Rotation-equivariance is an essential yet challenging property in oriented
object detection. While general object detectors naturally leverage robustness
to spatial shifts due to the translation-equivariance of the conventional CNNs,
achieving rotation-equivariance remains an elusive goal. Current detectors
deploy various alignment techniques to derive rotation-invariant features, but
still rely on high capacity models and heavy data augmentation with all
possible rotations. In this paper, we introduce a Fully Rotation-Equivariant
Oriented Object Detector (FRED), whose entire process from the image to the
bounding box prediction is strictly equivariant. Specifically, we decouple the
invariant task (object classification) and the equivariant task (object
localization) to achieve end-to-end equivariance. We represent the bounding box
as a set of rotation-equivariant vectors to implement rotation-equivariant
localization. Moreover, we utilized these rotation-equivariant vectors as
offsets in the deformable convolution, thereby enhancing the existing
advantages of spatial adaptation. Leveraging full rotation-equivariance, our
FRED demonstrates higher robustness to image-level rotation compared to
existing methods. Furthermore, we show that FRED is one step closer to non-axis
aligned learning through our experiments. Compared to state-of-the-art methods,
our proposed method delivers comparable performance on DOTA-v1.0 and
outperforms by 1.5 mAP on DOTA-v1.5, all while significantly reducing the model
parameters to 16%.

全旋转等变定向物体检测器 (FRED) 严格等变地实现了从图像到边界框预测的整个过程，利用旋转等变向量表示边界框实现旋转等变定位，并通过修正卷积操作进一步增强对图像层级旋转的鲁棒性，相比现有方法，FRED 在 DOTA-v1.0 上达到可比性能，而在 DOTA-v1.5 上提升了 1.5 mAP，同时模型参数显著减少至 16%。

FRED：实现航拍图像目标检测的完全旋转等变性

FRED: Towards a Full Rotation-Equivariance in Aerial Image Object  Detection

Table structure recognition aims to extract the logical and physical
structure of unstructured table images into a machine-readable format. The
latest end-to-end image-to-text approaches simultaneously predict the two
structures by two decoders, where the prediction of the physical structure (the
bounding boxes of the cells) is based on the representation of the logical
structure. However, the previous methods struggle with imprecise bounding boxes
as the logical representation lacks local visual information. To address this
issue, we propose an end-to-end sequential modeling framework for table
structure recognition called VAST. It contains a novel coordinate sequence
decoder triggered by the representation of the non-empty cell from the logical
structure decoder. In the coordinate sequence decoder, we model the bounding
box coordinates as a language sequence, where the left, top, right and bottom
coordinates are decoded sequentially to leverage the inter-coordinate
dependency. Furthermore, we propose an auxiliary visual-alignment loss to
enforce the logical representation of the non-empty cells to contain more local
visual details, which helps produce better cell bounding boxes. Extensive
experiments demonstrate that our proposed method can achieve state-of-the-art
results in both logical and physical structure recognition. The ablation study
also validates that the proposed coordinate sequence decoder and the
visual-alignment loss are the keys to the success of our method.

提出了一种名为 VAST 的表结构识别框架，其中包含一个新颖的坐标序列解码器以及一个辅助的视觉对齐损失。通过该框架，可以实现在逻辑和物理结构识别方面的最先进的结果。