Search Results for author:
Found 9 papers, 2 papers with code
Human-to-Human Interaction Detection
First, based on the popular AVA dataset created for action detection, we establish a new HID benchmark, termed AVA-Interaction (AVA-I), by adding annotations on interactive relations in a frame-by-frame manner.
BoPR: Body-aware Part Regressor for Human Shape and Pose Estimation
This paper presents a novel approach for estimating human body shape and pose from monocular images that effectively addresses the challenges of occlusions and depth ambiguity.
Ranked #16 on
3D Human Pose Estimation
on 3DPW
LoTE-Animal: A Long Time-span Dataset for Endangered Animal Behavior Understanding
To break the deadlock, we present LoTE-Animal, a large-scale endangered animal dataset collected over 12 years, to foster the application of deep learning in rare species conservation.
Geometric Structure Preserving Warp for Natural Image Stitching
Further, an optimized GES energy term is presented to reasonably determine the weights of the sampling points on the geometric structure, and the term is added into the Global Similarity Prior (GSP) stitching model called GES-GSP to achieve a smooth transition between local alignment and geometric structure preservation.
Hyperspectral Image Denoising via Global Spatial-Spectral Total Variation Regularized Nonconvex Local Low-Rank Tensor Approximation
Instead of traditional bandwise total variation, we use the SSTV regularization to simultaneously consider global spatial structure and spectral correlation of neighboring bands.
Enhanced nonconvex low-rank approximation of tensor multi-modes for tensor completion
Higher-order low-rank tensor arises in many data processing applications and has attracted great interests.
Hyperspectral Image Restoration via Global Total Variation Regularized Local nonconvex Low-Rank matrix Approximation
From one aspect, local LR of HSIs is formulated using a non-convex $L_{\gamma}$-norm, which provides a closer approximation to the matrix rank than the traditional NN.
Tensor completion using enhanced multiple modes low-rank prior and total variation
In this paper, we propose a novel model to recover a low-rank tensor by simultaneously performing double nuclear norm regularized low-rank matrix factorizations to the all-mode matricizations of the underlying tensor.
Object Tracking via Dual Linear Structured SVM and Explicit Feature Map
Structured support vector machine (SSVM) based methods has demonstrated encouraging performance in recent object tracking benchmarks.
