| Synthesized classifiers for zero-shot learning S Changpinyo, WL Chao, B Gong, F Sha IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 5327-5336, 2016 | 839 | 2016 |
| An empirical study and analysis of generalized zero-shot learning for object recognition in the wild WL Chao, S Changpinyo, B Gong, F Sha European Conference on Computer Vision (ECCV), 52-68, 2016 | 544 | 2016 |
| Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts S Changpinyo, P Sharma, N Ding, R Soricut IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 | 324 | 2021 |
| Predicting visual exemplars of unseen classes for zero-shot learning S Changpinyo, WL Chao, F Sha IEEE International Conference on Computer Vision (ICCV), 3496-3505, 2017 | 185 | 2017 |
| The power of sparsity in convolutional neural networks S Changpinyo, M Sandler, A Zhmoginov arXiv preprint arXiv:1702.06257, 2017 | 144 | 2017 |
| Connecting vision and language with localized narratives J Pont-Tuset, J Uijlings, S Changpinyo, R Soricut, V Ferrari European Conference on Computer Vision (ECCV), 647-664, 2020 | 128 | 2020 |
| PaLI: A Jointly-Scaled Multilingual Language-Image Model X Chen, X Wang, S Changpinyo, AJ Piergiovanni, P Padlewski, D Salz, ... International Conference on Learning Representations (ICLR), 2023 | 99 | 2023 |
| Multi-Task Learning for Sequence Tagging: An Empirical Study S Changpinyo, H Hu, F Sha 27th International Conference on Computational Linguistics (COLING), 2965–2977, 2018 | 72 | 2018 |
| Classifier and exemplar synthesis for zero-shot learning S Changpinyo, WL Chao, B Gong, F Sha International Journal of Computer Vision (IJCV) 128 (1), 166-201, 2020 | 41 | 2020 |
| Similarity component analysis S Changpinyo, K Liu, F Sha Advances in Neural Information Processing Systems (NIPS), 1511-1519, 2013 | 29 | 2013 |
| On Model Calibration for Long-Tailed Object Detection and Instance Segmentation TY Pan, C Zhang, Y Li, H Hu, D Xuan, S Changpinyo, B Gong, WL Chao Advances in Neural Information Processing Systems (NeurIPS), 2021 | 26 | 2021 |
| All You May Need for VQA are Image Captions S Changpinyo, D Kukliansky, I Szpektor, X Chen, N Ding, R Soricut Conference of the North American Chapter of the Association for …, 2022 | 22 | 2022 |
| MosaicOS: A Simple and Effective Use of Object-Centric Images for Long-Tailed Object Detection C Zhang, TY Pan, Y Li, H Hu, D Xuan, S Changpinyo, B Gong, WL Chao IEEE/CVF International Conference on Computer Vision (ICCV), 2021 | 22 | 2021 |
| Robust Visual Reasoning via Language Guided Neural Module Networks AR Akula, V Jampani, S Changpinyo, SC Zhu Advances in Neural Information Processing Systems (NeurIPS), 2021 | 19 | 2021 |
| CrossVQA: Scalably Generating Benchmarks for Systematically Testing VQA Generalization A Akula, S Changpinyo, B Gong, P Sharma, S Zhu, R Soricut Conference on Empirical Methods in Natural Language Processing (EMNLP), 2148 …, 2021 | 17 | 2021 |
| Decoupled box proposal and featurization with ultrafine-grained semantic labels improve image captioning and visual question answering S Changpinyo, B Pang, P Sharma, R Soricut Conference on Empirical Methods in Natural Language Processing and 9th …, 2019 | 14 | 2019 |
| Telling the What while Pointing to the Where: Multimodal Queries for Image Retrieval S Changpinyo, J Pont-Tuset, V Ferrari, R Soricut IEEE/CVF International Conference on Computer Vision (ICCV), 2021 | 13 | 2021 |
| Learning image attributes using the Indian Buffet Process S Changpinyo, EB Sudderth Brown University, 2012 | 8 | 2012 |
| PreSTU: Pre-Training for Scene-Text Understanding J Kil, S Changpinyo, X Chen, H Hu, S Goodman, WL Chao, R Soricut arXiv preprint arXiv:2209.05534, 2022 | 5 | 2022 |
| Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions? Y Chen, H Hu, Y Luan, H Sun, S Changpinyo, A Ritter, MW Chang arXiv preprint arXiv:2302.11713, 2023 | 3 | 2023 |
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