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Today's deep learning methods focus on how to design the most appropriate objective functions so that the prediction results of the model can be closest to the ground truth.
Deeply-supervised nets
Chen-Yu Lee, Saining Xie, Patrick Gallagher, Zhengyou Zhang, and Zhuowen Tu · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Deep learning and the information bottleneck principle
Naftali Tishby and Noga Zaslavsky · 2015
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Training deeper convolutional networks with deep supervision
Liwei Wang, Chen-Yu Lee, Zhuowen Tu, and Svetlana Lazebnik · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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The reversible residual network: Backpropagation without storing activations
Aidan N Gomez, Mengye Ren, Raquel Urtasun, and Roger B Grosse · 2017
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Boundary-aware instance segmentation
Zeeshan Hayder, Xuming He, and Mathieu Salzmann · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 2017
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YOLO9000: better, faster, stronger
Joseph Redmon and Ali Farhadi · 2017
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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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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YOLOv3: An incremental improvement
Joseph Redmon and Ali Farhadi · 2018
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Res2Net: A new multi-scale backbone architecture
Shang-Hua Gao, Ming-Ming Cheng, Kai Zhao, Xin-Yu Zhang, Ming-Hsuan Yang, and Philip Torr · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova · 2019
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Generalized intersection over union: A metric and a loss for bounding box regression
Hamid Rezatofighi, Nathan Tsoi, JunYoung Gwak, Amir Sadeghian, Ian Reid, and Silvio Savarese · 2019
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Object detection from scratch with deep supervision
Zhiqiang Shen, Zhuang Liu, Jianguo Li, Yu-Gang Jiang, Yurong Chen, and Xiangyang Xue · 2019
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FCOS: Fully convolutional one-stage object detection
Zhi Tian, Chunhua Shen, Hao Chen, and Tong He · 2019
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IoU loss for 2D/3D object detection
Dingfu Zhou, Jin Fang, Xibin Song, Chenye Guan, Junbo Yin, Yuchao Dai, and Ruigang Yang · 2019
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YOLOv4: Optimal speed and accuracy of object detection
Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao · 2020
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End-to-end object detection with transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
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AP-loss for accurate one-stage object detection
Kean Chen, Weiyao Lin, Jianguo Li, John See, Ji Wang, and Junni Zou · 2020
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AugFPN: Improving multi-scale feature learning for object detection
Chaoxu Guo, Bin Fan, Qian Zhang, Shiming Xiang, and Chunhong Pan · 2020
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DATNet: Dense auxiliary tasks for object detection
Alex Levinshtein, Alborz Rezazadeh Sereshkeh, and Konstantinos Derpanis · 2020
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CBNet: A novel composite backbone network architecture for object detection
Yudong Liu, Yongtao Wang, Siwei Wang, TingTing Liang, Qijie Zhao, Zhi Tang, and Haibin Ling · 2020
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A ranking-based, balanced loss function unifying classification and localisation in object detection
Kemal Oksuz, Baris Can Cam, Emre Akbas, and Sinan Kalkan · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Distance-IoU loss: Faster and better learning for bounding box regression
Zhaohui Zheng, Ping Wang, Wei Liu, Jinze Li, Rongguang Ye, and Dongwei Ren · 2020
Cited alongside, same era.
AutoAssign: Differentiable label assignment for dense object detection
Benjin Zhu, Jianfeng Wang, Zhengkai Jiang, Fuhang Zong, Songtao Liu, Zeming Li, and Jian Sun · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2021
MaxVIT: Multi-axis vision transformer
Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, and Yinxiao Li · 2022
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PVT v2: Improved baselines with pyramid vision transformer
Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao · 2022
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SimMIM: A simple framework for masked image modeling
Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, and Han Hu · 2022
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PP-YOLOE: An evolved version of YOLO
Shangliang Xu, Xinxin Wang, Wenyu Lv, Qinyao Chang, Cheng Cui, Kaipeng Deng, Guanzhong Wang, Qingqing Dang, Shengyu Wei, Yuning Du, et al · 2022
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DAMO-YOLO: A report on real-time object detection design
Xianzhe Xu, Yiqi Jiang, Weihua Chen, Yilun Huang, Yuan Zhang, and Xiuyu Sun · 2022
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Cited alongside, same era.
TOOD: Task-aligned one-stage object detection
Chengjian Feng, Yujie Zhong, Yu Gao, Matthew R Scott, and Weilin Huang · 2021
Cited alongside, same era.
OTA: Optimal transport assignment for object detection
Zheng Ge, Songtao Liu, Zeming Li, Osamu Yoshie, and Jian Sun · 2021
Cited alongside, same era.
YOLOX: Exceeding YOLO series in 2021
Zheng Ge, Songtao Liu, Feng Wang, Zeming Li, and Jian Sun · 2021
Cited alongside, same era.
