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Found 12 papers, 4 papers with code
Visual Context-Aware Person Fall Detection
During training, pixel-based transformations are applied to segmented objects, and the models are then evaluated on raw images without segmentation.
CrashCar101: Procedural Generation for Damage Assessment
For part segmentation, we show that the segmentation models trained on a combination of real data and our synthetic data outperform all models trained only on real data.
Learning the What and How of Annotation in Video Object Segmentation
To reduce this annotation cost, in this paper, we propose EVA-VOS, a human-in-the-loop annotation framework for video object segmentation.
Learning Program Representations for Food Images and Cooking Recipes
To validate our idea, we crowdsource programs for cooking recipes and show that: (a) projecting the image-recipe embeddings into programs leads to better cross-modal retrieval results; (b) generating programs from images leads to better recognition results compared to predicting raw cooking instructions; and (c) we can generate food images by manipulating programs via optimizing the latent code of a GAN.
Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidents
In this work, we present the Incidents1M Dataset, a large-scale multi-label dataset which contains 977, 088 images, with 43 incident and 49 place categories.
Scaling up instance annotation via label propagation
Through a large-scale experiment to populate 1M unlabeled images with object segmentation masks for 80 object classes, we show that (1) we obtain 1M object segmentation masks with an total annotation time of only 290 hours; (2) we reduce annotation time by 76x compared to manual annotation; (3) the segmentation quality of our masks is on par with those from manually annotated datasets.
Detecting natural disasters, damage, and incidents in the wild
While most studies on social media are limited to text, images offer more information for understanding disaster and incident scenes.
How to make a pizza: Learning a compositional layer-based GAN model
From a visual perspective, every instruction step can be seen as a way to change the visual appearance of the dish by adding extra objects (e. g., adding an ingredient) or changing the appearance of the existing ones (e. g., cooking the dish).
Extreme clicking for efficient object annotation
We crowd-source extreme point annotations for PASCAL VOC 2007 and 2012 and show that (1) annotation time is only 7s per box, 5x faster than the traditional way of drawing boxes [62]; (2) the quality of the boxes is as good as the original ground-truth drawn the traditional way; (3) detectors trained on our annotations are as accurate as those trained on the original ground-truth.
How hard can it be? Estimating the difficulty of visual search in an image
We address the problem of estimating image difficulty defined as the human response time for solving a visual search task.
Training object class detectors with click supervision
Training object class detectors typically requires a large set of images with objects annotated by bounding boxes.
We don't need no bounding-boxes: Training object class detectors using only human verification
Training object class detectors typically requires a large set of images in which objects are annotated by bounding-boxes.
