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Bo-Wen Yin, Zheng Lin

In this paper, we explore how to develop salient object detection models using adder neural networks (ANNs), which are more energy efficient than convolutional neural networks (CNNs), especially for real-world applications. Based on our empirical studies, we show that directly replacing the convolutions in CNN-based models with adder layers leads to a substantial loss of activations in the decoder part. This makes the feature maps learned in the decoder lack pattern diversity and hence results in a significant performance drop. To alleviate this issue, by investigating the statistics of the feature maps produced by adder layers, we introduce a simple yet effective differential merging strategy to augment the feature representations learned by adder layers and present a simple baseline for SOD using ANNs. Experiments on popular salient object detection benchmarks demonstrate that our proposed method with a simple feature pyramid network (FPN) architecture achieves comparable performance to previous state-of-theart CNN-based models and consumes much less energy. We hope this work could facilitate the development of ANNs in binary segmentation tasks.

Ellen Yi-Ge, Leo Shawn

High-quality, pixel-level annotated datasets are crucial for training deep learning models, while their creation is often labor-intensive, time-consuming, and costly. Generative diffusion models have then gained prominence for producing synthetic datasets, yet existing text-to-data methods struggle with generating complex scenes involving multiple objects and intricate spatial arrangements. To address these limitations, we introduce FlexDataset, a framework that pioneers the composition-to-data (C2D) paradigm. FlexDataset generates high-fidelity synthetic datasets with versatile annotations, tailored for tasks like salient object detection, depth estimation, and segmentation. Leveraging a meticulously designed composition-to-image (C2I) framework, it offers precise positional and categorical control. Our Versatile Annotation Generation (VAG) Plan A further enhances efficiency by exploiting rich latent representations through tuned perception decoders, reducing annotation time by nearly fivefold. FlexDataset allows unlimited generation of customized, multi-instance and multi-category (MIMC) annotated data. Extensive experiments show that FlexDataset sets a new standard in synthetic dataset generation across multiple datasets and tasks, including zero-shot and long-tail scenarios.

Chengyang Ye, Yunzhi Zhuge, Pingping Zhang

Recently, deep learning based methods have revolutionized remote sensing image segmentation. However, these methods usually rely on a predefined semantic class set, thus needing additional image annotation and model training when adapting to new classes. More importantly, they are unable to segment arbitrary semantic classes. In this work, we introduce Open-Vocabulary Remote Sensing Image Semantic Segmentation (OVRSISS), which aims to segment arbitrary semantic classes in remote sensing images. To address the lack of OVRSISS datasets, we develop LandDiscover50K, a comprehensive dataset of 51,846 images covering 40 diverse semantic classes. In addition, we propose a novel framework named GSNet that integrates domain priors from special remote sensing models and versatile capabilities of general vision-language models. Technically, GSNet consists of a Dual-Stream Image Encoder (DSIE), a Query-Guided Feature Fusion (QGFF), and a Residual Information Preservation Decoder (RIPD). DSIE first captures comprehensive features from both special models and general models in dual streams. Then, with the guidance of variable vocabularies, QGFF integrates specialist and generalist features, enabling them to complement each other. Finally, RIPD is proposed to aggregate multi-source features for more accurate mask predictions. Experiments show that our method outperforms other methods by a large margin, and our proposed LandDiscover50K improves the performance of OVRSISS methods. The dataset and method will be publicly available.

Zhiwei Yang, Yucong Meng, Kexue Fu, Shuo Wang, Zhijian Song

Weakly Supervised Semantic Segmentation (WSSS) with image-level labels typically uses Class Activation Maps (CAM) to achieve dense predictions. Recently, Vision Transformer (ViT) has provided an alternative to generate localization maps from class-patch attention. However, due to insufficient constraints on modeling such attention, we observe that the Localization Attention Maps (LAM) often struggle with the artifact issue, i.e., patch regions with minimal semantic relevance are falsely activated by class tokens. In this work, we propose MoRe to address this issue and further explore the potential of LAM. Our findings suggest that imposing additional regularization on class-patch attention is necessary. To this end, we first view the attention as a novel directed graph and propose the Graph Category Representation module to implicitly regularize the interaction among class-patch entities. It ensures that class tokens dynamically condense the related patch information and suppress unrelated artifacts at a graph level. Second, motivated by the observation that CAM from classification weights maintains smooth localization of objects, we devise the Localization-informed Regularization module to explicitly regularize the class-patch attention. It directly mines the token relations from CAM and further supervises the consistency between class and patch tokens in a learnable manner. Extensive experiments on PASCAL VOC and MS COCO validate that MoRe effectively addresses the artifact issue and achieves state-of-the-art performance, surpassing recent single-stage and even multi-stage methods.

