Assigning orders to drivers under localized spatiotemporal context (micro-view order-dispatching) is a major task in Didi, as it influences ride-hailing service experience. Existing industrial solutions mainly follow a two-stage pattern that incorporate heuristic or learning-based algorithms with naive combinatorial methods, tackling the uncertainty of both sides' behaviors, including emerging timings, spatial relationships, and travel duration, etc. In this paper, we propose a one-stage end-to-end reinforcement learning based order-dispatching approach that solves behavior prediction and combinatorial optimization uniformly in a sequential decision-making manner. Specifically, we employ a two-layer Markov Decision Process framework to model this problem, and present Deep Double Scalable Network (D2SN), an encoder-decoder structure network to generate order-driver assignments directly and stop assignments accordingly. Besides, by leveraging contextual dynamics, our approach can adapt to the behavioral patterns for better performance. Extensive experiments on Didi's real-world benchmarks justify that the proposed approach significantly outperforms competitive baselines in optimizing matching efficiency and user experience tasks. In addition, we evaluate the deployment outline and discuss the gains and experiences obtained during the deployment tests from the view of large-scale engineering implementation.

Achieving optimal order dispatching has been a long-standing challenge for online ride-hailing platforms. Early methods would make shortsighted matchings as they only consider order prices alone as the edge weights in the driver-order bipartite graph, thus harming the platform's revenue. To address this problem, recent works evaluate the value of the order's destination region to be the long-term income a driver could obtain in average in such region and incorporate it into the order's edge weight to influence the matching results. However, they often result in insufficient driver supplies in many regions, as the values evaluated in different regions vary greatly, mainly because the impact of one region's value on the future number of drivers and revenue in other regions is overlooked. This paper models such impact within a cooperative Markov game, which involves each value's impact over the platform's revenue with the goal to find the optimal region values for revenue maximization. To solve this game, our work proposes a novelgoal-reaching collaboration (GRC) algorithm that realizes credit assignment from a novel goal-reaching perspective, addressing the difficulty for accurate credit assignment with large-scale agents of previous methods and resolving the conflict between credit assignment and offline reinforcement learning. Specifically, during training, GRC predicts the city's future state through an environment model and utilizes a scoring model to rate the predicted states to judge their levels of profitability, where high-scoring states are regarded as the goal states. Then, the policies in the game are updated to promote the city to stay in the goal states for as long as possible. To evaluate GRC, we deploy a baseline policy online in several cities for three weeks to collect real-world dataset. Training and testing results on the collected dataset indicate that our GRC consistently outperforms the baselines in different cities and peak periods.

Learning visual representations from web data has recently attracted attention for object recognition. Previous studies have mainly focused on overcoming label noise and data bias and have shown promising results by learning directly from web data. However, we argue that it might be better to transfer knowledge from existing human labeling resources to improve performance at nearly no additional cost. In this paper, we propose a new semi-supervised method for learning via web data. Our method has the unique design of exploiting strong supervision, i.e., in addition to standard image-level labels, our method also utilizes detailed annotations including object bounding boxes and part landmarks. By transferring as much knowledge as possible from existing strongly supervised datasets to weakly supervised web images, our method can benefit from sophisticated object recognition algorithms and overcome several typical problems found in webly-supervised learning. We consider the problem of fine-grained visual categorization, in which existing training resources are scarce, as our main research objective. Comprehensive experimentation and extensive analysis demonstrate encouraging performance of the proposed approach, which, at the same time, delivers a new pipeline for fine-grained visual categorization that is likely to be highly effective for real-world applications.

Image-based clothing retrieval is receiving increasing interest with the growth of online shopping. In practice, users may often have a desired piece of clothing in mind (e.g., either having seen it before on the street or requiring certain specific clothing attributes), but may be unable to supply an image as a query. We model this problem as a new type of image retrieval task, in which the target image resides only in the user's mind (called “mental image retrieval” hereafter). Because of the absence of an explicit query image, we propose solving this problem through relevance feedback. Specifically, a new Bayesian formulation is proposed that simultaneously models the retrieval target and its high-level representation in the mind of the user (called the “user metric” hereafter) as posterior distributions of prefetched shop images and heterogeneous features extracted from multiple clothing attributes, respectively. Requiring only clicks as user feedback, the proposed algorithm is able to account for the variability in human decision-making. Experiments with real users demonstrate the effectiveness of the proposed algorithm.

Multi-instance learning (MIL) is widely acknowledged as a fundamental method to solve weakly supervised problems. While MIL is usually effective in standard weakly supervised object recognition tasks, in this paper, we investigate the applicability of MIL on an extreme case of weakly supervised learning on the task of fine-grained visual categorization, in which intra-class variance could be larger than inter-class due to the subtle differences between subordinate categories. For this challenging task, we propose a new method that generalizes the standard multi-instance learning framework, for which a novel multi-task co-localization algorithm is proposed to take advantage of the relationship among fine-grained categories and meanwhile performs as an effective initialization strategy for the non-convex multi-instance objective. The localization results also enable object-level domain-specific fine-tuning of deep neural networks, which significantly boosts the performance. Experimental results on three fine-grained datasets reveal the effectiveness of the proposed method, especially the importance of exploiting inter-class relationships between object categories in weakly supervised fine-grained recognition.

