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Found 26 papers, 8 papers with code
KiPT: Knowledge-injected Prompt Tuning for Event Detection
Then, knowledge-injected prompts are constructed using external knowledge bases, and a prompt tuning strategy is leveraged to optimize the prompts.
Qwen Look Again: Guiding Vision-Language Reasoning Models to Re-attention Visual Information
Inference time scaling drives extended reasoning to enhance the performance of Vision-Language Models (VLMs), thus forming powerful Vision-Language Reasoning Models (VLRMs).
GMM-Based Comprehensive Feature Extraction and Relative Distance Preservation For Few-Shot Cross-Modal Retrieval
Additionally, we introduce a new strategy for cross-modal semantic alignment, which constrains the relative distances between image and text feature distributions, thereby improving the accuracy of cross-modal representations.
GraphSOS: Graph Sampling and Order Selection to Help LLMs Understand Graphs Better
The success of Large Language Models (LLMs) in various domains has led researchers to apply them to graph-related problems by converting graph data into natural language text.
Domaino1s: Guiding LLM Reasoning for Explainable Answers in High-Stakes Domains
However, current LLMs for high-stakes domain tasks, such as financial investment and legal QA, typically generate brief answers without reasoning processes and explanations.
Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning
Dynamic graph augmentation is used to improve the performance of dynamic GNNs.
Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions
Consequently, we have devised Multiview Image Augmented VLM (MIAVLM), incorporating a Multiview Attributes Perceiver (MAP) submodule capable of simultaneously eliminating the influence of input image order and aligning visual information from multiview images with Large Language Models (LLMs).
Order Matters: Exploring Order Sensitivity in Multimodal Large Language Models
Furthermore, we demonstrate that popular MLLMs pay special attention to certain multimodal context positions, particularly the beginning and end.
Towards Defining an Efficient and Expandable File Format for AI-Generated Contents
Recently, AI-generated content (AIGC) has gained significant traction due to its powerful creation capability.
Supportiveness-based Knowledge Rewriting for Retrieval-augmented Language Modeling
Retrieval-augmented language models (RALMs) have recently shown great potential in mitigating the limitations of implicit knowledge in LLMs, such as untimely updating of the latest expertise and unreliable retention of long-tail knowledge.
Joint Event Extraction via Structural Semantic Matching
Event Extraction (EE) is one of the essential tasks in information extraction, which aims to detect event mentions from text and find the corresponding argument roles.
NVTC: Nonlinear Vector Transform Coding
In theory, vector quantization (VQ) is always better than scalar quantization (SQ) in terms of rate-distortion (R-D) performance.
Exploiting Hybrid Semantics of Relation Paths for Multi-hop Question Answering Over Knowledge Graphs
Answering natural language questions on knowledge graphs (KGQA) remains a great challenge in terms of understanding complex questions via multi-hop reasoning.
Eliciting Knowledge from Pretrained Language Models for Prototypical Prompt Verbalizer
The distances between the embedding at the masked position of input and prototypical embeddings are used as classification criterion.
The Parameter Sensitivities of a Jump-diffusion Process in Basic Credit Risk Analysis
We detect the parameter sensitivities of bond pricing which is driven by a Brownian motion and a compound Poisson process as the discontinuous case in credit risk research.
Pricing S&P 500 Index Options with Lévy Jumps
We analyze various jumps for Heston model, non-IID model and three L\'evy jump models for S&P 500 index options.
Enhancing Neural Models with Vulnerability via Adversarial Attack
2) There is no previous work considering adversarial attack to improve the performance of NLSM tasks.
What Do Questions Exactly Ask? MFAE: Duplicate Question Identification with Multi-Fusion Asking Emphasis
We firstly propose to explore the asking emphasis of a question as a key factor in DQI.
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Blind Quality Assessment for Image Superresolution Using Deep Two-Stream Convolutional Networks
Numerous image superresolution (SR) algorithms have been proposed for reconstructing high-resolution (HR) images from input images with lower spatial resolutions.
Prior-enlightened and Motion-robust Video Deblurring
Various blur distortions in video will cause negative impact on both human viewing and video-based applications, which makes motion-robust deblurring methods urgently needed.
Region Normalization for Image Inpainting
In this work, we show that the mean and variance shifts caused by full-spatial FN limit the image inpainting network training and we propose a spatial region-wise normalization named Region Normalization (RN) to overcome the limitation.
Asynchronous Episodic Deep Deterministic Policy Gradient: Towards Continuous Control in Computationally Complex Environments
Not limited to the control tasks in computationally complex environments, AE-DDPG also achieves higher rewards and 2- to 4-fold improvement in sample efficiency on average compared to other variants of DDPG in MuJoCo environments.
Review of Deep Learning
In recent years, China, the United States and other countries, Google and other high-tech companies have increased investment in artificial intelligence.
Video-based Sign Language Recognition without Temporal Segmentation
Worse still, isolated SLR methods typically require strenuous labeling of each word separately in a sentence, severely limiting the amount of attainable training data.
An influence-based fast preceding questionnaire model for elderly assessments
The practice in an elderly-care company shows that the FPQM can reduce the number of attributes by 90. 56% with a prediction accuracy of 98. 39%.
Comparative Deep Learning of Hybrid Representations for Image Recommendations
In many image-related tasks, learning expressive and discriminative representations of images is essential, and deep learning has been studied for automating the learning of such representations.