Perceiver: General perception with iterative attention
Andrew Jaegle, Felix Gimeno, Andy Brock, Oriol Vinyals, Andrew Zisserman, and Joao Carreira · 2021
Cited alongside, same era.
Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
Cited alongside, same era.
Rank & sort loss for object detection and instance segmentation
Kemal Oksuz, Baris Can Cam, Emre Akbas, and Sinan Kalkan · 2021
Cited alongside, same era.
What makes for end-to-end object detection?
Peize Sun, Yi Jiang, Enze Xie, Wenqi Shao, Zehuan Yuan, Changhu Wang, and Ping Luo · 2021
Cited alongside, same era.
Uni-perceiver: Pre-training unified architecture for generic perception for zero-shot and few-shot tasks
Xizhou Zhu, Jinguo Zhu, Hao Li, Xiaoshi Wu, Hongsheng Li, Xiaohua Wang, and Jifeng Dai · 2022
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Reversible column networks
Yuxuan Cai, Yizhuang Zhou, Qi Han, Jianjian Sun, Xiangwen Kong, Jun Li, and Xiangyu Zhang · 2023
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YOLO-MS: rethinking multi-scale representation learning for real-time object detection
Yuming Chen, Xinbin Yuan, Ruiqi Wu, Jiabao Wang, Qibin Hou, and Ming-Ming Cheng · 2023
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Mamba: Linear-time sequence modeling with selective state spaces
Albert Gu and Tri Dao · 2023
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RevColV2: Exploring disentangled representations in masked image modeling
Qi Han, Yuxuan Cai, and Xiangyu Zhang · 2023
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YOLOCS: Object detection based on dense channel compression for feature spatial solidification
Lin Huang, Weisheng Li, Linlin Shen, Haojie Fu, Xue Xiao, and Suihan Xiao · 2023
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YOLOv6 v3.0: A full-scale reloading
Chuyi Li, Lulu Li, Yifei Geng, Hongliang Jiang, Meng Cheng, Bo Zhang, Zaidan Ke, Xiaoming Xu, and Xiangxiang Chu · 2023
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Uni-perceiver v2: A generalist model for large-scale vision and vision-language tasks
Hao Li, Jinguo Zhu, Xiaohu Jiang, Xizhou Zhu, Hongsheng Li, Chun Yuan, Xiaohua Wang, Yu Qiao, Xiaogang Wang, Wenhai Wang, et al · 2023
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DynamicDet: A unified dynamic architecture for object detection
Zhihao Lin, Yongtao Wang, Jinhe Zhang, and Xiaojie Chu · 2023
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DETRs beat YOLOs on real-time object detection
Wenyu Lv, Shangliang Xu, Yian Zhao, Guanzhong Wang, Jinman Wei, Cheng Cui, Yuning Du, Qingqing Dang, and Yi Liu · 2023
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Perceiver-actor: A multi-task transformer for robotic manipulation
Mohit Shridhar, Lucas Manuelli, and Dieter Fox · 2023
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Perceiver-VL: Efficient vision-and-language modeling with iterative latent attention
Zineng Tang, Jaemin Cho, Jie Lei, and Mohit Bansal · 2023
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Gold-YOLO: Efficient object detector via gather-and-distribute mechanism
Chengcheng Wang, Wei He, Ying Nie, Jianyuan Guo, Chuanjian Liu, Kai Han, and Yunhe Wang · 2023
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YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
Chien-Yao Wang, Alexey Bochkovskiy, and Hong-Yuan Mark Liao · 2023
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Designing network design strategies through gradient path analysis
Chien-Yao Wang, Hong-Yuan Mark Liao, and I-Hau Yeh · 2023
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You only learn one representation: Unified network for multiple tasks
Chien-Yao Wang, I-Hau Yeh, and Hong-Yuan Mark Liao · 2023
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ConvNeXt v2: Co-designing and scaling convnets with masked autoencoders
Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, and Saining Xie · 2023
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MonoDETR: Depth-guided transformer for monocular 3D object detection
Renrui Zhang, Han Qiu, Tai Wang, Ziyu Guo, Ziteng Cui, Yu Qiao, Hongsheng Li, and Peng Gao · 2023
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DETRs with collaborative hybrid assignments training
Zhuofan Zong, Guanglu Song, and Yu Liu · 2023
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YOLOv8 release v8.1.0
Jocher Glenn · 2024
Closest in time.
Vmamba: Visual state space model
Yue Liu, Yunjie Tian, Yuzhong Zhao, Hongtian Yu, Lingxi Xie, Yaowei Wang, Qixiang Ye, and Yunfan Liu · 2024
Closest in time.
Vision mamba: Efficient visual representation learning with bidirectional state space model
Lianghui Zhu, Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, and Xinggang Wang · 2024
Closest in time.