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Found 6 papers, 2 papers with code
Video Repurposing from User Generated Content: A Large-scale Dataset and Benchmark
The demand for producing short-form videos for sharing on social media platforms has experienced significant growth in recent times.
Frame Order Matters: A Temporal Sequence-Aware Model for Few-Shot Action Recognition
In this paper, we propose a novel Temporal Sequence-Aware Model (TSAM) for few-shot action recognition (FSAR), which incorporates a sequential perceiver adapter into the pre-training framework, to integrate both the spatial information and the sequential temporal dynamics into the feature embeddings.
Envisioning Class Entity Reasoning by Large Language Models for Few-shot Learning
Few-shot learning (FSL) aims to recognize new concepts using a limited number of visual samples.
OmniCLIP: Adapting CLIP for Video Recognition with Spatial-Temporal Omni-Scale Feature Learning
In this paper, we propose OmniCLIP, a framework that adapts CLIP for video recognition by focusing on learning comprehensive features encompassing spatial, temporal, and dynamic spatial-temporal scales, which we refer to as omni-scale features.
Fully Fine-tuned CLIP Models are Efficient Few-Shot Learners
Prompt tuning, which involves training a small set of parameters, effectively enhances the pre-trained Vision-Language Models (VLMs) to downstream tasks.
Zero-Shot Long-Form Video Understanding through Screenplay
The Long-form Video Question-Answering task requires the comprehension and analysis of extended video content to respond accurately to questions by utilizing both temporal and contextual information.
