# Object Detection in Video with Spatiotemporal Sampling Networks

Source: [Object Detection in Video with Spatiotemporal Sampling Networks](<https://www.sota2.com/research/paper/68fa649a1e209131cea0573b>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:07:03.168Z

Authors: Gedas Bertasius, Lorenzo Torresani, Jianbo Shi

Published: 2018-03-15T00:23:22.000Z

## Abstract

We propose a Spatiotemporal Sampling Network (STSN) that uses deformable convolutions across time for object detection in videos. Our STSN performs object detection in a video frame by learning to spatially sample features from the adjacent frames. This naturally renders the approach robust to occlusion or motion blur in individual frames. Our framework does not require additional supervision, as it optimizes sampling locations directly with respect to object detection performance. Our STSN outperforms the state-of-the-art on the ImageNet VID dataset and compared to prior video object detection methods it uses a simpler design, and does not require optical flow data for training.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Video Object Detection on ImageNet VID (val)](<https://www.sota2.com/research/sota/video-object-detection-on-imagenet-vid-val.md>): Video Object Detection; ImageNet VID (val); mAP (%): 80.4.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1803.05549>)

[PDF](<https://arxiv.org/pdf/1803.05549.pdf>)