# Frame-to-Frame Aggregation of Active Regions in Web Videos for Weakly Supervised Semantic Segmentation

arXiv: https://arxiv.org/abs/1908.04501 · HTML: https://mlsanity.com/paper/1908.04501/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2019.

## Built on by (most cited first, 18 of 18)

- [Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2103.08896/) (2021)
- [BBAM: Bounding Box Attribution Map for Weakly Supervised Semantic and Instance Segmentation](https://mlsanity.com/paper/2103.08907/) (2021)
- [Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrast](https://mlsanity.com/paper/2110.07110/) (2021)
- [Complementary Patch for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2108.03852/) (2021)
- [Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2110.06530/) (2021)
- [Weakly Supervised Semantic Segmentation using Out-of-Distribution Data](https://mlsanity.com/paper/2203.03860/) (2022)
- [WEDGE: Web-Image Assisted Domain Generalization for Semantic Segmentation](https://mlsanity.com/paper/2109.14196/) (2021)
- [Weakly-supervised segmentation of referring expressions](https://mlsanity.com/paper/2205.04725/) (2022)
- [Mask-Free Video Instance Segmentation](https://mlsanity.com/paper/2303.15904/) (2023)
- [Weakly Supervised Instance Segmentation for Videos with Temporal Mask Consistency](https://mlsanity.com/paper/2103.12886/) (2021)
- [Weakly Supervised Semantic Segmentation via Alternative Self-Dual Teaching](https://mlsanity.com/paper/2112.09459/) (2021)
- [Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised Segmentation](https://mlsanity.com/paper/2108.07413/) (2021)
- [Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization](https://mlsanity.com/paper/2204.04890/) (2022)
- [WeClick: Weakly-Supervised Video Semantic Segmentation with Click Annotations](https://mlsanity.com/paper/2107.03088/) (2021)
- [Enabling the Network to Surf the Internet](https://mlsanity.com/paper/2102.12205/) (2021)
- [Simpler Does It: Generating Semantic Labels with Objectness Guidance](https://mlsanity.com/paper/2110.10335/) (2021)
- [Temporal-consistent CAMs for Weakly Supervised Video Segmentation in Waste Sorting](https://mlsanity.com/paper/2502.01455/) (2025)
- [Improving Weakly-supervised Video Instance Segmentation by Leveraging Spatio-temporal Consistency](https://mlsanity.com/paper/2408.16661/) (2024)

## Abstract (arXiv)

When a deep neural network is trained on data with only image-level labeling, the regions activated in each image tend to identify only a small region of the target object. We propose a method of using videos automatically harvested from the web to identify a larger region of the target object by using temporal information, which is not present in the static image. The temporal variations in a video allow different regions of the target object to be activated. We obtain an activated region in each frame of a video, and then aggregate the regions from successive frames into a single image, using a warping technique based on optical flow. The resulting localization maps cover more of the target object, and can then be used as proxy ground-truth to train a segmentation network. This simple approach outperforms existing methods under the same level of supervision, and even approaches relying on extra annotations. Based on VGG-16 and ResNet 101 backbones, our method achieves the mIoU of 65.0 and 67.4, respectively, on PASCAL VOC 2012 test images, which represents a new state-of-the-art.