2022-11-22 更新
Attention-based Class Activation Diffusion for Weakly-Supervised Semantic Segmentation
Authors:Jianqiang Huang, Jian Wang, Qianru Sun, Hanwang Zhang
Extracting class activation maps (CAM) is a key step for weakly-supervised semantic segmentation (WSSS). The CAM of convolution neural networks fails to capture long-range feature dependency on the image and result in the coverage on only foreground object parts, i.e., a lot of false negatives. An intuitive solution is coupling'' the CAM with the long-range attention matrix of visual transformers (ViT) We find that the directcoupling’’, e.g., pixel-wise multiplication of attention and activation, achieves a more global coverage (on the foreground), but unfortunately goes with a great increase of false positives, i.e., background pixels are mistakenly included. This paper aims to tackle this issue. It proposes a new method to couple CAM and Attention matrix in a probabilistic Diffusion way, and dub it AD-CAM. Intuitively, it integrates ViT attention and CAM activation in a conservative and convincing way. Conservative is achieved by refining the attention between a pair of pixels based on their respective attentions to common neighbors, where the intuition is two pixels having very different neighborhoods are rarely dependent, i.e., their attention should be reduced. Convincing is achieved by diffusing a pixel’s activation to its neighbors (on the CAM) in proportion to the corresponding attentions (on the AM). In experiments, our results on two challenging WSSS benchmarks PASCAL VOC and MS~COCO show that AD-CAM as pseudo labels can yield stronger WSSS models than the state-of-the-art variants of CAM.
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Plug and Play Active Learning for Object Detection
Authors:Chenhongyi Yang, Lichao Huang, Elliot J. Crowley
Annotating data for supervised learning is expensive and tedious, and we want to do as little of it as possible. To make the most of a given “annotation budget” we can turn to active learning (AL) which aims to identify the most informative samples in a dataset for annotation. Active learning algorithms are typically uncertainty-based or diversity-based. Both have seen success in image classification, but fall short when it comes to object detection. We hypothesise that this is because: (1) it is difficult to quantify uncertainty for object detection as it consists of both localisation and classification, where some classes are harder to localise, and others are harder to classify; (2) it is difficult to measure similarities for diversity-based AL when images contain different numbers of objects. We propose a two-stage active learning algorithm Plug and Play Active Learning (PPAL) that overcomes these difficulties. It consists of (1) Difficulty Calibrated Uncertainty Sampling, in which we used a category-wise difficulty coefficient that takes both classification and localisation into account to re-weight object uncertainties for uncertainty-based sampling; (2) Category Conditioned Matching Similarity to compute the similarities of multi-instance images as ensembles of their instance similarities. PPAL is highly generalisable because it makes no change to model architectures or detector training pipelines. We benchmark PPAL on the MS-COCO and Pascal VOC datasets using different detector architectures and show that our method outperforms the prior state-of-the-art. Code is available at https://github.com/ChenhongyiYang/PPAL
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Computational Optics Meet Domain Adaptation: Transferring Semantic Segmentation Beyond Aberrations
Authors:Qi Jiang, Hao Shi, Shaohua Gao, Jiaming Zhang, Kailun Yang, Lei Sun, Kaiwei Wang
Semantic scene understanding with Minimalist Optical Systems (MOS) in mobile and wearable applications remains a challenge due to the corrupted imaging quality induced by optical aberrations. However, previous works only focus on improving the subjective imaging quality through computational optics, i.e. Computational Imaging (CI) technique, ignoring the feasibility in semantic segmentation. In this paper, we pioneer to investigate Semantic Segmentation under Optical Aberrations (SSOA) of MOS. To benchmark SSOA, we construct Virtual Prototype Lens (VPL) groups through optical simulation, generating Cityscapes-ab and KITTI-360-ab datasets under different behaviors and levels of aberrations. We look into SSOA via an unsupervised domain adaptation perspective to address the scarcity of labeled aberration data in real-world scenarios. Further, we propose Computational Imaging Assisted Domain Adaptation (CIADA) to leverage prior knowledge of CI for robust performance in SSOA. Based on our benchmark, we conduct experiments on the robustness of state-of-the-art segmenters against aberrations. In addition, extensive evaluations of possible solutions to SSOA reveal that CIADA achieves superior performance under all aberration distributions, paving the way for the applications of MOS in semantic scene understanding. Code and dataset will be made publicly available at https://github.com/zju-jiangqi/CIADA.
