Hotels-50K: A Global Hotel Recognition Dataset

Recognizing a hotel from an image of a hotel room is important for human trafficking investigations. Images directly link victims to places and can help verify where victims have been trafficked, and where their traffickers might move them or others in the future. Recognizing the hotel from images is challenging because of low image quality, uncommon camera perspectives, large occlusions (often the victim), and the similarity of objects (e.g., furniture, art, bedding) across different hotel rooms. To support efforts towards this hotel recognition task, we have curated a dataset of over 1 million annotated hotel room images from 50,000 hotels. These images include professionally captured photographs from travel websites and crowd-sourced images from a mobile application, which are more similar to the types of images analyzed in real-world investigations. We present a baseline approach based on a standard network architecture and a collection of data-augmentation approaches tuned to this problem domain

PanDA: Panoptic Data Augmentation

The recently proposed panoptic segmentation task presents a significant challenge of image understanding with computer vision by unifying semantic segmentation and instance segmentation tasks. In this paper we present an efficient and novel panoptic data augmentation (PanDA) method which operates exclusively in pixel space, requires no additional data or training, and is computationally cheap to implement. By retraining original state-of-the-art models on PanDA augmented datasets generated with a single frozen set of parameters, we show robust performance gains in panoptic segmentation, instance segmentation, as well as detection across models, backbones, dataset domains, and scales. Finally, the effectiveness of unrealistic-looking training images synthesized by PanDA suggest that one should rethink the need for image realism for efficient data augmentation

Gathering Information on the Web by Consistent Entity Augmentation

Users usually want to gather information about what they are interested in, which could be achieved by entity augmentation using a vast amount of web tables. Existing techniques assume that web tables are entity-attribute binary tables. As for tables having multiple columns to be augmented, they will be split into several entity-attribute binary relations, which would cause semantic fragmentation. Furthermore, the result table consolidated by binary relations will suffer from entity inconsistency and low precision. The objective of our research is to return a consistent result table for entity augmentation when given a set of entities and attribute names. In this paper we propose a web information gathering framework based on consistent entity augmentation. To ensure high consistency and precision of the result table we propose that answer tables for building result table should have consistent matching relationships with each other. Instead of splitting tables into pieces we regard web tables as nodes and consistent matching relationships as edges to make a consistent clique and expand it until its coverage for augmentation query reaches certain threshold gamma. It is proved in this paper that a consistent result table could be built by considering tables in consistent clique to be answer tables. We tested our method on four real-life datasets, compared it with different answer table selection methods and state-of-the-art entity augmentation technique based on table fragmentation as well. The results of a comprehensive set of experiments indicate that our entity augmentation framework is more effective than the existing method in getting consistent entity augmentation results with high accuracy and reliability

Flow-based Influence Graph Visual Summarization

Visually mining a large influence graph is appealing yet challenging. People are amazed by pictures of newscasting graph on Twitter, engaged by hidden citation networks in academics, nevertheless often troubled by the unpleasant readability of the underlying visualization. Existing summarization methods enhance the graph visualization with blocked views, but have adverse effect on the latent influence structure. How can we visually summarize a large graph to maximize influence flows? In particular, how can we illustrate the impact of an individual node through the summarization? Can we maintain the appealing graph metaphor while preserving both the overall influence pattern and fine readability? To answer these questions, we first formally define the influence graph summarization problem. Second, we propose an end-to-end framework to solve the new problem. Our method can not only highlight the flow-based influence patterns in the visual summarization, but also inherently support rich graph attributes. Last, we present a theoretic analysis and report our experiment results. Both evidences demonstrate that our framework can effectively approximate the proposed influence graph summarization objective while outperforming previous methods in a typical scenario of visually mining academic citation networks.Comment: to appear in IEEE International Conference on Data Mining (ICDM), Shen Zhen, China, December 201