CEZSAR: A Contrastive Embedding Method for Zero-Shot Action Recognition

• URL: http://arxiv.org/abs/2605.01165v1
• Date: Fri, 01 May 2026 23:47:29 GMT
• Title: CEZSAR: A Contrastive Embedding Method for Zero-Shot Action Recognition
• Abstract summary: This paper proposes a novel Zero-Shot Action Recognition(ZSAR) method based on contrastive learning.<n>Two well-known problems remain in ZSAR: the semantic gap and the domain shift.<n>We propose a new model that encodes videos and sentences in a joint embedding space, trained by aligning videos with their natural-language descriptions.
• Score: 3.971295713554491
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper proposes a novel Zero-Shot Action Recognition~(ZSAR) method based on contrastive learning. In ZSAR, we aim to classify examples from classes that were missing during training. Two well-known problems remain in ZSAR: the semantic gap and the domain shift. A semantic gap occurs because label representations come from the textual domain (i.e., language models) and must be associated with visual representations (i.e., CNNs, RNNs, transformer-based). This multimodal nature implies that the semantic properties of the two spaces are not identical. On the other hand, the domain shift arises from differences between the training and test sets and is inherent to ZSAR once the test set is unknown. One of the most promising methods to address both issues is learning joint embedding spaces. Therefore, we propose a new model that encodes videos and sentences in a joint embedding space, trained by aligning videos with their natural-language descriptions. We design an automatic negative sampling procedure to augment the training dataset and generate unpaired data, i.e., visual appearance and unrelated descriptions. Our results are state-of-the-art on the UCF-101 and Kinetics-400 datasets under several split configurations. Our code is available at https://github.com/valterlej/cezsar.

Related papers

• ZoRI: Towards Discriminative Zero-Shot Remote Sensing Instance Segmentation [23.40908829241552]
  We propose a novel task called zero-shot remote sensing instance segmentation, aimed at identifying aerial objects that are absent from training data.<n>We introduce a knowledge-injected adaptation strategy that decouples semantic-related information to preserve the pretrained vision-language alignment.<n>We establish new experimental protocols and benchmarks, and extensive experiments convincingly demonstrate that ZoRI achieves the state-of-art performance.
  arXiv  Detail & Related papers  (2024-12-17T11:00:56Z)
• SEER-ZSL: Semantic Encoder-Enhanced Representations for Generalized Zero-Shot Learning [0.6792605600335813]
  Zero-Shot Learning (ZSL) presents the challenge of identifying categories not seen during training.<n>We introduce a Semantic-Enhanced Representations for Zero-Shot Learning (SEER-ZSL)<n>First, we aim to distill meaningful semantic information using a probabilistic encoder, enhancing the semantic consistency and robustness.<n>Second, we distill the visual space by exploiting the learned data distribution through an adversarially trained generator. Third, we align the distilled information, enabling a mapping of unseen categories onto the true data manifold.
  arXiv  Detail & Related papers  (2023-12-20T15:18:51Z)
• Dual Feature Augmentation Network for Generalized Zero-shot Learning [14.410978100610489]
  Zero-shot learning (ZSL) aims to infer novel classes without training samples by transferring knowledge from seen classes. Existing embedding-based approaches for ZSL typically employ attention mechanisms to locate attributes on an image. We propose a novel Dual Feature Augmentation Network (DFAN), which comprises two feature augmentation modules.
  arXiv  Detail & Related papers  (2023-09-25T02:37:52Z)
• Global Semantic Descriptors for Zero-Shot Action Recognition [2.0206441617430695]
  This work introduces a new ZSAR method based on the relationships of actions-objects and actions-descriptive sentences. We show how to estimate probabilities over the set of action classes based only on a set of sentences without hard human labeling. Our results are state-of-the-art in the Kinetics-400 dataset and are competitive on UCF-101.
  arXiv  Detail & Related papers  (2022-09-24T18:15:47Z)
• I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification [123.90912800376039]
  Online textual documents, e.g., Wikipedia, contain rich visual descriptions about object classes. We propose I2DFormer, a novel transformer-based ZSL framework that jointly learns to encode images and documents. Our method leads to highly interpretable results where document words can be grounded in the image regions.
  arXiv  Detail & Related papers  (2022-09-21T12:18:31Z)
• Cross-modal Representation Learning for Zero-shot Action Recognition [67.57406812235767]
  We present a cross-modal Transformer-based framework, which jointly encodes video data and text labels for zero-shot action recognition (ZSAR) Our model employs a conceptually new pipeline by which visual representations are learned in conjunction with visual-semantic associations in an end-to-end manner. Experiment results show our model considerably improves upon the state of the arts in ZSAR, reaching encouraging top-1 accuracy on UCF101, HMDB51, and ActivityNet benchmark datasets.
  arXiv  Detail & Related papers  (2022-05-03T17:39:27Z)
• Tell me what you see: A zero-shot action recognition method based on natural language descriptions [3.136605193634262]
  We propose using video captioning methods to extract semantic information from videos. To the best of our knowledge, this is the first work to represent both videos and labels with descriptive sentences. We build a shared semantic space employing BERT-based embedders pre-trained in the paraphrasing task on multiple text datasets.
  arXiv  Detail & Related papers  (2021-12-18T17:44:07Z)
• Mitigating Generation Shifts for Generalized Zero-Shot Learning [52.98182124310114]
  Generalized Zero-Shot Learning (GZSL) is the task of leveraging semantic information (e.g., attributes) to recognize the seen and unseen samples, where unseen classes are not observable during training. We propose a novel Generation Shifts Mitigating Flow framework for learning unseen data synthesis efficiently and effectively. Experimental results demonstrate that GSMFlow achieves state-of-the-art recognition performance in both conventional and generalized zero-shot settings.
  arXiv  Detail & Related papers  (2021-07-07T11:43:59Z)
• i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning [117.63815437385321]
  We propose i-Mix, a simple yet effective domain-agnostic regularization strategy for improving contrastive representation learning. In experiments, we demonstrate that i-Mix consistently improves the quality of learned representations across domains.
  arXiv  Detail & Related papers  (2020-10-17T23:32:26Z)
• Information Bottleneck Constrained Latent Bidirectional Embedding for Zero-Shot Learning [59.58381904522967]
  We propose a novel embedding based generative model with a tight visual-semantic coupling constraint. We learn a unified latent space that calibrates the embedded parametric distributions of both visual and semantic spaces. Our method can be easily extended to transductive ZSL setting by generating labels for unseen images.
  arXiv  Detail & Related papers  (2020-09-16T03:54:12Z)
• Transferring Cross-domain Knowledge for Video Sign Language Recognition [103.9216648495958]
  Word-level sign language recognition (WSLR) is a fundamental task in sign language interpretation. We propose a novel method that learns domain-invariant visual concepts and fertilizes WSLR models by transferring knowledge of subtitled news sign to them.
  arXiv  Detail & Related papers  (2020-03-08T03:05:21Z)

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.