Given input images, scene graph generation (SGG) aims to produce comprehensive, graphical representations describing visual relationships among salient objects. Recently, more efforts have been paid to the long tail problem in SGG; however, the imbalance in the fraction of missing labels of different classes, or reporting bias, exacerbating the long tail is rarely considered and cannot be solved by the existing debiasing methods. In this paper we show that, due to the missing labels, SGG can be viewed as a "Learning from Positive and Unlabeled data" (PU learning) problem, where the reporting bias can be removed by recovering the unbiased probabilities from the biased ones by utilizing label frequencies, i.e., the per-class fraction of labeled, positive examples in all the positive examples. To obtain accurate label frequency estimates, we propose Dynamic Label Frequency Estimation (DLFE) to take advantage of training-time data augmentation and average over multiple training iterations to introduce more valid examples. Extensive experiments show that DLFE is more effective in estimating label frequencies than a naive variant of the traditional estimate, and DLFE significantly alleviates the long tail and achieves state-of-the-art debiasing performance on the VG dataset. We also show qualitatively that SGG models with DLFE produce prominently more balanced and unbiased scene graphs. The source code is publicly available.

Indoor localization is a fundamental problem in location-based applications. Current approaches to this problem typically rely on Radio Frequency technology, which requires not only supporting infrastructures but human efforts to measure and calibrate the signal. Moreover, data collection for all locations is indispensable in existing methods, which in turn hinders their large-scale deployment. In this paper, we propose a novel neural network based architecture Graph Location Networks (GLN) to perform infrastructure-free, multi-view image based indoor localization. GLN makes location predictions based on robust location representations extracted from images through message-passing networks. Furthermore, we introduce a novel zero-shot indoor localization setting and tackle it by extending the proposed GLN to a dedicated zero-shot version, which exploits a novel mechanism Map2Vec to train location-aware embeddings and make predictions on novel unseen locations. Our extensive experiments show that the proposed approach outperforms state-of-the-art methods in the standard setting, and achieves promising accuracy even in the zero-shot setting where data for half of the locations are not available. The source code and datasets are publicly available.

Recognizing ongoing events based on acoustic clues has been a critical research problem for a variety of AI applications. Compared to visual inputs, acoustic cues tend to be less descriptive and less consistent in time domain. The duration of a sound event can be quite short, which creates great difficulties for, especially weakly labeled, audio tagging. To solve these challenges, we present a novel end-to-end multi-level attention model that first makes segment-level predictions with temporal modeling, followed by advanced aggregations along both time and feature domains. Our model adopts class-aware attention based temporal fusion to highlight/suppress the relevant/irrelevant segments to each class. Moreover, to improve the representation ability of acoustic inputs, a new multi-level feature fusion method is proposed to obtain more accurate segment-level predictions, as well as to perform more effective multi-layer aggregation of clip-level predictions. We additionally introduce a weight sharing strategy to reduce model complexity and overfitting. Comprehensive experiments have been conducted on the AudioSet and the DCASE17 datasets. Experimental results show that our proposed method works remarkably well and obtains the state-of-the-art audio tagging results on both datasets. Furthermore, we show that our proposed multi-level fusion based model can be easily integrated with existing systems where additional performance gain can be obtained.