Search Results for author:
Found 14 papers, 13 papers with code
Towards reporting bias in visual-language datasets: bimodal augmentation by decoupling object-attribute association
In this paper, we focus on the wide existence of reporting bias in visual-language datasets, embodied as the object-attribute association, which can subsequentially degrade models trained on them.
CoNe: Contrast Your Neighbours for Supervised Image Classification
Moreover, to further boost the performance, we propose ``distributional consistency" as a more informative regularization to enable similar instances to have a similar probability distribution.
SimMatchV2: Semi-Supervised Learning with Graph Consistency
Semi-Supervised image classification is one of the most fundamental problem in computer vision, which significantly reduces the need for human labor.
LightViT: Towards Light-Weight Convolution-Free Vision Transformers
Vision transformers (ViTs) are usually considered to be less light-weight than convolutional neural networks (CNNs) due to the lack of inductive bias.
Green Hierarchical Vision Transformer for Masked Image Modeling
We present an efficient approach for Masked Image Modeling (MIM) with hierarchical Vision Transformers (ViTs), allowing the hierarchical ViTs to discard masked patches and operate only on the visible ones.
Learning Where to Learn in Cross-View Self-Supervised Learning
In this paper, we present a new approach, Learning Where to Learn (LEWEL), to adaptively aggregate spatial information of features, so that the projected embeddings could be exactly aligned and thus guide the feature learning better.
SimMatch: Semi-supervised Learning with Similarity Matching
Learning with few labeled data has been a longstanding problem in the computer vision and machine learning research community.
HRFormer: High-Resolution Vision Transformer for Dense Predict
We present a High-Resolution Transformer (HRFormer) that learns high-resolution representations for dense prediction tasks, in contrast to the original Vision Transformer that produces low-resolution representations and has high memory and computational cost.
HRFormer: High-Resolution Transformer for Dense Prediction
We present a High-Resolution Transformer (HRFormer) that learns high-resolution representations for dense prediction tasks, in contrast to the original Vision Transformer that produces low-resolution representations and has high memory and computational cost.
Ranked #3 on
Pose Estimation
on AIC
Self-Adaptive Training: Bridging Supervised and Self-Supervised Learning
We propose self-adaptive training -- a unified training algorithm that dynamically calibrates and enhances training processes by model predictions without incurring an extra computational cost -- to advance both supervised and self-supervised learning of deep neural networks.
Self-Adaptive Training: beyond Empirical Risk Minimization
We propose self-adaptive training---a new training algorithm that dynamically corrects problematic training labels by model predictions without incurring extra computational cost---to improve generalization of deep learning for potentially corrupted training data.
Beyond Human Parts: Dual Part-Aligned Representations for Person Re-Identification
On the other hand, there still exist many useful contextual cues that do not fall into the scope of predefined human parts or attributes.
Ranked #59 on
Person Re-Identification
on DukeMTMC-reID
Interlaced Sparse Self-Attention for Semantic Segmentation
There are two successive attention modules each estimating a sparse affinity matrix.
OCNet: Object Context Network for Scene Parsing
To capture richer context information, we further combine our interlaced sparse self-attention scheme with the conventional multi-scale context schemes including pyramid pooling~\citep{zhao2017pyramid} and atrous spatial pyramid pooling~\citep{chen2018deeplab}.
Ranked #9 on
Semantic Segmentation
on Trans10K
