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Found 11 papers, 8 papers with code
DDF: A Novel Dual-Domain Image Fusion Strategy for Remote Sensing Image Semantic Segmentation with Unsupervised Domain Adaptation
Semantic segmentation of remote sensing images is a challenging and hot issue due to the large amount of unlabeled data.
Hand-Centric Motion Refinement for 3D Hand-Object Interaction via Hierarchical Spatial-Temporal Modeling
To address this problem, we propose a data-driven method for coarse motion refinement.
Prior-Free Continual Learning with Unlabeled Data in the Wild
Existing CL methods usually reduce forgetting with task priors, \ie using task identity or a subset of previously seen samples for model training.
Continual Learning with Strong Experience Replay
Experience Replay (ER) is a simple and effective rehearsal-based strategy, which optimizes the model with current training data and a subset of old samples stored in a memory buffer.
Temporal Action Localization with Multi-temporal Scales
However, the temporal features of a low-level scale lack enough semantics for action classification while a high-level scale cannot provide rich details of the action boundaries.
Effective Abstract Reasoning with Dual-Contrast Network
As a step towards improving the abstract reasoning capability of machines, we aim to solve Raven's Progressive Matrices (RPM) with neural networks, since solving RPM puzzles is highly correlated with human intelligence.
Unsupervised Abstract Reasoning for Raven's Problem Matrices
Raven's Progressive Matrices (RPM) is highly correlated with human intelligence, and it has been widely used to measure the abstract reasoning ability of humans.
Solving Raven's Progressive Matrices with Neural Networks
Based on the design of the pseudo target, MCPT converts the unsupervised learning problem to a supervised task.
Fast Video Object Segmentation via Mask Transfer Network
To overcome this limitation, we propose a novel mask transfer network (MTN), which can greatly boost the processing speed of VOS and also achieve a reasonable accuracy.
Explainable Video Action Reasoning via Prior Knowledge and State Transitions
Finally, by sequentially examining each state transition in the video graph, our method can detect and explain how those actions are executed with prior knowledge, just like the logical manner of thinking by humans.
Unsupervised Online Video Object Segmentation with Motion Property Understanding
Moreover, our method achieves better performance than the best unsupervised offline algorithm on the DAVIS-2016 benchmark dataset.
