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Found 22 papers, 11 papers with code
Four-hour thunderstorm nowcasting using deep diffusion models of satellite
In terms of application, our system operates efficiently (forecasting 4 hours of convection in 8 minutes), and is highly transferable with the potential to collaborate with multiple satellites for global convection nowcasting.
Codebook Transfer with Part-of-Speech for Vector-Quantized Image Modeling
To this end, we propose a novel codebook transfer framework with part-of-speech, called VQCT, which aims to transfer a well-trained codebook from pretrained language models to VQIM for robust codebook learning.
TinyPredNet: A Lightweight Framework for Satellite Image Sequence Prediction
In this article, we propose a lightweight prediction framework TinyPredNet for satellite image sequence prediction, in which a spatial encoder and decoder model the intra-frame appearance features and a temporal translator captures inter-frame motion patterns.
DiffCast: A Unified Framework via Residual Diffusion for Precipitation Nowcasting
A unified and flexible framework that can equip any type of spatio-temporal models is proposed based on residual diffusion, which effectively tackles the shortcomings of previous methods.
HPCR: Holistic Proxy-based Contrastive Replay for Online Continual Learning
Inspired by this finding, we propose a novel replay-based method called proxy-based contrastive replay (PCR), which replaces anchor-to-sample pairs with anchor-to-proxy pairs in the contrastive-based loss to alleviate the phenomenon of forgetting.
UER: A Heuristic Bias Addressing Approach for Online Continual Learning
By decomposing the dot-product logits into an angle factor and a norm factor, we empirically find that the bias problem mainly occurs in the angle factor, which can be used to learn novel knowledge as cosine logits.
MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning
Its key idea is learning a deep model in a bi-level optimization manner, where the outer-loop process learns a shared gradient descent algorithm (i. e., its hyperparameters), while the inner-loop process leverage it to optimize a task-specific model by using only few labeled data.
PCR: Proxy-based Contrastive Replay for Online Class-Incremental Continual Learning
It aims to continuously learn new classes from data stream and the samples of data stream are seen only once, which suffers from the catastrophic forgetting issue, i. e., forgetting historical knowledge of old classes.
MSTCGAN: Multiscale time conditional generative adversarial network for long-term satellite image sequence prediction
To address the sequential error accumulation issue, MSTCGAN adopts a parallel prediction framework to produce the future image sequences by a one-hot time condition input.
MetaDT: Meta Decision Tree with Class Hierarchy for Interpretable Few-Shot Learning
Then, resorting to the prior, we split each few-shot task to a set of subtasks with different concept levels and then perform class prediction via a model of decision tree.
SGMNet: Scene Graph Matching Network for Few-Shot Remote Sensing Scene Classification
In this framework, a scene graph construction module is carefully designed to represent each test remote sensing image or each scene class as a scene graph, where the nodes reflect these co-occurrence objects meanwhile the edges capture the spatial correlations between these co-occurrence objects.
RAP-Net: Region Attention Predictive Network for Precipitation Nowcasting
Natural disasters caused by heavy rainfall often cost huge loss of life and property.
A multi-domain splitting framework for time-varying graph structure
To address the anomaly detection problem for datasets with a spatial-temporal structure, in this work, we propose a novel graph multi-domain splitting framework, called GMDS, by integrating the time, vertex, and frequency features to locate the anomalies.
Prototype Completion for Few-Shot Learning
In this paper, 1) we figure out the reason, i. e., in the pre-trained feature space, the base classes already form compact clusters while novel classes spread as groups with large variances, which implies that fine-tuning feature extractor is less meaningful; 2) instead of fine-tuning feature extractor, we focus on estimating more representative prototypes.
MetaNODE: Prototype Optimization as a Neural ODE for Few-Shot Learning
Although the existing meta-optimizers can also be adapted to our framework, they all overlook a crucial gradient bias issue, \emph{i. e.}, the mean-based gradient estimation is also biased on sparse data.
Prototype Completion with Primitive Knowledge for Few-Shot Learning
To avoid the prototype completion error caused by primitive knowledge noises or class differences, we further develop a Gaussian based prototype fusion strategy that combines the mean-based and completed prototypes by exploiting the unlabeled samples.
Multi-source Heterogeneous Domain Adaptation with Conditional Weighting Adversarial Network
In reality, however, it is not uncommon to obtain samples from multiple heterogeneous domains.
MetaConcept: Learn to Abstract via Concept Graph for Weakly-Supervised Few-Shot Learning
To this end, we propose a novel meta-learning framework, called MetaConcept, which learns to abstract concepts via the concept graph.
Enhancing Cross-target Stance Detection with Transferable Semantic-Emotion Knowledge
Specifically, a semantic-emotion heterogeneous graph is constructed from external semantic and emotion lexicons, which is then fed into a graph convolutional network to learn multi-hop semantic connections between words and emotion tags.
Inter-sequence Enhanced Framework for Personalized Sequential Recommendation
Then, based on the inter-sequence correlation encoder, we build GRU network and attention network in the intra-sequence correlation encoder to model the item sequential correlation within each individual sequence and temporal dynamics for predicting users' preferences over candidate items.
Heterogeneous Domain Adaptation via Soft Transfer Network
Heterogeneous domain adaptation (HDA) aims to facilitate the learning task in a target domain by borrowing knowledge from a heterogeneous source domain.
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling
The DRR framework treats recommendation as a sequential decision making procedure and adopts an "Actor-Critic" reinforcement learning scheme to model the interactions between the users and recommender systems, which can consider both the dynamic adaptation and long-term rewards.
