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Few-class object detection is a critical task in numerous scenes, such as autonomous driving and intelligent surveillance. The current researches mainly focus on the correlation or decoupling between classification and regression subtasks in object detection. However, they rarely take advantage of the potential of re-decoupling the classification subtask. In this paper, we propose to re-decouple the classification branch in object detection for few-class scenes by reducing multi-classification features to multiple binary-classification features. Since multi-classification losses cannot supervise the network to learn decoupled binary-classification features, we introduce a single-class loss to supervise decoupled multiple binary-classification branches. In particular, we propose a basic feature degradation head (FD-Head) structure that decouples the classification branches and applies binary-classification loss to encourage each branch to learn only the degraded single-class features. In addition, based on the mutual exclusion between classes, we propose a mutual exclusion constraint (FD-Head-M) module to constrain the scores of all classes, promoting the detector performance. Finally, we replace the original convolution with more powerful feature extraction modules to form the enhanced FD-Head (FD-Head-E). Notably, our method can be used as a universal module and embedded into the existing object detectors to boost their performance. When applying our method to typical object detectors, it experimentally achieves performance gains of 1.2–2.2%, 1.7–2.5% on the KITTI-3, SeaShips datasets respectively. When using ResNet50 as the backbone network, our method gains an accuracy of 45.9% on the MS COCO dataset. • A feature degradation head for few-class scenes is proposed. • Guiding all sub-branches to learn more significant single-class features. • A mutually exclusive constraint module is proposed to reduce false detection. • Feature degradation head has also been extended to multi-class scenes. [ABSTRACT FROM AUTHOR]

6DoF camera localization is an important component of autonomous driving and navigation. Deep learning has achieved impressive results in localization, but its robustness in dynamic environments has not been adequately addressed. In this paper, we propose a framework based on hybrid attention mechanism which can be generally applied to existing CNN-based pose regressors to improve their robustness in dynamic environments. Specifically, we propose a novel Transformer Bottleneck (TBo) block including convolution, channel attention, and a position-aware self-attention mechanism, which extracts more geometrically robust features by capturing the corresponding long-term dependencies between pixels. Furthermore, we introduce shuffle attention (SA) before the pose regressor, which integrates feature information in both spatial and channel dimensions, forcing the network to learn geometrically robust features, reducing the effects of dynamic objects and illumination conditions to improve camera localization accuracy. We evaluate our method on commonly benchmarked indoor and outdoor datasets and the experimental results show that our proposed method can significantly improve localization performance compared compare favorably to contemporary pose regressors schemes. In addition, extensive ablation evaluations are conducted to prove the effectiveness of our proposed hybrid attention bottleneck block for pose regression networks. • We propose a novel Transformer Bottleneck block with self-attention and channel attention to overcome the limitations of convolution. This coupling allows them to be optimized in a mutually reinforcing manner, significantly improving fine-grained feature extraction for accurate localization. • We propose a novel end-to-end hybrid attention network for single image localization, which improves the accuracy and robustness of camera localization, especially in dynamic scenes. • We conduct extensive experiments in both indoor and outdoor dataset, which shows that our model performs better than the existing competitive methods. [ABSTRACT FROM AUTHOR]