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Label Mask AutoEncoder(L-MAE): A Pure Transformer Method to Augment Semantic Segmentation Datasets
Authors:Jiaru Jia, Mingzhe Liu, Jiake Xie, Xin Chen, Aiqing Yang, Xin Jiang, Hong Zhang, Yong Tang
Semantic segmentation models based on the conventional neural network can achieve remarkable performance in such tasks, while the dataset is crucial to the training model process. Significant progress in expanding datasets has been made in semi-supervised semantic segmentation recently. However, completing the pixel-level information remains challenging due to possible missing in a label. Inspired by Mask AutoEncoder, we present a simple yet effective Pixel-Level completion method, Label Mask AutoEncoder(L-MAE), that fully uses the existing information in the label to predict results. The proposed model adopts the fusion strategy that stacks the label and the corresponding image, namely Fuse Map. Moreover, since some of the image information is lost when masking the Fuse Map, direct reconstruction may lead to poor performance. Our proposed Image Patch Supplement algorithm can supplement the missing information, as the experiment shows, an average of 4.1% mIoU can be improved. The Pascal VOC2012 dataset (224 crop size, 20 classes) and the Cityscape dataset (448 crop size, 19 classes) are used in the comparative experiments. With the Mask Ratio setting to 50%, in terms of the prediction region, the proposed model achieves 91.0% and 86.4% of mIoU on Pascal VOC 2012 and Cityscape, respectively, outperforming other current supervised semantic segmentation models. Our code and models are available at https://github.com/jjrccop/Label-Mask-Auto-Encoder.
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ElegantSeg: End-to-End Holistic Learning for Extra-Large Image Semantic Segmentation
Authors:Wei Chen, Yansheng Li, Bo Dang, Yongjun Zhang
This paper presents a new paradigm for Extra-large image semantic Segmentation, called ElegantSeg, that capably processes holistic extra-large image semantic segmentation (ELISS). The extremely large sizes of extra-large images (ELIs) tend to cause GPU memory exhaustion. To tackle this issue, prevailing works either follow the global-local fusion pipeline or conduct the multi-stage refinement. These methods can only process limited information at one time, and they are not able to thoroughly exploit the abundant information in ELIs. Unlike previous methods, ElegantSeg can elegantly process holistic ELISS by extending the tensor storage from GPU memory to host memory. To the best of our knowledge, it is the first time that ELISS can be performed holistically. Besides, ElegantSeg is specifically designed with three modules to utilize the characteristics of ELIs, including the multiple large kernel module for developing long-range dependency, the efficient class relation module for building holistic contextual relationships, and the boundary-aware enhancement module for obtaining complete object boundaries. ElegantSeg outperforms previous state-of-the-art on two typical ELISS datasets. We hope that ElegantSeg can open a new perspective for ELISS. The code and models will be made publicly available.
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KD-SCFNet: Towards More Accurate and Efficient Salient Object Detection via Knowledge Distillation
Authors:Jin Zhang, Qiuwei Liang, Yanjiao Shi
Most existing salient object detection (SOD) models are difficult to apply due to the complex and huge model structures. Although some lightweight models are proposed, the accuracy is barely satisfactory. In this paper, we design a novel semantics-guided contextual fusion network (SCFNet) that focuses on the interactive fusion of multi-level features for accurate and efficient salient object detection. Furthermore, we apply knowledge distillation to SOD task and provide a sizeable dataset KD-SOD80K. In detail, we transfer the rich knowledge from a seasoned teacher to the untrained SCFNet through unlabeled images, enabling SCFNet to learn a strong generalization ability to detect salient objects more accurately. The knowledge distillation based SCFNet (KDSCFNet) achieves comparable accuracy to the state-of-the-art heavyweight methods with less than 1M parameters and 174 FPS real-time detection speed. Extensive experiments demonstrate the robustness and effectiveness of the proposed distillation method and SOD framework. Code and data: https://github.com/zhangjinCV/KD-SCFNet.