Yiming Yang, Yueru Luo, Bingkun He, Erlong Li, Zhipeng Cao, Chao Zheng, Shuqi Mei, Zhen Li

Extracting lane topology from perspective views (PV) is crucial for planning and control in autonomous driving. This approach extracts potential drivable trajectories for self-driving vehicles without relying on high-definition (HD) maps. However, the unordered nature and weak long-range perception of the DETR-like framework can result in misaligned segment endpoints and limited topological prediction capabilities. Inspired by the learning of contextual relationships in language models, the connectivity relations in roads can be characterized as explicit topology sequences. In this paper, we introduce Topo2Seq, a novel approach for enhancing topology reasoning via topology sequences learning. The core concept of Topo2Seq is a randomized order prompt-to-sequence learning between lane segment decoder and topology sequence decoder. The dual-decoder branches simultaneously learn the lane topology sequences extracted from the Directed Acyclic Graph (DAG) and the lane graph containing geometric information. Randomized order prompt-to-sequence learning extracts unordered key points from the lane graph predicted by the lane segment decoder, which are then fed into the prompt design of the topology sequence decoder to reconstruct an ordered and complete lane graph. In this way, the lane segment decoder learns powerful long-range perception and accurate topological reasoning from the topology sequence decoder. Notably, topology sequence decoder is only introduced during training and does not affect the inference efficiency. Experimental evaluations on the OpenLane-V2 dataset demonstrate the state-of-the-art performance of Topo2Seq in topology reasoning.

Xin Yang, Xin Zhang, Xinchao Wang

As climate change reshapes global weather patterns, the increasing frequency and intensity of extreme rainfall events have amplified the safety imperatives for autonomous driving systems. During such events, rainfall can escalate from heavy to violent, as defined by the World Meteorological Organization, severely impairing images with diverse and significant degradations. Many existing semantic segmentation models perform well under light to heavy rain, but there is a notable absence of datasets addressing violent rain conditions for these models to validate and learn from. In this paper, we introduce the Extreme RainFall (ERF) dataset for semantic segmentation in both image and video tasks under violent rain conditions. Our dataset comprises 14,757 unlabeled frames and 100 labeled frames, all captured during four different violent rainfall periods. We use our dataset to evaluate the robustness of various methods against violent rainfall, focusing on four approaches: 1) image-based foundation models, 2) image-based domain generalization methods, 3) image-based domain adaptation methods, and 4) video-based methods. The results reveal that none of the existing models tested is capable of withstanding the extreme challenges posed by violent rainfall conditions. By analyzing the results, we offer insights and suggestions for developing more robust models under extreme rainfall events.

Xin Yang, Wending Yan, Yuan Yuan, Michael Bi Mi, Robby T. Tan

Existing semantic segmentation methods face challenges when processing input images degraded by raindrops on the lens or windshield. Unlike other adverse conditions such as fog and nighttime, which degrade visual quality, raindrops not only impair visual appearances but also introduce misleading occlusion, leading to significant performance drops in current models. The novelty of our approach lies in our two-stage, dual teacher-student framework. We tackle the complex problem of raindrop degradation by dividing it into two distinct challenges: degraded visual appearance and raindrop occlusion. These challenges are then addressed individually in two stages, utilizing two pairs of teacher-student networks. This division enables the networks to develop specialized expertise in handling each aspect of raindrop degradation, enabling their collaboration to achieve superior performance. In the first stage, one teacher-student pair focuses on learning to extract information from visual degraded areas. Building on this, the second teacher-student pair focuses specially on the raindrop occlusion. As such, unlike the existing methods, our approach employs a collaborative approach to decompose and address raindrop-induced degradations. In the second stage, we introduce a mask-based recovery technique to identify and rectify areas that likely contain misleading information, thus further refining the predictions. Additionally, this stage encourages both pairs to expand knowledge by swapping their specialized expertise. Our method achieves a performance of 60.3 mIoU on Rainy WCity and 72.8 mIoU on ACDC Rainy, representing an improvement of +4.4 mIoU and +2.3 mIoU over the existing state-of-the-art methods, respectively.

