Search Results for author:
Found 11 papers, 2 papers with code
Compress3D: a Compressed Latent Space for 3D Generation from a Single Image
In this paper, we present a triplane autoencoder, which encodes 3D models into a compact triplane latent space to effectively compress both the 3D geometry and texture information.
Reconstructing 3D Human Pose from RGB-D Data with Occlusions
Our key idea is to constrain the solution space of the human body by considering the occluded body parts and visible body parts separately: modeling all plausible poses where the occluded body parts do not penetrate the scene, and constraining the visible body parts using depth data.
CBNet: A Plug-and-Play Network for Segmentation-Based Scene Text Detection
Recently, segmentation-based methods are quite popular in scene text detection, which mainly contain two steps: text kernel segmentation and expansion.
Shape Completion with Points in the Shadow
The core challenge is to generate plausible geometries to fill the unobserved part of the object based on a partial scan, which is under-constrained and suffers from a huge solution space.
A Learned Index for Exact Similarity Search in Metric Spaces
In this paper, we propose a novel indexing approach called LIMS that uses data clustering, pivot-based data transformation techniques and learned indexes to support efficient similarity query processing in metric spaces.
Semantic Data Augmentation for End-to-End Mandarin Speech Recognition
Augmentation related issues, such as comparison of different strategies and ratios for data combination are also investigated.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
TravelNet: Self-Supervised Physically Plausible Hand Motion Learning From Monocular Color Images
In this paper, we embed physical constraints on the per-frame estimated motions in both spatial and temporal space.
Informative Scene Decomposition for Crowd Analysis, Comparison and Simulation Guidance
The method takes as input raw and noisy data with highly mixed multi-dimensional (space, time and dynamics) information, and automatically structure it by learning the correlations among these dimensions.
City Metro Network Expansion with Reinforcement Learning
In this method, we formulate the metro expansion as a process of sequential station selection, and design feasibility rules based on the selected station sequence to ensure the reasonable connection patterns of metro line.
Retrieving Similar Trajectories from Cellular Data at City Scale
Retrieving similar trajectories from a large trajectory dataset is important for a variety of applications, like transportation planning and mobility analysis.
Improving Shadow Suppression for Illumination Robust Face Recognition
2D face analysis techniques, such as face landmarking, face recognition and face verification, are reasonably dependent on illumination conditions which are usually uncontrolled and unpredictable in the real world.
