Surficial geologic map of the Des Moines Lobe of Iowa, Phase 5: Polk County

https://ir.uiowa.edu/igs_ofm/1030/thumbnail.jp

Label Scarcity in Computer Vision: From Long Tail to Zero-shot

In the era of big data, we have access to various sources of potentially unlimited data, but collecting labels for those data is still very costly for computer vision. For example, object detection requires the images to be annotated with labels and bounding boxes for all objects, and instance segmentation requires pixel level annotation of images. Given the limited budget and the non-uniform distribution of real world data, the available labels we have usually follows a long tail distribution, where some frequent classes have a lot of annotations while rare classes have very few labels. With the rapid growth of the Internet, people create new content and concepts almost every day, and it is hard for machines to recognize and classify such novel content, which gives rise to another kind of label scarcity named zero-shot recognition, where we want to train models to recognize new classes that they never see during training. In this work, we study the two types of label scarcity (i.e., long tail distribution of classes and novel classes without annotations) in different applications. On one hand, we study dealing with long tail distribution in scene graph parsing, which requires the model to not only detect objects in the input images but also predict the relations between those objects. We propose a general framework that can be applied to and improve many existing models, by decomposing the problem into classification and ranking sub-problems. On the other hand, to deal with label scarcity caused by novel classes with no annotations, we design generative models as well as utilize external knowledge from text to solve different zero-shot recognition problems in image classification. Specifically, we propose a unified framework for single label zero-shot recognition with generative adversarial networks, and use graph convolutional networks to bridge the gap between seen and unseen classes for multi-label zero-shot image recognition. Additionally, we propose a translational embedding model that recognize new attribute-object compositions. All the works mentioned above use open-source public datasets like ImageNet, MS-COCO, NUS-WIDE and CUB

One representative trial from one TF amputee subject (TF01) when the prosthesis mode was switched from ramp descent to level-ground walking at the beginning of single stance phase after he stepped on the level ground (SS_2 indicated in Fig 2).

<p>One representative trial from one TF amputee subject (TF01) when the prosthesis mode was switched from ramp descent to level-ground walking at the beginning of single stance phase after he stepped on the level ground (SS_2 indicated in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133965#pone.0133965.g002" target="_blank">Fig 2</a>).</p

Illustration of investigated mode switch timings across two gait cycles.

<p>Illustration of investigated mode switch timings across two gait cycles.</p

Forest plot for meta-analysis comparing risk of stroke in HCV infected patients compared to that in non-infected controls.

<p>After omitting the study which induced heterogeneity, adjusted ORs from the other three studies and the pooled OR was shown. Dimension of shaded OR for individual studies is proportional to their total weight in calculation of the pooled estimator.</p

Flow diagram of study identification.

<p>Flow diagram of study identification.</p

Forest plot for meta-analysis comparing unadjusted OR of carotid atherosclerosis in HCV infected patients compared to that in non-infected controls.

<p>Unadjusted ORs from included studies and the pooled OR are shown. Dimension of shaded OR for individual studies is proportional to their total weight in calculation of the pooled estimator.</p

Subgroup analysis of the adjusted risk of carotid atherosclerosis with HCV infection.

<p>* As estimates in multivariate analyses were fully-adjusted, "Comparability" was not considered in Newcastle-Ottawa Scale in this study (maximum score = 7). Score = 7 was defined as high quality.</p><p>Subgroup analysis of the adjusted risk of carotid atherosclerosis with HCV infection.</p

Begg’s funnel plot (with pseudo 95% confidence intervals) to detect any publication bias.

<p>Begg’s funnel plot (with pseudo 95% confidence intervals) to detect any publication bias.</p

Experimental Setup on one able-bodied subject AB 01(left) and one TF amputee TF01(right).

<p>Experimental Setup on one able-bodied subject AB 01(left) and one TF amputee TF01(right).</p