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检索条件"机构=National Engineering Laboratory of Visual Information Processing Applications"

共 370 条记录，以下是11-20 订阅

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MSE-LVIO: Multi-Modal Semantic-Enhanced LiDAR-visual-Inertial Odometry in Dynamic Traffic Scenes 27

MSE-LVIO: Multi-Modal Semantic-Enhanced LiDAR-Visual-Inertia...

引用

27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Wang, Dan Zhu, Ziyu Hai, Renwei Shen, Yanqing Xin, Jingmin Zheng, Nanning National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Shaanxi Xi'an710049 China

ISBN: (纸本)9798331505929

Reliable localization and mapping are the key technologies for autonomous driving. In complex and dynamic traffic scenarios, a single sensor cannot provide sufficient information to achieve reliable and accurate Simultaneous Localization and Mapping (SLAM). Therefore, more and more multi-sensor fusion SLAM works have emerged. However, previous multi-sensor fusion SLAM systems mainly utilize geometric information, but not fully leverage semantic information, which plays a crucial role in understanding complex scenes. This paper proposes a semantic-enhanced LiDAR-visual-Inertial Odometry system named MSE-LVIO, which utilizes the spatial consistency between image semantic segmentation and point cloud clustering to construct a semantic map integrating object attributes, dynamic, and static information. By fully leveraging semantic and object information, real-time dynamic obstacle filtering can be achieved during the front-end registration phase. Our method has been validated in the Carla simulation environment, KITTI raw dataset, and M2DGR dataset. The results show that our approach performs better in dynamic scenes. © 2024 IEEE.

关键词： Mapping

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Refiner: Fine-grained Cross-modal Concepts Refinement for Compositional Zero-Shot Learning

Refiner: Fine-grained Cross-modal Concepts Refinement for Co...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Zhang, Xiao Jing, Haodong Chen, Hui Ma, Yongqiang Zheng, Nanning National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Shaanxi China

ISBN: (纸本)9798350368741

Recent Compositional Zero-Shot Learning (CZSL) methods increasingly adopt the pre-trained vision-language models to capture the contextual relations between image and text spaces. However, the single-class-token design from Transformer-based encoder inevitably captures contextual information from unrelated objects and background, thus hindering the modeling of fine-grained class-specific visual features. Suffering from cross-modal gap, prior methods also struggle to improve compositional recognition performance. To address these issues, we propose a fine-grained cross-modal concepts refinement framework, termed as Refiner, which comprises two pivotal components: (i) the fine-grained concepts refinement of image embeddings to capture state-object context within visual scenes, and (ii) the cross-modal information fusion to mitigate the modality gap. By leveraging learnable query vectors to capture region-specific semantic information pertinent to composition labels, our approach refines visual representations with fine-grained state-object context information. As for cross-modal information fusion, we construct a robust image-to-text mapping by aligning visual embeddings with states, objects, and compositions, respectively. Extensive experiments demonstrate that our Refiner achieves new state-of-the-art performance across all popular benchmarks in both closed- and open-world settings. © 2025 IEEE.

关键词： Compositional Zero-shot Learning Cross-Modal Fusion Fine-Grained Refinement Multimodal Models

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EFormer-VPR: Fusing Events and Frames with Transformer for visual Place Recognition 27

EFormer-VPR: Fusing Events and Frames with Transformer for V...

引用

27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Zhang, Yangjing Shen, Yanqing Zhu, Ziyu Hai, Renwei Chen, Shitao Zheng, Nanning National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Shaanxi Xi'an710049 China

ISBN: (纸本)9798331505929

visual place recognition (VPR) is a challenging task faced by mobile robots and autonomous driving systems. In scenarios with glare or high-speed motion, image blurring makes it difficult for traditional cameras to perform reliable and accurate place recognition. In contrast, event cameras can capture target motion information without blur in high-speed motion scenes, but lack texture information in low-speed motion scenes. To leverage the complementary characteristics of these two sensors and improve the performance and robustness of VPR algorithms, we propose EFormer- VPR, which fuses target motion events and frames with transformer. This method firstly preprocesses the events stream within an adaptive time window using a clustering method, then uses transformer-based networks to extract features from motion frames and events separately and fuse them through a scoring module. Finally, features are aggregated using a VLAD layer and the whole pipeline is supervised by a triplet ranking loss. To verify the effectiveness of the proposed algorithm, we compare it with other VPR methods on the event-based driving datasets (Brisbane-Event-VPR, NeuroGPR) with challenging scenarios. Experimental results show that on the Brisbane- Event- Vprand NeuroGPR datasets, our method achieves state-of-the-art performance. © 2024 IEEE.

关键词： Mobile robots

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Diffusion Model with Cross Attention as an Inductive Bias for Disentanglement 38

Diffusion Model with Cross Attention as an Inductive Bias fo...

