Vectorized glyphs are widely used in poster design, network animation, art display, and various other fields due to their scalability and flexibility. In typography, they are often seen as special sequences composed of ordered strokes. This concept extends to the token sequence prediction abilities of large language models (LLMs), enabling vectorized character generation through stroke modeling. In this paper, we propose a novel Large Vectorized Glyph Model (LVGM) designed to generate vectorized Chinese glyphs by predicting the next stroke. Initially, we encode strokes into discrete latent variables called stroke embeddings. Subsequently, we train our LVGM via fine-tuning DeepSeek LLM by predicting the next stroke embedding. With limited strokes given, it can generate complete characters, semantically elegant words, and even unseen verses in vectorized form. Moreover, we release a new large-scale Chinese SVG dataset containing 907, 267 samples based on strokes for dynamically vectorized glyph generation. Experimental results show that our model has scaling behaviors on data scales. Our generated vectorized glyphs have been validated by experts and relevant individuals.

Data-rich documents are ubiquitous in various applications, yet they often rely solely on textual descriptions to convey data insights. Prior research primarily focused on providing visualization-centric augmentation to data-rich documents. However, few have explored using automatically generated word-scale visualizations to enhance the document-centric reading process. As an exploratory step, we propose GistVis, an automatic pipeline that extracts and visualizes data insight from text descriptions. GistVis decomposes the generation process into four modules: Discoverer, Annotator, Extractor, and Visualizer, with the first three modules utilizing the capabilities of large language models and the fourth using visualization design knowledge. Technical evaluation including a comparative study on Discoverer and an ablation study on Annotator reveals decent performance of GistVis. Meanwhile, the user study (N=12) showed that GistVis could generate satisfactory word-scale visualizations, indicating its effectiveness in facilitating users' understanding of data-rich documents (+5.6% accuracy) while significantly reducing their mental demand (p=0.016) and perceived effort (p=0.033).

Existing context-based offline meta-reinforcement learning (COMRL) methods primarily focus on task representation learning and given-context adaptation performance. They often assume that the adaptation context is collected using task-specific behavior policies or through multiple rounds of collection. However, in real applications, the context should be collected by a policy in a one-shot manner to ensure efficiency and safety. We find that intrinsic context ambiguity across multiple tasks and out-of-distribution (OOD) issues due to distribution shift significantly affect the performance of one-shot adaptation, which has been largely overlooked in most COMRL research. To address this problem, we propose using heteroscedastic uncertainty in representation learning to identify ambiguous and OOD contexts, and train an uncertainty-aware context collecting policy for effective one-shot online adaptation. The proposed method can be integrated into various COMRL frameworks, including classifier-based, reconstrution-based and contrastive learning-based approaches. Empirical evaluations on benchmark tasks show that our method can improve one-shot adaptation performance by up to 36% and zero-shot adaptation performance by up to 34% compared to existing baseline COMRL methods.

Using Quadrics as the object representation has the benefits of both generality and closed-form projection derivation between image and world spaces. Although numerous constraints have been proposed for dual quadric reconstruction, we found that many of them are imprecise and provide minimal improvements to localization. After scrutinizing the existing constraints, we introduce a concise yet more precise convex hull-based algebraic constraint for object landmarks, which is applied to object reconstruction, frontend pose estimation, and backend bundle adjustment. This constraint is designed to fully leverage precise semantic segmentation, effectively mitigating mismatches between complex-shaped object contours and dual quadrics. Experiments on public datasets demonstrate that our approach is applicable to both monocular and RGB-D SLAM and achieves improved object mapping and localization than existing quadric SLAM methods. The implementation of our method is available at https://github.com/tiev-tongji/convexhull-based-algebraic-constraint.

Automated Chinese ancient character restoration (ACACR) remains a challenging task due to its historical significance and aesthetic complexity. Existing methods are constrained by non-professional masks and even overfitting when training on small-scale datasets, which hinder their interdisciplinary application to traditional fields. In this paper, we are proud to introduce the Chinese Ancient Rubbing and Manuscript Character Dataset (ARMCD), which consists of 15,553 real-world ancient single-character images with 42 rubbings and manuscripts, covering the works of over 200 calligraphy artists spanning from 200 to 1,800 AD. We are also dedicated to providing professional synthetic masks by extracting localized erosion from real eroded images. Moreover, we propose DiffACR (Diffusion model for automated Chinese Ancient Character Restoration), a diffusion-based method for the ACACR task. Specifically, we regard the synthesis of eroded images as a special form of cold diffusion on uneroded ones and extract the prior mask directly from the eroded images. Our experiments demonstrate that our method comprehensively outperforms most existing methods on the proposed ARMCD. Dataset and code are available at https://github.com/lhl322001/DiffACR.

