Search Results for author:
Found 12 papers, 4 papers with code
Towards Self-Adaptive Pseudo-Label Filtering for Semi-Supervised Learning
In this work, we observe that the distribution gap between the confidence values of correct and incorrect pseudo labels emerges at the very beginning of the training, which can be utilized to filter pseudo labels.
Referring Image Segmentation Using Text Supervision
Hence, we propose a novel weakly-supervised RIS framework to formulate the target localization problem as a classification process to differentiate between positive and negative text expressions.
BiFormer: Vision Transformer with Bi-Level Routing Attention
As the core building block of vision transformers, attention is a powerful tool to capture long-range dependency.
Ranked #9 on
Object Detection
on COCO 2017
(mAP metric)
Adaptive Illumination Mapping for Shadow Detection in Raw Images
In this paper, we propose a novel method to detect shadows from raw images.
Laplacian Denoising Autoencoder
This can be naturally generalized to span multiple scales with a Laplacian pyramid representation of the input data.
Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset
Second, to better cover the stochastic distribution of real rain streaks, we propose a novel SPatial Attentive Network (SPANet) to remove rain streaks in a local-to-global manner.
Ranked #3 on
Single Image Deraining
on RainCityscapes
Active Matting
Our aim is to discover the most informative sequence of regions for user input in order to produce a good alpha matte with minimum labeling efforts.
Look Deeper into Depth: Monocular Depth Estimation with Semantic Booster and Attention-Driven Loss
Monocular depth estimation benefits greatly from learning based techniques.
VITAL: VIsual Tracking via Adversarial Learning
To augment positive samples, we use a generative network to randomly generate masks, which are applied to adaptively dropout input features to capture a variety of appearance changes.
Image Correction via Deep Reciprocating HDR Transformation
Given an input LDR image, we first reconstruct the missing details in the HDR domain.
CREST: Convolutional Residual Learning for Visual Tracking
Our method integrates feature extraction, response map generation as well as model update into the neural networks for an end-to-end training.
Learning Fully Convolutional Networks for Iterative Non-blind Deconvolution
In this paper, we propose a fully convolutional networks for iterative non-blind deconvolution We decompose the non-blind deconvolution problem into image denoising and image deconvolution.
