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Found 8 papers, 7 papers with code
Towards Impartial Multi-task Learning
Multi-task learning (MTL) has been widely used in representation learning.
SegViTv2: Exploring Efficient and Continual Semantic Segmentation with Plain Vision Transformers
This paper investigates the capability of plain Vision Transformers (ViTs) for semantic segmentation using the encoder-decoder framework and introduces \textbf{SegViTv2}.
Ranked #16 on
Semantic Segmentation
on ADE20K
Group Fisher Pruning for Practical Network Compression
Our method can be used to prune any structures including those with coupled channels.
Group R-CNN for Weakly Semi-supervised Object Detection with Points
The core of this task is to train a point-to-box regressor on well-labeled images that can be used to predict credible bounding boxes for each point annotation.
Pseudo-mask Matters in Weakly-supervised Semantic Segmentation
For these matters, we propose the following designs to push the performance to new state-of-art: (i) Coefficient of Variation Smoothing to smooth the CAMs adaptively; (ii) Proportional Pseudo-mask Generation to project the expanded CAMs to pseudo-mask based on a new metric indicating the importance of each class on each location, instead of the scores trained from binary classifiers.
Ranked #27 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
Decomposing Disease Descriptions for Enhanced Pathology Detection: A Multi-Aspect Vision-Language Pre-training Framework
Medical vision language pre-training (VLP) has emerged as a frontier of research, enabling zero-shot pathological recognition by comparing the query image with the textual descriptions for each disease.
BPKD: Boundary Privileged Knowledge Distillation For Semantic Segmentation
Current knowledge distillation approaches in semantic segmentation tend to adopt a holistic approach that treats all spatial locations equally.
Diverse Message Passing for Attribute with Heterophily
Based on this attribute homophily rate, we propose a Diverse Message Passing (DMP) framework, which specifies every attribute propagation weight on each edge.
