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Found 22 papers, 12 papers with code
CoupleFace: Relation Matters for Face Recognition Distillation
In this work, we observe that mutual relation knowledge between samples is also important to improve the discriminative ability of the learned representation of the student model, and propose an effective face recognition distillation method called CoupleFace by additionally introducing the Mutual Relation Distillation (MRD) into existing distillation framework.
PseCo: Pseudo Labeling and Consistency Training for Semi-Supervised Object Detection
Specifically, for pseudo labeling, existing works only focus on the classification score yet fail to guarantee the localization precision of pseudo boxes; For consistency training, the widely adopted random-resize training only considers the label-level consistency but misses the feature-level one, which also plays an important role in ensuring the scale invariance.
Knowledge Distillation for Object Detection via Rank Mimicking and Prediction-guided Feature Imitation
Based on the two observations, we propose Rank Mimicking (RM) and Prediction-guided Feature Imitation (PFI) for distilling one-stage detectors, respectively.
One to Transfer All: A Universal Transfer Framework for Vision Foundation Model with Few Data
Transferring with few data in a general way to thousands of downstream tasks is becoming a trend of the foundation model's application.
INTERN: A New Learning Paradigm Towards General Vision
Enormous waves of technological innovations over the past several years, marked by the advances in AI technologies, are profoundly reshaping the industry and the society.
CycleMLP: A MLP-like Architecture for Dense Prediction
We build a family of models which surpass existing MLPs and even state-of-the-art Transformer-based models, e. g., Swin Transformer, while using fewer parameters and FLOPs.
Ranked #10 on
Semantic Segmentation
on DensePASS
PVTv2: Improved Baselines with Pyramid Vision Transformer
We hope this work will facilitate state-of-the-art Transformer researches in computer vision.
Ranked #39 on
Object Detection
on COCO minival
GroupLink: An End-to-end Multitask Method for Word Grouping and Relation Extraction in Form Understanding
Forms are a common type of document in real life and carry rich information through textual contents and the organizational structure.
PAN++: Towards Efficient and Accurate End-to-End Spotting of Arbitrarily-Shaped Text
By systematically comparing with existing scene text representations, we show that our kernel representation can not only describe arbitrarily-shaped text but also well distinguish adjacent text.
Inter-class Discrepancy Alignment for Face Recognition
In this study, we make a key observation that the local con-text represented by the similarities between the instance and its inter-class neighbors1plays an important role forFR.
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
Unlike the recently-proposed Transformer model (e. g., ViT) that is specially designed for image classification, we propose Pyramid Vision Transformer~(PVT), which overcomes the difficulties of porting Transformer to various dense prediction tasks.
Ranked #20 on
Semantic Segmentation
on DensePASS
Segmenting Transparent Object in the Wild with Transformer
This work presents a new fine-grained transparent object segmentation dataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale transparent object segmentation dataset.
Ranked #3 on
Semantic Segmentation
on Trans10K
DAM: Discrepancy Alignment Metric for Face Recognition
To estimate the LID of each face image in the verification process, we propose two types of LID Estimation (LIDE) methods, which are reference-based and learning-based estimation methods, respectively.
DocStruct: A Multimodal Method to Extract Hierarchy Structure in Document for General Form Understanding
The table detection and handcrafted features in previous works cannot apply to all forms because of their requirements on formats.
AE TextSpotter: Learning Visual and Linguistic Representation for Ambiguous Text Spotting
Unlike previous works that merely employed visual features for text detection, this work proposes a novel text spotter, named Ambiguity Eliminating Text Spotter (AE TextSpotter), which learns both visual and linguistic features to significantly reduce ambiguity in text detection.
Scene Text Image Super-Resolution in the Wild
For example, it outperforms LapSRN by over 5% and 8%on the recognition accuracy of ASTER and CRNN.
PolarMask: Single Shot Instance Segmentation with Polar Representation
In this paper, we introduce an anchor-box free and single shot instance segmentation method, which is conceptually simple, fully convolutional and can be used as a mask prediction module for instance segmentation, by easily embedding it into most off-the-shelf detection methods.
Ranked #61 on
Instance Segmentation
on COCO test-dev
Knowledge Distillation via Route Constrained Optimization
However, we find that the representation of a converged heavy model is still a strong constraint for training a small student model, which leads to a high lower bound of congruence loss.
Pyramid Mask Text Detector
Scene text detection, an essential step of scene text recognition system, is to locate text instances in natural scene images automatically.
Ranked #1 on
Scene Text Detection
on ICDAR 2017 MLT
Dynamic Multi-path Neural Network
Although deeper and larger neural networks have achieved better performance, the complex network structure and increasing computational cost cannot meet the demands of many resource-constrained applications.
FOTS: Fast Oriented Text Spotting with a Unified Network
Incidental scene text spotting is considered one of the most difficult and valuable challenges in the document analysis community.
Ranked #4 on
Scene Text Detection
on ICDAR 2015
DeepID3: Face Recognition with Very Deep Neural Networks
Very deep neural networks recently achieved great success on general object recognition because of their superb learning capacity.
Ranked #12 on
Face Verification
on Labeled Faces in the Wild
