# Feature Flow: In-network Feature Flow Estimation for Video Object Detection Source: [Feature Flow: In-network Feature Flow Estimation for Video Object Detection]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:50.713Z Authors: Ruibing Jin, Guosheng Lin, Changyun Wen, Jianliang Wang, Fayao Liu Published: 2020-09-21T07:55:50.000Z ## Abstract Optical flow, which expresses pixel displacement, is widely used in many computer vision tasks to provide pixel-level motion information. However, with the remarkable progress of the convolutional neural network, recent state-of-the-art approaches are proposed to solve problems directly on feature-level. Since the displacement of feature vector is not consistent to the pixel displacement, a common approach is to:forward optical flow to a neural network and fine-tune this network on the task dataset. With this method,they expect the fine-tuned network to produce tensors encoding feature-level motion information. In this paper, we rethink this de facto paradigm and analyze its drawbacks in the video object detection task. To mitigate these issues, we propose a novel network (IFF-Net) with an \\textbf{I}n-network \\textbf{F}eature \\textbf{F}low estimation module (IFF module) for video object detection. Without resorting pre-training on any additional dataset, our IFF module is able to directly produce \\textbf{feature flow} which indicates the feature displacement. Our IFF module consists of a shallow module, which shares the features with the detection branches. This compact design enables our IFF-Net to accurately detect objects, while maintaining a fast inference speed. Furthermore, we propose a transformation residual loss (TRL) based on \\textit{self-supervision}, which further improves the performance of our IFF-Net. Our IFF-Net outperforms existing methods and sets a state-of-the-art performance on ImageNet VID. ## 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)](): Video Object Detection; ImageNet VID (val); mAP (%): 77.1. ## Original sources and implementation [Original paper]() [PDF]()