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Found 17 papers, 10 papers with code
High-Dimension Human Value Representation in Large Language Models
there is an urgent need to understand the scope and nature of human values injected into these models before their release.
Measuring Political Bias in Large Language Models: What Is Said and How It Is Said
We propose to measure political bias in LLMs by analyzing both the content and style of their generated content regarding political issues.
Subobject-level Image Tokenization
Transformer-based vision models typically tokenize images into fixed-size square patches as input units, which lacks the adaptability to image content and overlooks the inherent pixel grouping structure.
Few-shot Adaptation of Multi-modal Foundation Models: A Survey
Therefore, in this survey, we introduce and analyze the research advancements in few-shot adaptation methods for multi-modal models, summarizing commonly used datasets and experimental setups, and comparing the results of different methods.
Towards Joint Modeling of Dialogue Response and Speech Synthesis based on Large Language Model
This paper explores the potential of constructing an AI spoken dialogue system that "thinks how to respond" and "thinks how to speak" simultaneously, which more closely aligns with the human speech production process compared to the current cascade pipeline of independent chatbot and Text-to-Speech (TTS) modules.
Visual Instruction Tuning with Polite Flamingo
This side effect negatively impacts the model's ability to format responses appropriately -- for instance, its "politeness" -- due to the overly succinct and unformatted nature of raw annotations, resulting in reduced human preference.
RemoteCLIP: A Vision Language Foundation Model for Remote Sensing
However, these models primarily learn low-level features and require annotated data for fine-tuning.
Ranked #2 on
Cross-Modal Retrieval
on RSITMD
(using extra training data)
Taming Diffusion Models for Music-driven Conducting Motion Generation
Generating the motion of orchestral conductors from a given piece of symphony music is a challenging task since it requires a model to learn semantic music features and capture the underlying distribution of real conducting motion.
Few-shot Classification via Ensemble Learning with Multi-Order Statistics
To address this need, we prove theoretically that leveraging ensemble learning on the base classes can correspondingly reduce the true error in the novel classes.
ProtoCLIP: Prototypical Contrastive Language Image Pretraining
Based on this understanding, in this paper, Prototypical Contrastive Language Image Pretraining (ProtoCLIP) is introduced to enhance such grouping by boosting its efficiency and increasing its robustness against the modality gap.
A Simple Baseline for Adversarial Domain Adaptation-based Unsupervised Flood Forecasting
Specifically, training of FloodDAN includes two stages: in the first stage, we train a rainfall encoder and a prediction head to learn general transferable hydrological knowledge on large-scale source domain data; in the second stage, we transfer the knowledge in the pretrained encoder into the rainfall encoder of target domain through adversarial domain alignment.
Self-Supervised Music-Motion Synchronization Learning for Music-Driven Conducting Motion Generation
Therefore, we propose a novel Music Motion Synchronized Generative Adversarial Network (M2S-GAN), which generates motions according to the automatically learned music representations.
Survey of Hallucination in Natural Language Generation
This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation and data-to-text generation.
VirtualConductor: Music-driven Conducting Video Generation System
In this demo, we present VirtualConductor, a system that can generate conducting video from any given music and a single user's image.
Significant Wave Height Prediction based on Wavelet Graph Neural Network
Several parallel graph neural networks are separately trained on wavelet decomposed data, and the reconstruction of each model's prediction forms the final SWH prediction.
Deep Learning Based Single Sample Per Person Face Recognition: A Survey
Face recognition under this situation is referred to as single sample face recognition and poses significant challenges to the effective training of deep models.
A Review of Automated Diagnosis of COVID-19 Based on Scanning Images
The pandemic of COVID-19 has caused millions of infections, which has led to a great loss all over the world, socially and economically.
