Search Results for author:
Found 9 papers, 3 papers with code
Combining ConvNets with Hand-Crafted Features for Action Recognition Based on an HMM-SVM Classifier
This paper proposes a new framework for RGB-D-based action recognition that takes advantages of hand-designed features from skeleton data and deeply learned features from depth maps, and exploits effectively both the local and global temporal information.
Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks
Recently, Convolutional Neural Networks (ConvNets) have shown promising performances in many computer vision tasks, especially image-based recognition.
A Neural Network for Detailed Human Depth Estimation from a Single Image
To achieve this goal, we separate the depth map into a smooth base shape and a residual detail shape and design a network with two branches to regress them respectively.
Box Re-Ranking: Unsupervised False Positive Suppression for Domain Adaptive Pedestrian Detection
Therefore, it yields our attention to suppress false positive in each target domain in an unsupervised way.
FaceInpainter: High Fidelity Face Adaptation to Heterogeneous Domains
In this work, we propose a novel two-stage framework named FaceInpainter to implement controllable Identity-Guided Face Inpainting (IGFI) under heterogeneous domains.
Information Bottleneck Disentanglement for Identity Swapping
In this work, we propose a novel information disentangling and swapping network, called InfoSwap, to extract the most expressive information for identity representation from a pre-trained face recognition model.
Extracting knowledge from features with multilevel abstraction
Knowledge distillation aims at transferring the knowledge from a large teacher model to a small student model with great improvements of the performance of the student model.
Recognize Anything: A Strong Image Tagging Model
We are releasing the RAM at \url{https://recognize-anything. github. io/} to foster the advancements of large models in computer vision.
The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions
This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.
