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Found 17 papers, 5 papers with code
Investigating LLM Applications in E-Commerce
The emergence of Large Language Models (LLMs) has revolutionized natural language processing in various applications especially in e-commerce.
Coarse-to-Fine Detection of Multiple Seams for Robotic Welding
The RGB image is used to obtain the region of interest by approximately localizing the weld seams, and the point cloud is used to achieve the fine-edge extraction of the weld seams within the region of interest using region growth.
Self-Tuning: Instructing LLMs to Effectively Acquire New Knowledge through Self-Teaching
Motivated by the remarkable success of the Feynman Technique in efficient human learning, we introduce Self-Tuning, a learning framework aimed at improving an LLM's ability to effectively acquire new knowledge from raw documents through self-teaching.
DisenStudio: Customized Multi-subject Text-to-Video Generation with Disentangled Spatial Control
To tackle the problems, in this paper, we propose DisenStudio, a novel framework that can generate text-guided videos for customized multiple subjects, given few images for each subject.
Integrating Present and Past in Unsupervised Continual Learning
We formulate a unifying framework for unsupervised continual learning (UCL), which disentangles learning objectives that are specific to the present and the past data, encompassing stability, plasticity, and cross-task consolidation.
Causal Graph Discovery with Retrieval-Augmented Generation based Large Language Models
This method leverages knowledge compressed in LLMs and knowledge LLMs extracted from scientific publication database as well as experiment data about factors of interest to achieve this goal.
VideoDreamer: Customized Multi-Subject Text-to-Video Generation with Disen-Mix Finetuning
The video generator is further customized for the given multiple subjects by the proposed Disen-Mix Finetuning and Human-in-the-Loop Re-finetuning strategy, which can tackle the attribute binding problem of multi-subject generation.
DisenBooth: Identity-Preserving Disentangled Tuning for Subject-Driven Text-to-Image Generation
To tackle the problems, we propose DisenBooth, an identity-preserving disentangled tuning framework for subject-driven text-to-image generation.
On the Security Vulnerabilities of Text-to-SQL Models
Although it has been demonstrated that Natural Language Processing (NLP) algorithms are vulnerable to deliberate attacks, the question of whether such weaknesses can lead to software security threats is under-explored.
Action-conditioned On-demand Motion Generation
We propose a novel framework, On-Demand MOtion Generation (ODMO), for generating realistic and diverse long-term 3D human motion sequences conditioned only on action types with an additional capability of customization.
Ranked #1 on
Human action generation
on UESTC RGB-D
Fine-Grained Visual Entailment
In this paper, we propose an extension of this task, where the goal is to predict the logical relationship of fine-grained knowledge elements within a piece of text to an image.
Delving into Rectifiers in Style-Based Image Translation
The AdaReLU can dynamically adjust the slope parameters according to the target style and can be utilized to increase the controllability by combining with Adaptive Instance Normalization (AdaIN).
Conditional Sequential Slate Optimization
To that end, we propose a hybrid framework extended from traditional slate optimization to solve the conditional slate optimization problem.
Disentangling Transfer and Interference in Multi-Domain Learning
Humans are incredibly good at transferring knowledge from one domain to another, enabling rapid learning of new tasks.
AutoGL: A Library for Automated Graph Learning
To fill this gap, we present Automated Graph Learning (AutoGL), the first dedicated library for automated machine learning on graphs.
Towards A Friendly Online Community: An Unsupervised Style Transfer Framework for Profanity Redaction
Offensive and abusive language is a pressing problem on social media platforms.
Defect Detection from UAV Images based on Region-Based CNNs
In this work, we study the performance of the region-based CNN for the electrical equipment defect detection by using the UAV images.
