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Found 8 papers, 4 papers with code
MaskConver: Revisiting Pure Convolution Model for Panoptic Segmentation
With ResNet50 backbone, our MaskConver achieves 53. 6% PQ on the COCO panoptic val set, outperforming the modern convolution-based model, Panoptic FCN, by 9. 3% as well as transformer-based models such as Mask2Former (+1. 7% PQ) and kMaX-DeepLab (+0. 6% PQ).
Ranked #8 on
Panoptic Segmentation
on COCO test-dev
Two-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Sampling
Training high-quality instance segmentation models requires an abundance of labeled images with instance masks and classifications, which is often expensive to procure.
AdaMV-MoE: Adaptive Multi-Task Vision Mixture-of-Experts
Instead of compressing multiple tasks' knowledge into a single model, MoE separates the parameter space and only utilizes the relevant model pieces given task type and its input, which provides stabilized MTL training and ultra-efficient inference.
Dilated SpineNet for Semantic Segmentation
Scale-permuted networks have shown promising results on object bounding box detection and instance segmentation.
Ranked #5 on
Semantic Segmentation
on PASCAL VOC 2012 val
Batch norm with entropic regularization turns deterministic autoencoders into generative models
The variational autoencoder is a well defined deep generative model that utilizes an encoder-decoder framework where an encoding neural network outputs a non-deterministic code for reconstructing an input.
MatrixNets: A New Scale and Aspect Ratio Aware Architecture for Object Detection
We present MatrixNets (xNets), a new deep architecture for object detection.
Matrix Nets: A New Deep Architecture for Object Detection
We present Matrix Nets (xNets), a new deep architecture for object detection.
Ranked #97 on
Object Detection
on COCO test-dev
Online Structure Learning for Feed-Forward and Recurrent Sum-Product Networks
Sum-product networks have recently emerged as an attractive representation due to their dual view as a special type of deep neural network with clear semantics and a special type of probabilistic graphical model for which inference is always tractable.