A primary challenge for visual-based Reinforcement Learning (RL) is to generalize effectively across unseen environments. Although previous studies have explored different auxiliary tasks to enhance generalization, few adopt image reconstruction due to concerns about exacerbating overfitting to task-irrelevant features during training. Perceiving the pre-eminence of image reconstruction in representation learning, we propose SMG (\blue{S}eparated \blue{M}odels for \blue{G}eneralization), a novel approach that exploits image reconstruction for generalization. SMG introduces two model branches to extract task-relevant and task-irrelevant representations separately from visual observations via cooperatively reconstruction. Built upon this architecture, we further emphasize the importance of task-relevant features for generalization. Specifically, SMG incorporates two additional consistency losses to guide the agent's focus toward task-relevant areas across different scenarios, thereby achieving free from overfitting. Extensive experiments in DMC demonstrate the SOTA performance of SMG in generalization, particularly excelling in video-background settings. Evaluations on robotic manipulation tasks further confirm the robustness of SMG in real-world applications. Source code is available at \url{https://anonymous.4open.science/r/SMG/}.

Intersections are key nodes and also bottlenecks of urban road networks, so improving the traffic efficiency at intersections is beneficial to improving overall traffic throughput and mitigating traffic congestion. Previous methods such as rule-based, planning-based, and single-agent reinforcement learning usually oversimplify the policies of the surrounding vehicles and thus have difficulty modeling the complex interaction behaviors between vehicles, which limits the performance of these methods to some extent. Instead, we adopt a multi-agent reinforcement learning (MARL) approach to train and coordinate the policies of all vehicles to handle unsignalized intersection scenarios. Nevertheless, due to complex interactions between multiple agents, it is challenging to efficiently explore the environment and obtain high-reward samples. We therefore propose to pre-train the policy using demonstration data consisting of expert data and interaction data to improve the initial performance of agents and improve exploration, as well as to reduce the distributional shift between the demonstration data and the environmental interaction data. We experimentally prove that using interaction data generated by the algorithm in the demonstration data improves training stability. The proposed method enables effective exploration and greatly speeds up the training process.

Designing and deriving effective model-based reinforcement learning (MBRL) algorithms with a performance improvement guarantee is challenging, mainly attributed to the high coupling between model learning and policy optimization. Many prior methods that rely on return discrepancy to guide model learning ignore the impacts of model shift, which can lead to performance deterioration due to excessive model updates. Other methods use performance difference bound to explicitly consider model shift. However, these methods rely on a fixed threshold to constrain model shift, resulting in a heavy dependence on the threshold and a lack of adaptability during the training process. In this paper, we theoretically derive an optimization objective that can unify model shift and model bias and then formulate a fine-tuning process. This process adaptively adjusts the model updates to get a performance improvement guarantee while avoiding model overfitting. Based on these, we develop a straightforward algorithm USB-PO (Unified model Shift and model Bias Policy Optimization). Empirical results show that USB-PO achieves state-of-the-art performance on several challenging benchmark tasks.

The scale ambiguity problem is inherently unsolvable to monocular SLAM without the metric baseline between moving cameras. In this paper, we present a novel scale estimation approach based on an object-level SLAM system. To obtain the absolute scale of the reconstructed map, we formulate an optimization problem to make the scaled dimensions of objects conform to the distribution of their sizes in the physical world, without relying on any prior information about gravity direction. The dual quadric is adopted to represent objects for its ability to describe objects compactly and accurately, thus providing reliable dimensions for scale estimation. In the proposed monocular object-level SLAM system, semantic objects are initialized first from fitted 3-D oriented bounding boxes and then further optimized under constraints of 2-D detections and 3-D map points. Experiments on indoor and outdoor public datasets show that our approach outperforms existing methods in terms of accuracy and robustness.

