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Despite the notable accomplishments of deep object detection models, a major challenge that persists is the requirement for extensive amounts of training data.
The Pascal Visual Object Classes (VOC) Challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2009
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Microsoft COCO: Common Objects in Context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Deep Unsupervised Learning Using Nonequilibrium Thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Generating Images from Captions with Attention
Elman Mansimov, Emilio Parisotto, Jimmy Lei Ba, and Ruslan Salakhutdinov · 2015
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U-Net: Convolutional Networks for Biomedical Image Segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Region-Based Convolutional Networks for Accurate Object Detection and Segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2016
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You Only Look Once: Unified, Real-Time Object Detection
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi · 2016
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What’s the Point: Semantic Segmentation with Point Supervision
Amy Bearman, Olga Russakovsky, Vittorio Ferrari, and Li Fei-Fei · 2016
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ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2017
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SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla · 2017
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Mask R-CNN
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Attention Is All You Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Neural Discrete Representation Learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
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YOLO9000: Better, Faster, Stronger
Joseph Redmon and Ali Farhadi · 2017
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Photographic Text-to-Image Synthesis with a Hierarchically-Nested Adversarial Network
Zizhao Zhang, Yuanpu Xie, and Lin Yang · 2018
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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YOLOv3: An Incremental Improvement
Joseph Redmon and Ali Farhadi · 2018
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Path Aggregation Network for Instance Segmentation
Shu Liu, Lu Qi, Haifang Qin, Jianping Shi, and Jiaya Jia · 2018
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Mixed Precision Training
P. Micikevicius, S. Narang, J. Alben, G. Diamos, E. Elsen, D. Garcia, B. Ginsburg, M. Houston, O. Kuchaiev, G. Venkatesh, and H. Wu · 2018
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A Style-Based Generator Architecture for Generative Adversarial Networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Countering Language Drift via Visual Grounding
Jason Lee, Kyunghyun Cho, and Douwe Kiela · 2019
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Deep Learning vs. Traditional Computer Vision
Niall O’Mahony, Sean Campbell, Anderson Carvalho, Suman Harapanahalli, Gustavo Velasco Hernandez, Lenka Krpalkova, Daniel Riordan, and Joseph Walsh · 2020
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MinneApple: A Benchmark Dataset for Apple Detection and Segmentation
Nicolai Häni, Pravakar Roy, and Volkan Isler · 2020
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Generative Adversarial Networks
8-bit Optimizers via Block-wise Quantization
Tim Dettmers, Mike Lewis, Sam Shleifer, and Luke Zettlemoyer · 2021
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Self-Attention Does Not Need O ( n 2 ) O(n^{2}) Memory
Markus N Rabe and Charles Staats · 2021
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Hierarchical Text-Conditional Image Generation with CLIP Latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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High-Resolution Image Synthesis with Latent Diffusion Models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
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Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2020
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Semantic Object Accuracy for Generative Text-to-Image Synthesis
Tobias Hinz, Stefan Heinrich, and Stefan Wermter · 2020
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Denoising Diffusion Probabilistic Models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Countering Language Drift with Seeded Iterated Learning
Yuchen Lu, Soumye Singhal, Florian Strub, Aaron Courville, and Olivier Pietquin · 2020
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CSPNet: A New Backbone that Can Enhance Learning Capability of CNN
Chien-Yao Wang, Hong-Yuan Mark Liao, Yueh-Hua Wu, Ping-Yang Chen, Jun-Wei Hsieh, and I-Hau Yeh · 2020
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Cross-Modal Contrastive Learning for Text-to-Image Generation
Han Zhang, Jing Yu Koh, Jason Baldridge, Honglak Lee, and Yinfei Yang · 2021
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Taming Transformers for High-Resolution Image Synthesis
Patrick Esser, Robin Rombach, and Bjorn Ommer · 2021
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Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al · 2022
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Dreambooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation
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VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidance
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DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis
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Fundamentals and Challenges of Generative Adversarial Networks for Image-based Applications
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An Image is Worth One Word: Personalizing Text-to-Image Generation Using Textual Inversion
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Personalizing Text-to-Image Generation via Aesthetic Gradients
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Reproducible Scaling Laws for Contrastive Language-Image Learning
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