Search Results
A Simple Single-Scale Vision Transformer for Object Localization and Instance Segmentation
In this paper, we comprehensively study three architecture design choices on ViT -- spatial reduction, doubled channels, and multiscale features -- and demonstrate that a vanilla ViT architecture can fulfill this goal without handcrafting multiscale features, maintaining the original ViT design philosophy.
Towards End-to-End Unified Scene Text Detection and Layout Analysis
tensorflow/models
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CVPR 2022
In this paper, we bring them together and introduce the task of unified scene text detection and layout analysis.
Contextualized Spatio-Temporal Contrastive Learning with Self-Supervision
tensorflow/models
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CVPR 2022
Modern self-supervised learning algorithms typically enforce persistency of instance representations across views.
Proper Reuse of Image Classification Features Improves Object Detection
tensorflow/models
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CVPR 2022
A common practice in transfer learning is to initialize the downstream model weights by pre-training on a data-abundant upstream task.
Adversarial Training Methods for Semi-Supervised Text Classification
We extend adversarial and virtual adversarial training to the text domain by applying perturbations to the word embeddings in a recurrent neural network rather than to the original input itself.
Ranked #16 on
Sentiment Analysis
on IMDb
MobileNetV2: Inverted Residuals and Linear Bottlenecks
tensorflow/models
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CVPR 2018
In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes.
Ranked #7 on
Retinal OCT Disease Classification
on OCT2017
AutoAugment: Learning Augmentation Policies from Data
In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch.
Ranked #4 on
Data Augmentation
on ImageNet
Identity Mappings in Deep Residual Networks
Deep residual networks have emerged as a family of extremely deep architectures showing compelling accuracy and nice convergence behaviors.
Ranked #16 on
Image Classification
on Kuzushiji-MNIST
Cognitive Mapping and Planning for Visual Navigation
tensorflow/models
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CVPR 2017
The accumulated belief of the world enables the agent to track visited regions of the environment.
Ensemble Adversarial Training: Attacks and Defenses
tensorflow/models
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ICLR 2018
We show that this form of adversarial training converges to a degenerate global minimum, wherein small curvature artifacts near the data points obfuscate a linear approximation of the loss.
