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Found 17 papers, 11 papers with code
SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation
We hope that our work will inspire future research into what can be achieved by versatile multimodal foundation models in real-world applications.
Wills Aligner: A Robust Multi-Subject Brain Representation Learner
We meticulously evaluate the performance of our approach across coarse-grained and fine-grained visual decoding tasks.
Deep Coupling Network For Multivariate Time Series Forecasting
Multivariate time series (MTS) forecasting is crucial in many real-world applications.
Frequency Spectrum is More Effective for Multimodal Representation and Fusion: A Multimodal Spectrum Rumor Detector
Multimodal content, such as mixing text with images, presents significant challenges to rumor detection in social media.
ViT-Lens: Towards Omni-modal Representations
In this paper, we present ViT-Lens-2 that facilitates efficient omni-modal representation learning by perceiving novel modalities with a pretrained ViT and aligning them to a pre-defined space.
Frequency-domain MLPs are More Effective Learners in Time Series Forecasting
FreTS mainly involves two stages, (i) Domain Conversion, that transforms time-domain signals into complex numbers of frequency domain; (ii) Frequency Learning, that performs our redesigned MLPs for the learning of real and imaginary part of frequency components.
ViT-Lens: Initiating Omni-Modal Exploration through 3D Insights
A well-trained lens with a ViT backbone has the potential to serve as one of these foundation models, supervising the learning of subsequent modalities.
Ranked #2 on
Zero-Shot Transfer 3D Point Cloud Classification
on ModelNet40
(using extra training data)
A Survey on Deep Learning based Time Series Analysis with Frequency Transformation
Despite the growing attention and the proliferation of research in this emerging field, there is currently a lack of a systematic review and in-depth analysis of deep learning-based time series models with FT.
Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspective
To bridge such significant gap, we formulate the fairness modeling problem as learning informative representations attending to both advantaged and disadvantaged variables.
RILS: Masked Visual Reconstruction in Language Semantic Space
Both masked image modeling (MIM) and natural language supervision have facilitated the progress of transferable visual pre-training.
Edge-Varying Fourier Graph Networks for Multivariate Time Series Forecasting
The key problem in multivariate time series (MTS) analysis and forecasting aims to disclose the underlying couplings between variables that drive the co-movements.
Distributional Drift Adaptation with Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecasting
Most existing MTS forecasting models greatly suffer from distribution drift and degrade the forecasting performance over time.
Masked Image Modeling with Denoising Contrast
Since the development of self-supervised visual representation learning from contrastive learning to masked image modeling (MIM), there is no significant difference in essence, that is, how to design proper pretext tasks for vision dictionary look-up.
mc-BEiT: Multi-choice Discretization for Image BERT Pre-training
Image BERT pre-training with masked image modeling (MIM) becomes a popular practice to cope with self-supervised representation learning.
PENCIL: Deep Learning with Noisy Labels
It is easy to collect a dataset with noisy labels, but such noise makes networks overfit seriously and accuracies drop dramatically.
Recommending POIs for Tourists by User Behavior Modeling and Pseudo-Rating
Our mechanism include two components: one is a probabilistic model that reveals the user behaviors in tourism; the other is a pseudo rating mechanism to handle the cold-start issue in POIs recommendations.
Probabilistic End-to-end Noise Correction for Learning with Noisy Labels
Deep learning has achieved excellent performance in various computer vision tasks, but requires a lot of training examples with clean labels.
Ranked #25 on
Image Classification
on Clothing1M
(using extra training data)
