The contribution of this study lies in the integration of deep learning with cross-modal supervision, providing new perspectives for enhancing the robustness and accuracy of target pose recognition.Xu, DongpoLiu, YunqingWang, QianWang, Liang
Self-Supervised MultiModal Versatile Networks Multi-modal Self-Supervision from Generalized Data Transformations VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text Video Self-supervised Learning (特指单模态) 目前主要分两类,基于对比学习的(拓展SimCLR, MoCo, BYOL, etc...
In this work we propose a technique that transfers supervision between images from different modalities. We use learned representations from a large labeled modality as supervisory signal for training representations for a new unlabeled paired modality. Our method enables learning of rich representations ...
To further reduce the amount of supervision, we propose Prompts-in-The-Loop (PiTL) that prompts knowledge from large language models (LLMs) to describe images. Concretely, given a category label of an image, e.g. refinery, the knowledge, e.g. a refinery could be seen with large storage...
2.S. Gupta, J. Hoffman, and J. Malik. Cross modal distillationfor supervision transfer. InCVPR, 2016. 3. J. Hoffman, S. Gupta, and T. Darrell. Learning with sideinformation through modality hallucination. InCVPR, 2016
[CVPR 2023 Highlight 💡] Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal Supervision deep-learning optical-flow autonomous-driving mobile-robotics motion-segmentation scene-flow cross-modal-learning 4d-radar automotive-radar ego-motion-estimation Updated Jul 17, 2023 Python RunpeiDong...
(a) xMUDA learns from supervision on the source domain (plain lines) and self-supervision on the tar- get domain (dashed lines), while benefiting from the cross- modal predictions of 2D/3D. (b) We consider four data sub- sets: Source 2D, Target 2D...
In prelimi-nary experiments, we extended this work to anothermodality: we found out that, in VQG, without anysupervision between the images and the questions,the cross-modal alignment was not successfullylearnt. This discrepancy between multi-lingual andmulti-modal results might f ind its root ...
@InProceedings{Nagrani20d, author = "Arsha Nagrani and Joon~Son Chung and Samuel Albanie and Andrew Zisserman", title = "Disentangled Speech Embeddings using Cross-Modal Self-Supervision", booktitle = "International Conference on Acoustics, Speech, and Signal Processing", year = "2020", } Li...
In other words, two modalities constantly extract supervision information to help the opposite side to refine propagation result until attaining a stable state. Finally, we integrate two modalities-induced propagation results into a refined saliency map. We compare our model with the state-of-the-...