Zero-Shot Video Retrieval
33 papers with code • 8 benchmarks • 7 datasets
Zero-shot video retrieval is the task of retrieving relevant videos based on a query (usually in text form) without any prior training on specific examples of those videos. Unlike traditional retrieval methods that rely on supervised learning with annotated datasets, zero-shot retrieval leverages pre-trained models, typically based on large-scale vision-language learning, to understand semantic relationships between textual descriptions and video content.
This approach enables retrieval of unseen video concepts by generalizing knowledge from diverse training data, making it highly useful for domains with limited labeled data, such as broadcast media, surveillance, and historical archives.
Libraries
Use these libraries to find Zero-Shot Video Retrieval models and implementationsMost implemented papers
LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment
We thus propose VIDAL-10M with Video, Infrared, Depth, Audio and their corresponding Language, naming as VIDAL-10M.
Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval
Our objective in this work is video-text retrieval - in particular a joint embedding that enables efficient text-to-video retrieval.
CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval
In this paper, we propose a CLIP4Clip model to transfer the knowledge of the CLIP model to video-language retrieval in an end-to-end manner.
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
We train VATT end-to-end from scratch using multimodal contrastive losses and evaluate its performance by the downstream tasks of video action recognition, audio event classification, image classification, and text-to-video retrieval.
End-to-End Learning of Visual Representations from Uncurated Instructional Videos
Annotating videos is cumbersome, expensive and not scalable.
mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video
In contrast to predominant paradigms of solely relying on sequence-to-sequence generation or encoder-based instance discrimination, mPLUG-2 introduces a multi-module composition network by sharing common universal modules for modality collaboration and disentangling different modality modules to deal with modality entanglement.
ImageBind: One Embedding Space To Bind Them All
We show that all combinations of paired data are not necessary to train such a joint embedding, and only image-paired data is sufficient to bind the modalities together.
VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding
We present VideoCLIP, a contrastive approach to pre-train a unified model for zero-shot video and text understanding, without using any labels on downstream tasks.
Florence: A New Foundation Model for Computer Vision
Computer vision foundation models, which are trained on diverse, large-scale dataset and can be adapted to a wide range of downstream tasks, are critical for this mission to solve real-world computer vision applications.
Bridging Video-text Retrieval with Multiple Choice Questions
As an additional benefit, our method achieves competitive results with much shorter pre-training videos on single-modality downstream tasks, e. g., action recognition with linear evaluation.