Realtime bus waiting time information is of great importance to the intelligent public transportation system and is beneficial for improving user satisfaction by online map services. While there are limited realtime bus waiting time services in a city, because of the expensive cost of sensor deployment and sophisticated traffic conditions. To address the above problem, we propose Du-Bus, a multi-source data fusion based system, which estimates the realtime bus waiting time based on approximating the realtime locations of buses without GPS sensors, by a variety of urban datasets, including historical bus trip data reported by a limited number of GPS equipped buses, transportation network data, traffic condition data, user mobility data, and temporal data. Du-Bus approximates the realtime locations of buses without GPS sensors by jointly modeling the bus timetable and the bus realtime travel time, which can be estimated by a variety of data sources. Specifically, we first propose a BiLSTM based end-to-end model for each bus route to estimate the bus departure interval and generate the corresponding departure timetable. Then, we estimate the travel time for each individual bus via a deep neural network component by incorporating the traffic conditions, geolocation, and map query information. Finally, we estimate the bus waiting time for arbitrary stations in the city by jointly modeling the estimated bus departure timetable and travel time. We evaluate our system on two real-world datasets, and the results verify the effectiveness of Du-Bus compared with historical average based and headway based methods. Since early 2019, Du-Bus has been deployed on Baidu Maps, one of the world’s largest map services, servicing over 20 major cities in China.

Logical reasoning consistently plays a fundamental and significant role in the domains of knowledge engineering and artificial intelligence. Recently, Large Language Models (LLMs) have emerged as a noteworthy innovation in natural language processing (NLP). However, the question of whether LLMs can effectively address the task of logical reasoning, which requires gradual cognitive inference similar to human intelligence, remains unanswered. To this end, we aim to bridge this gap and provide comprehensive evaluations in this paper. First, to offer systematic evaluations, we select fifteen typical logical reasoning datasets and organize them into deductive, inductive, abductive and mixed-form reasoning settings. Considering the comprehensiveness of evaluations, we include 3 early-era representative LLMs and 4 trending LLMs. Second, different from previous evaluations relying only on simple metrics (e.g., accuracy), we propose fine-level evaluations in objective and subjective manners, covering both answers and explanations, including answer correctness, explain correctness, explain completeness and explain redundancy. Additionally, to uncover the logical flaws of LLMs, problematic cases will be attributed to five error types from two dimensions, i.e., evidence selection process and reasoning process. Third, to avoid the influences of knowledge bias and concentrate purely on benchmarking the logical reasoning capability of LLMs, we propose a new dataset with neutral content. Based on the in-depth evaluations, this paper finally forms a general evaluation scheme of logical reasoning capability from six dimensions (i.e., Correct, Rigorous, Self-aware, Active, Oriented and No hallucination). It reflects the pros and cons of LLMs and gives guiding directions for future works.

An integral part of video analysis and surveillance is temporal activity detection, which means to simultaneously recognize and localize activities in long untrimmed videos. Currently, the most effective methods of temporal activity detection are based on deep learning, and they typically perform very well with large scale annotated videos for training. However, these methods are limited in real applications due to the unavailable videos about certain activity classes and the time-consuming data annotation. To solve this challenging problem, we propose a novel task setting called zero-shot temporal activity detection (ZSTAD), where activities that have never been seen in training still need to be detected. We design an end-to-end deep transferable network TN-ZSTAD as the architecture for this solution. On the one hand, this network utilizes an activity graph transformer to predict a set of activity instances that appear in the video, rather than produces many activity proposals in advance. On the other hand, this network captures the common semantics of seen and unseen activities from their corresponding label embeddings, and it is optimized with an innovative loss function that considers the classification property on seen activities and the transfer property on unseen activities together. Experiments on the THUMOS’14, Charades, and ActivityNet datasets show promising performance in terms of detecting unseen activities.

Visual question answering requires a system to provide an accurate natural language answer given an image and a natural language question. However, it is widely recognized that previous generic VQA methods often tend to memorize biases present in the training data rather than learning proper behaviors, such as grounding images before predicting answers. Therefore, these methods usually achieve high in-distribution but poor out-of-distribution performance. In recent years, various datasets and debiasing methods have been proposed to evaluate and enhance the VQA robustness, respectively. This paper provides the first comprehensive survey focused on this emerging fashion. Specifically, we first provide an overview of the development process of datasets from in-distribution and out-of-distribution perspectives. Then, we examine the evaluation metrics employed by these datasets. Third, we propose a typology that presents the development process, similarities and differences, robustness comparison, and technical features of existing debiasing methods. Furthermore, we analyze and discuss the robustness of representative vision-and-language pre-training models on VQA. Finally, through a thorough review of the available literature and experimental analysis, we discuss the key areas for future research from various viewpoints.

Visual question answering (VQA) is a task that machines should provide an accurate natural language answer given an image and a question about the image. Many studies have found that the current VQA methods are heavily driven by the surface correlation or statistical bias in the training data, and lack sufficient image grounding. To address this issue, we devise a novel end-to-end architecture that uses multitask learning to promote more sufficient image grounding and learn effective multimodality representations. The tasks consist of VQA and our proposed image cloze (IC) task requires machines to fill in the blanks accurately given an image and a textual description of the image. To ensure our model performs sufficient image grounding as much as possible, we propose a novel word-masking algorithm to develop the multimodal IC task based on the part-of-speech of words. Our model predicts the VQA answer and fills in the blanks after the multimodality representation learning that is shared by the two tasks. Experimental results show that our model achieves almost the equivalent, state-of-the-art, second-best performance on the VQA v2.0, VQA-changing priors (CP) v2, and grounded question answering (GQA) datasets, respectively, with fewer parameters and without additional data compared with baselines.

Few-shot semantic segmentation (FSS) has been developed to perform pixel-level segmentation with only a few dense labeled examples for training, which relieves the expensive annotation problem in traditional segmentation models. Current researches on FSS generally act the labeled masks on the corresponding support images to obtain the class-specific embeddings, and predict the pixel-level masks for query images by matching their pixels to these class-specific embeddings. Their performance is difficult to further break through because of the limited supervision from single-view support images and the neglect of position information from similar pixels between query and support images. To solve these issues, we propose a novel robust prior mask guided model named RPMG-FSS for the challenging FSS task. The core of RPMG-FSS is to produce a robust prior mask with good generalization ability on novel classes to better assist the following query mask prediction. Note that each element in the prior mask corresponds to one pixel in query image. It not only considers the interaction within one view and between multiple views of the support image, but also fuses the top- k similarity values to all support pixels and these pixels’ position information. The parameters in RPMG-FSS are optimized with the combination of segmentation loss and multi-view contrastive loss. Comprehensive experiments on two datasets show that our RPMG-FSS achieves outstanding performance comparing with the current popular baselines. The code is released on https://github.com/dxzxy12138/RPMG-FSS/tree/master.