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Found 6 papers, 6 papers with code
Side Adapter Network for Open-Vocabulary Semantic Segmentation
A side network is attached to a frozen CLIP model with two branches: one for predicting mask proposals, and the other for predicting attention bias which is applied in the CLIP model to recognize the class of masks.
A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language Model
However, semantic segmentation and the CLIP model perform on different visual granularity, that semantic segmentation processes on pixels while CLIP performs on images.
Bootstrap Your Object Detector via Mixed Training
We introduce MixTraining, a new training paradigm for object detection that can improve the performance of existing detectors for free.
End-to-End Semi-Supervised Object Detection with Soft Teacher
This paper presents an end-to-end semi-supervised object detection approach, in contrast to previous more complex multi-stage methods.
Ranked #6 on
Semi-Supervised Object Detection
on COCO 100% labeled data
(using extra training data)
Progressive and Aligned Pose Attention Transfer for Person Image Generation
This paper proposes a new generative adversarial network for pose transfer, i. e., transferring the pose of a given person to a target pose.
Asymmetric Non-local Neural Networks for Semantic Segmentation
The non-local module works as a particularly useful technique for semantic segmentation while criticized for its prohibitive computation and GPU memory occupation.
Ranked #15 on
Semantic Segmentation
on COCO-Stuff test
