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Found 17 papers, 6 papers with code
Investigation of the effectiveness of applying ChatGPT in Dialogic Teaching Using Electroencephalography
However, students who engaged in dialogue with ChatGPT exhibited lower performance on the transfer test.
Large Language Models for Robotics: Opportunities, Challenges, and Perspectives
Notably, in the realm of robot task planning, LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions.
Understanding LLMs: A Comprehensive Overview from Training to Inference
Low-cost training and deployment of LLMs represent the future development trend.
Holistic Evaluation of GPT-4V for Biomedical Imaging
GPT-4V represents a breakthrough in artificial general intelligence (AGI) for computer vision, with applications in the biomedical domain.
PhotoVerse: Tuning-Free Image Customization with Text-to-Image Diffusion Models
Personalized text-to-image generation has emerged as a powerful and sought-after tool, empowering users to create customized images based on their specific concepts and prompts.
Collaborative Tracking Learning for Frame-Rate-Insensitive Multi-Object Tracking
Multi-object tracking (MOT) at low frame rates can reduce computational, storage and power overhead to better meet the constraints of edge devices.
Review of Large Vision Models and Visual Prompt Engineering
This review aims to summarize the methods employed in the computer vision domain for large vision models and visual prompt engineering, exploring the latest advancements in visual prompt engineering.
Prompt Engineering for Healthcare: Methodologies and Applications
Prompt engineering is a critical technique in the field of natural language processing that involves designing and optimizing the prompts used to input information into models, aiming to enhance their performance on specific tasks.
Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models
This paper presents a comprehensive survey of ChatGPT-related (GPT-3. 5 and GPT-4) research, state-of-the-art large language models (LLM) from the GPT series, and their prospective applications across diverse domains.
Spatial-Temporal Convolutional Attention for Mapping Functional Brain Networks
To validate the performance of the proposed method, we evaluate the approach on HCP-rest dataset.
Discovering Dynamic Functional Brain Networks via Spatial and Channel-wise Attention
More importantly, our proposed hybrid attention modules (SA and CA) do not enforce assumptions of linearity and independence as previous methods, and thus provide a novel approach to better understanding dynamic functional brain networks.
Unsupervised Person Re-Identification with Wireless Positioning under Weak Scene Labeling
To this end, we propose to explore unsupervised person re-identification with both visual data and wireless positioning trajectories under weak scene labeling, in which we only need to know the locations of the cameras.
Learning Linear Non-Gaussian Graphical Models with Multidirected Edges
In this paper we propose a new method to learn the underlying acyclic mixed graph of a linear non-Gaussian structural equation model given observational data.
Vision Meets Wireless Positioning: Effective Person Re-identification with Recurrent Context Propagation
Existing person re-identification methods rely on the visual sensor to capture the pedestrians.
An End-to-End Foreground-Aware Network for Person Re-Identification
By presenting a target attention loss, the pedestrian features extracted from the foreground branch become more insensitive to the backgrounds, which greatly reduces the negative impacts of changing backgrounds on matching an identical across different camera views.
WIDER Face and Pedestrian Challenge 2018: Methods and Results
This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian.
Spatial and Temporal Mutual Promotion for Video-based Person Re-identification
How to explore the abundant spatial-temporal information in video sequences is the key to solve this problem.