引用

38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Yang, Tao Lan, Cuiling Lu, Yan Zheng, Nanning National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University China Microsoft Research Asia China

Disentangled representation learning strives to extract the intrinsic factors within the observed data. Factoring these representations in an unsupervised manner is notably challenging and usually requires tailored loss functions or specific structural designs. In this paper, we introduce a new perspective and framework, demonstrating that diffusion models with cross-attention itself can serve as a powerful inductive bias to facilitate the learning of disentangled representations. We propose to encode an image into a set of concept tokens and treat them as the condition of the latent diffusion model for image reconstruction, where cross attention over the concept tokens is used to bridge the encoder and the U-Net of the diffusion model. We analyze that the diffusion process inherently possesses the time-varying information bottlenecks. Such information bottlenecks and cross attention act as strong inductive biases for promoting disentanglement. Without any regularization term in the loss function, this framework achieves superior disentanglement performance on the benchmark datasets, surpassing all previous methods with intricate designs. We have conducted comprehensive ablation studies and visualization analyses, shedding a light on the functioning of this model. We anticipate that our findings will inspire more investigation on exploring diffusion model for disentangled representation learning towards more sophisticated data analysis and understanding. © 2024 Neural information processing systems foundation. All rights reserved.

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Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction 41

Self-Consistency Training for Density-Functional-Theory Hami...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Zhang, He Liu, Chang Wang, Zun Wei, Xinran Liu, Siyuan Zheng, Nanning Shao, Bin Liu, Tie-Yan National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University China Microsoft Research AI for Science United States

Predicting the mean-field Hamiltonian matrix in density functional theory is a fundamental formulation to leverage machine learning for solving molecular science ***, its applicability is limited by insufficient labeled data for *** this work, we highlight that Hamiltonian prediction possesses a self-consistency principle, based on which we propose self-consistency training, an exact training method that does not require labeled *** distinguishes the task from predicting other molecular properties by the following benefits: (1) it enables the model to be trained on a large amount of unlabeled data, hence addresses the data scarcity challenge and enhances generalization;(2) it is more efficient than running DFT to generate labels for supervised training, since it amortizes DFT calculation over a set of *** empirically demonstrate the better generalization in data-scarce and out-of-distribution scenarios, and the better efficiency over DFT *** benefits push forward the applicability of Hamiltonian prediction to an ever-larger scale. Copyright 2024 by the author(s)

关键词： Density functional theory

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Measuring Mutual Policy Divergence for Multi-Agent Sequential Exploration 38

Measuring Mutual Policy Divergence for Multi-Agent Sequentia...

引用

38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Dou, Haowen Dang, Lujuan Luan, Zhirong Chen, Badong National Key Laboratory of Human-Machine Hybrid Augmented Intelligence China National Engineering Research Center for Visual Information and Applications China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University China School of Electrical Engineering Xi'an University of Technology China

Despite the success of Multi-Agent Reinforcement Learning (MARL) algorithms in cooperative tasks, previous works, unfortunately, face challenges in heterogeneous scenarios since they simply disable parameter sharing for agent specialization. Sequential updating scheme was thus proposed, naturally diversifying agents by encouraging agents to learn from preceding ones. However, the exploration strategy in sequential scheme has not been investigated. Benefiting from updating one-by-one, agents have the access to the information from preceding agents. Thus, in this work, we propose to exploit the preceding information to enhance exploration and heterogeneity sequentially. We present Multi-Agent Divergence Policy Optimization (MADPO), equipped with mutual policy divergence maximization framework. We quantify the discrepancies between episodes to enhance exploration and between agents to heterogenize agents, termed intra-agent divergence and inter-agent divergence. To address the issue that traditional divergence measurements lack stability and directionality, we propose to employ the conditional Cauchy-Schwarz divergence to provide entropy-guided exploration incentives. Extensive experiments show that the proposed method outperforms state-of-the-art sequential updating approaches in two challenging multi-agent tasks with various heterogeneous scenarios. Source code is available at https://***/hwdou6677/MADPO. © 2024 Neural information processing systems foundation. All rights reserved.

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Make Your LLM Fully Utilize the Context 38

Make Your LLM Fully Utilize the Context

引用

38th Conference on Neural information processing Systems, NeurIPS 2024

作者： An, Shengnan Ma, Zexiong Lin, Zeqi Zheng, Nanning Lou, Jian-Guang Chen, Weizhu National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center of Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University China Microsoft United States Peking University China

