Activity Driven Weakly Supervised Object Detection

Weakly supervised object detection aims at reducing the amount of supervision required to train detection models. Such models are traditionally learned from images/videos labelled only with the object class and not the object bounding box. In our work, we try to leverage not only the object class labels but also the action labels associated with the data. We show that the action depicted in the image/video can provide strong cues about the location of the associated object. We learn a spatial prior for the object dependent on the action (e.g. "ball" is closer to "leg of the person" in "kicking ball"), and incorporate this prior to simultaneously train a joint object detection and action classification model. We conducted experiments on both video datasets and image datasets to evaluate the performance of our weakly supervised object detection model. Our approach outperformed the current state-of-the-art (SOTA) method by more than 6% in mAP on the Charades video dataset.Comment: CVPR'19 camera read

Multiple Instance Curriculum Learning for Weakly Supervised Object Detection

When supervising an object detector with weakly labeled data, most existing approaches are prone to trapping in the discriminative object parts, e.g., finding the face of a cat instead of the full body, due to lacking the supervision on the extent of full objects. To address this challenge, we incorporate object segmentation into the detector training, which guides the model to correctly localize the full objects. We propose the multiple instance curriculum learning (MICL) method, which injects curriculum learning (CL) into the multiple instance learning (MIL) framework. The MICL method starts by automatically picking the easy training examples, where the extent of the segmentation masks agree with detection bounding boxes. The training set is gradually expanded to include harder examples to train strong detectors that handle complex images. The proposed MICL method with segmentation in the loop outperforms the state-of-the-art weakly supervised object detectors by a substantial margin on the PASCAL VOC datasets.Comment: Published in BMVC 201

Minimizing Supervision in Multi-label Categorization

Multiple categories of objects are present in most images. Treating this as a multi-class classification is not justified. We treat this as a multi-label classification problem. In this paper, we further aim to minimize the supervision required for providing supervision in multi-label classification. Specifically, we investigate an effective class of approaches that associate a weak localization with each category either in terms of the bounding box or segmentation mask. Doing so improves the accuracy of multi-label categorization. The approach we adopt is one of active learning, i.e., incrementally selecting a set of samples that need supervision based on the current model, obtaining supervision for these samples, retraining the model with the additional set of supervised samples and proceeding again to select the next set of samples. A crucial concern is the choice of the set of samples. In doing so, we provide a novel insight, and no specific measure succeeds in obtaining a consistently improved selection criterion. We, therefore, provide a selection criterion that consistently improves the overall baseline criterion by choosing the top k set of samples for a varied set of criteria. Using this criterion, we are able to show that we can retain more than 98% of the fully supervised performance with just 20% of samples (and more than 96% using 10%) of the dataset on PASCAL VOC 2007 and 2012. Also, our proposed approach consistently outperforms all other baseline metrics for all benchmark datasets and model combinations.Comment: Accepted in CVPR-W 202

Deep Self-Taught Learning for Weakly Supervised Object Localization

Most existing weakly supervised localization (WSL) approaches learn detectors by finding positive bounding boxes based on features learned with image-level supervision. However, those features do not contain spatial location related information and usually provide poor-quality positive samples for training a detector. To overcome this issue, we propose a deep self-taught learning approach, which makes the detector learn the object-level features reliable for acquiring tight positive samples and afterwards re-train itself based on them. Consequently, the detector progressively improves its detection ability and localizes more informative positive samples. To implement such self-taught learning, we propose a seed sample acquisition method via image-to-object transferring and dense subgraph discovery to find reliable positive samples for initializing the detector. An online supportive sample harvesting scheme is further proposed to dynamically select the most confident tight positive samples and train the detector in a mutual boosting way. To prevent the detector from being trapped in poor optima due to overfitting, we propose a new relative improvement of predicted CNN scores for guiding the self-taught learning process. Extensive experiments on PASCAL 2007 and 2012 show that our approach outperforms the state-of-the-arts, strongly validating its effectiveness.Comment: Accepted as spotlight paper by CVPR 201

Saliency Guided End-to-End Learning for Weakly Supervised Object Detection

Weakly supervised object detection (WSOD), which is the problem of learning detectors using only image-level labels, has been attracting more and more interest. However, this problem is quite challenging due to the lack of location supervision. To address this issue, this paper integrates saliency into a deep architecture, in which the location in- formation is explored both explicitly and implicitly. Specifically, we select highly confident object pro- posals under the guidance of class-specific saliency maps. The location information, together with semantic and saliency information, of the selected proposals are then used to explicitly supervise the network by imposing two additional losses. Meanwhile, a saliency prediction sub-network is built in the architecture. The prediction results are used to implicitly guide the localization procedure. The entire network is trained end-to-end. Experiments on PASCAL VOC demonstrate that our approach outperforms all state-of-the-arts.Comment: Accepted to appear in IJCAI 201

Review of the New World genera of the Leafhopper Tribe Erythroneurini (Hemiptera: Cicadellidae: Typhlocycbinae)

