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Found 10 papers, 2 papers with code
Inter- and intra-uncertainty based feature aggregation model for semi-supervised histopathology image segmentation
To address these issues, we first propose a novel inter- and intra-uncertainty regularization method to measure and constrain both inter- and intra-inconsistencies in the teacher-student architecture.
Prototype as Query for Few Shot Semantic Segmentation
In this way, the spatial details can be better captured and the semantic features of target class in the query image can be focused.
Ranked #11 on
Few-Shot Semantic Segmentation
on COCO-20i (5-shot)
The Third Place Solution for CVPR2022 AVA Accessibility Vision and Autonomy Challenge
Finally, our approach achieves 63. 008\%AP@0. 50:0. 95 on the test set of CVPR2022 AVA Challenge.
Bilateral Network with Channel Splitting Network and Transformer for Thermal Image Super-Resolution
Firstly, we designed a context branch based on channel splitting network with transformer to obtain sufficient context information.
The Second Place Solution for The 4th Large-scale Video Object Segmentation Challenge--Track 3: Referring Video Object Segmentation
The referring video object segmentation task (RVOS) aims to segment object instances in a given video referred by a language expression in all video frames.
The Second Place Solution for ICCV2021 VIPriors Instance Segmentation Challenge
Finally, our approach can achieve 40. 2\%AP@0. 50:0. 95 on the test set of ICCV2021 VIPriors instance segmentation challenge.
Stronger Baseline for Person Re-Identification
Person re-identification (re-ID) aims to identify the same person of interest across non-overlapping capturing cameras, which plays an important role in visual surveillance applications and computer vision research areas.
TBN-ViT: Temporal Bilateral Network with Vision Transformer for Video Scene Parsing
Based on VSPW, we design a Temporal Bilateral Network with Vision Transformer.
EMface: Detecting Hard Faces by Exploring Receptive Field Pyraminds
Modern face detectors employ feature pyramids to deal with scale variation.
Generator Pyramid for High-Resolution Image Inpainting
Thus, our model consists of a pyramid of fully convolutional GANs, wherein the content GAN is responsible for completing contents in the lowest-resolution masked image, and each texture GAN is responsible for synthesizing textures in a higher-resolution image.
