Tell Me Where I Am Object level Scene Context Prediction

Tell Me Where I Am Object Level Scene Context Prediction-PDF Download

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Shape Discriminator and Layout Discriminator The input to the shape discriminator is the generated shape or the real. shape of size 16 16 72 The shape discriminator first processes the input with a series of 3 3 stride 2 convolutions All. the convolutional layers are followed by batch normalization and Leaky ReLU except for the first layer where only Leaky. ReLU is applied Then a 4 4 convolution and a fully connected layer are added to output the probability of the input being. real or fake Both layers are followed by Leaky ReLU The layout discriminator shares a similar architecture with the shape. discriminator but has a different number of convolutional layers. 2 User Studies, We describe how we conducted the user studies on Amazon Mechanical Turk as discussed in Section 4 4 in the main. paper We asses the quality of the generated scene layouts by conducting two user studies a plausibility evaluation and. b fitness evaluation In a our goal is to evaluate whether the objects in generated scene layouts have plausible spatial. relations In b we aim to evaluate whether the generated scene layouts provide convincing context for the respective input. object s Details of the experiments are described below. a Plausibility evaluation Figure 2 is an example of our MTurk experiment for plausibility evaluation AMT workers. were given a sequence of scene layouts selected randomly from three sources Ours Baseline and GT and asked to evaluate. whether the objects in the scene layouts have plausible spatial relations For the scene layouts judged to be implausible they. were asked to label at least one pair of objects that are in incorrect spatial relation Each Human Intelligence Task HIT. contains 50 scene layouts along with 10 duplicate layouts for consistency check We discarded the responses from the worker. who has less than 80 consistency on the duplicate questions We end up with 30 workers in our experiments and have each. scene layout evaluated by 10 workers, b Fitness evaluation Figure 3 is an example of our MTurk experiment for fitness evaluation The AMT workers were. presented with an input object layout along with two scene layouts generated from the input and asked to select which scene. layout illustrates a better context for the input objects In each comparison we displayed two scene layouts chosen randomly. from three sources Ours Baseline and GT side by side in randomized order We used 150 pairwise comparisons 50 for. Ours vs Baseline 50 for Ours vs GT and 50 for Baseline vs GT We randomly divided the 150 comparisons into three. HITs uniformly In each HIT we added 5 duplicate comparisons for consistency check We discarded the responses from. the worker who has less than 80 consistency on the duplicate comparisons We have a total of 9 workers in the experiment. and each comparison was evaluated by 3 workers,3 Qualitative Results of Scene Layout Generation. Figure 4 shows more qualitative results of our method compared with a baseline method 3 Overall our method can. predict plausible scene layouts that fit the input object s well given large variations of input object s in terms of category. shape and position, Figure 5 shows more results of our method and the baseline when varying the categories and shapes of input objects and. the spatial relation between input objects Note that when inputs are changed our method can generate the scene contexts. that better adapt to the inputs For instance when changing an input object from car to elephant the third column to the. fourth column at the top part of Figure 5 our predicted context change the region supporting the input object from road to. grass Moreover when changing the spatial relation between a person and a surfboard from hperson above surf boardi. to hperson right surf boardi the first column to the second column at the bottom part of Figure 5 the region below the. person changes from sea to sand accordingly, Figure 6 visualizes the object bounding boxes predicted by our model As can be seen our predictions are able to recall.
most of the important object regions even though they are not exactly the same as the ground truth due to the multi modal. nature of scene context prediction,References, 1 Q Chen and V Koltun Photographic image synthesis with cascaded refinement networks In ICCV 2017 1. 2 S Ioffe and C Szegedy Batch normalization Accelerating deep network training by reducing internal covariate shift In ICML 2015. 3 P Isola J Y Zhu T Zhou and A A Efros Image to image translation with conditional adversarial networks In CVPR 2017 2. 4 A L Maas A Y Hannun and A Y Ng Rectifier nonlinearities improve neural network acoustic models In ICML 2013 1. Figure 2 An example of the plausibility evaluation in our user study. Figure 3 An example of the fitness evaluation in our user study. tree sky sky,tree building,bus bus bus tree tree,car car car train train train. grass grass,road railroad railroad, building building building building building building building. road grass road grass,sky tree sky tree,person person person. person person person, snowboard snowboard snowboard motorcycle motorcycle motorcycle.
snow grass road grass,sky sky tree,boat boat boat,boat boat boat. grass sand grass,tree sky sky sky,person kite kite kite. cat cat cat,grass sand road,sky sky building tree,truck truck truck. sand sand sand sea sand sand sand road road grass,Input Ours Baseline Input Ours Baseline. Figure 4 Qualitative results from our model and the baseline Given the input object layouts left diagrams in each column which contains. one or two standalone objects we generate the output scene layouts using our model middle diagrams in each column and the baseline. right diagrams in each column,car elephant airplane.
person person person person,skateboard snowboard,building tree sky sky building sky sky. car tree elephant airplane,person grass person person person. road road person tree,skateboard snowboard pavement pavement. snow grass,tree tree tree tree tree,person person car elephant airplane. person person person person,sky sky person,skateboard snowboard grass grass grass.
grass grass,person person,person person person,surfboard surfboard. sky sea sea,person person sea,person person person. sea surfboard sand sand,surfboard surfboard,tree tree sky tree tree. person person tree,person person person,grass surfboard grass surfboard grass. grass grass, Figure 5 Qualitative results of our model and the baseline by varying the category and shape of the input objects top and the spatial.
relation between the input objects bottom,person person. Predicted Boxes,tree sky building sky sky sky,Predicted Layout. person person,boat bus horse,bicycle pavement sea,skis road grass sand. fog sky clouds tree sea,person building,boat bus tree horse. snow bush bird,bicycle pavement sea,road grass sand.
Figure 6 Object bounding boxes predicted by our model For each input object layout first row we show our predicted object bounding. boxes second row and the ground truth bounding boxes third row We also compare our scene layout prediction fourth row and the. Tell Me Where I Am Object level Scene Context Prediction Supplementary Material Xiaotian Qiao Quanlong Zheng Ying Cao Rynson W H Lau City University of Hong Kong In this supplementary material we provide the details of our network the details ofour user studies and additional qualitative results of scene layout generation 1 Details of Our

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