In the context of fine-grained visual categorization, the ability to interpret models as human-understandable visual manuals is sometimes as important as achieving high classification accuracy. In this paper, we propose a novel Part-Stacked CNN architecture that explicitly explains the finegrained recognition process by modeling subtle differences from object parts. Based on manually-labeled strong part annotations, the proposed architecture consists of a fully convolutional network to locate multiple object parts and a two-stream classification network that encodes object-level and part-level cues simultaneously. By adopting a set of sharing strategies between the computation of multiple object parts, the proposed architecture is very efficient running at 20 frames/sec during inference. Experimental results on the CUB-200-2011 dataset reveal the effectiveness of the proposed architecture, from multiple perspectives of classification accuracy, model interpretability, and efficiency. Being able to provide interpretable recognition results in realtime, the proposed method is believed to be effective in practical applications.

Cross-domain clothing retrieval is a challenging task due to significant differences between online shop images taken in controlled conditions of clean backgrounds, good lighting, and fixed poses, and street photos captured in uncontrollable conditions. In recent years, Convolutional Neural Networks (CNNs) have demonstrated its effectiveness for various computer vision problems including image retrieval. There are two mainstream CNNs based models addressing image retrieval tasks: triplet network models [1] and siamese network models [2]. In this paper, we first make a thorough comparison between the two types of models, and investigate the impact of different domain adaptation schemes including parameter sharing, non-sharing, and a new partial-sharing strategy between the street domain and the shop domain. Extensive experiments have revealed that the proposed partial-sharing scheme is able to reduce the number of parameters by a significant margin, while achieving comparable retrieval accuracy as the state-of-the-art scheme using triplet loss with non-sharing parameters.

In this paper, we present multinomial latent logistic regression (MLLR), a new learning paradigm that introduces latent variables to logistic regression. By inheriting the advantages of logistic regression, MLLR is efficiently optimized using the second-order derivatives and provides effective probabilistic analysis on output predictions. MLLR is particularly effective in weakly supervised settings where the latent variable has an exponential number of possible values. The effectiveness of MLLR is demonstrated on four different image understanding applications, including a new challenging architectural style classification task. Furthermore, we show that MLLR can be generalized to general structured output prediction, and in doing so, we provide a thorough investigation of the connections and differences between MLLR and existing related algorithms, including latent structural SVMs and hidden conditional random fields.

We propose a new method for fine-grained object recognition that employs part-level annotations and deep convolutional neural networks (CNNs) in a unified framework. Although both schemes have been widely used to boost recognition performance, due to the difficulty in acquiring detailed part annotations, strongly supervised fine-grained datasets are usually too small to keep pace with the rapid evolution of CNN architectures. In this paper, we solve this problem by exploiting inexhaustible web data. The proposed method improves classification accuracy in two ways: more discriminative CNN feature representations are generated using a training set augmented by collecting a large number of part patches from weakly supervised web images, and more robust object classifiers are learned using a multi-instance learning algorithm jointly on the strong and weak datasets. Despite its simplicity, the proposed method delivers a remarkable performance improvement on the CUB200-2011 dataset compared to baseline part-based R-CNN methods, and achieves the highest accuracy on this dataset even in the absence of test image annotations.

Topic models have been widely used for analyzing text corpora and achieved great success in applications including content organization and information retrieval. However, different from traditional text data, social tags in the web containers are usually of small amounts, unordered, and non-iid, i.e., it is highly dependent on contextual information such as users and objects. Considering the specific characteristics of social tags, we here introduce a new model named Joint Latent Dirichlet Allocation (JLDA) to capture the relationships among users, objects, and tags. The model assumes that the latent topics of users and those of objects jointly influence the generation of tags. The latent distributions is then inferred with Gibbs sampling. Experiments on two social tag data sets have demonstrated that the model achieves a lower predictive error and generates more reasonable topics. We also present an interesting application of this model to object recommendation.

An image in social media, termed a social image, exhibits characteristics different from images widely discussed in image processing. They can be described by both content and social related attributes, called social image attributes, including visual contents, users, tags, and timestamps. There are strong coupling relationships between social image attributes, which make social images not independent and identically distributed (non-IID). By analyzing the relationships among these attributes, we can better understand the semantic activities conducted on such non-IID social images, hence enabling new applications including content organization, recommendation, and social activity understanding. In this article, we present a novel algorithm to analyze the coupling relationships between social images, which involves not only intra-coupled similarity within a social image attribute, but also inter-coupled similarity between attributes, in analyzing the non-IIDness of the similarity between social images. In particular, we propose a multi-entry version of the coupled similarity metric to deal with attributes (i.e., tags) which have a many-to-one relationship with respect to images. Experimental results on a Flickr group dataset show that the proposed algorithm captures coupling relationships and therefore achieves promising results in various applications, including image clustering and tagging.