Detecting objects in nighttime low-light and weak-illumination scenarios presents a persistent challenge, as detectors trained on daytime well-illuminated data exhibit significant performance degradation on nighttime data due to low visibility. Previous methods mitigate day-to-night domain adaptation problems by leveraging teacher-student framework and image enhancement techniques. However, these methods uniformly process all foreground and background regions, resulting in an over-reliance on background scenes and struggling to learn effective features for typically sparse and small objects. To address this challenge, we propose a Semantic-Aware Salient Guidance day-to-night domain adaptive object detection framework (SASG). We first design an object Semantic Saliency Guidance (SSG) module, which leverages object semantic information to saliency guide the detection network to focus on the object regions (i.e., pedestrians, traffic signs) in the image, reducing attention to background scenes (i.e., roads, sky). Next, we introduce a compact Image Relighting Network (IRN) to narrow the intensity distribution gap between daytime and nighttime images, reducing the detector sensitivity to illumination variations. Extensive experiments demonstrate that our approach achieves superior nighttime low-light and weak-illumination generalization performance on the BDD100K and SHIFT datasets, outperforming the state-of-the-art methods by 2.5% and 1.2%, respectively.

Fact verification has attracted a lot of research attention recently, e.g., in journalism, marketing, and policymaking, as misinformation and disinformation online can sway one's opinion and affect one's actions.While fact-checking is a hard task in general, in many cases, false statements can be easily debunked based on analytics over tables with reliable information.Hence, table-based fact verification has recently emerged as an important and growing research area.Yet, progress has been limited due to the lack of datasets that can be used to pre-train language models (LMs) to be aware of common table operations, such as aggregating a column or comparing tuples.To bridge this gap, in this paper we introduce PASTA, a novel state-of-the-art framework for table-based fact verification via pre-training with synthesized sentence-table cloze questions.In particular, we design six types of common sentence-table cloze tasks, including Filter, Aggregation, Superlative, Comparative, Ordinal, and Unique, based on which we synthesize a large corpus consisting of 1.2 million sentence-table pairs from WikiTables.PASTA uses a recent pre-trained LM, DeBERTaV3, and further pretrains it on our corpus.Our experimental results show that PASTA achieves new state-ofthe-art performance on two table-based fact verification benchmarks: TabFact and SEM-TAB-FACTS.In particular, on the complex set of TabFact, which contains multiple operations, PASTA largely outperforms the previous state of the art by 4.7 points (85.6% vs. 80.9%), and the gap between PASTA and human performance on the small TabFact test set is narrowed to just 1.5 points (90.6% vs. 92.1%). 1 * Xiaoman Zhao is the corresponding author.

The prevalence of misinformation, both online and offline, has prompted a great demand of fact verification. Table-based fact verification aims to check whether a textual claim is supported or refuted based on relational tables. However, most of the existing approaches are in a closed-domain setting, which may not be realistic in practice. To address this problem, in this paper, we introduce OpenTFV, a user-friendly system that supports open domain table-based fact verification. Given a claim input by an end-user, OpenTFV retrieves the relevant tables, and provides a verification result for each table with an intuitive interpretation in natural language. We have implemented OpenTFV and demonstrated OpenTFV in two representative scenarios, COVID-19 claims fact verification based on academic tables and general fact verification on Wiki-tables.

Behavioral targeting plays an important role in social media advertising for capturing users' preferences of ads. While existing studies of behavioral targeting mainly focus on the user behaviors that have explicit correlations with ads, such as ad clicking and web search, many implicit relationships between users and ads, which reside in a variety of heterogeneous sources in social media platforms, are not utilized to enhance the prediction of users' preferences of ads.In this paper, we propose a two-pronged approach to behavioral targeting that effectively addresses the above difficulties. First, we model the implicit relationships between users and ads as a heterogeneous information network (HIN), and propose a method that first performs representation learning in the HIN and then uses the learned representations to train a prediction model for boosting the performance of behavioral targeting. Second, we develop a human-in-the-loop framework to address the incompleteness challenge in HIN construction that may result in inferior performance of model prediction. The framework judiciously selects the most "beneficial" tasks to ask human for completing the HIN and utilizes the results from human to update the representation learning of HIN. We validate the effectiveness of our approach through extensive experiments on real datasets collected from WeChat, the largest social media platform in China. The experimental results show that our approach is effective at constructing a high-quality HIN at a low cost of human involvement, and the HIN can significantly improve the performance of social behavioral targeting.

Social in-feed advertising delivers ads that seamlessly fit inside a user’s feed, and allows users to engage in social actions (likes or comments) with the ads. Many businesses pay higher attention to “engagement marketing” that maximizes social actions, as social actions can effectively promote brand awareness. This paper studies social action prediction for in-feed advertising. Most existing works overlook the social influence as a user’s action may be affected by her friends’ actions. This paper introduces an end-to-end approach that leverages social influence for action prediction, and focuses on addressing the high sparsity challenge for in-feed ads. We propose to learn influence structure that models who tends to be influenced. We extract a subgraph with the near neighbors a user interacts with, and learn topological features of the subgraph by developing structure-aware graph encoding methods. We also introduce graph attention networks to learn influence dynamics that models how a user is influenced by neighbors’ actions. We conduct extensive experiments on real datasets from the commercial advertising platform of WeChat and a public dataset. The experimental results demonstrate that social influence learned by our approach can significantly boost performance of social action prediction.