Few-shot semantic segmentation is the task of learning to locate each pixel of the novel class in the query image with only a few annotated support images. The current correlation-based methods construct pair-wise feature correlations to establish the many-to-many matching because the typical prototype-based approaches cannot learn fine-grained correspondence relations. However, the existing methods still suffer from the noise contained in naive correlations and the lack of context semantic information in correlations. To alleviate these problems mentioned above, we propose a Feature-Enhanced Context-Aware Network (FECANet). Specifically, a feature enhancement module is proposed to suppress the matching noise caused by inter-class local similarity and enhance the intra-class relevance in the naive correlation. In addition, we propose a novel correlation reconstruction module that encodes extra correspondence relations between foreground and background and multi-scale context semantic features, significantly boosting the encoder to capture a reliable matching pattern. Experiments on PASCAL- $5^{i}$ and COCO- $20^{i}$ datasets demonstrate that our proposed FECANet leads to remarkable improvement compared to previous state-of-the-arts, demonstrating its effectiveness. The source codes and models have been made available at https://github.com/NUST-Machine-Intelligence-Laboratory/FECANET .

Weakly supervised semantic segmentation with only image-level labels aims to reduce annotation costs for the segmentation task. Existing approaches generally leverage class activation maps (CAMs) to locate the object regions for pseudo label generation. However, CAMs can only discover the most discriminative parts of objects, thus leading to inferior pixel-level pseudo labels. To address this issue, we propose a saliency guided Inter- and Intra-Class Relation Constrained (I²CRC) framework to assist the expansion of the activated object regions in CAMs. Specifically, we propose a saliency guided class-agnostic distance module to pull the intra-category features closer by aligning features to their class prototypes. Further, we propose a class-specific distance module to push the inter-class features apart and encourage the object region to have a higher activation than the background. Besides strengthening the capability of the classification network to activate more integral object regions in CAMs, we also introduce an object guided label refinement module to take a full use of both the segmentation prediction and the initial labels for obtaining superior pseudo-labels. Extensive experiments on PASCAL VOC 2012 and COCO datasets demonstrate well the effectiveness of I²CRC over other state-of-the-art counterparts.

One-shot semantic image segmentation aims to segment the object regions for the novel class with only one annotated image. Recent works adopt the episodic training strategy to mimic the expected situation at testing time. However, these existing approaches simulate the test conditions too strictly during the training process, and thus cannot make full use of the given label information. Besides, these approaches mainly focus on the foreground-background target class segmentation setting. They only utilize binary mask labels for training. In this paper, we propose to leverage the multi-class label information during the episodic training. It will encourage the network to generate more semantically meaningful features for each category. After integrating the target class cues into the query features, we then propose a pyramid feature fusion module to mine the fused features for the final classifier. Furthermore, to take more advantage of the support image-mask pair, we propose a self-prototype guidance branch to support image segmentation. It can constrain the network for generating more compact features and a robust prototype for each semantic class. For inference, we propose a fused prototype guidance branch for the segmentation of the query image. Specifically, we leverage the prediction of the query image to extract the pseudo-prototype and combine it with the initial prototype. Then we utilize the fused prototype to guide the final segmentation of the query image. Extensive experiments demonstrate the superiority of our proposed approach. The source codes and models have been made available at https://github.com/NUST-Machine-Intelligence-Laboratory/SMCP .

Performances of deep neural networks are prone to be degraded by label noise due to their powerful capability in fitting training data. Deeming low-loss instances as clean data is one of the most promising strategies in tackling label noise and has been widely adopted by state-of-the-art methods. However, prior works tend to drop high-loss instances directly, neglecting their valuable information. To address this issue, we propose an end-to-end framework named Co-LDL, which incorporates the low-loss sample selection strategy with label distribution learning. Specifically, we simultaneously train two deep neural networks and let them communicate useful knowledge by selecting low-loss and high-loss samples for each other. Low-loss samples are leveraged conventionally for updating network parameters. On the contrary, high-loss samples are trained in a label distribution learning manner to update network parameters and label distributions concurrently. Moreover, we propose a self-supervised module to further boost the model performance by enhancing the learned representations. Comprehensive experiments on both synthetic and real-world noisy datasets are provided to demonstrate the superiority of our Co-LDL method over state-of-the-art approaches in learning with noisy labels. The source code and models have been made available at https://github.com/NUST-Machine-Intelligence-Laboratory/CoLDL .

Unsupervised domain adaptation for semantic segmentation aims to transfer knowledge from a labeled source domain to another unlabeled target domain. However, due to the label noise and domain mismatch, learning directly from source domain data tends to have poor performance. Though adversarial learning methods strive to reduce domain discrepancies by aligning feature distributions, traditional methods suffer from the training imbalance and feature distortion problems. Besides, due to the absence of target domain labels, the classifier is blind to features from the target domain during training. Consequently, the final classifier overfits the source domain features and usually fails to predict the structured outputs of the target domain. To alleviate these problems, we focus on enhancing the adversarial learning based feature alignment from three perspectives. First, a classification constrained discriminator is proposed to balance the adversarial training and alleviate the feature distortion problem. Next, to alleviate the classifier overfitting problem, self-training is collaboratively used to learn a domain robust classifier with target domain pseudo labels. Moreover, an efficient class centroid calculation module is proposed and the domain discrepancy is further reduced by aligning the feature centroids of the same class from different domains. Experimental evaluations on GTA5 $\rightarrow$ Cityscapes and SYNTHIA $\rightarrow$ Cityscapes demonstrate state-of-the-art results compared to other counterpart methods. The source code and models have been made available at. ^{1 1}[Online]. Available: https://github.com/NUST-Machine-Intelligence-Laboratory/EFA.

The lack of sufficient training data has been one obstacle to fine-grained visual classification research because labeling subcategories generally requires specialist knowledge. As one optional approach to alleviating the data-hunger problem, leveraging web images as training data is drawing increasing attention. Nevertheless, web images potentially have false labels, which can misguide the training process. Although several works have been proposed to deal with label noise, it still can be difficult for the network to tackle complex real-world noisy labels without any prior knowledge. In the literature, we propose to leverage a small and clean meta-set to provide reliable prior knowledge for tackling noisy web images. Specifically, our method trains a network with two peer predicting heads, which learn from noisy web images (web head) and meta ones (meta head), respectively. The meta head produces pseudo soft labels for web images to revise their training loss, which can overcome the high noise ratio problem. Furthermore, a selection net is trained in a meta-learning strategy to identify in- and out-of-distribution noisy images. Then in-distribution ones are reused for training with pseudo soft labels produced by the meta head as supervision, while out-of-distribution ones are discarded. In this manner, the misguidance caused by label noise is remarkably alleviated and in-distribution noisy samples are properly exploited to boost model performance. The superiority of our proposed approach is demonstrated by mathematical theory with great interpretability as well as extensive experimental results on the real-world dataset WebFG-496. Our source code is available at:.