Enquan Yang, Peng Xing, Hanyang Sun, Wenbo Guo, Yuanwei Ma, Zechao Li, Dan Zeng

Industrial anomaly detection achieves progress thanks to datasets such as MVTec-AD and VisA. However, they suffer from limitations in terms of the number of defect samples, types of defects, and availability of real-world scenes. These constraints inhibit researchers from further exploring the performance of industrial detection with higher accuracy. To this end, we propose a new large-scale anomaly detection dataset called 3CAD, which is derived from real 3C production lines. Specifically, the proposed 3CAD includes eight different types of manufactured parts, totaling 27,039 high-resolution images labeled with pixel-level anomalies. The key features of 3CAD are that it covers anomalous regions of different sizes, multiple anomaly types, and the possibility of multiple anomalous regions and multiple anomaly types per anomaly image. This is the largest and first anomaly detection dataset dedicated to 3C product quality control for community exploration and development. Meanwhile, we introduce a simple yet effective framework for unsupervised anomaly detection: a Coarse-to-Fine detection paradigm with Recovery Guidance (CFRG). To detect small defect anomalies, the proposed CFRG utilizes a coarse-to-fine detection paradigm. Specifically, we utilize a heterogeneous distillation model for coarse localization and then fine localization through a segmentation model. In addition, to better capture normal patterns, we introduce recovery features as guidance. Finally, we report the results of our CFRG framework and popular anomaly detection methods on the 3CAD dataset, demonstrating strong competitiveness and providing a highly challenging benchmark to promote the development of the anomaly detection field.

Chuanguang Yang, XinQiang Yu, Han Yang, Zhulin An, Chengqing Yu, Libo Huang, Yongjun Xu

Multi-teacher Knowledge Distillation (KD) transfers diverse knowledge from a teacher pool to a student network. The core problem of multi-teacher KD is how to balance distillation strengths among various teachers. Most existing methods often develop weighting strategies from an individual perspective of teacher performance or teacher-student gaps, lacking comprehensive information for guidance. This paper proposes Multi-Teacher Knowledge Distillation with Reinforcement Learning (MTKD-RL) to optimize multi-teacher weights. In this framework, we construct both teacher performance and teacher-student gaps as state information to an agent. The agent outputs the teacher weight and can be updated by the return reward from the student. MTKD-RL reinforces the interaction between the student and teacher using an agent in an RL-based decision mechanism, achieving better matching capability with more meaningful weights. Experimental results on visual recognition tasks, including image classification, object detection, and semantic segmentation tasks, demonstrate that MTKD-RL achieves state-of-the-art performance compared to the existing multi-teacher KD works.

Ke Yan, Qing Cai, Fan Zhang, Ziyan Cao, Zhi Liu

Although semi-supervised learning has made significant advances in the field of medical image segmentation, fully annotating a volumetric sample slice by slice remains a costly and time-consuming task. Even worse, most of the existing approaches pay much attention to image-level information and ignore semantic features, resulting in the inability to perceive weak boundaries. To address these issues, we propose a novel Semantic-Guided Triplet Co-training (SGTC) framework, which achieves high-end medical image segmentation by only annotating three orthogonal slices of a few volumetric samples, significantly alleviating the burden of radiologists. Our method consist of two main components. Specifically, to enable semantic-aware, fine-granular segmentation and enhance the quality of pseudo-labels, a novel semantic-guided auxiliary learning mechanism is proposed based on the pretrained CLIP. In addition, focusing on a more challenging but clinically realistic scenario, a new triple-view disparity training strategy is proposed, which uses sparse annotations (i.e., only three labeled slices of a few volumes) to perform co-training between three sub-networks, significantly improving the robustness. Extensive experiments on three public medical datasets demonstrate that our method outperforms most state-of-the-art semi-supervised counterparts under sparse annotation settings.