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ACSeg: Adaptive Conceptualization for Unsupervised Semantic Segmentation
Authors:Kehan Li, Zhennan Wang, Zesen Cheng, Runyi Yu, Yian Zhao, Guoli Song, Chang Liu, Li Yuan, Jie Chen
Recently, self-supervised large-scale visual pre-training models have shown great promise in representing pixel-level semantic relationships, significantly promoting the development of unsupervised dense prediction tasks, e.g., unsupervised semantic segmentation (USS). The extracted relationship among pixel-level representations typically contains rich class-aware information that semantically identical pixel embeddings in the representation space gather together to form sophisticated concepts. However, leveraging the learned models to ascertain semantically consistent pixel groups or regions in the image is non-trivial since over/ under-clustering overwhelms the conceptualization procedure under various semantic distributions of different images. In this work, we investigate the pixel-level semantic aggregation in self-supervised ViT pre-trained models as image Segmentation and propose the Adaptive Conceptualization approach for USS, termed ACSeg. Concretely, we explicitly encode concepts into learnable prototypes and design the Adaptive Concept Generator (ACG), which adaptively maps these prototypes to informative concepts for each image. Meanwhile, considering the scene complexity of different images, we propose the modularity loss to optimize ACG independent of the concept number based on estimating the intensity of pixel pairs belonging to the same concept. Finally, we turn the USS task into classifying the discovered concepts in an unsupervised manner. Extensive experiments with state-of-the-art results demonstrate the effectiveness of the proposed ACSeg.
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Sparse4D: Multi-view 3D Object Detection with Sparse Spatial-Temporal Fusion
Authors:Xuewu Lin, Tianwei Lin, Zixiang Pei, Lichao Huang, Zhizhong Su
Bird-eye-view (BEV) based methods have made great progress recently in multi-view 3D detection task. Comparing with BEV based methods, sparse based methods lag behind in performance, but still have lots of non-negligible merits. To push sparse 3D detection further, in this work, we introduce a novel method, named Sparse4D, which does the iterative refinement of anchor boxes via sparsely sampling and fusing spatial-temporal features. (1) Sparse 4D Sampling: for each 3D anchor, we assign multiple 4D keypoints, which are then projected to multi-view/scale/timestamp image features to sample corresponding features; (2) Hierarchy Feature Fusion: we hierarchically fuse sampled features of different view/scale, different timestamp and different keypoints to generate high-quality instance feature. In this way, Sparse4D can efficiently and effectively achieve 3D detection without relying on dense view transformation nor global attention, and is more friendly to edge devices deployment. Furthermore, we introduce an instance-level depth reweight module to alleviate the ill-posed issue in 3D-to-2D projection. In experiment, our method outperforms all sparse based methods and most BEV based methods on detection task in the nuScenes dataset.
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Dynamic Focus-aware Positional Queries for Semantic Segmentation
Authors:Haoyu He, Jianfei Cai, Zizheng Pan, Jing Liu, Jing Zhang, Dacheng Tao, Bohan Zhuang
The DETR-like segmentors have underpinned the most recent breakthroughs in semantic segmentation, which end-to-end train a set of queries representing the class prototypes or target segments. Recently, masked attention is proposed to restrict each query to only attend to the foreground regions predicted by the preceding decoder block for easier optimization. Although promising, it relies on the learnable parameterized positional queries which tend to encode the dataset statistics, leading to inaccurate localization for distinct individual queries. In this paper, we propose a simple yet effective query design for semantic segmentation termed Dynamic Focus-aware Positional Queries (DFPQ), which dynamically generates positional queries conditioned on the cross-attention scores from the preceding decoder block and the positional encodings for the corresponding image features, simultaneously. Therefore, our DFPQ preserves rich localization information for the target segments and provides accurate and fine-grained positional priors. In addition, we propose to efficiently deal with high-resolution cross-attention by only aggregating the contextual tokens based on the low-resolution cross-attention scores to perform local relation aggregation. Extensive experiments on ADE20K and Cityscapes show that with the two modifications on Mask2former, our framework achieves SOTA performance and outperforms Mask2former by clear margins of 1.1%, 1.9%, and 1.1% single-scale mIoU with ResNet-50, Swin-T, and Swin-B backbones on the ADE20K validation set, respectively. Source code is at https://github.com/zip-group/FASeg.
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