178 papers with code • 15 benchmarks • 27 datasets
The objective of video retrieval is as follows: given a text query and a pool of candidate videos, select the video which corresponds to the text query. Typically, the videos are returned as a ranked list of candidates and scored via document retrieval metrics.
These leaderboards are used to track progress in Video Retrieval
LibrariesUse these libraries to find Video Retrieval models and implementations
Most implemented papers
Learning a Text-Video Embedding from Incomplete and Heterogeneous Data
We evaluate our method on the task of video retrieval and report results for the MPII Movie Description and MSR-VTT datasets.
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.
Dense-Captioning Events in Videos
We also introduce ActivityNet Captions, a large-scale benchmark for dense-captioning events.
HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips
In this work, we propose instead to learn such embeddings from video data with readily available natural language annotations in the form of automatically transcribed narrations.
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.
ECO: Efficient Convolutional Network for Online Video Understanding
In this paper, we introduce a network architecture that takes long-term content into account and enables fast per-video processing at the same time.
IMAE for Noise-Robust Learning: Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude's Variance Matters
In this work, we study robust deep learning against abnormal training data from the perspective of example weighting built in empirical loss functions, i. e., gradient magnitude with respect to logits, an angle that is not thoroughly studied so far.
Use What You Have: Video Retrieval Using Representations From Collaborative Experts
The rapid growth of video on the internet has made searching for video content using natural language queries a significant challenge.