Zhongxing Xu, Feilong Tang, Zhe Chen, Yingxue Su, Zhiyi Zhao, Ge Zhang, Jionglong Su, Zongyuan Ge

The application of Contrastive Language-Image Pre-training (CLIP) in Weakly Supervised Semantic Segmentation (WSSS) research demonstrates powerful cross-modal semantic understanding capabilities. Existing methods attempt to optimize input text prompts for improved alignment of images and text, specifically by finely adjusting text prototypes to facilitate semantic matching. Nevertheless, given the modality gap between text and vision spaces, the text prototypes employed by these methods have not effectively established a close correspondence with pixel-level vision features. In this work, our theoretical analysis indicates that the inherent modality gap results in misalignment of text and region features, and that this gap cannot be sufficiently reduced by minimizing contrast loss in CLIP. To mitigate the impact of the modality gap, we propose a Vision Prototype Learning (VPL) framework, by introducing more representative vision prototypes. The core of this framework is to learn class-specific vision prototypes in vision space with the help of text prototypes, to capture high-quality localization maps. Moreover, we propose a regional semantic contrast module that contrasts regions embedding with corresponding prototypes, leading to more comprehensive and robust feature learning. Experimental results show that our proposed framework achieves state-of-the-art performance on two benchmark datasets.

Xinhua Xu, Hong Liu, Jianbing Wu, Jinfu Liu

The integration of RGB and depth modalities significantly enhances the accuracy of segmenting complex indoor scenes, with depth data from RGB-D cameras playing a crucial role in this improvement. However, collecting an RGB-D dataset is more expensive than an RGB dataset due to the need for specialized depth sensors. Aligning depth and RGB images also poses challenges due to sensor positioning and issues like missing data and noise. In contrast, Pseudo Depth (PD) from high-precision depth estimation algorithms can eliminate the dependence on RGB-D sensors and alignment processes, as well as provide effective depth information and show significant potential in semantic segmentation. Therefore, to explore the practicality of utilizing pseudo depth instead of real depth for semantic segmentation, we design an RGB-PD segmentation pipeline to integrate RGB and pseudo depth and propose a Pseudo Depth Aggregation Module (PDAM) for fully exploiting the informative clues provided by the diverse pseudo depth maps. The PDAM aggregates multiple pseudo depth maps into a single modality, making it easily adaptable to other RGB-D segmentation methods. In addition, the pre-trained diffusion model serves as a strong feature extractor for RGB segmentation tasks, but multi-modal diffusion-based segmentation methods remain unexplored. Therefore, we present a Pseudo Depth Diffusion Model (PDDM) that adopts a large-scale text-image diffusion model as a feature extractor and a simple yet effective fusion strategy to integrate pseudo depth. To verify the applicability of pseudo depth and our PDDM, we perform extensive experiments on the NYUv2 and SUNRGB-D datasets. The experimental results demonstrate that pseudo depth can effectively enhance segmentation performance, and our PDDM achieves state-of-the-art performance, outperforming other methods by +6.98 mIoU on NYUv2 and +2.11 mIoU on SUNRGB-D.

Wenting Xu, Viorela Ila, Luping Zhou, Craig T. Jin

The concept of function and affordance is a critical aspect of 3D scene understanding and supports task-oriented objectives. In this work, we develop a model that learns to structure and vary functional affordance across a 3D hierarchical scene graph representing the spatial organization of a scene. The varying functional affordance is designed to integrate with the varying spatial context of the graph. More specifically, we develop an algorithm that learns to construct a 3D hierarchical scene graph (3DHSG) that captures the spatial organization of the scene. Starting from segmented object point clouds and object semantic labels, we develop a 3DHSG with a top node that identifies the room label, child nodes that define local spatial regions inside the room with region-specific affordances, and grand-child nodes indicating object locations and object-specific affordances. To support this work, we create a custom 3DHSG dataset that provides ground truth data for local spatial regions with region-specific affordances and also object-specific affordances for each object. We employ a Transformer Based Hierarchical Scene Understanding (TB-HSU) model to learn the 3DHSG. We use a multi-task learning framework that learns both room classification and learns to define spatial regions within the room with region-specific affordances. Our work improves on the performance of state-of-the-art baseline models and shows one approach for applying transformer models to 3D scene understanding and the generation of 3DHSGs that capture the spatial organization of a room. The code and dataset are publicly available.

