Contrastive Learning of Visual-Semantic Embeddings

• URL: http://arxiv.org/abs/2110.08872v1
• Date: Sun, 17 Oct 2021 17:28:04 GMT
• Title: Contrastive Learning of Visual-Semantic Embeddings
• Authors: Anurag Jain and Yashaswi Verma
• Abstract summary: We propose two loss functions based on normalized cross-entropy to perform the task of learning joint visual-semantic embedding. We compare our results with existing visual-semantic embedding methods on cross-modal image-to-text and text-to-image retrieval tasks.
• Score: 4.7464518249313805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contrastive learning is a powerful technique to learn representations that are semantically distinctive and geometrically invariant. While most of the earlier approaches have demonstrated its effectiveness on single-modality learning tasks such as image classification, recently there have been a few attempts towards extending this idea to multi-modal data. In this paper, we propose two loss functions based on normalized cross-entropy to perform the task of learning joint visual-semantic embedding using batch contrastive training. In a batch, for a given anchor point from one modality, we consider its negatives only from another modality, and define our first contrastive loss based on expected violations incurred by all the negatives. Next, we update this loss and define the second contrastive loss based on the violation incurred only by the hardest negative. We compare our results with existing visual-semantic embedding methods on cross-modal image-to-text and text-to-image retrieval tasks using the MS-COCO and Flickr30K datasets, where we outperform the state-of-the-art on the MS-COCO dataset and achieve comparable results on the Flickr30K dataset.

Related papers

• Separating common from salient patterns with Contrastive Representation Learning [2.250968907999846]
  Contrastive Analysis aims at separating common factors of variation between two datasets. Current models based on Variational Auto-Encoders have shown poor performance in learning semantically-expressive representations. We propose to leverage the ability of Contrastive Learning to learn semantically expressive representations well adapted for Contrastive Analysis.
  arXiv  Detail & Related papers  (2024-02-19T08:17:13Z)
• Active Mining Sample Pair Semantics for Image-text Matching [6.370886833310617]
  This paper proposes a novel image-text matching model, called Active Mining Sample Pair Semantics image-text matching model (AMSPS) Compared with the single semantic learning mode of the commonsense learning model with triplet loss function, AMSPS is an active learning idea.
  arXiv  Detail & Related papers  (2023-11-09T15:03:57Z)
• LSEH: Semantically Enhanced Hard Negatives for Cross-modal Information Retrieval [0.4264192013842096]
  Visual Semantic Embedding (VSE) aims to extract the semantics of images and their descriptions, and embed them into the same latent space for information retrieval. Most existing VSE networks are trained by adopting a hard negatives loss function which learns an objective margin between the similarity of relevant and irrelevant image-description embedding pairs. This paper presents a novel approach that comprises two main parts: (1) finds the underlying semantics of image descriptions; and (2) proposes a novel semantically enhanced hard negatives loss function.
  arXiv  Detail & Related papers  (2022-10-10T15:09:39Z)
• Mix-up Self-Supervised Learning for Contrast-agnostic Applications [33.807005669824136]
  We present the first mix-up self-supervised learning framework for contrast-agnostic applications. We address the low variance across images based on cross-domain mix-up and build the pretext task based on image reconstruction and transparency prediction.
  arXiv  Detail & Related papers  (2022-04-02T16:58:36Z)
• On Modality Bias Recognition and Reduction [70.69194431713825]
  We study the modality bias problem in the context of multi-modal classification. We propose a plug-and-play loss function method, whereby the feature space for each label is adaptively learned. Our method yields remarkable performance improvements compared with the baselines.
  arXiv  Detail & Related papers  (2022-02-25T13:47:09Z)
• Robust Contrastive Learning against Noisy Views [79.71880076439297]
  We propose a new contrastive loss function that is robust against noisy views. We show that our approach provides consistent improvements over the state-of-the-art image, video, and graph contrastive learning benchmarks.
  arXiv  Detail & Related papers  (2022-01-12T05:24:29Z)
• Weakly Supervised Contrastive Learning [68.47096022526927]
  We introduce a weakly supervised contrastive learning framework (WCL) to tackle this issue. WCL achieves 65% and 72% ImageNet Top-1 Accuracy using ResNet50, which is even higher than SimCLRv2 with ResNet101.
  arXiv  Detail & Related papers  (2021-10-10T12:03:52Z)
• Dense Contrastive Visual-Linguistic Pretraining [53.61233531733243]
  Several multimodal representation learning approaches have been proposed that jointly represent image and text. These approaches achieve superior performance by capturing high-level semantic information from large-scale multimodal pretraining. We propose unbiased Dense Contrastive Visual-Linguistic Pretraining to replace the region regression and classification with cross-modality region contrastive learning.
  arXiv  Detail & Related papers  (2021-09-24T07:20:13Z)
• Incremental False Negative Detection for Contrastive Learning [95.68120675114878]
  We introduce a novel incremental false negative detection for self-supervised contrastive learning. During contrastive learning, we discuss two strategies to explicitly remove the detected false negatives. Our proposed method outperforms other self-supervised contrastive learning frameworks on multiple benchmarks within a limited compute.
  arXiv  Detail & Related papers  (2021-06-07T15:29:14Z)
• Distilling Localization for Self-Supervised Representation Learning [82.79808902674282]
  Contrastive learning has revolutionized unsupervised representation learning. Current contrastive models are ineffective at localizing the foreground object. We propose a data-driven approach for learning in variance to backgrounds.
  arXiv  Detail & Related papers  (2020-04-14T16:29:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.