One of the roadblocks for instance segmentation today is heavy computational overhead and model parameters. Previous methods based on Polar Representation made the initial mark to address this challenge by formulating instance segmentation as polygon detection, but failed to align with mainstream methods in performance. In this paper, we highlight that Representation Errors, arising from the limited capacity of polygons to capture boundary details, have long been overlooked, which results in severe performance degradation. Observing that optimal starting point selection effectively alleviates this issue, we propose an Adaptive Polygonal Sample Decision strategy to dynamically capture the positional variation of representation errors across samples. Additionally, we design a Union-aligned Rasterization Module to incorporate these errors into polygonal assessment, further advancing the proposed strategy. With these components, our framework PolarNeXt achieves a remarkable performance boost of over 4.8% AP compared to other polar-based methods. PolarNeXt is markedly more lightweight and efficient than state-of-the-art instance segmentation methods, while achieving comparable segmentation accuracy. We expect this work will open up a new direction for instance segmentation in high-resolution images and resource-limited scenarios. Codes can be found at https://github.com/Sun15194/PolarNeXt.

Inadequate bounding box modeling in regression tasks constrains the performance of one-stage 3D object detection. We pinpoint two key factors behind this limitation: (1) Restricted center-offset prediction severely impairs bounding box localization, as many peak response positions deviate significantly from object centers. (2) Low-quality samples ignored in regression tasks notably affect bounding box prediction, leading to unreliable IoU-based quality adjustments. To tackle these problems, we propose Decoupled and Interactive Regression Modeling (DIRM) for one-stage detection. Decoupled Attribute Regression (DAR) is implemented to facilitate long regression range modeling for the center attribute through an adaptive multi-sample assignment strategy. Additionally, to enhance the reliability of IoU predictions for low-quality results, Interactive Quality Prediction (IQP) integrates the classification task, proficient in modeling negative samples, with quality prediction for joint optimization. Extensive experiments on Waymo and ONCE datasets demonstrate that DIRM achieves state-of-the-art performance, balancing accuracy and computational cost for real-time autonomous driving.

In this paper, we propose a Dual-Constraint Diffusion Model (DCDM) to contrast aging appearance for facial age estimation, addressing the key issue of noisy labels. Existing methods for face age estimation are plagued by class imbalance and noisy supervision signals, which disrupt the ordinal relationships between age categories and hinder effective feature decoupling in existing models. To overcome these challenges, the proposed DCDM develops a label-independent Paired Comparison, ensuring accurate sample labeling and maintaining continuity in age estimation. Moreover, we incorporate a Dual-Constraint Diffusion Model to effectively separate and recombine age-related and unrelated features, thus facilitating the generation of high-fidelity and continuous age-progressed facial representations. Lastly, we optimize our model parameters by exploiting the age difference information via an active learning framework. Comparative evaluations on several in-the-wild datasets demonstrate that our DCDM significantly achieves superior results compared to existing state-of-the-art methods in facial age estimation.

Most semi-supervised domain object detection (SDAOD) methods are based on the mean-teacher framework. This framework primarily utilizes object-level features provided by pseudo-labels. However, the pseudo-labels generated by the Teacher model often contain notable noise, which limits the detector’s performance. Unlike pseudo-labels, domain labels are more precise and can offer accurate domain-level features. Motivated by this, we incorporate domain-level features into contrastive learning by designing different label assignment strategies and thus propose Contrastive-Domain Mean Teacher (CDMT) for SDAOD. Specifically, domain-level features include both inter-domain and intra-domain features. For inter-domain features, our strategy regards samples with the same domain label as positive pairs, enabling contrastive learning to extract global feature representations. While, intra-domain features from the same image are treated as positive pairs, which helps contrastive learning to extract fine-grained feature representations. Thorough experiments demonstrate that CDMT achieves state-of-the-art performance on Foggy Cityscapes and Clipart combined with recent Mean Teacher framework methods. Notably, for more challenging foggiest images (’0.02’ split) based on the Probabilistic Teacher (PT) baseline, CDMT outperforms the previously best CMT by 4.1% on mAP, which shows its priority on cross-domain detection tasks.

Center-based 3D object detection has underperformed recently compared to advanced techniques. We experimentally find that the root lies in two weaknesses of the basic sample mechanism: (1) Unreasonable assignment that close-range and high-frequency objects dominate the network optimization since samples are equally assigned to each object. (2) Ambiguous encoding that samples exhibit suboptimal object discrimination ability, as the encoding process is restricted to a limited receptive field. To realize a reasonable assignment, Dynamic Multi-Quality Assignment (DMQA) is proposed, which dynamically assigns and supervises samples through fine-grained control. Concretely, initial samples are defined based on prior attributes (category and distance) per object, and dynamically adjusted upon the learning effect (classification and localization confidence). Besides, multi-scale auxiliary losses are introduced, ensuring precise sample learning. As for ambiguous encoding, Interactive Enhancement (IE) is introduced to improve sample representation through cross-task and cross-sample interaction. Cross-task interaction first aggregates neighborhood context from another task map. Parallel attention further performs cross-sample interaction on both local and global levels. Based on DMQA and IE, we propose a novel 3D detector named Sample Enhanced 3D Object Detection (SampleDet3D). Comprehensive experiments demonstrate that SampleDet3D effectively enhances center-based detection and achieves state-of-the-art performance on both Waymo and ONCE datasets.

