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Found 12 papers, 5 papers with code
CM-Diff: A Single Generative Network for Bidirectional Cross-Modality Translation Diffusion Model Between Infrared and Visible Images
In this work, we present the cross-modality translation diffusion model (CM-Diff) for simultaneously modeling data distributions in both the infrared and visible modalities.
IV-tuning: Parameter-Efficient Transfer Learning for Infrared-Visible Tasks
By fine-tuning approximately 3% of the backbone parameters, IV-tuning outperforms full fine-tuning across various baselines in infrared-visible semantic segmentation and object detection, as well as previous state-of-the-art methods.
Deep Learning in Long-Short Stock Portfolio Allocation: An Empirical Study
This paper provides an empirical study explores the application of deep learning algorithms-Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Transformer-in constructing long-short stock portfolios.
IVGF: The Fusion-Guided Infrared and Visible General Framework
Besides, the attention-guided fusion module is proposed for effectively fusing by exploring the complementary information of two modalities.
Advanced Financial Fraud Detection Using GNN-CL Model
The innovative GNN-CL model proposed in this paper marks a breakthrough in the field of financial fraud detection by synergistically combining the advantages of graph neural networks (gnn), convolutional neural networks (cnn) and long short-term memory (LSTM) networks.
Extroversion or Introversion? Controlling The Personality of Your Large Language Models
Our investigation revealed a hierarchy of effectiveness in control: Prompt > SFT > RLHF > Continual Pre-train.
Advancing Financial Risk Prediction Through Optimized LSTM Model Performance and Comparative Analysis
This paper focuses on the application and optimization of LSTM model in financial risk prediction.
AlignGPT: Multi-modal Large Language Models with Adaptive Alignment Capability
Multimodal Large Language Models (MLLMs) are widely regarded as crucial in the exploration of Artificial General Intelligence (AGI).
Ranked #155 on
Visual Question Answering
on MM-Vet
Emotion-Anchored Contrastive Learning Framework for Emotion Recognition in Conversation
To achieve this, we utilize label encodings as anchors to guide the learning of utterance representations and design an auxiliary loss to ensure the effective separation of anchors for similar emotions.
Ranked #7 on
Emotion Recognition in Conversation
on EmoryNLP
DAMSDet: Dynamic Adaptive Multispectral Detection Transformer with Competitive Query Selection and Adaptive Feature Fusion
To effectively mine the complementary information and adapt to misalignment situations, we propose a Multispectral Deformable Cross-attention module to adaptively sample and aggregate multi-semantic level features of infrared and visible images for each object.
InfMAE: A Foundation Model in the Infrared Modality
Finally, based on the fact that infrared images do not have a lot of details and texture information, we design an infrared decoder module, which further improves the performance of downstream tasks.
