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Found 20 papers, 8 papers with code
iBARLE: imBalance-Aware Room Layout Estimation
iBARLE consists of (1) Appearance Variation Generation (AVG) module, which promotes visual appearance domain generalization, (2) Complex Structure Mix-up (CSMix) module, which enhances generalizability w. r. t.
Adaptive Trajectory Prediction via Transferable GNN
To the best of our knowledge, our work is the pioneer which fills the gap in benchmarks and techniques for practical pedestrian trajectory prediction across different domains.
Semi-supervised Domain Adaptive Structure Learning
Semi-supervised domain adaptation (SSDA) is quite a challenging problem requiring methods to overcome both 1) overfitting towards poorly annotated data and 2) distribution shift across domains.
Slow Learning and Fast Inference: Efficient Graph Similarity Computation via Knowledge Distillation
For slow learning of graph similarity, this paper proposes a novel early-fusion approach by designing a co-attention-based feature fusion network on multilevel GNN features.
Sign Language Recognition via Skeleton-Aware Multi-Model Ensemble
Current Sign Language Recognition (SLR) methods usually extract features via deep neural networks and suffer overfitting due to limited and noisy data.
MemREIN: Rein the Domain Shift for Cross-Domain Few-Shot Learning
In this paper, we propose a novel framework, MemREIN, which considers Memorized, Restitution, and Instance Normalization for cross-domain few-shot learning.
Skeleton Aware Multi-modal Sign Language Recognition
Sign language is commonly used by deaf or speech impaired people to communicate but requires significant effort to master.
Ranked #2 on
Sign Language Recognition
on WLASL-2000
Aspect-based Sentiment Classification via Reinforcement Learning
As texts always contain a large proportion of task-irrelevant words, accurate alignment between aspects and their sentimental descriptions is the most crucial and challenging step.
I3DOL: Incremental 3D Object Learning without Catastrophic Forgetting
Moreover, the performance of advanced approaches degrades dramatically for past learned classes (i. e., catastrophic forgetting), due to the irregular and redundant geometric structures of 3D point cloud data.
Job2Vec: Job Title Benchmarking with Collective Multi-View Representation Learning
However, it is still a challenging task since (1) the job title and job transition (job-hopping) data is messy which contains a lot of subjective and non-standard naming conventions for the same position (e. g., Programmer, Software Development Engineer, SDE, Implementation Engineer), (2) there is a large amount of missing title/transition information, and (3) one talent only seeks limited numbers of jobs which brings the incompleteness and randomness modeling job transition patterns.
Collaborative Attention Mechanism for Multi-View Action Recognition
Extensive experiments on four action datasets illustrate the proposed CAM achieves better results for each view and also boosts multi-view performance.
Inductive and Unsupervised Representation Learning on Graph Structured Objects
Inductive and unsupervised graph learning is a critical technique for predictive or information retrieval tasks where label information is difficult to obtain.
Contradictory Structure Learning for Semi-supervised Domain Adaptation
Current adversarial adaptation methods attempt to align the cross-domain features, whereas two challenges remain unsolved: 1) the conditional distribution mismatch and 2) the bias of the decision boundary towards the source domain.
Correlative Channel-Aware Fusion for Multi-View Time Series Classification
Multi-view time series classification (MVTSC) aims to improve the performance by fusing the distinctive temporal information from multiple views.
PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation
Specifically, most general-purpose DA methods that struggle for global feature alignment and ignore local geometric information are not suitable for 3D domain alignment.
Ranked #1 on
Unsupervised Domain Adaptation
on PreSIL to KITTI
Generative Multi-View Human Action Recognition
Multi-view action recognition targets to integrate complementary information from different views to improve classification performance.
EV-Action: Electromyography-Vision Multi-Modal Action Dataset
To make up this, we introduce a new, large-scale EV-Action dataset in this work, which consists of RGB, depth, electromyography (EMG), and two skeleton modalities.
Ranked #4 on
Multimodal Activity Recognition
on EV-Action
An Efficient Approach to Informative Feature Extraction from Multimodal Data
We further generalize the framework to handle more than two modalities and missing modalities.
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
To solve these problems, we propose the very deep residual channel attention networks (RCAN).
Ranked #15 on
Image Super-Resolution
on BSD100 - 4x upscaling
