scispace - formally typeset
Search or ask a question

Answers from top 10 papers

More filters
Papers (10)Insight
Experimental results on two public vehicle re-identification datasets including VeRi-776 and VehicleID have shown that our proposed method achieves significant improvements over the state-of-the-art methods.
Extensive experiments conducted on two commonly used datasets VeRi-776 and VehicleID have demonstrated that the proposed DQAL approach outperforms multiple recently reported vehicle Re-ID methods.
Open accessProceedings ArticleDOI
23 Jul 2018
201 Citations
Compared with the global appearance, local regions such as decorations and inspection stickers attached to the windshield, may be more distinctive for vehicle Re-ID.
Proceedings ArticleDOI
Yi Zhou, Ling Shao 
12 Mar 2018
54 Citations
Experimental results illustrate that our approach achieves consistent improvements over state-of-the-art vehicle re-ID methods.
The extensive experiments on a large-scale traffic surveillance vehicle dataset demonstrate that our proposed approach substantially outperforms the state-of-the-art methods on vehicle Re-Id.
Moreover, the approach is generic and has been the first attempt to tackle the vehicle re-ID problem in an unsupervised manner.
The experimental results show that our method outperforms most state-of-the-art approaches for vehicle Re-ID, even if only the appearance module is used.
Experimental results demonstrate the effectiveness of our method and show that it achieves the state-of-the-art performance on both vehicle re-identification and retrieval.
By conducting comprehensive experiments on two vehicle re-identification benchmark datasets VeRi and VehicleID, we demonstrate that the proposed model achieves superior performance over state-of-the-art methods.
Extensive experiments show that the proposed JFSDL method is superior to multiple state-of-the-art vehicle re-identification methods on both the VehicleID and VeRi data sets.

