Search Results for author:
Found 21 papers, 15 papers with code
VideoAgent: A Memory-augmented Multimodal Agent for Video Understanding
We explore how reconciling several foundation models (large language models and vision-language models) with a novel unified memory mechanism could tackle the challenging video understanding problem, especially capturing the long-term temporal relations in lengthy videos.
Systematic Assessment of Tabular Data Synthesis Algorithms
Data synthesis has been advocated as an important approach for utilizing data while protecting data privacy.
Parameter-Efficient Fine-Tuning for Pre-Trained Vision Models: A Survey
Large-scale pre-trained vision models (PVMs) have shown great potential for adaptability across various downstream vision tasks.
CLOVA: A Closed-Loop Visual Assistant with Tool Usage and Update
However, these methods often overlook the potential for continual learning, typically by freezing the utilized tools, thus limiting their adaptation to environments requiring new knowledge.
LaplaceConfidence: a Graph-based Approach for Learning with Noisy Labels
We introduce LaplaceConfidence, a method that to obtain label confidence (i. e., clean probabilities) utilizing the Laplacian energy.
Knowledge-refined Denoising Network for Robust Recommendation
Knowledge graph (KG), which contains rich side information, becomes an essential part to boost the recommendation performance and improve its explainability.
Towards Explainable Collaborative Filtering with Taste Clusters Learning
In recent decades, there have been significant advancements in latent embedding-based CF methods for improved accuracy, such as matrix factorization, neural collaborative filtering, and LightGCN.
Spatial-Temporal Graph Convolutional Gated Recurrent Network for Traffic Forecasting
We design an attention module to capture long-term dependency by mining periodic information in traffic data.
Self-Guided Learning to Denoise for Robust Recommendation
Besides, our method can automatically switch its learning phase at the memorization point from memorization to self-guided learning, and select clean and informative memorized data via a novel adaptive denoising scheduler to improve the robustness.
HAKG: Hierarchy-Aware Knowledge Gated Network for Recommendation
Furthermore, we propose a dual item embeddings design to represent and propagate collaborative signals and knowledge associations separately, and leverage the gated aggregation to distill discriminative information for better capturing user behavior patterns.
Ranked #1 on
Recommendation Systems
on Alibaba-iFashion
MetaKG: Meta-learning on Knowledge Graph for Cold-start Recommendation
Inspired by the success of meta-learning on scarce training samples, we propose a novel meta-learning based framework called MetaKG, which encompasses a collaborative-aware meta learner and a knowledge-aware meta learner, to capture meta users' preference and entities' knowledge for cold-start recommendations.
Deep Spatially and Temporally Aware Similarity Computation for Road Network Constrained Trajectories
Trajectory similarity computation has drawn massive attention, as it is core functionality in a wide range of applications such as ride-sharing, traffic analysis, and social recommendation.
Two Wrongs Don't Make a Right: Combating Confirmation Bias in Learning with Label Noise
We show that our method successfully alleviates the damage of both label noise and confirmation bias.
Ranked #2 on
Image Classification
on mini WebVision 1.0
Finding Materialized Models for Model Reuse
It uses a Gaussian mixture-based metric called separation degree to rank materialized models.
Generation, augmentation, and alignment: A pseudo-source domain based method for source-free domain adaptation
The proposed method firstly generates and augments the pseudo-source domain, and then employs distribution alignment with four novel losses based on pseudo-label based strategy.
AdaRNN: Adaptive Learning and Forecasting of Time Series
This paper proposes Adaptive RNNs (AdaRNN) to tackle the TCS problem by building an adaptive model that generalizes well on the unseen test data.
Semi-Supervised Learning with Multi-Head Co-Training
Co-training, extended from self-training, is one of the frameworks for semi-supervised learning.
Cross-domain error minimization for unsupervised domain adaptation
Unsupervised domain adaptation aims to transfer knowledge from a labeled source domain to an unlabeled target domain.
Learning transferable and discriminative features for unsupervised domain adaptation
In this paper, a novel method called \textit{learning TransFerable and Discriminative Features for unsupervised domain adaptation} (TFDF) is proposed to optimize these two objectives simultaneously.
Dual Adversarial Domain Adaptation
Recent experiments have shown that when the discriminator is provided with domain information in both domains and label information in the source domain, it is able to preserve the complex multimodal information and high semantic information in both domains.
Ranked #5 on
Domain Adaptation
on ImageCLEF-DA
Homogeneous Online Transfer Learning with Online Distribution Discrepancy Minimization
In this paper, we propose a novel online transfer learning method which seeks to find a new feature representation, so that the marginal distribution and conditional distribution discrepancy can be online reduced simultaneously.
