We address the problem of cross-modal fine-grained action retrieval between text and video.
Cross-modal retrieval is commonly achieved through learning a shared embedding space …

Trained on large datasets, deep learning (DL) can accurately classify videos into hundreds
of diverse classes. However, video data is expensive to annotate. Zero-shot learning (ZSL) …

Learning to classify video data from classes not included in the training data, ie video-based
zero-shot learning, is challenging. We conjecture that the natural alignment between the …

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 …

Abstract Zero-Shot Learning (ZSL) has rapidly advanced in recent years. Towards
overcoming the annotation bottleneck in the Dynamic Hand Gesture Recognition (DHGR) …

This paper tackles the problem of zero-shot sign language recognition (ZSSLR), where the
goal is to leverage models learned over the seen sign classes to recognize the instances of …

Utilizing vision and language models (VLMs) pre-trained on large-scale image-text pairs is
becoming a promising paradigm for open-vocabulary visual recognition. In this work, we …

In this paper, we study current and upcoming frontiers across the landscape of skeleton-
based human action recognition. To study skeleton-action recognition in the wild, we …

While video action recognition has been an active area of research for several years, zero-
shot action recognition has only recently started gaining traction. In this work, we propose a …

Most methods tackle zero-shot video classification by aligning visual-semantic
representations within seen classes, which limits generalization to unseen classes. To …