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Found 11 papers, 4 papers with code
DexGraspNet 2.0: Learning Generative Dexterous Grasping in Large-scale Synthetic Cluttered Scenes
Grasping in cluttered scenes remains highly challenging for dexterous hands due to the scarcity of data.
TVCondNet: A Conditional Denoising Neural Network for NMR Spectroscopy
Nuclear Magnetic Resonance (NMR) spectroscopy is a widely-used technique in the fields of bio-medicine, chemistry, and biology for the analysis of chemicals and proteins.
PnP Restoration with Domain Adaptation for SANS
The prior in PR-SANS is initially trained on a set of generic images and subsequently fine-tuned using a limited amount of experimental SANS data.
UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policy
Trained on our synthesized large-scale dexterous grasp dataset, this model enables us to sample diverse and high-quality dexterous grasp poses for the object point cloud. For the second stage, we propose to replace the motion planning used in parallel gripper grasping with a goal-conditioned grasp policy, due to the complexity involved in dexterous grasping execution.
DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation
Robotic dexterous grasping is the first step to enable human-like dexterous object manipulation and thus a crucial robotic technology.
End-to-end Weakly-supervised Single-stage Multiple 3D Hand Mesh Reconstruction from a Single RGB Image
In this paper, we consider the challenging task of simultaneously locating and recovering multiple hands from a single 2D image.
Weakly-Supervised Multi-Face 3D Reconstruction
3D face reconstruction plays a very important role in many real-world multimedia applications, including digital entertainment, social media, affection analysis, and person identification.
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020.
Attribute-aware Pedestrian Detection in a Crowd
To tackle this critical problem, we propose an attribute-aware pedestrian detector to explicitly model people's semantic attributes in a high-level feature detection fashion.
Efficient Large-Scale Approximate Nearest Neighbor Search on OpenCL FPGA
The proposed method significantly outperforms state-of-the-art methods on CPU and GPU for high dimensional nearest neighbor queries on billion-scale datasets in terms of query time and accuracy regardless of the batch size.
Feature Agglomeration Networks for Single Stage Face Detection
In this paper, we propose a novel simple yet effective framework of "Feature Agglomeration Networks" (FANet) to build a new single stage face detector, which not only achieves state-of-the-art performance but also runs efficiently.
