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Found 6 papers, 6 papers with code
PointNetGPD: Detecting Grasp Configurations from Point Sets
In this paper, we propose an end-to-end grasp evaluation model to address the challenging problem of localizing robot grasp configurations directly from the point cloud.
Robotics
Nearest Neighborhood-Based Deep Clustering for Source Data-absent Unsupervised Domain Adaptation
To reach this goal, we construct the nearest neighborhood for every target data and take it as the fundamental clustering unit by building our objective on the geometry.
SwinLSTM: Improving Spatiotemporal Prediction Accuracy using Swin Transformer and LSTM
In this paper, we propose a new recurrent cell, SwinLSTM, which integrates Swin Transformer blocks and the simplified LSTM, an extension that replaces the convolutional structure in ConvLSTM with the self-attention mechanism.
Ranked #7 on
Video Prediction
on Moving MNIST
SwinLSTM:Improving Spatiotemporal Prediction Accuracy using Swin Transformer and LSTM
In this paper, we propose a new recurrent cell, SwinLSTM, which integrates Swin Transformer blocks and the simplified LSTM, an extension that replaces the convolutional structure in ConvLSTM with the self-attention mechanism.
Source-Free Domain Adaptation with Frozen Multimodal Foundation Model
We find that directly applying the ViL model to the target domain in a zero-shot fashion is unsatisfactory, as it is not specialized for this particular task but largely generic.
Unified Source-Free Domain Adaptation
To tackle this unified SFDA problem, we propose a novel approach called Latent Causal Factors Discovery (LCFD).