One-stage 3D object detection, known for its simplicity and high-speed inference, is attracting increasing attention in autonomous driving scenarios. However, current one-stage detectors tend to perform sub-optimally compared to two-stage competitors. Our experimental findings suggest that one-stage detectors underperform due to the underutilization of spatial information in feature embedding and classification. Concretely, the spatial context is severely lost during feature propagation, inducing distorted spatial awareness. On the other hand, category recognition relies on the full utilization of spatial information, which is neglected by current detectors. This inadequate spatial awareness of the classification branch can exacerbate misclassification. To address these issues, we propose Spatial-aware Learning in Feature Embedding and Classification for One-stage 3D Object Detection (SLDet). Specifically, to restore the distorted spatial awareness, Category-wise Spatial Augmentation (CSA) is proposed to adaptively bring the network with pre-encoding multi-scale spatial contexts. As for misclassification, Spatial Guiding Classification (SGC) is introduced to guide the classification using explicit scale information. It employs the natural scale divergences among categories to rectify misclassification. Comprehensive experiments demonstrate that SLDet efficiently utilizes spatial information and achieves newly state-of-the-art performance on both the Waymo Open Dataset and the ONCE Dataset. Furthermore, additional experiments demonstrate the excellent generalization capacity of SLDet.

Accurately detecting multi-class objects in a single pass is critical but challenging for real-world autonomous driving scenarios. Several single-class anchor-based methods have recently achieved the state-of-the-art performance in the car category, but when extending to multi-class detection tasks, their performance on small objects (i.e., pedestrians and cyclists) is limited. We find that the core problem that causes this phenomenon lies in the unbalanced sample quality and the classification objective. To address this problem, we proposed a single-model multi-class 3D object detector with balanced sample assignment and objective, named BSAODet. Specifically, the quality-balanced sample assignment (QBSA) is introduced to dynamically collect stable high-quality samples for each class according to the predicted sample performance and geometric constraints. In conjunction with the QBSA, the class-balanced classification objective (CBCO) performs instance-wise label normalization and weighting on positive samples, preventing the model from biasing toward objects with more samples. Extensive experiments on the popular KITTI dataset, the latest large-scale ONCE dataset, and the challenging Waymo Open Dataset show that our method steadily improves the performance of current state-of-the-art detectors by 2–7 mAP in pedestrians and cyclists while maintaining competitiveness in cars. Moreover, our best model achieves 66.31 mAP on three classes, outperforming all published LiDAR-only detectors on the KITTI benchmark.

Face aging aims to estimate aged facial textures given a certain face image. A number of 2D face-aging methods have been developed, but there have been few studies on 3D face aging, which would be valuable in several real-world applications. The lack of 3D face-aging data has had a significant impact on the development of 3D face aging, but we hypothesized that the large amounts of 2D face-aging data on the internet could be leveraged for 3D aged facial textures. In this paper, we propose a novel 3D aging framework, which we call UV-transformation texture estimation based on generative adversarial networks (UVTE-GAN), to achieve 3D face aging. Specifically, the proposed framework has three parts: 1) a 3D vertex and texture estimator, which accurately estimates the face’s spatial vertices and textures; 2) a texture-aging GAN, which is responsible for aging the estimated texture map via adversarial learning; and 3) a 2D & 3D rendering rebuilder, which recovers 2D & 3D faces using the estimated facial vertex map and aged facial texture map. In addition, we also design a plugin layer that allows us to train the whole model in an end-to-end manner. Experimental results demonstrate the effectiveness of the proposed method in synthesizing visually pleasing 3D aged face pictures, and state-of-the-art performance is achieved on several public datasets.

Image dehazing aims to remove haze in images to improve their image quality. However, most image dehazing methods heavily depend on strict prior knowledge and paired training strategy, which would hinder generalization and performance when dealing with unseen scenes. In this paper, to address the above problem, we propose Bidirectional Normalizing Flow (BiN-Flow), which exploits no prior knowledge and constructs a neural network through weakly-paired training with better generalization for image dehazing. Specifically, BiN-Flow designs 1) Feature Frequency Decoupling (FFD) for mining the various texture details through multi-scale residual blocks and 2) Bidirectional Propagation Flow (BPF) for exploiting the one-to-many relationships between hazy and haze-free images using a sequence of invertible Flow. In addition, BiN-Flow constructs a reference mechanism (RM) that uses a small number of paired hazy and haze-free images and a large number of haze-free reference images for weakly-paired training. Essentially, the mutual relationships between hazy and haze-free images could be effectively learned to further improve the generalization and performance for image dehazing. We conduct extensive experiments on five commonly-used datasets to validate the BiN-Flow. The experimental results that BiN-Flow outperforms all state-of-the-art competitors demonstrate the capability and generalization of our BiN-Flow. Besides, our BiN-Flow could produce diverse dehazing images for the same image by considering restoration diversity.