Multimodal Federated Learning via Contrastive Representation Ensemble Q Yu, Y Liu, Y Wang, K Xu, J Liu ICLR, 2023 PUB 视觉问答 Visual Question Answer Think locally, act globally: Federated learning with local and global representations PP Liang, T Liu, L Ziyin, NB Allen, RP Auerbach, D Br...
Additionally, a positive and negative sample selection strategy based on different images is explored during contrastive learning. Averaging pixel values across different training images for each category to set positive and negative samples compares global pixel information while also limiting sample ...
Similar to unimodal self-supervised contrastive learning, this approach can be seen as enforcing a strict identity constraint in a multimodal context. However, due to the inherent complexity of remote sensing images, which cannot be easily described in a single sentence, and the fact that remote ...
Temporal augmented contrastive learning for micro-expression recognition. Pattern Recognit. Lett. 2023, 167, 122–131. [Google Scholar] [CrossRef] Kim, D.H.; Baddar, W.J.; Ro, Y.M. Micro-expression recognition with expression-state constrained spatio-temporal feature representations. In ...
multimodal abstractive summarization; cross-modal fusion; contrastive learning; supervised and unsupervised learning1. Introduction The last two decades have witnessed a surge of information on the internet. Extensive digital resources in a variety of formats (text, image and video) have enriched our ...
CLIP uses contrastive learning, a technique largely popular in the field of unsupervised learning. CLIP has several components, one of which is an image encoder, which utilizes architectures and ResNet models to produce high-dimensional vector representations. CLIP also has a text encoder, which ...
Contrastive learning operates as a self-supervised framework. It employs contrastive learning to augment data, enhancing the effectiveness of visual representations. To further purify the connection between existing features, we perform enhanced contrast fusion on the original image feature 𝑋=(𝑥1,…...
Self-Supervised Learning Representation for Abnormal Acoustic Event Detection Based on Attentional Contrastive Learning. Digit. Signal Process. 2023, 142, 104199. [Google Scholar] [CrossRef] Bresson, X.; Laurent, T. Residual Gated Graph ConvNets. arXiv 2018, arXiv:1711.07553. [Google Scholar] ...
2.3. Contrastive Learning Contrastive learning is a subfield of metric learning that aims to explicitly shape the latent feature space learned by a neural network. This is usually performed by formulating training losses that enforce that latent features of samples belonging to the same class are clo...
UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Virtual Event, 1...