Image-level Weakly Supervised Semantic Segmentation (WSSS) has garnered significant attention due to its low annotation costs. Current single-stage state-of-the-art WSSS methods mainly rely on Vision Transformer (ViT) to extract features from input images, generating more complete segmentation results based on comprehensive semantic information. However, these ViT-based methods often suffer from over-smoothing issues in segmentation results. In this paper, we identify that attenuated high-frequency features mislead the decoder of ViT-based WSSS models, resulting in over-smoothed false segmentation. To address this, we propose a Frequency Feature Rectification (FFR) framework to rectify the false segmentations caused by attenuated high-frequency features and enhance the learning of high-frequency features in the decoder. Quantitative and qualitative experimental results demonstrate that our FFR framework can effectively address the attenuated high-frequency caused over-smoothed segmentation issue and achieve new state-of-the-art WSSS performances. Codes are available at https://github.com/yay97/FFR.

Long-tailed distribution exists in real-world scenario and compromises the performance of recognition models. In this article, we point out that a neural network classifier has a shared feature bias, which tends to regard the shared features among different classes as head-class discriminative features, leading to misclassifications on tail-class samples under long-tailed scenarios. To solve this issue, we propose a shared feature bias mitigating (SFBM) framework. Specifically, we create two parallel classifiers trained concurrently with the baseline classifier, using our special training loss. The parallel classifier weight sums are then used for estimating the shared feature components in baseline classifier weights. Finally, we rectify the baseline classifier by removing the estimated shared feature components from it while supplementing the parallel classifier weights class by class to the rectified classifier weights, mitigating shared feature bias. Our proposed SFBM demonstrates broad compatibility with nearly all recognition methods while maintaining high computational efficiency, as it introduces no additional computation during inference. Extensive experiments on CIFAR10/100-LT, ImageNet-LT, and iNaturalist 2018 demonstrate that simply incorporating SFBM during the training phase consistently boosts the performance of various state-of-the-art methods by significant margins. The complete source code will be made publicly available at https://github.com/bzbz-bot/SFBM.

Image-level weakly supervised semantic segmentation has received increasing attention due to its low annotation cost. Existing methods mainly rely on Class Activation Mapping (CAM) to obtain pseudo-labels for training semantic segmentation models. In this work, we are the first to demonstrate that long-tailed distribution in training data can cause the CAM calculated through classifier weights over-activated for head classes and under-activated for tail classes due to the shared features among head- and tail- classes. This degrades pseudo-label quality and further influences final semantic segmentation performance. To address this issue, we propose a Shared Feature Calibration (SFC) method for CAM generation. Specifically, we leverage the class prototypes which carry positive shared features and propose a Multi-Scaled Distribution-Weighted (MSDW) consistency loss for narrowing the gap between the CAMs generated through classifier weights and class prototypes during training. The MSDW loss counterbalances over-activation and under-activation by calibrating the shared features in head-/tail-class classifier weights. Experimental results show that our SFC significantly improves CAM boundaries and achieves new state-of-the-art performances. The project is available at https://github.com/Barrett-python/SFC.

Image-level Weakly Supervised Semantic Segmentation (WSSS) has received increasing attention due to its low an-notation cost. Class Activation Mapping (CAM) generated through classifier weights in WSSS inevitably ignores cer-tain useful cues, while the CAM generated through class prototypes can alleviate that. However, because of the dif-ferent goals of image classification and semantic segmentation, the class prototypes still focus on activating primary discriminative pixels learned from classification loss, leading to incomplete CAM. In this paper, we propose a plug-and-play Prototype-based Secondary Discriminative Pixels Mining (PSDPM) framework for enabling class prototypes to activate more secondary discriminative pixels, thus gen-erating a more complete CAM. Specifically, we introduce a Foreground Pixel Estimation Module (FPEM) for esti-mating potential foreground pixels based on the correlations between primary and secondary discriminative pix-els and the semantic segmentation results of baseline meth-ods. Then, we enable WSSS model to learn discriminative features from secondary discriminative pixels through a consistency loss calculated between FPEM result and class-prototype CAM. Experimental results show that our PSDPM improves various baseline methods significantly and achieves new state-of-the-art performances on WSSS benchmarks. Codes are available at https://github.com/xinqiaozhao/PSDPM.

Image-level weakly supervised semantic segmentation (WSSS) has received substantial attention due to its cost-effective annotation process. In WSSS, Class Activation Maps (CAMs) generated via classifier weights tend to focus on the most discriminative region, while the CAMs derived from class prototypes are significantly enhanced to cover more complete regions. However, the prototype CAMs still exhibit limitations such as incomplete localization maps on target objects and the presence of background noise. In this paper, we propose a novel WSSS framework called Classifier-Prototype Mutual Calibration (CPMC) that leverages the characteristics of both classifier and prototype CAMs to address the above issues. Specifically, an iterative refinement strategy based on context feature dependency is applied to refine the original classifier CAMs, which helps to generate improved prototype CAMs. Subsequently, local prototypes are constructed based on the false negative regions and false positive regions extracted from the previous two CAMs, which contribute to completing missing parts of the target object and suppressing background noise respectively. Therefore, CPMC can alleviate the aforementioned issues. Extensive experimental results on standard WSSS benchmarks (PASCAL VOC and MS COCO) show that our method significantly improves the quality of CAMs and achieves state-of-the-art performance. Our source code will be released.