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Found 9 papers, 8 papers with code
Unified Vision and Language Prompt Learning
Prompt tuning, a parameter- and data-efficient transfer learning paradigm that tunes only a small number of parameters in a model's input space, has become a trend in the vision community since the emergence of large vision-language models like CLIP.
On-Device Domain Generalization
Another interesting observation is that the teacher-student gap on out-of-distribution data is bigger than that on in-distribution data, which highlights the capacity mismatch issue as well as the shortcoming of KD.
Open-Vocabulary DETR with Conditional Matching
To this end, we propose a novel open-vocabulary detector based on DETR -- hence the name OV-DETR -- which, once trained, can detect any object given its class name or an exemplar image.
Ranked #6 on
Open Vocabulary Object Detection
on MSCOCO
FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentation
We propose a simple yet effective method, Feature Augmentation and Sampling Adaptation (FASA), that addresses the data scarcity issue by augmenting the feature space especially for rare classes.
Seesaw Loss for Long-Tailed Instance Segmentation
Instances of head classes dominate a long-tailed dataset and they serve as negative samples of tail categories.
1st Place Solutions for OpenImage2019 -- Object Detection and Instance Segmentation
Given such good instance bounding box, we further design a simple instance-level semantic segmentation pipeline and achieve the 1st place on the segmentation challenge.
KPNet: Towards Minimal Face Detector
The small receptive field and capacity of minimal neural networks limit their performance when using them to be the backbone of detectors.
Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network
Recently, some methods have been proposed to tackle arbitrary-shaped text detection, but they rarely take the speed of the entire pipeline into consideration, which may fall short in practical applications. In this paper, we propose an efficient and accurate arbitrary-shaped text detector, termed Pixel Aggregation Network (PAN), which is equipped with a low computational-cost segmentation head and a learnable post-processing.
Ranked #6 on
Scene Text Detection
on SCUT-CTW1500
Scene Text Detection with Supervised Pyramid Context Network
We propose a supervised pyramid context network (SPCNET) to precisely locate text regions while suppressing false positives.
Ranked #2 on
Scene Text Detection
on ICDAR 2013