This paper proposes G-VIDO, a vehicle dynamics, and intermittent Global Navigation Satellite System (GNSS)-aided visual-inertial state estimator, to address the state estimation problem of autonomous vehicle localization (i.e., position and orientation estimation in the global coordinate system) under various GNSS states. A dynamics pre-integration theory is proposed on the basis of a two-degree-of-freedom (DOF) vehicle dynamics model, and dynamics constraints are built in the optimization back-end, considering the unobservable problem of the monocular visual-inertial system under degenerate motions. The proposed highly nonlinear system can be robustly initialized by loosely aligning the monocular structure from motion (SfM) results, pre-integrated IMU measurements, and vehicle motion information. GNSS is used for reference frame transformation and constraint construction in the sliding window. The cumulative error can be corrected with the aid of GNSS, and the vehicle’s position in the global coordinate system can be determined. A GNSS anomaly detection algorithm is proposed to improve the system robustness under intermittent GNSS. Experiments have shown that G-VIDO can provide real-time, robust, and seamless localization in multiple GNSS states, with an RMSE of less than 30 cm (with GNSS). Moreover, we proved that the initialization and local odometry modules in G-VIDO outperform several state-of-the-art VIO systems and our preliminary work VINS-Vehicle.

Precisely localizing a vehicle in the GNSS-denied urban area is crucial for autonomous driving. The occupancy grid-based 2D LiDAR SLAM methods scale poorly to outdoor road scenarios, while the 3D point cloud-based LiDAR SLAM methods suffer from huge computation and storage costs. Aiming at the precise real-time LiDAR SLAM for both indoor and outdoor, this paper proposed a direct 2.5D heightmap-based SLAM system. This system extended our previously proposed DLO (the direct 2.5D LiDAR odometry) method by introducing the 2.5D segment features for efficient loop closure detection. We experimented our SLAM method on the KITTI datasets and shown it superior performance compared with the existing LiDAR SLAM methods.

Intelligent vehicles need to detect new classes of traffic objects while keeping the performance of old ones. Deep convolution neural network (DCNN) based detector has shown superior performance, however, DCNN is ill-equipped for incremental learning, i.e., a DCNN based vehicle detector trained on traffic sign dataset will catastrophic forget how to detect vehicles. In this paper, we propose a novel method to alleviate this problem, our key insight is that the original class of objects also appears in new task data, by utilizing these objects, our method effectively keeps the detection accuracy of original models while incremental learning to detect new classes of objects. Detailed experiments on PASCAL VOC dataset and TSD-max database verified the effectiveness of our method.

For autonomous vehicles, high-precision real-time localization is the guarantee of stable driving. Compared with the visual odometry (VO), the LiDAR odometry (LO) has the advantages of higher accuracy and better stability. However, 2D LO is only suitable for the indoor environment, and 3D LO has less efficiency in general. Both are not suitable for the online localization of an autonomous vehicle in an outdoor driving environment. In this paper, a direct LO method based on the 2.5D grid map is proposed. The fast semi-dense direct method proposed for VO is employed to register two 2.5D maps. Experiments show that this method is superior to both the 3D-NDT and LOAM in the outdoor environment.

With the rapid development of driverless cars, pedestrian detection has been a canonical instance of object detection. Although recent deep learning detectors such as RPN+BF and MS-CNN have shown excellent performance for pedestrian detection, they have limited success for detecting pedestrian, and the importance of final feature receptive field has been awared by previous leading deep learning pedestrian detectors. Applying the dilated convolution to the feature learning of pedestrian detection, we constructed a pedestrian detection framework along with the region proposal network and boosted decision trees. Pipeline of our proposed framework can be briefly generalized as follows: firstly, the fine-tuned RPN with specified aspect ratio is used to get boxes and scores. Secondly, the designed dilated convolution feature extraction model is used to get features. As different dilation factors provide different receptive field scales, we concat the features of different layers with the dilated convolutional features to get the final features. Finally, the candidate boxes are sent to the boosted decision trees to be classified using the scores and features. We evaluated our method on the Caltech Pedestrian Detection Benchmark. Comparing with other state-of-the-art detection methods, the proposed framework with dilated convolution has better performance.