Objective: Surgical activity recognition is a fundamental step in computer-assisted
interventions. This paper reviews the state-of-the-art in methods for automatic recognition of …

Vision-language models (VLMs) pre-trained on large-scale image-text pairs have
demonstrated impressive transferability on various visual tasks. Transferring knowledge …

Transferring knowledge from task-agnostic pre-trained deep models for downstream tasks is
an important topic in computer vision research. Along with the growth of computational …

Abstract Contrastive Language-Image Pretraining (CLIP) has demonstrated impressive zero-
shot learning abilities for image understanding, yet limited effort has been made to …

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 …

Transferring knowledge from pre-trained deep models for downstream tasks, particularly
with limited labeled samples, is a fundamental problem in computer vision research. Recent …

Despite significant results achieved by Contrastive Language-Image Pretraining (CLIP) in
zero-shot image recognition, limited effort has been made exploring its potential for zero …

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 …

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 …