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Found 24 papers, 6 papers with code
LMP: Leveraging Motion Prior in Zero-Shot Video Generation with Diffusion Transformer
A reweighted motion transfer module is designed to allow the target video to reference the motion from the reference video.
A High-Performance Thermal Infrared Object Detection Framework with Centralized Regulation
In this study, we present a novel and efficient thermal infrared object detection framework, known as CRT-YOLO, that is based on centralized feature regulation, enabling the establishment of global-range interaction on TIR information.
ABC-GS: Alignment-Based Controllable Style Transfer for 3D Gaussian Splatting
Moreover, a style transfer loss function based on feature alignment is proposed to ensure that the outcomes of style transfer accurately reflect the global style of the reference image.
DeepCircuitX: A Comprehensive Repository-Level Dataset for RTL Code Understanding, Generation, and PPA Analysis
This paper introduces DeepCircuitX, a comprehensive repository-level dataset designed to advance RTL (Register Transfer Level) code understanding, generation, and power-performance-area (PPA) analysis.
DeepCell: Multiview Representation Learning for Post-Mapping Netlists
Representation learning for post-mapping (PM) netlists is a critical challenge in Electronic Design Automation (EDA), driven by the diverse and complex nature of modern circuit designs.
OpenLS-DGF: An Adaptive Open-Source Dataset Generation Framework for Machine Learning Tasks in Logic Synthesis
This paper introduces OpenLS-DGF, an adaptive logic synthesis dataset generation framework, to enhance machine learning~(ML) applications within the logic synthesis process.
RuleAlign: Making Large Language Models Better Physicians with Diagnostic Rule Alignment
Large Language Models (LLMs) like GPT-4, MedPaLM-2, and Med-Gemini achieve performance competitively with human experts across various medical benchmarks.
FIND: Fine-tuning Initial Noise Distribution with Policy Optimization for Diffusion Models
In this paper, we introduce a Fine-tuning Initial Noise Distribution (FIND) framework with policy optimization, which unleashes the powerful potential of pre-trained diffusion networks by directly optimizing the initial distribution to align the generated contents with user-input prompts.
A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions
With the advent of Large Language Models (LLMs), medical artificial intelligence (AI) has experienced substantial technological progress and paradigm shifts, highlighting the potential of LLMs to streamline healthcare delivery and improve patient outcomes.
Towards Automatic Evaluation for LLMs' Clinical Capabilities: Metric, Data, and Algorithm
Applying such paradigm, we construct an evaluation benchmark in the field of urology, including a LCP, a SPs dataset, and an automated RAE.
Unified Hallucination Detection for Multimodal Large Language Models
Despite significant strides in multimodal tasks, Multimodal Large Language Models (MLLMs) are plagued by the critical issue of hallucination.
RJUA-QA: A Comprehensive QA Dataset for Urology
We introduce RJUA-QA, a novel medical dataset for question answering (QA) and reasoning with clinical evidence, contributing to bridge the gap between general large language models (LLMs) and medical-specific LLM applications.
From Beginner to Expert: Modeling Medical Knowledge into General LLMs
(3) Specifically for multi-choice questions in the medical domain, we propose a novel Verification-of-Choice approach for prompting engineering, which significantly enhances the reasoning ability of LLMs.
Think-in-Memory: Recalling and Post-thinking Enable LLMs with Long-Term Memory
Memory-augmented Large Language Models (LLMs) have demonstrated remarkable performance in long-term human-machine interactions, which basically relies on iterative recalling and reasoning of history to generate high-quality responses.
Simple but Effective Unsupervised Classification for Specified Domain Images: A Case Study on Fungi Images
Addressing this, an unsupervised classification method with three key ideas is introduced: 1) dual-step feature dimensionality reduction using a pre-trained model and manifold learning, 2) a voting mechanism from multiple clustering algorithms, and 3) post-hoc instead of prior manual annotation.
AdaptMVSNet: Efficient Multi-View Stereo with adaptive convolution and attention fusion
To this end, adaptive convolution is introduced to significantly improve the efficiency in speed and metrics compared to current methods.
CeRF: Convolutional Neural Radiance Fields for New View Synthesis with Derivatives of Ray Modeling
In recent years, novel view synthesis has gained popularity in generating high-fidelity images.
Who Would be Interested in Services? An Entity Graph Learning System for User Targeting
At the online stage, the system offers the ability of user targeting in real-time based on the entity graph from the offline stage.
Time Series Domain Adaptation via Sparse Associative Structure Alignment
To reduce the difficulty in the discovery of causal structure, we relax it to the sparse associative structure and propose a novel sparse associative structure alignment model for domain adaptation.
Deeply Supervised Active Learning for Finger Bones Segmentation
Segmentation is a prerequisite yet challenging task for medical image analysis.
Causal Mechanism Transfer Network for Time Series Domain Adaptation in Mechanical Systems
However, the mainstream domain adaptation methods cannot achieve ideal performance on time series data, because most of them focus on static samples and even the existing time series domain adaptation methods ignore the properties of time series data, such as temporal causal mechanism.
Data Driven Chiller Plant Energy Optimization with Domain Knowledge
Refrigeration and chiller optimization is an important and well studied topic in mechanical engineering, mostly taking advantage of physical models, designed on top of over-simplified assumptions, over the equipments.
An Encoder-Decoder Framework Translating Natural Language to Database Queries
These techniques help the neural network better focus on understanding semantics of operations in natural language and save the efforts on SQL grammar learning.
Selective Annotation of Sentence Parts: Identification of Relevant Sub-sentential Units
Many NLP tasks involve sentence-level annotation yet the relevant information is not encoded at sentence level but at some relevant parts of the sentence.