Feilong Xu, Feiyang Yang, Xiongfei Li, Xiaoli Zhang

In utilizing deep learning techniques for medical image segmentation, two types of imbalance issues are observed: inter-class imbalance between majority and minority classes and intra-class imbalance between easy and hard samples. However, existing loss functions typically confuse these issues, leading to enhancements that cater to only one aspect. Moreover, loss functions optimized for specific tasks often exhibit limited generalizability. To address these issues, we propose Inter-class and Intra-class Balance loss, as well as a unified loss termed Balance loss. The Inter-class Balance loss controls the extent of hard sample mining for majority class samples by considering the frequency of minority classes present in each input image. This approach requires no manual adjustment weights and adapts automatically to different datasets. The Intra-class Balance loss enhances the network's ability to learn from hard samples by performing mining on hard samples within each class. We evaluate our loss functions on five segmentation tasks with varying degrees of class imbalance. The experimental results show that our proposed Balance loss enhances segmentation performance compared with the current loss functions and exhibits superior robustness.

FeiFan Xu, Tianyi Chen, Fan Yang, Yunfei Zhang, Si Wu

The rapid advancement of 3D Generative Adversarial Networks (GANs) has significantly enhanced the diversity and quality of generated 3D images. Despite these breakthroughs, the manipulation capabilities of 3D GANs remain unexplored, presenting substantial challenges for practical applications where user interaction and modification are essential. Current manipulation methods often lack the precision needed for fine-grained attribute manipulation, and struggle to maintain multi-view consistency during the editing process. To address these limitations, we propose 3DHumanEdit, a novel approach for 3D human body part-aware manipulation. 3DHumanEdit leverages multi-modal feature fusion and body part-aware feature alignment to achieve precise manipulation of individual body parts based on detailed text inputs and segmentation images. By exploring 3D prior for accurate editing and enforcing correspondence in latent space, 3DHumanEdit ensures coherence across multiple views. Experiments demonstrate that 3DHumanEdit outperforms existing methods in both editing fidelity and multi-view consistency, offering a robust solution for fine-grained 3D manipulation.

Cheng Xu, Weiwen Zhang, Hongrui Zhang, Xuemiao Xu, Huaidong Zhang, Jing Zou, Jing Qin

In clinical imaging, medical segmentation networks typically require continually adapting to new data from multiple sites over time, as aggregating all data for learning at once can be impractical due to storage limitations and privacy concerns. However, existing methods basically overlook domain-specific characteristics and fall short of adequately capturing domain-invariant knowledge during continual learning, leading to undesired catastrophic forgetting of previous sites and inferior generalization to new sites. To tackle this issue, this paper introduces FR2Seg, to sufficiently exploit both domain-specific and domain-invariant knowledge for efficient continual learning with the aid of low-frequency cues. For the former aspect, we propose a Fourier style replay module to synthesize pseudo images with old-site styles for data augmentation during new-site training, effectively preventing catastrophic forgetting without sacrificing data privacy. For the latter, we present a Fourier adaptive consistency regularization to identify and constrain the optimization of domain-invariant parameters with explicit awareness of knowledge transferability across sites, ensuring excellent generalizability to new sites. Experimental results on two public datasets confirm our method's superiority over existing state-of-the-art continual learning methods.

Jingqiao Xiu, Mengze Li, Zongxin Yang, Wei Ji, Yifang Yin, Roger Zimmermann

Audio-Visual Semantic Segmentation (AVSS) has gained significant attention in the multi-modal domain, aiming to segment video objects that produce specific sounds in the corresponding audio. Despite notable progress, existing methods still struggle to handle new classes not included in the original training set. To this end, we introduce Few-Shot Incremental Learning (FSIL) to the AVSS task, which seeks to seamlessly integrate new classes with limited incremental samples while preserving the knowledge of old classes. Two challenges arise in this new setting: (1) To reduce labeling costs, old classes within the incremental samples are treated as background, similar to silent objects. Training the model directly with background annotations may worsen the loss of distinctive knowledge about old classes, such as their outlines and sounds. (2) Most existing models adopt early cross-modal fusion with a single-tower design, incorporating more characteristics into class representations, which impedes knowledge transfer between classes based on similarity. To address these issues, we propose a Few-shot Incremental learning framework via class-centric foregrouNd aggreGation and dual-tower knowlEdge tRansfer (FINGER) for the AVSS task, which comprises two targeted modules: (1) The class-centric foreground aggregation gathers class-specific features for each foreground class while disregarding background features. The background class is excluded during training and inferred from the foreground predictions. (2) The dual-tower knowledge transfer postpones cross-modal fusion to separately conduct knowledge transfer for each modality. Extensive experiments validate the effectiveness of the FINGER model, significantly surpassing state-of-the-art methods.