While many contemporary large language models (LLMs) can process lengthy input, they still struggle to fully utilize information within the long context, known as the lost-in-the-middle challenge. We hypothesize that it stems from insufficient explicit supervision during the long-context training, which fails to emphasize that any position in a long context can hold crucial information. Based on this intuition, our study presents information-INtensive (IN2) training, a purely data-driven solution to overcome lost-in-the-middle. Specifically, IN2 training leverages a synthesized long-context question-answer dataset, where the answer requires (1) fine-grained information awareness on a short segment (∼128 tokens) within a synthesized long context (4K−32K tokens), and (2) the integration and reasoning of information from two or more short segments. Through applying this information-intensive training on Mistral-7B, we present FILM-7B (FILl-in-the-Middle). To thoroughly assess the ability of FILM-7B for utilizing long contexts, we design three probing tasks that encompass various context styles (document, code, and structured-data context) and information retrieval patterns (forward, backward, and bi-directional retrieval). The probing results demonstrate that FILM-7B can robustly retrieve information from different positions in its 32K context window. Beyond these probing tasks, FILM-7B significantly improves the performance on real-world long-context tasks (e.g., 23.5→26.9 F1 score on NarrativeQA), while maintaining a comparable performance on short-context tasks (e.g., 59.3→59.2 accuracy on MMLU). © 2024 Neural information processing systems foundation. All rights reserved.

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Can LLMs Learn From Mistakes? An Empirical Study on Reasoning Tasks

Can LLMs Learn From Mistakes? An Empirical Study on Reasonin...

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2024 Conference on Empirical Methods in Natural Language processing, EMNLP 2024

作者： An, Shengnan Ma, Zexiong Cai, Siqi Lin, Zeqi Zheng, Nanning Lou, Jian-Guang Chen, Weizhu National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center of Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University China Microsoft United States Peking University China

ISBN: (纸本)9798891761681

Towards enhancing the chain-of-thought (CoT) reasoning of large language models (LLMs), much existing work has revealed the effectiveness of straightforward learning on annotated/generated CoT paths. However, there is less evidence yet that reasoning capabilities can be enhanced through a reverse learning process, i.e., learning from potential mistakes in reasoning. To investigate whether LLMs can learn from mistakes, we construct mistake-correction datasets, using GPT-4 to identify and correct the mistakes in inaccurate CoTs. With these mistake-correction datasets, we fine-tune open-source LLMs and arrive at the following conclusions. (1) LLMs can indeed learn from mistakes to enhance their CoT reasoning performances. (2) Compared to CoT data, the mistake-correction data provides additional knowledge on the explanations and reasons for the potential mistakes in CoTs, which consistently contributes to the effectiveness of learning from mistakes. (3) Evolution techniques, especially the correction-centric evolution we introduced, can further enhance the effectiveness of learning from mistakes. © 2024 Association for Computational Linguistics.

关键词： Contrastive Learning

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Parking Space Recognition Methods Based on visual Image and Deep Learning

Parking Space Recognition Methods Based on Visual Image and ...

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2023 China Automation Congress, CAC 2023

作者： Deng, Siyi Xia, Yuchen Zuo, Weiliang Xin, Jingmin Zhou, Sanping Zheng, Nanning Institute of Artificial Intelligence and Robotics Xi'An Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Xi'an China

ISBN: (纸本)9798350303759

Parking space recognition technology is of great significance for intelligent parking lot management. To address the shortcomings of current parking lot management methods, we conducted research and practice on parking space recognition based on image processing and deep learning methods, resulting in a comprehensive and effective parking space recognition method. By preprocessing the parking lot images to divide the parking area and using the VGG16 neural network for parking space recognition training, we processed the parking lot video with the trained model. The experimental results show that most of the parking spaces in the video can be accurately identified, with an accuracy rate of 98.32%. The link to the parking space recognition video is: https://***/9xMph9USgW4. © 2023 IEEE.

关键词： Image processing

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SUNOD: Synthetic Underwater Non-Natural Object Detection Dataset

SUNOD: Synthetic Underwater Non-Natural Object Detection Dat...

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2023 China Automation Congress, CAC 2023

作者： Xia, Yuchen Deng, Siyi Li, Yuxi Zuo, Weiliang Xin, Jingmin Zheng, Nanning Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Xi'an China

ISBN: (纸本)9798350303759

Deep-learning-based non-natural object detection in complex underwater environments is widely needed in many application scenarios. However, obtaining image data of underwater non-natural objects is difficult, so there is basically no relevant dataset. Besides, the underwater environments and lighting conditions are complex, which will lead to image quality degradation and increase the difficulty of detecting. For this purpose, we build a Synthetic Underwater Non-natural Object Detection (SUNOD) dataset. We first utilize a free software, Unreal Engine 5 (UE5), to create a realistic three-dimensional virtual underwater environment containing 12 kinds of non-natural objects. Then we take pictures of these non-natural objects and manually label 2,840 images. To make SUNOD more challenging and be able to create object detection models with more generalization capabilities and better robustness, we expand it to contain 72,943 images by 26 kinds of data augmentation methods. In addition, we report the performance of 5 YOLOv8 object detection models with different volumes on SUNOD. SUNOD can be used both to fine-tune pretrained models to accomplish the underwater non-natural object detection task and to be a benchmark to evaluate new models for this task. This study provides the first synthetic dataset for underwater non-natural object detection, which contributes to the research on the intelligence of submarines, exploration robots, and other underwater devices. © 2023 IEEE.

关键词： Object detection

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