Video Object Detection
34 papers with code • 2 benchmarks • 4 datasets
Video object detection is the task of detecting objects from a video as opposed to images.
( Image credit: Learning Motion Priors for Efficient Video Object Detection )
Benchmarks
| Trend | Dataset | Best Model | Paper Title | Paper | Code | Compare |
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DAVIS
DAVIS 2017
YT-BB
SYNTHIA-AL
Greatest papers with code
Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection
tensorflow/models
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CVPR 2020
In this paper we propose a method that leverages temporal context from the unlabeled frames of a novel camera to improve performance at that camera.
Looking Fast and Slow: Memory-Guided Mobile Video Object Detection
Models and examples built with TensorFlow
Ranked #13 on
Video Object Detection
on ImageNet VID
Mobile Video Object Detection with Temporally-Aware Feature Maps
tensorflow/models
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CVPR 2018
This paper introduces an online model for object detection in videos designed to run in real-time on low-powered mobile and embedded devices.
Emerging Properties in Self-Supervised Vision Transformers
In this paper, we question if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets).
Ranked #1 on
Video Object Detection
on DAVIS 2017
Sequence Level Semantics Aggregation for Video Object Detection
open-mmlab/mmtracking
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ICCV 2019
In this work, we argue that aggregating features in the full-sequence level will lead to more discriminative and robust features for video object detection.
Ranked #3 on
Video Object Detection
on ImageNet VID
Flow-Guided Feature Aggregation for Video Object Detection
open-mmlab/mmtracking
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ICCV 2017
The accuracy of detection suffers from degenerated object appearances in videos, e. g., motion blur, video defocus, rare poses, etc.
Ranked #8 on
Video Object Detection
on ImageNet VID
TSM: Temporal Shift Module for Efficient Video Understanding
MIT-HAN-LAB/temporal-shift-module
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ICCV 2019
The explosive growth in video streaming gives rise to challenges on performing video understanding at high accuracy and low computation cost.
Ranked #10 on
Video Object Detection
on ImageNet VID
Memory Enhanced Global-Local Aggregation for Video Object Detection
Scalsol/mega.pytorch
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CVPR 2020
We argue that there are two important cues for humans to recognize objects in videos: the global semantic information and the local localization information.
Ranked #2 on
Video Object Detection
on ImageNet VID
Relation Distillation Networks for Video Object Detection
Scalsol/mega.pytorch
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ICCV 2019
In this paper, we introduce a new design to capture the interactions across the objects in spatio-temporal context.
Optimizing Video Object Detection via a Scale-Time Lattice
guanfuchen/video_obj
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CVPR 2018
High-performance object detection relies on expensive convolutional networks to compute features, often leading to significant challenges in applications, e. g. those that require detecting objects from video streams in real time.