Vehicle natural language description lables from CityFlow-NL dataset.

Vehicle natural language description lables from CityFlow-NL dataset.

<p>Vehicle natural language description lables from CityFlow-NL dataset.</p>

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Changhao Zhang (547149) (author)
مؤلفون آخرون:	Zhandong Liu (516336) (author), Ke Li (106849) (author), Yong Li (15029) (author), Xiangwei Qi (22032822) (author), Nan Ding (710349) (author)
منشور في:	2025
الموضوعات:	Ecology Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified test data set significantly enhanced image retrieving tracked vehicles natural language descriptions mean reciprocal ranking eliminate erroneous information camera surveillance scenarios video retrieval capabilities modal vehicle retrieval modal retrieval framework matching control system div >< p mvr system integrates view retrieval system designed mvr system utilizes clip traffic environments th </ recent advances ranked result processing techniques previous top paper introduces method achieves feature extraction contrastive learning based attributes
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

مواد مشابهة

Different video frames and natural language (NL) descriptions of the same vehicle are included in the CityFlow-NL training set.
حسب: Changhao Zhang (547149)
منشور في: (2025)

Top-3 video-to-text retrieval results on CityFlow-NL dataset.
حسب: Changhao Zhang (547149)
منشور في: (2025)

Examples of video frame images extracted from the Cityflow-nl dataset.
حسب: Changhao Zhang (547149)
منشور في: (2025)

Leaderboard for the tracked vehicle retrieval challenge using natural language descriptions.
حسب: Changhao Zhang (547149)
منشور في: (2025)

Example of a vehicle motion module.
حسب: Changhao Zhang (547149)
منشور في: (2025)

Ablation study analysis of our MVR method. MRR_I: Improvement in MRR, F_MRR: Final MRR Value.
حسب: Changhao Zhang (547149)
منشور في: (2025)

The example of control match system.
حسب: Changhao Zhang (547149)
منشور في: (2025)

The core architecture of our video recognition module involves different processing steps for visual and textual data.
حسب: Changhao Zhang (547149)
منشور في: (2025)

Overview of our proposed MVR framework.
حسب: Changhao Zhang (547149)
منشور في: (2025)

Ablation study results of MDKG-RL model on ICDAR 2023 and AIDA corpus datasets: Comparison of performance metrics with component removal.
حسب: Yali Chen (24494)
منشور في: (2025)

Statistical significance of performance improvements between MDKG-RL and BERT-GNN models across multiple metrics.
حسب: Yali Chen (24494)
منشور في: (2025)

Comparative performance of different models on ICDAR 2023 and AIDA corpus datasets based on MRR, NDCG, , and ELA.
حسب: Yali Chen (24494)
منشور في: (2025)

Details of MDKG-RL model performance evaluation metrics.
حسب: Yali Chen (24494)
منشور في: (2025)

Experimental environment configuration details.
حسب: Yali Chen (24494)
منشور في: (2025)

Details of public datasets used in the experiment (ICDAR 2023 & AIDA Corpus).
حسب: Yali Chen (24494)
منشور في: (2025)

Confusion matrix showing the entity linking error distribution for core entities, long-tail entities, cross-modal ambiguity, events, locations, and organizations by the MDKG-RL model on the AIDA Corpus dataset.
حسب: Yali Chen (24494)
منشور في: (2025)

Dynamic changes in reward values and retrieval accuracy over training iterations during deep reinforcement learning (DRL) training.
حسب: Yali Chen (24494)
منشور في: (2025)

Boxplot comparing the response time distribution of MDKG-RL, BERT-GNN, and Baseline models on ICDAR 2023 and AIDA Corpus datasets.
حسب: Yali Chen (24494)
منشور في: (2025)

Heatmap of the probability distribution of retrieval result ranking positions for MDKG-RL and Baseline models on the ICDAR 2023 dataset.
حسب: Yali Chen (24494)
منشور في: (2025)

MDKG-RL model component ablation experiment performance comparison (ICDAR 2023 vs AIDA Corpus).
حسب: Yali Chen (24494)
منشور في: (2025)

Performance comparison of different models on ICDAR 2023 and AIDA Corpus datasets (MRR, NDCG, response time, entity linking accuracy).
حسب: Yali Chen (24494)
منشور في: (2025)

Operational mechanism diagram of deep reinforcement learning retrieval optimization module, based on the user interaction retrieval strategy optimization process, encodes the user query vector, historical interaction record and knowledge graph subgraph representation into state space, designs reward function, and trains the policy network through the proximal policy optimization algorithm (PPO) to optimize the retrieval strategy.
حسب: Yali Chen (24494)
منشور في: (2025)

Schematic diagram of the construction and reasoning of the graph neural network and knowledge graph fusion module, covering the entity alignment in graph construction (mapping multimodal entity representations to a unified space based on the TransE model), relational reasoning (aggregating neighbor node features using the GraphSAGE model), and the process of logical rule injection and external knowledge fusion (through the SWRL rule engine and access to the general knowledge base).
حسب: Yali Chen (24494)
منشور في: (2025)

The working principle diagram of the multimodal Transformer feature extraction module, presenting the feature extraction and fusion process of text, image, and audio data, the specific steps of feature extraction of text data through the BERT model, image data with the help of Vision Transformer (ViT), and audio data with Audio Transformer, and finally the process of realizing multimodal feature fusion through the joint attention mechanism.
حسب: Yali Chen (24494)
منشور في: (2025)

Schematic diagram of the overall architecture of the MDKG-RL model, showing the process of multimodal archival data processing, knowledge graph construction and retrieval optimization.
حسب: Yali Chen (24494)
منشور في: (2025)

African Market OS- MVR API Python SDK
حسب: Farouk Mark Mukiibi (22233445)
منشور في: (2025)

SCOLIA25-Lesavourey_Hubert.pdf
حسب: International Workshop on Scholarly Information Access (21153161)
منشور في: (2025)

SCOLIA25-Karl_Scherp.pdf
حسب: International Workshop on Scholarly Information Access (21153161)
منشور في: (2025)

The ARP, ARR Results for Corel-10k.
حسب: Aiza Shabir (20896271)
منشور في: (2025)

The Average Precision, Average Recall Result for Tropical-Fruits.
حسب: Aiza Shabir (20896271)
منشور في: (2025)

Zubud Database Mean Average Recall (%).
حسب: Aiza Shabir (20896271)
منشور في: (2025)

Zubud Database Mean Average Precision (%).
حسب: Aiza Shabir (20896271)
منشور في: (2025)

Zubud Database F-Measure.
حسب: Aiza Shabir (20896271)
منشور في: (2025)

Zubud Database Average Recall (%).
حسب: Aiza Shabir (20896271)
منشور في: (2025)

Zubud Database Average Precision (%).
حسب: Aiza Shabir (20896271)
منشور في: (2025)

(a).Corel-10k Mean Average Precision (%). (b).Corel-10k Mean Average Recall (%).
حسب: Aiza Shabir (20896271)
منشور في: (2025)

Corel-10k F-measure.
حسب: Aiza Shabir (20896271)
منشور في: (2025)

Corel-10k Database Average Recall (%).
حسب: Aiza Shabir (20896271)
منشور في: (2025)

Corel-10k Database Average Precision (%).
حسب: Aiza Shabir (20896271)
منشور في: (2025)

(a) Cifar-10 Database F-Measure. (b) Cifar-10 Database Mean Average Precision (%). (c) Cifar-10 Database Mean Average Recall (%).
حسب: Aiza Shabir (20896271)
منشور في: (2025)

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