See what other people are reading

What is corporate environmental performance?
5 answers
Corporate environmental performance (CEP) refers to the impact and contribution of corporations to environmental protection during their operations. It is crucial for companies to operate in an environmentally friendly manner, especially in the context of initiatives like China's double carbon target. Studies have shown that CEP has a significant association with financial performance, where good performers tend to disclose better CEP in their reports, positively impacting firm value. Furthermore, improved CEP has been found to reduce financing costs for companies in heavily polluting industries, optimizing their financial performance and encouraging environmental investment. Sustainable development acts as a moderator, weakening the inhibitory effect of CEP on financing costs and playing a regulatory role in promoting corporate sustainable development within heavy pollution industries.
What are the most effective methods for deterring wild monkeys from human habitats?
5 answers
Electric fences have been identified as the most effective method for deterring wild monkeys from human habitats, as they were successful in keeping both baboons and vervets out of crop fields. Other methods like model snakes, bioacoustic sounds, leopard models, and human sounds showed limited effectiveness in deterring primates from crop foraging. Reflective, rotating prisms like the SunGunOne have also been suggested as a potential deterrent for baboons raiding crop fields, with the premise that reflected sunlight startles approaching baboons, altering their movement path and reducing access to human-derived food sources. Additionally, employing machine learning algorithms like YOLO V4 has shown promise in detecting and identifying endangered wild animals, potentially aiding in the mitigation of human-wildlife conflict.
What is the carbon footprint of ChatGPT by OpenAI?
5 answers
The carbon footprint of ChatGPT by OpenAI is not explicitly discussed in the provided contexts. However, the technology behind ChatGPT, which is based on large language models (LLMs) and deep learning algorithms, requires significant computational power and energy consumption. The training process of ChatGPT involves massive amounts of data, which can contribute to a high carbon footprint due to the energy-intensive nature of training deep learning models. While the exact carbon footprint of ChatGPT is not quantified in the literature, it is important to consider the environmental impact of AI technologies like ChatGPT, especially given the energy requirements for training and operating such models. Further research and analysis may be needed to assess and mitigate the carbon footprint associated with ChatGPT and similar AI technologies.
What ist the Carbon Footprint of AI?
5 answers
The carbon footprint of AI is a significant concern due to the environmental impact of training high-end models. Research indicates that training a single high-end AI model can result in emissions exceeding 500t of CO2eq, highlighting the substantial carbon impact of AI development and deployment. Strategies to mitigate this impact include exploring green development approaches for AI industry alliances, promoting green consumption among consumers, and implementing regulations by governments to limit the carbon footprint during the early stages of AI system development. Additionally, distributed and federated learning methods are proposed as alternatives to reduce energy consumption and carbon emissions in AI by efficiently distributing learning tasks across low-power devices. These findings emphasize the importance of understanding and addressing the carbon footprint of AI to promote sustainable development.
What is the knowledge about artificial intelligence in medical imaging?
5 answers
Medical imaging students have fair knowledge about artificial intelligence (AI) and its applications in medical imaging, but lack in-depth knowledge, especially regarding AI's emergence in radiography practice in Ghana. Radiologists and radiologic technologists in Saudi Arabia, Sudan, and Yemen show varying levels of awareness about AI in medical imaging, with radiologists generally being more knowledgeable than technologists. AI, including machine learning (ML) and deep learning (DL), has the potential to revolutionize biomedical imaging by enhancing efficiency, precision in image processing and interpretation, and aiding in the detection of abnormalities that may be missed by human radiologists. The integration of AI into radiology workflows can improve diagnostic accuracy and workflow efficiency across various imaging modalities like MRI, X-ray, and ultrasound.
What is gda?
4 answers
GDA, or Genetic Data Analysis, encompasses various applications across different domains. In the context of the provided research papers, GDA refers to different concepts. One instance of GDA is a software tool for genetic analysis, focusing on measures of genetic diversity, population differentiation, genetic assignment, kinship coefficients, and genetic distances. Another interpretation of GDA is a dedicated augmentation technique for relational texts, aimed at improving relation extraction tasks by preserving semantic consistency and syntax structures in sentences through a generative formulation and entity hints. Additionally, GDA can also stand for Global Cellular Automata, a model that extends the traditional Cellular Automata model by incorporating global neighbors and pointers, enabling parallel processing and application in data parallel applications.
What is gDNA?
5 answers
gDNA, or genomic DNA, refers to the complete set of genetic material present in an organism's genome. It serves as a template for various genetic processes, including gene expression and replication. Techniques for gDNA extraction vary, with methods like the EtNa protocol utilizing hot alkaline ethanol lysis for efficient extraction from bacteria and yeast. In disease monitoring, gDNA qPCR has shown superiority over standard qRT-PCR in detecting disease status, particularly in Chronic Myeloid Leukemia (CML) patients undergoing Tyrosine Kinase Inhibitor (TKI) therapy. The extraction and analysis of gDNA play crucial roles in genetic research, diagnostics, and therapeutic monitoring, highlighting its significance in understanding genetic information and disease progression.
How does blockchain technology consensus mechanisms trust models and reputation systems in blockchain - cloud integration? models and reputation`1111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111?
5 answers
Blockchain technology integrates consensus mechanisms like PoS and PBFT to enhance trust and security. These mechanisms incentivize honest behavior through trust scores and rewards, crucial for block validation and ordering processes. Blockchain's immutability and trustworthiness revolutionize various fields, including business intelligence, by enabling secure data-sharing ecosystems. Consensus mechanisms are pivotal in ensuring security and performance, with different models compared for suitability in diverse scenarios. To manage consensus in opinion dynamics models, a Blockchain-based linguistic model is proposed, emphasizing trust-building mechanisms to achieve consensus while maintaining privacy and eliminating bias. Integrating these elements in blockchain-cloud integration enhances trust, security, and efficient decision-making processes.
What are the results produced from the topic "Optimization of the operational state’s routing for mobile wireless sensor networks"?
5 answers
The research on optimizing the operational state's routing for mobile wireless sensor networks has yielded significant results. Various algorithms and protocols have been proposed to enhance energy efficiency, network performance, and data transmission in such networks. For instance, the proposed Optimum Deterministic Data Gathering Sub-Path Finding (ODDGSPF) algorithm aims to optimize data collection routes and reduce energy consumption. Additionally, the Adaptive fuzzy optimized routing based on Maximum Energy support routing protocol using the SSAMR algorithm has shown improved network performance and minimized delivery time for data transmission. Moreover, the Hybrid Optimization-based Efficient Routing Protocol (HOERP) utilizes grey wolf optimization and particle swarm optimization to minimize energy consumption in mobile wireless sensor networks, demonstrating superior energy efficiency compared to existing routing protocols.
What to do after llm?
5 answers
After working with Large Language Models (LLMs), one can explore various avenues based on the specific context. For instance, in the realm of LLMs, researchers have delved into post-training quantization methods to enhance model efficiency. Additionally, the Lin, Lunin, Maldacena (LLM) ansatz in D = 11 has been studied extensively, revealing insights into the geometry and fundamental constants governing the spacetime. Moreover, a novel framework called LLM-Brain has been proposed, utilizing LLMs as a robotic brain to integrate memory and control for embodied AI systems, showcasing applications in tasks like active exploration and embodied question answering. Therefore, after engaging with LLMs, one can further explore optimization techniques, theoretical implications, or practical applications in the field of artificial intelligence and robotics.
What is the role of social media in political influence in india?
5 answers
Social media plays a crucial role in political influence in India by enabling direct communication between political entities and voters, spreading political messages, and shaping public opinion. Platforms like Facebook, Twitter, Google, and YouTube have become significant players in Indian elections, allowing politicians to engage with a vast audience, particularly the youth. Social media's impact on voter behavior, campaign management, and public participation has been substantial, especially since 2014. Additionally, social media platforms like Twitter have been studied for their role in political polarization among Indian politicians, with discussions on controversial topics leading to higher levels of polarization. Overall, social media in India serves as a powerful tool for political communication, outreach, and influencing public discourse.