Introspective Deep Metric Learning for Image Retrieval

This paper proposes an introspective deep metric learning (IDML) framework for uncertainty-aware comparisons of images. Conventional deep metric learning methods produce confident semantic distances between images regardless of the uncertainty level. However, we argue that a good similarity model should consider the semantic discrepancies with caution to better deal with ambiguous images for more robust training. To achieve this, we propose to represent an image using not only a semantic embedding but also an accompanying uncertainty embedding, which describes the semantic characteristics and ambiguity of an image, respectively. We further propose an introspective similarity metric to make similarity judgments between images considering both their semantic differences and ambiguities. The proposed IDML framework improves the performance of deep metric learning through uncertainty modeling and attains state-of-the-art results on the widely used CUB-200-2011, Cars196, and Stanford Online Products datasets for image retrieval and clustering. We further provide an in-depth analysis of our framework to demonstrate the effectiveness and reliability of IDML. Code is available at: https://github.com/wzzheng/IDML.Comment: The extended version of this paper is accepted to T-PAMI. Source code available at https://github.com/wzzheng/IDM

The mean-variance relation: A story of night and day

The traditional financial framework theorizes a positive mean-variance relation, which, however, is not fully supported by empirical evidence. We provide a new explanation for the weak mean-variance relation by separately testing the relation overnight and intraday. Results at the global level present a positive mean-variance relation overnight but a negative relation intraday, while results of individual markets reveal a high degree of heterogeneity. We employ cultural dimensions, market integrity, and market development to examine the drivers of the observed cross-market differences, showing that all the three factors influence the mean-variance relation, and notably, the influence varies across night and day

Investor sentiment and stock market returns: A story of night and day

Some financial relations have been confirmed to be different overnight and intraday due to different clienteles. In this paper, we assess the impact of investor sentiment on stock market returns in 30 international stock markets overnight and intraday. At the global level, empirical evidence reveals a negative sentiment-return relation in both non-trading and trading hours, and the relation is stronger intraday than overnight, indicating that overnight traders are more rational than intraday traders. The separation between developed and emerging markets does not distort the negative relation or the stronger impact intraday. At the individual market level, results reveal a high degree of heterogeneity in the sentiment-return relation, in terms of both influence direction and magnitude. The heterogeneity can be explained by cross-market differences in cultural dimensions and market integrity, and notably, such influence varies across night and day, suggesting that the influence of the two aspects may be more complex than we used to theorize and therefore, future studies applying the cross-market analytical framework may take different clienteles into account