Safe autonomous driving requires both accurate HD map construction and persistent awareness of traffic rules, even when their associated signs are no longer visible. However, existing methods either focus solely on geometric elements or treat rules as temporary classifications, failing to capture their persistent effectiveness across extended driving sequences. In this paper, we present PAMR (Persistent Autoregressive Mapping with Traffic Rules), a novel framework that performs autoregressive co-construction of lane vectors and traffic rules from visual observations. Our approach introduces two key mechanisms: Map-Rule Co-Construction for processing driving scenes in temporal segments, and Map-Rule Cache for maintaining rule consistency across these segments. To properly evaluate continuous and consistent map generation, we develop MapDRv2, featuring improved lane geometry annotations. Extensive experiments demonstrate that PAMR achieves superior performance in joint vector-rule mapping tasks, while maintaining persistent rule effectiveness throughout extended driving sequences.

Large-scale map construction is foundational for critical applications such as autonomous driving and navigation systems. Traditional large-scale map construction approaches mainly rely on costly and inefficient special data collection vehicles and labor-intensive annotation processes. While existing satellite-based methods have demonstrated promising potential in enhancing the efficiency and coverage of map construction, they exhibit two major limitations: (1) inherent drawbacks of satellite data (e.g., occlusions, outdatedness) and (2) inefficient vectorization from perception-based methods, resulting in discontinuous and rough roads that require extensive post-processing. This paper presents a novel generative framework, UniMapGen, for large-scale map construction, offering three key innovations: (1) representing lane lines as discrete sequence and establishing an iterative strategy to generate more complete and smooth map vectors than traditional perception-based methods. (2) proposing a flexible architecture that supports multi-modal inputs, enabling dynamic selection among BEV, PV, and text prompt, to overcome the drawbacks of satellite data. (3) developing a state update strategy for global continuity and consistency of the constructed large-scale map. UniMapGen achieves state-of-the-art performance on the OpenSatMap dataset. Furthermore, UniMapGen can infer occluded roads and predict roads missing from dataset annotations.

Existing researches on handwritten Chinese characters are mainly based on recognition network designed to solve the complex structure and numerous amount characteristics of Chinese characters. In this paper, we investigate Chinese characters from the perspective of error correction, which is to diagnose a handwritten character to be right or wrong and provide a feedback on error analysis. For this handwritten Chinese character error correction task, we define a benchmark by unifying both the evaluation metrics and data splits for the first time. Then we design a diagnosis system that includes decomposition, judgement and correction stages. Specifically, a novel tree-structure analysis network (TAN) is proposed to model a Chinese character as a tree layout, which mainly consists of a CNN-based encoder and a tree-structure based decoder. Using the predicted tree layout for judgement, correction operation is performed for the wrongly written characters to do error analysis. The correction stage is composed of three steps: fetch the ideal character, correct the errors and locate the errors. Additionally, we propose a novel bucketing mining strategy to apply triplet loss at radical level to alleviate feature dispersion. Experiments on handwritten character dataset demonstrate that our proposed TAN shows great superiority on all three metrics comparing with other state-of-the-art recognition models. Through quantitative analysis, TAN is proved to capture more accurate spatial position information than regular encoder-decoder models, showing better generalization ability.

In recent years, tree decoders become more popular than LaTeX string decoders in the field of handwritten mathematical expression recognition (HMER) as they can capture the hierarchical tree structure of mathematical expressions. However previous tree decoders converted the tree structure labels into a fixed and ordered sequence, which could not make full use of the diversified expression of tree labels. In this study, we propose a novel tree decoder (TDv2) to fully utilize the tree structure labels. Compared with previous tree decoders, this new model does not require a fixed priority for different branches of a node during training and inference, which can effectively improve the model generalization capability. The input and output of the model make full use of the tree structure label, so that there is no need to find the parent node in the decoding process, which simplifies the decoding process and adds a prior information to help predict the node. We verified the effectiveness of each part of the model through comprehensive ablation experiments and attention visualization analysis. On the authoritative CROHME 14/16/19 datasets, our method achieves the state-of-the-art results.

Chinese character recognition has attracted much interest due to its high challenge and various applications. The whole-character modeling method can recognize common characters well but unable to handle unseen situation. Some radical-based modeling methods have successfully achieved great performance in unseen condition but need RNN-based decoder for sequence decoding. Therefore, a compact model which can recognize unseen characters needs to be proposed. First, this paper introduces a novel radical counter network (RCN) to recognize Chinese characters by identifying radicals and spatial structures. The proposed RCN first extracts visual features from input by employing DenseNet as encoder. Then a decoder based on fully connected layer is employed, aiming at synchronously estimating the number of each caption in character. Additionally, we design a multi-task learning to combine global feature extraction capability of whole-character modeling and local feature extraction capability of radical-based modeling, which further improves the model generalization. Experiments on natural scene character dataset demonstrate that the proposed model significantly outperforms WCN by 5.48% and achieve comparable performance with RAN in lower model complexity. That shows great robustness and simplicity of our model.

Recently, many researches propose to employ attention based encoder-decoder models to convert a sequence of trajectory points into a LaTeX string for online handwritten mathematical expression recognition (OHMER), and the recognition performance of these models critically relies on the accuracy of the attention. In this paper, unlike previous methods which basically employ a soft attention model, we propose to employ a posterior attention model, which modifies the attention probabilities after observing the output probabilities generated by the soft attention model. In order to further improve the posterior attention mechanism, we propose a stroke average pooling layer to aggregate point-level features obtained from the encoder into stroke-level features. We argue that posterior attention is better to be implemented on stroke-level features than point-level features as the output probabilities generated by stroke is more convincing than generated by point, and we prove that through experimental analysis. Validated on the CROHME competition task, we demonstrate that stroke based posterior attention achieves expression recognition rates of 54.26% on CROHME 2014 and 51.75% on CROHME 2016. According to attention visualization analysis, we empirically demonstrate that the posterior attention mechanism can achieve better alignment accuracy than the soft attention mechanism.

Recently, a novel radical analysis network (RAN) has the capability of effectively recognizing unseen Chinese character classes and largely reducing the requirement of training data by treating a Chinese character as a hierarchical composition of radicals rather than a single character class. However, when dealing with more challenging issues, such as the recognition of complicated characters, low-frequency character categories, and characters in natural scenes, RAN still has a lot of room for improvement. In this paper, we explore options to further improve the structure generalization and robustness capability of RAN with the Transformer architecture, which has achieved start-of-the-art results for many sequence-to-sequence tasks. More specifically, we propose to replace the original attention module in RAN with the transformer decoder, which is named as a transformer-based radical analysis network (RTN). The experimental results show that the proposed approach can significantly outperform the RAN on both printed Chinese character database and natural scene Chinese character database. Meanwhile, further analysis proves that RTN can be better generalized to complex samples and low-frequency characters, and has better robustness in recognizing Chinese characters with different attributes.