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Found 8 papers, 3 papers with code
Goal-Conditioned Reinforcement Learning with Disentanglement-based Reachability Planning
In the paper, we propose a goal-conditioned RL algorithm combined with Disentanglement-based Reachability Planning (REPlan) to solve temporally extended tasks.
3D Part Assembly Generation with Instance Encoded Transformer
It is desirable to enable robots capable of automatic assembly.
Weakly Supervised Disentangled Representation for Goal-conditioned Reinforcement Learning
In the paper, we propose a skill learning framework DR-GRL that aims to improve the sample efficiency and policy generalization by combining the Disentangled Representation learning and Goal-conditioned visual Reinforcement Learning.
Diverse Knowledge Distillation for End-to-End Person Search
Person search aims to localize and identify a specific person from a gallery of images.
Mask Encoding for Single Shot Instance Segmentation
To date, instance segmentation is dominated by twostage methods, as pioneered by Mask R-CNN.
Part-Guided Attention Learning for Vehicle Instance Retrieval
Finally, we aggregate the global appearance and part features to improve the feature performance further.
Self-training with progressive augmentation for unsupervised cross-domain person re-identification
In this work, we develop a self-training method with progressive augmentation framework (PAST) to promote the model performance progressively on the target dataset.
Ranked #11 on
Unsupervised Domain Adaptation
on Market to Duke
Adversarial Generation of Training Examples: Applications to Moving Vehicle License Plate Recognition
Our experimental results on a few data sets demonstrate the effectiveness of using GAN images: an improvement of 7. 5% over a strong baseline with moderate-sized real data being available.
