Botian Xu

Botao Ren, Botian Xu, Tengyu Liu et al.

Neuroscience studies have shown that the human visual system utilizes high-level feedback information to guide lower-level perception, enabling adaptation to signals of different characteristics. In light of this, we propose Feedback multi-Level feature Extractor (Flex) to incorporate a similar mechanism for object detection. Flex refines feature selection based on image-wise and instance-level feedback information in response to image quality variation and classification uncertainty. Experimental results show that Flex offers consistent improvement to a range of existing SOTA methods on the challenging aerial object detection datasets including DOTA-v1.0, DOTA-v1.5, and HRSC2016. Although the design originates in aerial image detection, further experiments on MS COCO also reveal our module's efficacy in general detection models. Quantitative and qualitative analyses indicate that the improvements are closely related to image qualities, which match our motivation.

Botao Ren, Botian Xu, Xue Yang et al.

In most modern object detection pipelines, the detection proposals are processed independently given the feature map. Therefore, they overlook the underlying relationships between objects and the surrounding background, which could have provided additional context for accurate detection. Because aerial imagery is almost orthographic, the spatial relations in image space closely align with those in the physical world, and inter-object and object-background relationships become particularly significant. To address this oversight, we propose a framework that leverages the strengths of Transformer-based models and Contrastive Language-Image Pre-training (CLIP) features to capture such relationships. Specifically, Building on two-stage detectors, we treat Region of Interest (RoI) proposals as tokens, accompanied by CLIP Tokens obtained from multi-level image segments. These tokens are then passed through a Transformer encoder, where specific spatial and geometric relations are incorporated into the attention weights, which are adaptively modulated and regularized. Additionally, we introduce self-supervised constraints on CLIP Tokens to ensure consistency. Extensive experiments on three benchmark datasets demonstrate that our approach achieves consistent improvements, setting new state-of-the-art results with increases of 1.37 mAP$_{50}$ on DOTA-v1.0, 5.30 mAP$_{50}$ on DOTA-v1.5, 2.30 mAP$_{50}$ on DOTA-v2.0 and 3.23 mAP$_{50}$ on DIOR-R.