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Found 17 papers, 4 papers with code
Local-Adaptive Face Recognition via Graph-based Meta-Clustering and Regularized Adaptation
Leveraging the environment-specific local data after the deployment of the initial global model, LaFR aims at getting optimal performance by training local-adapted models automatically and un-supervisely, as opposed to fixing their initial global model.
Privacy-preserving Online AutoML for Domain-Specific Face Detection
Thanks to HyperFD, each local task (client) is able to effectively leverage the learning "experience" of previous tasks without uploading raw images to the platform; meanwhile, the meta-feature extractor is continuously learned to better trade off the bias and variance.
Beyond 3D Siamese Tracking: A Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds
3D single object tracking (3D SOT) in LiDAR point clouds plays a crucial role in autonomous driving.
Ranked #1 on
Object Tracking
on KITTI
CRFace: Confidence Ranker for Model-Agnostic Face Detection Refinement
Our confidence ranker is model-agnostic, so we can augment the data by choosing the pairs from multiple face detectors during the training, and generalize to a wide range of face detectors during the testing.
Personalized Face Modeling for Improved Face Reconstruction and Motion Retargeting
Traditional methods for image-based 3D face reconstruction and facial motion retargeting fit a 3D morphable model (3DMM) to the face, which has limited modeling capacity and fail to generalize well to in-the-wild data.
JNR: Joint-based Neural Rig Representation for Compact 3D Face Modeling
In this paper, we introduce a novel approach to learn a 3D face model using a joint-based face rig and a neural skinning network.
Animating Face using Disentangled Audio Representations
All previous methods for audio-driven talking head generation assume the input audio to be clean with a neutral tone.
Joint Face Detection and Facial Motion Retargeting for Multiple Faces
Facial motion retargeting is an important problem in both computer graphics and vision, which involves capturing the performance of a human face and transferring it to another 3D character.
Real-time Burst Photo Selection Using a Light-Head Adversarial Network
The system is designed to run in the viewfinder mode and capture a burst sequence of frames before and after the shutter is pressed.
Personalized Exposure Control Using Adaptive Metering and Reinforcement Learning
We propose a reinforcement learning approach for real-time exposure control of a mobile camera that is personalizable.
Understanding and Predicting The Attractiveness of Human Action Shot
Selecting attractive photos from a human action shot sequence is quite challenging, because of the subjective nature of the "attractiveness", which is mainly a combined factor of human pose in action and the background.
Exposure: A White-Box Photo Post-Processing Framework
Retouching can significantly elevate the visual appeal of photos, but many casual photographers lack the expertise to do this well.
Personalized Cinemagraphs using Semantic Understanding and Collaborative Learning
Cinemagraphs are a compelling way to convey dynamic aspects of a scene.
FC4: Fully Convolutional Color Constancy With Confidence-Weighted Pooling
However, the patch-based CNNs that exist for this problem are faced with the issue of estimation ambiguity, where a patch may contain insufficient information to establish a unique or even a limited possible range of illumination colors.
Unsupervised Extraction of Video Highlights Via Robust Recurrent Auto-encoders
With the growing popularity of short-form video sharing platforms such as \em{Instagram} and \em{Vine}, there has been an increasing need for techniques that automatically extract highlights from video.
Harvesting Discriminative Meta Objects with Deep CNN Features for Scene Classification
Recent work on scene classification still makes use of generic CNN features in a rudimentary manner.
Automatic Photo Adjustment Using Deep Neural Networks
Many photographic styles rely on subtle adjustments that depend on the image content and even its semantics.
