing ratios in the PRD (47 ± 38 pptv and 22 ± 5 pptv, respectively) were 3-30 times lower than elevated values that have been measured elsewhere in East Asia (Japan and Korea) at this time of year. Poor correlations were found among the three RSCs in the whole group of 188 samples, suggesting their complex and variable sources in the region. By means of backward Lagrangian particle release simulations, air samples originating from the inner PRD, urban Hong Kong and South China Sea were identified. The mean mixing ratio of OCS in the inner PRD was significantly higher than that in Hong Kong urban air and South China Sea marine air (p < 0.001), whereas no statistical differences were found for DMS and CS2 among the three regions (p > 0.05). Using a linear regression method based on correlations with the urban tracer CO, the estimated OCS emission in inner PRD (49.6 ± 4.7 Gg yr-1) was much higher than that in Hong Kong (0.32 ± 0.05 Gg yr-1), whereas the estimated CS2 and DMS emissions in the study region accounted for a very few percentage of the total CS2 and DMS emission in China. These findings lay the foundation for better understanding sulfur chemistry in the greater PRD region of southern China. © 2010 Elsevier Ltd

    Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors

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    The impressive performance of deep convolutional neural networks in single-view 3D reconstruction suggests that these models perform non-trivial reasoning about the 3D structure of the output space. However, recent work has challenged this belief, showing that complex encoder-decoder architectures perform similarly to nearest-neighbor baselines or simple linear decoder models that exploit large amounts of per category data in standard benchmarks. On the other hand settings where 3D shape must be inferred for new categories with few examples are more natural and require models that generalize about shapes. In this work we demonstrate experimentally that naive baselines do not apply when the goal is to learn to reconstruct novel objects using very few examples, and that in a \emph{few-shot} learning setting, the network must learn concepts that can be applied to new categories, avoiding rote memorization. To address deficiencies in existing approaches to this problem, we propose three approaches that efficiently integrate a class prior into a 3D reconstruction model, allowing to account for intra-class variability and imposing an implicit compositional structure that the model should learn. Experiments on the popular ShapeNet database demonstrate that our method significantly outperform existing baselines on this task in the few-shot setting
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