# Enhancing Weakly-Supervised Object Detection on Static Images through (Hallucinated) Motion

Source: [Enhancing Weakly-Supervised Object Detection on Static Images through (Hallucinated) Motion](<https://www.sota2.com/research/paper/68fa74ca1e209131cea63d86>)

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

Data updated: 2025-10-26T03:18:28.201Z

Authors: Cagri Gungor, Adriana Kovashka

Published: 2024-09-15T05:32:48.000Z

## Abstract

While motion has garnered attention in various tasks, its potential as a modality for weakly-supervised object detection (WSOD) in static images remains unexplored. Our study introduces an approach to enhance WSOD methods by integrating motion information. This method involves leveraging hallucinated motion from static images to improve WSOD on image datasets, utilizing a Siamese network for enhanced representation learning with motion, addressing camera motion through motion normalization, and selectively training images based on object motion. Experimental validation on the COCO and YouTube-BB datasets demonstrates improvements over a state-of-the-art method.

## 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.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2409.09616>)

[PDF](<https://arxiv.org/pdf/2409.09616.pdf>)