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 )

Greatest papers with code

Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection

tensorflow/models 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.

Video Object Detection Video Understanding

Mobile Video Object Detection with Temporally-Aware Feature Maps

tensorflow/models 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.

Video Object Detection

Emerging Properties in Self-Supervised Vision Transformers

lucidrains/vit-pytorch 29 Apr 2021

In this paper, we question if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets).

Copy Detection Self-Supervised Image Classification +3

Sequence Level Semantics Aggregation for Video Object Detection

open-mmlab/mmtracking 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.

Video Object Detection Video Recognition

Flow-Guided Feature Aggregation for Video Object Detection

open-mmlab/mmtracking ICCV 2017

The accuracy of detection suffers from degenerated object appearances in videos, e. g., motion blur, video defocus, rare poses, etc.

Video Object Detection Video Recognition

TSM: Temporal Shift Module for Efficient Video Understanding

MIT-HAN-LAB/temporal-shift-module ICCV 2019

The explosive growth in video streaming gives rise to challenges on performing video understanding at high accuracy and low computation cost.

Action Classification Action Recognition +3

Memory Enhanced Global-Local Aggregation for Video Object Detection

Scalsol/mega.pytorch 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.

Video Object Detection

Relation Distillation Networks for Video Object Detection

Scalsol/mega.pytorch ICCV 2019

In this paper, we introduce a new design to capture the interactions across the objects in spatio-temporal context.

Region Proposal Video Object Detection

Optimizing Video Object Detection via a Scale-Time Lattice

guanfuchen/video_obj 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.

Video Object Detection