Zero-Shot Video Retrieval

30 papers with code • 8 benchmarks • 7 datasets

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Libraries

Use these libraries to find Zero-Shot Video Retrieval models and implementations
3 papers
3,044

Most implemented papers

Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval

m-bain/frozen-in-time ICCV 2021

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

ArrowLuo/CLIP4Clip 18 Apr 2021

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.

LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment

pku-yuangroup/languagebind 3 Oct 2023

We thus propose VIDAL-10M with Video, Infrared, Depth, Audio and their corresponding Language, naming as VIDAL-10M.

mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video

alibaba/AliceMind 1 Feb 2023

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.

VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text

google-research/google-research NeurIPS 2021

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.

VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding

pytorch/fairseq EMNLP 2021

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.

Bridging Video-text Retrieval with Multiple Choice Questions

tencentarc/mcq CVPR 2022

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.

Revealing Single Frame Bias for Video-and-Language Learning

jayleicn/singularity 7 Jun 2022

Training an effective video-and-language model intuitively requires multiple frames as model inputs.

InternVideo: General Video Foundation Models via Generative and Discriminative Learning

opengvlab/internvideo 6 Dec 2022

Specifically, InternVideo efficiently explores masked video modeling and video-language contrastive learning as the pretraining objectives, and selectively coordinates video representations of these two complementary frameworks in a learnable manner to boost various video applications.