Multi-adversarial Faster-RCNN for Unrestricted Object Detection (1907.10343v2)

Abstract: Conventional object detection methods essentially suppose that the training and testing data are collected from a restricted target domain with expensive labeling cost. For alleviating the problem of domain dependency and cumbersome labeling, this paper proposes to detect objects in an unrestricted environment by leveraging domain knowledge trained from an auxiliary source domain with sufficient labels. Specifically, we propose a multi-adversarial Faster-RCNN (MAF) framework for unrestricted object detection, which inherently addresses domain disparity minimization for domain adaptation in feature representation. The paper merits are in three-fold: 1) With the idea that object detectors often becomes domain incompatible when image distribution resulted domain disparity appears, we propose a hierarchical domain feature alignment module, in which multiple adversarial domain classifier submodules for layer-wise domain feature confusion are designed; 2) An information invariant scale reduction module (SRM) for hierarchical feature map resizing is proposed for promoting the training efficiency of adversarial domain adaptation; 3) In order to improve the domain adaptability, the aggregated proposal features with detection results are feed into a proposed weighted gradient reversal layer (WGRL) for characterizing hard confused domain samples. We evaluate our MAF on unrestricted tasks, including Cityscapes, KITTI, Sim10k, etc. and the experiments show the state-of-the-art performance over the existing detectors.

• The paper introduces Multi-adversarial Faster-RCNN (MAF), a framework using hierarchical and proposal feature alignment via multi-adversarial domain classifiers to improve object detection across domains.
• The framework incorporates a Scale Reduction Module (SRM) for training efficiency and a Weighted Gradient Reversal Layer (WGRL) to balance learning across different domain samples.
• Empirical evaluation demonstrates MAF's superior performance over baseline Faster-RCNN and state-of-the-art methods on benchmark datasets, enhancing robustness in challenging domain shifts.

Multi-adversarial Faster-RCNN for Unrestricted Object Detection

The research paper "Multi-adversarial Faster-RCNN for Unrestricted Object Detection" presents a novel approach to address the challenges of domain adaptation in object detection tasks, particularly in environments where data from different domains exhibit considerable disparity. The proposed Multi-adversarial Faster-RCNN (MAF) framework is designed to detect objects in unrestricted environments by leveraging domain knowledge from a well-labeled auxiliary source domain. This work fundamentally advances the state-of-the-art domain adaptation processes by introducing a multi-adversarial approach to achieve robust detection performance.

The core contributions of this work are articulated across three primary components: hierarchical domain feature alignment, proposal feature alignment, and the introduction of a scale reduction module (SRM) and a weighted gradient reversal layer (WGRL).

1. Hierarchical Domain Feature Alignment: The paper identifies a significant challenge in domain-adaptive object detection, which is the domain disparity at both image and feature levels. To mitigate this, the authors propose hierarchical domain feature alignment through multiple adversarial domain classifiers applied at different convolutional layers. This strategy effectively minimizes domain distribution discrepancies by ensuring comprehensive feature-level alignment, thereby improving domain-invariance of feature representations.
2. Proposal Feature Alignment: To further optimize object detection across domains, the paper introduces an aggregated proposal feature alignment module. This module incorporates detection results, such as classification scores and bounding box regression outputs, to reinforce semantic alignment. The weighted gradient reversal layer (WGRL) is employed to adjust the gradients during training, facilitating a balanced learning process that adapively focuses on hard-to-confuse samples, thus enhancing the model's robustness against domain shifts.
3. Scale Reduction Module (SRM): Training efficiency is a critical concern addressed by this work, particularly in large-scale domain adaptation tasks. The scale reduction module (SRM) efficiently downsizes feature maps without loss of essential domain feature information, thereby optimizing the computational load and accelerating training processes.

The empirical evaluation, conducted across several benchmark datasets including Cityscapes, Foggy Cityscapes, KITTI, and SIM10k, demonstrates the efficacy of the proposed MAF approach. Particularly, the framework shows superior performance when addressing the domain shift from synthetic data to real-world scenarios, and when adapting detection models from one environmental condition to another, such as from clear weather to foggy conditions. The MAF consistently outperforms the baseline Faster-RCNN and the state-of-the-art domain adaptive Faster-RCNN (DAF) by notable margins, underscoring the effectiveness of the multi-adversarial strategy.

Future developments following this research may explore further optimization in adversarial domain adaptation by refining adversarial classifiers or enhancing feature representation layers. Additionally, the approach opens avenues for extending multi-adversarial strategies to other machine learning domains requiring domain invariance, such as speech recognition or sentiment analysis, where domain-specific variance is prevalent.

Overall, the MAF framework substantially increases the robustness and adaptability of object detection systems, demonstrating its utility in real-world applications where heterogeneous data sources hinder performance. This work contributes significantly to the field of domain adaptation in computer vision, setting a precedent for future advancements in unrestricted object detection.