R2Det: Exploring Relaxed Rotation Equivariance in 2D object detection
Zhiqiang Wu, Yingjie Liu, Hanlin Dong, Xuan Tang, Jian Yang, Bo Jin,, Mingsong Chen, Xian Wei
TL;DR
This paper introduces R2Det, a novel 2D object detection framework that relaxes strict rotation equivariance constraints, enhancing robustness and generalization in real-world scenarios with symmetry-breaking.
Contribution
It proposes a new Relaxed Rotation-Equivariant GConv (R2GConv) and integrates it into R2Net and R2Det, improving adaptability to symmetry-breaking in 2D object detection.
Findings
R2GConv improves natural image classification performance.
R2Det achieves state-of-the-art results in 2D object detection.
The method enhances robustness and generalization in real-world conditions.
Abstract
Group Equivariant Convolution (GConv) empowers models to explore underlying symmetry in data, improving performance. However, real-world scenarios often deviate from ideal symmetric systems caused by physical permutation, characterized by non-trivial actions of a symmetry group, resulting in asymmetries that affect the outputs, a phenomenon known as Symmetry Breaking. Traditional GConv-based methods are constrained by rigid operational rules within group space, assuming data remains strictly symmetry after limited group transformations. This limitation makes it difficult to adapt to Symmetry-Breaking and non-rigid transformations. Motivated by this, we mainly focus on a common scenario: Rotational Symmetry-Breaking. By relaxing strict group transformations within Strict Rotation-Equivariant group , we redefine a Relaxed Rotation-Equivariant group and…
Peer Reviews
Decision·ICLR 2025 Poster
This work makes a valuable contribution to the field of object detection by addressing the limitations of traditional, strictly rotation-equivariant models and exploring the potential of RRE through ER2GConv. This work presents a significant contribution to the field of object detection by addressing a crucial limitation in handling real-world scenarios, where strict rotational symmetries are rarely observed. The authors introduce a novel approach, Relaxed Rotation-Equivariance (RRE), which eff
* The paper lacks sufficient exploration of the key parameter b, which controls the perturbation factor Δ, and more extensive experiments with intermediate values of b would strengthen the argument for the necessity of this perturbation parameter. ** In page 8, Figure 4(a) and Table 1, the results presented demonstrate a minimal improvement in AP when (b=0.1) compared to (b=0). ** Performance deteriorates when (b>0.1), and it would be beneficial to conduct more thorough experiments, especially
1. The paper introduces a novel relaxed rotation-equivariant group convolution (R2GConv), which extends existing equivariant neural networks (ENNs). Additionally, the resulting model, R2Det, shows strong performance across various datasets and tasks. 2. The authors enhance the R2GConv module by incorporating depth-wise and point-wise convolution, and conduct extensive comparative and ablation experiments to confirm its positive impact on the outcomes. 3. The paper is well-written and easy to u
1. Dataset Limitation: The selected datasets, PASCAL VOC and MS COCO 2017, do not emphasize rotation characteristics, which reduces the impact and relevance of the experimental results. To better highlight the effects of SRE and RRE modeling, rotation-specific object detection datasets should be used. 2. Insufficient Baseline Comparison: it would be beneficial to include comparisons with established models in rotation object detection, such as ReDet and FRED, to strengthen the evaluation and pr
- The method demonstrates impressive performance-compute trade-offs on the object detection task. - The authors evaluated the effect of the main contribution (i.e., the learnable perturbation) on performance. Furthermore, its effect on the learned features is well illustrated in Figure 5.
1. The provided background and naming of methods are misleading and partially incorrect. Indeed, **relaxed** equivariance is defined in (Kaba and Ravanbakhsh, 2023) as a relaxation that allows breaking the symmetry of inputs and mapping to arbitrary orbit types when necessary. Note that the output of the function is still predictable under the transformation of the input. While in R2Det, the relaxed equivariance is mistakenly defined (definition 1, line 161) using the definition of **$\epsilon$-
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Robotics and Sensor-Based Localization · Image and Object Detection Techniques
MethodsConvolution