The genus-level classification of New World Erythroneurini is revised based on results of a phylogenetic analysis of 100 morphological characters. The 704 known species are placed into 18 genera. Erasmoneura Young and Eratoneura Young, previously treated as subgenera of Erythroneura Fitch, and Erythridula Young, most recently treated as a subgenus of Arboridia Zachvatkin, are elevated to generic status. Three species previously included in Erasmoneura are placed in a new genus, Rossmoneura (type species, Erythroneura tecta McAtee). The concept of Erythroneura is thereby narrowed to include only those species previously included in the nominotypical subgenus. New World species previously included in Zygina Fieber are not closely related to the European type species of that genus and are therefore placed in new genera. Neozygina, n. gen., based on type species Erythroneura ceonothana Beamer, includes all species previously included in the ???ceonothana group???, and Zyginama, n. gen., based on type species Erythroneura ritana Beamer, includes most species previously included in the ???ritana group??? of New World Zygina. Five additional new genera are described to include other previously described North American Erythroneurini: Hepzygina, n. gen., based on type species Erythroneura milleri Beamer and also including E. aprica McAtee; Mexigina, n. gen., based on type species Erythroneura oculata McAtee; Nelionidia, n. gen., based on type species N. pueblensis, n. sp., three additional new species, and Erythroneura amicis Ross; Neoimbecilla, n. gen., based on type species Erythroneura kiperi Beamer and one new species; and Illinigina, n. gen., based on type species Erythroneura illinoiensis Gillette. Five new genera, based on previously undescribed species, are also recognized: Aztegina, n. gen, based on A. punctinota, n. sp., from Mexico; Amazygina, n. gen., based on type species A. decaspina, n. sp., and three additional new species from Ecuador; Hamagina, n. gen., based on type species H. spinigera, n. sp., and two additional new species from Peru and Ecuador; Napogina, n. gen., based on type species N. recta, n. sp., and one additional new species from Ecuador; Perugina, n. gen., based on type species P. denticula, n. sp., from Peru; and Spinigina, n. gen., based on type species S. hirsuta, n. sp., and an additional new species from Peru. Phylogenetic analysis suggests that the New World Erythroneurini consist of three lineages resulting from separate invasions from the Old World.published or submitted for publicationis peer reviewe

Seasonal and spatial variations in the ocean-coupled ambient wavefield of the Ross Ice Shelf

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Baker, M. G., Aster, R. C., Anthony, R. E., Chaput, J., Wiens, D. A., Nyblade, A., Bromirski, P. D., Gerstoft, P., & Stephen, R. A. Seasonal and spatial variations in the ocean-coupled ambient wavefield of the Ross Ice Shelf. Journal of Glaciology, 65(254), (2019): 912-925, doi:10.1017/jog.2019.64.The Ross Ice Shelf (RIS) is host to a broadband, multimode seismic wavefield that is excited in response to atmospheric, oceanic and solid Earth source processes. A 34-station broadband seismographic network installed on the RIS from late 2014 through early 2017 produced continuous vibrational observations of Earth's largest ice shelf at both floating and grounded locations. We characterize temporal and spatial variations in broadband ambient wavefield power, with a focus on period bands associated with primary (10–20 s) and secondary (5–10 s) microseism signals, and an oceanic source process near the ice front (0.4–4.0 s). Horizontal component signals on floating stations overwhelmingly reflect oceanic excitations year-round due to near-complete isolation from solid Earth shear waves. The spectrum at all periods is shown to be strongly modulated by the concentration of sea ice near the ice shelf front. Contiguous and extensive sea ice damps ocean wave coupling sufficiently so that wintertime background levels can approach or surpass those of land-sited stations in Antarctica.This research was supported by NSF grants PLR-1142518, 1141916, 1142126, 1246151 and 1246416. JC was additionally supported by Yates funds in the Colorado State University Department of Mathematics. PDB also received support from the California Department of Parks and Recreation, Division of Boating and Waterways under contract 11-106-107. We thank Reinhard Flick and Patrick Shore for their support during field work, Tom Bolmer in locating stations and preparing maps, and the US Antarctic Program for logistical support. The seismic instruments were provided by the Incorporated Research Institutions for Seismology (IRIS) through the PASSCAL Instrument Center at New Mexico Tech. Data collected are available through the IRIS Data Management Center under RIS and DRIS network code XH. The PSD-PDFs presented in this study were processed with the IRIS Noise Tool Kit (Bahavar and others, 2013). The facilities of the IRIS Consortium are supported by the National Science Foundation under Cooperative Agreement EAR-1261681 and the DOE National Nuclear Security Administration. The authors appreciate the support of the University of Wisconsin-Madison Automatic Weather Station Program for the data set, data display and information; funded under NSF grant number ANT-1543305. The Ross Ice Shelf profiles were generated using the Antarctic Mapping Tools (Greene and others, 2017). Regional maps were generated with the Generic Mapping Tools (Wessel and Smith, 1998). Topography and bathymetry data for all maps in this study were sourced from the National Geophysical Data Center ETOPO1 Global Relief Model (doi:10.7289/V5C8276M). We thank two anonymous reviewers for suggestions on the scope and organization of this paper