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Found 12 papers, 7 papers with code
CorrTalk: Correlation Between Hierarchical Speech and Facial Activity Variances for 3D Animation
In this study, we propose a novel framework, CorrTalk, which effectively establishes the temporal correlation between hierarchical speech features and facial activities of different intensities across distinct regions.
Center Contrastive Loss for Metric Learning
In this paper, we propose a novel metric learning function called Center Contrastive Loss, which maintains a class-wise center bank and compares the category centers with the query data points using a contrastive loss.
Ranked #5 on
Metric Learning
on CUB-200-2011
UniMoCo: Unsupervised, Semi-Supervised and Full-Supervised Visual Representation Learning
To fully utilize the label annotations, we propose Unified Momentum Contrast (UniMoCo), which extends MoCo to support arbitrary ratios of labeled data and unlabeled data training.
UP-DETR: Unsupervised Pre-training for Object Detection with Transformers
DEtection TRansformer (DETR) for object detection reaches competitive performance compared with Faster R-CNN via a transformer encoder-decoder architecture.
Listwise View Ranking for Image Cropping
However, the performance of ranking-based methods is often poor and this is mainly due to two reasons: 1) image cropping is a listwise ranking task rather than pairwise comparison; 2) the rescaling caused by pooling layer and the deformation in view generation damage the performance of composition learning.
BIT: Biologically Inspired Tracker
Visual tracking is challenging due to image variations caused by various factors, such as object deformation, scale change, illumination change and occlusion.
Deep Sampling Networks
With the powerful down-sampling process, the co-training DSN set a new state-of-the-art performance for image super-resolution.
A Joint Intrinsic-Extrinsic Prior Model for Retinex
We propose a joint intrinsic-extrinsic prior model to estimate both illumination and reflectance from an observed image.
FReLU: Flexible Rectified Linear Units for Improving Convolutional Neural Networks
Rectified linear unit (ReLU) is a widely used activation function for deep convolutional neural networks.
Single Image Super-Resolution Using Multi-Scale Convolutional Neural Network
However, the previous methods mainly restore images from one single area in the low resolution (LR) input, which limits the flexibility of models to infer various scales of details for high resolution (HR) output.
Multi-scale Convolutional Neural Networks for Crowd Counting
Crowd counting on static images is a challenging problem due to scale variations.
DehazeNet: An End-to-End System for Single Image Haze Removal
The key to achieve haze removal is to estimate a medium transmission map for an input hazy image.
Ranked #7 on
Image Dehazing
on RS-Haze
