Search Results for author:
Found 11 papers, 9 papers with code
CBNet: A Composite Backbone Network Architecture for Object Detection
With multi-scale testing, we push the current best single model result to a new record of 60. 1% box AP and 52. 3% mask AP without using extra training data.
Ranked #6 on
Object Detection
on COCO-O
(using extra training data)
RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition
We propose RepMLP, a multi-layer-perceptron-style neural network building block for image recognition, which is composed of a series of fully-connected (FC) layers.
Ranked #753 on
Image Classification
on ImageNet
Improving Image Restoration by Revisiting Global Information Aggregation
Our TLC converts global operations to local ones only during inference so that they aggregate features within local spatial regions rather than the entire large images.
Ranked #1 on
Color Image Denoising
on Urban100 sigma30
Simple Baselines for Image Restoration
Although there have been significant advances in the field of image restoration recently, the system complexity of the state-of-the-art (SOTA) methods is increasing as well, which may hinder the convenient analysis and comparison of methods.
Ranked #1 on
Deblurring
on MSU BASED
NAFSSR: Stereo Image Super-Resolution Using NAFNet
This paper inherits a strong and simple image restoration model, NAFNet, for single-view feature extraction and extends it by adding cross attention modules to fuse features between views to adapt to binocular scenarios.
HINet: Half Instance Normalization Network for Image Restoration
Specifically, we present a novel block: Half Instance Normalization Block (HIN Block), to boost the performance of image restoration networks.
Ranked #3 on
Single Image Deraining
on Test2800
DynamicDet: A Unified Dynamic Architecture for Object Detection
We also present a novel optimization strategy with an exiting criterion based on the detection losses for our dynamic detectors.
Training Protocol Matters: Towards Accurate Scene Text Recognition via Training Protocol Searching
The development of scene text recognition (STR) in the era of deep learning has been mainly focused on novel architectures of STR models.
IterVM: Iterative Vision Modeling Module for Scene Text Recognition
By combining the proposed IterVM with iterative language modeling module, we further propose a powerful scene text recognizer called IterNet.
A Simple and Generic Framework for Feature Distillation via Channel-wise Transformation
Specifically, we propose a learnable nonlinear channel-wise transformation to align the features of the student and the teacher model.
SCSC: Spatial Cross-scale Convolution Module to Strengthen both CNNs and Transformers
This paper presents a module, Spatial Cross-scale Convolution (SCSC), which is verified to be effective in improving both CNNs and Transformers.
