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Found 10 papers, 2 papers with code
SeqHAND: RGB-Sequence-Based 3D Hand Pose and Shape Estimation
In this paper, we attempt to not only consider the appearance of a hand but incorporate the temporal movement information of a hand in motion into the learning framework for better 3D hand pose estimation performance, which leads to the necessity of a large scale dataset with sequential RGB hand images.
WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents
Existing benchmarks for grounding language in interactive environments either lack real-world linguistic elements, or prove difficult to scale up due to substantial human involvement in the collection of data or feedback signals.
Depth Estimation with Simplified Transformer
Transformer and its variants have shown state-of-the-art results in many vision tasks recently, ranging from image classification to dense prediction.
Dynamic Iterative Refinement for Efficient 3D Hand Pose Estimation
While hand pose estimation is a critical component of most interactive extended reality and gesture recognition systems, contemporary approaches are not optimized for computational and memory efficiency.
SeqHAND:RGB-Sequence-Based 3D Hand Pose and Shape Estimation
In this paper, we attempt to not only consider the appearance of a hand but incorporate the temporal movement information of a hand in motion into the learning framework for better 3D hand pose estimation performance, which leads to the necessity of a large scale dataset with sequential RGB hand images.
Genetic-Gated Networks for Deep Reinforcement Learning
We introduce the Genetic-Gated Networks (G2Ns), simple neural networks that combine a gate vector composed of binary genetic genes in the hidden layer(s) of networks.
Sym-parameterized Dynamic Inference for Mixed-Domain Image Translation
Recent advances in image-to-image translation have led to some ways to generate multiple domain images through a single network.
Genetic-Gated Networks for Deep Reinforcement
We introduce the Genetic-Gated Networks (G2Ns), simple neural networks that combine a gate vector composed of binary genetic genes in the hidden layer(s) of networks.
Towards Governing Agent's Efficacy: Action-Conditional $β$-VAE for Deep Transparent Reinforcement Learning
We tackle the blackbox issue of deep neural networks in the settings of reinforcement learning (RL) where neural agents learn towards maximizing reward gains in an uncontrollable way.
Broadcasting Convolutional Network for Visual Relational Reasoning
In this paper, we propose the Broadcasting Convolutional Network (BCN) that extracts key object features from the global field of an entire input image and recognizes their relationship with local features.
