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Learning to segment images purely by relying on the image-text alignment from web data can lead to sub-optimal performance due to noise in the data.
Normalized cuts and image segmentation
Jianbo Shi and Jitendra Malik · 1997
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Kilian Q Weinberger and Lawrence K Saul · 2009
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The PASCAL visual object classes (VOC) challenge
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Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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YFCC100M: The New Data in Multimedia Research
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Ilya Loshchilov and Frank Hutter · 2016
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Chris J Maddison, Andriy Mnih, and Yee Whye Teh · 2016
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Deep metric learning via lifted structured feature embedding
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WebVision Database: Visual Learning and Understanding from Web Data
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
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Sampling matters in deep embedding learning
Chao-Yuan Wu, R Manmatha, Alexander J Smola, and Philipp Krahenbuhl · 2017
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Pyramid scene parsing network
Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia · 2017
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Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, and Laurens van der Maaten · 2018
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Yash Patel, Lluis Gomez, Raul Gomez, Marçal Rusiñol, Dimosthenis Karatzas, and CV Jawahar · 2018
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Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut · 2018
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Representation Learning with Contrastive Predictive Coding
Aäron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Deep metric learning to rank
Fatih Cakir, Kun He, Xide Xia, Brian Kulis, and Stan Sclaroff · 2019
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Large-scale weakly-supervised pre-training for video action recognition
Deepti Ghadiyaram, Matt Feiszli, Du Tran, Xueting Yan, Heng Wang, and Dhruv Mahajan · 2019
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Invariant Information Clustering for Unsupervised Image Classification and Segmentation
Xu Ji, João F. Henriques, and Andrea Vedaldi · 2019
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Sampling wisely: Deep image embedding by top-k precision optimization
Jing Lu, Chaofan Xu, Wei Zhang, Ling-Yu Duan, and Tao Mei · 2019
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Self-supervised visual representations for cross-modal retrieval
Yash Patel, Lluis Gomez, Marçal Rusiñol, Dimosthenis Karatzas, and CV Jawahar · 2019
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Smooth-ap: Smoothing the path towards large-scale image retrieval
Andrew Brown, Weidi Xie, Vicky Kalogeiton, and Andrew Zisserman · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals
Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, and Luc Van Gool · 2021
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Segformer: Simple and efficient design for semantic segmentation with transformers
Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo · 2021
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Filip: Fine-grained interactive language-image pre-training
Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiaodan Liang, Zhenguo Li, Xin Jiang, and Chunjing Xu · 2021
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Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers
Sixiao Zheng, Jiachen Lu, Hengshuang Zhao, Xiatian Zhu, Zekun Luo, Yabiao Wang, Yanwei Fu, Jianfeng Feng, Tao Xiang, Philip HS Torr, et al · 2021
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Improving Semantic Segmentation via Efficient Self-Training
Yi Zhu, Zhongyue Zhang, Chongruo Wu, Zhi Zhang, Tong He, Hang Zhang, R. Manmatha, Mu Li, and Alexander Smola · 2021
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Multi-modal alignment using representation codebook
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Unsupervised Semantic Segmentation by Distilling Feature Correspondences
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Mask dino: Towards a unified transformer-based framework for object detection and segmentation
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Image segmentation using text and image prompts
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Recall@ k surrogate loss with large batches and similarity mixup
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Flava: A foundational language and vision alignment model
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GroupViT Official Implementation
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Groupvit: Semantic segmentation emerges from text supervision
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A simple baseline for open-vocabulary semantic segmentation with pre-trained vision-language model
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CoCa: Contrastive Captioners are Image-Text Foundation Models
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Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations
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Extract free dense labels from clip
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Self-Supervised Learning of Object Parts for Semantic Segmentation
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