Video captioning focuses on generating natural language descriptions according to the
video content. Existing works mainly explore this multimodal learning with the paired source …

Video captioning, a challenging task targeting the automatic generation of accurate and
comprehensive descriptions based on video content, has witnessed substantial success …

Prompt tuning, an emerging parameter-efficient strategy, leverages the powerful knowledge
of large-scale pre-trained image-text models (eg, CLIP) to swiftly adapt to downstream tasks …

In the field of video captioning, recent works usually focus on multi-modal video content
understanding, in which transcripts are extracted from speech and are often adopted as an …

Video captioning aims at automatically generating descriptive sentences based on the given
video, establishing an association between the visual contents and textual languages, has …

Temporal dynamics represent the evolving of video content over time, which are critical for
action recognition. In this paper, we ask the question: can the off-the-shelf image transformer …

Schizophrenia (SZ) is a common and disabling mental illness, and most patients encounter
cognitive deficits. The eye-tracking technology has been increasingly used to characterize …

In video captioning, it is very challenging to comprehensively describe multi-modal content
information of a video, such as appearance, motion, and object. Prior arts often neglect …

In the context of video captioning, a conventional dual learning scheme involves two tasks: a
primal task, which translates frame features into natural language captions, and a dual task …

Traditional image captioning methods only have a local perspective at the dataset level,
allowing them to explore dispersed information within individual images. However, the lack …