Search Results for author:
Found 11 papers, 5 papers with code
Vid2Act: Activate Offline Videos for Visual RL
Pretraining RL models on offline video datasets is a promising way to improve their training efficiency in online tasks, but challenging due to the inherent mismatch in tasks, dynamics, and behaviors across domains.
MMDR: A Result Feature Fusion Object Detection Approach for Autonomous System
Recent research in this field has primarily centered around multimodal approaches for addressing this issue. In this paper, a multimodal fusion approach based on result feature-level fusion is proposed.
Obstacle-Transformer: A Trajectory Prediction Network Based on Surrounding Trajectories
Therefore, this paper proposes an Obstacle-Transformer to predict trajectory in a constant inference time.
Improving Masked Autoencoders by Learning Where to Mask
Masked image modeling is a promising self-supervised learning method for visual data.
Predictive Experience Replay for Continual Visual Control and Forecasting
In this paper, we present a new continual learning approach for visual dynamics modeling and explore its efficacy in visual control and forecasting.
HiFuse: Hierarchical Multi-Scale Feature Fusion Network for Medical Image Classification
A parallel hierarchy of local and global feature blocks is designed to efficiently extract local features and global representations at various semantic scales, with the flexibility to model at different scales and linear computational complexity relevant to image size.
Continual Predictive Learning from Videos
Can we develop predictive learning algorithms that can deal with more realistic, non-stationary physical environments?
Fully Context-Aware Image Inpainting with a Learned Semantic Pyramid
We train the prior learner and the image generator as a unified model without any post-processing.
Context-Aware Image Inpainting with Learned Semantic Priors
Based on the semantic priors, we further propose a context-aware image inpainting model, which adaptively integrates global semantics and local features in a unified image generator.
Learning Context Graph for Person Search
Person re-identification has achieved great progress with deep convolutional neural networks.
Depth Structure Preserving Scene Image Generation
Key to automatically generate natural scene images is to properly arrange among various spatial elements, especially in the depth direction.
