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Found 14 papers, 8 papers with code
An Empirical Study of Scaling Law for OCR
The laws of model size, data volume, computation and model performance have been extensively studied in the field of Natural Language Processing (NLP).
Ranked #1 on
Scene Text Recognition
on ICDAR2013
(using extra training data)
Optical Character Recognition
Optical Character Recognition (OCR)
+1
Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanism
In the past years, YOLO-series models have emerged as the leading approaches in the area of real-time object detection.
Boosting Semantic Segmentation from the Perspective of Explicit Class Embeddings
Following this observation, we propose ECENet, a new segmentation paradigm, in which class embeddings are obtained and enhanced explicitly during interacting with multi-stage image features.
Category Feature Transformer for Semantic Segmentation
Aggregation of multi-stage features has been revealed to play a significant role in semantic segmentation.
Bi-ViT: Pushing the Limit of Vision Transformer Quantization
Vision transformers (ViTs) quantization offers a promising prospect to facilitate deploying large pre-trained networks on resource-limited devices.
Redistribution of Weights and Activations for AdderNet Quantization
To this end, we first thoroughly analyze the difference on distributions of weights and activations in AdderNet and then propose a new quantization algorithm by redistributing the weights and the activations.
Network Amplification With Efficient MACs Allocation
In particular, the proposed method is used to enlarge models sourced by GhostNet, we achieve state-of-the-art 80. 9% and 84. 3% ImageNet top-1 accuracies under the setting of 600M and 4. 4B MACs, respectively.
Greedy Network Enlarging
Recent studies on deep convolutional neural networks present a simple paradigm of architecture design, i. e., models with more MACs typically achieve better accuracy, such as EfficientNet and RegNet.
GhostSR: Learning Ghost Features for Efficient Image Super-Resolution
Based on the observation that many features in SISR models are also similar to each other, we propose to use shift operation to generate the redundant features (i. e., ghost features).
Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts
In particular, we propose a novel joint-training framework to train plain CNN by leveraging the gradients of the ResNet counterpart.
Widening and Squeezing: Towards Accurate and Efficient QNNs
Quantization neural networks (QNNs) are very attractive to the industry because their extremely cheap calculation and storage overhead, but their performance is still worse than that of networks with full-precision parameters.
Learning Instance-wise Sparsity for Accelerating Deep Models
Exploring deep convolutional neural networks of high efficiency and low memory usage is very essential for a wide variety of machine learning tasks.
Attribute Aware Pooling for Pedestrian Attribute Recognition
This paper expands the strength of deep convolutional neural networks (CNNs) to the pedestrian attribute recognition problem by devising a novel attribute aware pooling algorithm.
Data-Free Learning of Student Networks
Learning portable neural networks is very essential for computer vision for the purpose that pre-trained heavy deep models can be well applied on edge devices such as mobile phones and micro sensors.