Jianxiang Xie, Yao Wu, Yachao Zhang, Zhongchao Shi, Jianping Fan, Yuan Xie, Yanyun Qu

Source-Free Domain Adaptation (SFDA) aims to transfer a pre-trained source model to the unlabeled target domain without accessing the source data, thereby effectively solving labeled data dependency and domain shift problems. However, the SFDA setting faces a bottleneck due to the absence of supervisory information. To mitigate this problem, Active Learning (AL) is introduced to combine with SFDA, endeavoring to actively label a small set of the most high-quality target points so that models with satisfactory performance can be obtained at an acceptable cost. Nevertheless, several issues remain unresolved, namely when to query new labels during training, what kind of samples deserve labeling to ensure rich information, and where the labels should be distributed to guarantee diversity. Thus we elaborate OmniQuery to omnibearing address the “When, What, and Where” problems about active points querying in source-free domain adaptation for cross-modal 3D semantic segmentation. The method consists of three main components: Query Decider, Point Ranker, and Budget Slicer. The Query Decider determines the optimal timing to query new points by fitting the validation curves during training. The Point Ranker nominates points for annotation by calculating the ambiguity of neighboring points in the feature space. The Budget Slicer allocates the annotation quota, i.e., labeling percentage of the point cloud, to different semantic regions by utilizing the advanced 2D semantic segmentation capabilities of the Segment Anything Model (SAM). Extensive experiments demonstrate the effectiveness of our proposed method, achieving up to 99.64% of fully supervised performance with only 3% of labels, and consistently outperforming comparison methods across various scenarios.

Yue Wu, Zhipeng Wang, Yongzhe Yuan, Maoguo Gong, Hao Li, Mingyang Zhang, Wenping Ma, Qiguang Miao

3D Change Detection (3DCD) has gradually become another research hotspot after image change detection. Recent works focus on using artificial labels for supervised or weakly-supervised training of siamese networks to segment changed points. However, labeling every points of multi-temporal point clouds is very expensive and time-consuming. In addition, these works lack effective self-supervised signals, and existing self-supervised signals often fail to capture sufficiently rich change information. To solve this problem, we assume that the powerful representation of 3D objects should model the consistency information of unchanged regions and distinguish different objects. Based on this assumption, we propose a new unsupervised framework called MUCD to learn change information of multi-temporal point clouds through bidirectional optimization of change segmentor and feature extractor. The training of network is divided into two stages. We first design a foreknowledge point contrastive loss based on the characteristics of the 3DCD task to initialize the feature extractor, and then propose a masked consistency loss to further learn the shared geometric information of unchanged regions in the multi-temporal point clouds, utilizing it as a free and powerful supervised signal to train a change segmentor. In the inference stage, only the segmentor is used to take multi-temporal point clouds as input and produce change segmentation result. Extensive experiments are conducted on SLPCCD and Urb3DCD, two real-world datasets of streets and urban buildings, to verify that our proposed unsupervised method is highly competitive and even outperforms supervised methods in scenes where semantic information changes occur, exhibiting better performance in generalization ability and robustness.

Yirui Wu, Yuhang Xia, Hao Li, Lixin Yuan, Junyang Chen, Jun Liu, Tong Lu, Shaohua Wan

Incremental few-shot semantic segmentation (IFSS) expands segmentation capacity of the trained model to segment new-class images with few samples. However, semantic meanings may shift from background to object class or vice versa during incremental learning. Moreover, new-class samples often lack representative attribute features when the new class greatly differs from the pre-learned old class. In this paper, we propose a causal framework to discuss the cause of semantic shift and incompleteness in IFSS, and we deconfound the revealed causal effects from two aspects. First, we propose a Causal Intervention Module (CIM) to resist semantic shift. CIM progressively and adaptively updates prototypes of old class, and removes the confounder in an intervention manner. Second, a Prototype Refinement Module (PRM) is proposed to complete the missing semantics. In PRM, knowledge gained from the episode learning scheme assists in fusing features of new-class and old-class prototypes. Experiments on both PASCAL-VOC 2012 and ADE20k benchmarks demonstrate the outstanding performance of our method.