Dynamic Coarse-to-Fine Learning for Oriented Tiny Object Detection (2304.08876v1)

Abstract: Detecting arbitrarily oriented tiny objects poses intense challenges to existing detectors, especially for label assignment. Despite the exploration of adaptive label assignment in recent oriented object detectors, the extreme geometry shape and limited feature of oriented tiny objects still induce severe mismatch and imbalance issues. Specifically, the position prior, positive sample feature, and instance are mismatched, and the learning of extreme-shaped objects is biased and unbalanced due to little proper feature supervision. To tackle these issues, we propose a dynamic prior along with the coarse-to-fine assigner, dubbed DCFL. For one thing, we model the prior, label assignment, and object representation all in a dynamic manner to alleviate the mismatch issue. For another, we leverage the coarse prior matching and finer posterior constraint to dynamically assign labels, providing appropriate and relatively balanced supervision for diverse instances. Extensive experiments on six datasets show substantial improvements to the baseline. Notably, we obtain the state-of-the-art performance for one-stage detectors on the DOTA-v1.5, DOTA-v2.0, and DIOR-R datasets under single-scale training and testing. Codes are available at https://github.com/Chasel-Tsui/mmrotate-dcfl.

Dynamic Coarse-to-Fine Learning for Oriented Tiny Object Detection

The paper by Xu et al. introduces a novel approach called Dynamic Coarse-to-Fine Learning (DCFL) to improve the detection of arbitrarily oriented tiny objects, a task that poses significant challenges to existing detectors, especially regarding label assignment. The authors identify key issues such as mismatch between position prior, feature, and instance, as well as imbalance in learning extreme-shaped objects. To address these, the paper proposes a dynamic approach integrating prior modeling, label assignment, and object representation.

One of the main contributions of the paper is the introduction of a dynamic prior capturing block (PCB), which leverages techniques from recent advances in object detection, such as the DETR and Sparse R-CNN frameworks. The PCB facilitates dynamic prior adjustments using deformable convolutional networks (DCN), thereby mitigating the mismatch issues common in static priors. The dynamic prior is modeled through the prior capturing block which allows for modifications in the spatial locations of features, improving the alignment between prediction and object morphology.

Moreover, the paper advances label assignment with the introduction of Cross-FPN-layer Coarse Positive Sample (CPS) candidates and dynamic posterior matching. This new assignment employs a coarse-to-fine strategy, using Generalized Jensen-Shannon Divergence (GJSD) to ensure that the CPS reflects a more representative sample range. The following steps—Medium Positive Sample (MPS) candidate selection and the application of a Dynamic Gaussian Mixture Model (DGMM)—further refine the positive samples by balancing between ground-truth alignment and prediction efficacy.

The numerical results are particularly compelling: DCFL demonstrates substantial improvements in mean Average Precision (mAP) on multiple benchmarks, achieving state-of-the-art performance on challenging datasets like DOTA-v1.5, DOTA-v2.0, and DIOR-R in oriented bounding box (OBB) tasks, even under single-scale training and testing conditions. The experimental evaluation highlights DCFL's capacity to rectify both quality and quantity imbalances present in previous object detection methods for tiny, oriented objects.

The implications of this work are manifold. Practically, the proposed method can lead to more accurate and reliable object detection systems, particularly in fields where tiny and oriented objects are prevalent, such as aerial imagery analysis. Theoretically, it showcases the potential for dynamic modeling constructs in alleviating complex mismatches in deep learning frameworks. The dynamic integration of prior learning directly into the training process presents a progressive route for future object detection research.

Speculation on future AI developments from this paper suggests an increasing focus on integrating dynamic modeling constructs that can adapt to data characteristics more fluidly than static models. Further research might extend this work by exploring real-time deployment scenarios or integrating multi-modal data to enhance detection from complementary data sources.

In conclusion, Chang Xu and colleagues have presented a comprehensive, technically robust paper that forwards the state of the art in tiny object detection by addressing long-standing issues with a well-motivated dynamic learning approach and thorough empirical validation. The work serves as a benchmark for future developments in this challenging domain.