Scene-Adaptive Person Search via Bilateral Modulations to Eliminate Background and Foreground Noise

Core Concepts

A novel Scene-Adaptive Person Search (SEAS) framework that employs bilateral modulations to eliminate background and foreground noise in person features, enabling consistent person representations across diverse scenes.

Abstract

The paper presents a Scene-Adaptive Person Search (SEAS) framework that addresses the challenge of person search in varied scenes. The key insight is that the person feature consists of scene noise, which can be divided into background noise from the detected bounding box and foreground noise caused by lighting conditions, visibility, etc. To eliminate the background noise, SEAS proposes a Background Modulation Network (BMN) that encodes the person feature into a multi-granularity embedding and applies a Background Noise Reduction (BNR) loss to specifically suppress the background noise. To mitigate the foreground noise, SEAS introduces a Foreground Modulation Network (FMN) that uses the scene feature to compute an offset to counteract the foreground noise in the person feature. By applying these bilateral modulations in an end-to-end manner, SEAS is able to obtain consistent person feature representations that adapt to diverse scenes. Experiments on the CUHK-SYSU and PRW benchmarks show that SEAS achieves state-of-the-art performance, outperforming existing methods by a significant margin.

Stats

SEAS achieves 97.1% mAP and 97.8% top-1 accuracy on the CUHK-SYSU dataset, outperforming the previous state-of-the-art by 0.7% and 0.8% respectively. On the PRW dataset, SEAS reaches 60.5% mAP, surpassing the previous best method by 0.7%. SEAS outperforms state-of-the-art methods by a large margin on the cross-camera setting of the PRW dataset, achieving 58.3% mAP.

Quotes

"To eliminate the residual background in the detected bounding box, SEAS proposes a Background Modulation Network (BMN) that incorporates our designed Multi-Granularity Embedding (MGE) to encode the feature embedding that integrate various granularities of person information to reduce the background noise at multiple levels instead of only at the global level." "To eliminate the effect of scene on the foreground of person, SEAS proposes a Foreground Modulation Network (FMN), which employs the scene feature to correct the deviation of person feature caused by the foreground noise."

Key Insights Distilled From

Scene-Adaptive Person Search via Bilateral Modulations

by Yimin Jiang,... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.02834.pdf

Deeper Inquiries

How can the proposed bilateral modulation approach be extended to other computer vision tasks beyond person search that also suffer from scene-dependent noise

The proposed bilateral modulation approach in the SEAS framework can be extended to various other computer vision tasks that also face challenges due to scene-dependent noise. One such application could be object detection in cluttered scenes. By incorporating bilateral modulations to eliminate background noise and maintain consistent object representations, the model can adapt to diverse scenes and improve detection accuracy. Additionally, this approach could be beneficial in image segmentation tasks where the presence of scene noise can hinder the accurate delineation of object boundaries. By modulating both background and foreground noise, the model can enhance segmentation results in complex scenes.

What are the potential limitations of the current SEAS framework, and how could it be further improved to handle more challenging real-world scenarios

While the SEAS framework shows promising results in addressing scene-dependent noise in person search tasks, there are potential limitations that could be further improved upon. One limitation is the reliance on pre-defined hyperparameters, such as the margin parameter M in the Bidirectional Online Instance Matching (BOIM) loss. To enhance adaptability to different datasets and scenarios, a more dynamic mechanism for adjusting hyperparameters based on data characteristics could be implemented. Moreover, the framework's performance may be impacted by extreme variations in lighting conditions or occlusions. Introducing robust feature fusion techniques and attention mechanisms to handle such challenging scenarios could further enhance the model's performance in real-world applications.

Given the effectiveness of the multi-granularity embedding and cross-attention based denoising in SEAS, how could these techniques be applied to enhance feature representations in other domains like natural language processing or speech recognition

The techniques of multi-granularity embedding and cross-attention-based denoising utilized in the SEAS framework can be applied to enhance feature representations in domains like natural language processing (NLP) and speech recognition. In NLP tasks, multi-granularity embedding can be leveraged to capture both global context and fine-grained details in textual data, improving the model's understanding of complex language structures. Similarly, cross-attention mechanisms can be employed in speech recognition to focus on relevant audio features while filtering out background noise, leading to more accurate transcription results. By adapting these techniques to different modalities, the models can effectively handle noisy input data and extract meaningful representations for downstream tasks.