Noisy correspondence in cross-modal retrieval introduces significant challenges due to its inherent difficulty in identification and correction. Although existing methods attempt to minimize the influence of noisy samples by the weighting mechanism, these methods still struggle with performance degradation under increasing noise levels. Specifically, the clean samples are assigned the same weight of 1, which ignores the sample hardness. In addition, the weights for noisy samples are approaching 0, leading to the overlook of sample diversity. To address these issues, we propose a Hardness and Noise-aware (HaNa) robust cross-modal retrieval method. HaNa introduces a momentum-based reweighting mechanism to adaptively balance learning difficulty across clean samples, avoiding overfitting risk and accumulative partitioning bias. Moreover, HaNa addresses the limitation that weights for noisy data are approaching 0 from a new perspective to fully employ the diversity of samples to further improve its generalization. It employs an Asymmetric Noise-aware Regularization Loss (ANRL) to treat identified noisy data as negative samples for optimization. Extensive experiments demonstrate that HaNa achieves superior matching accuracy and stability, especially in high-noise scenarios, outperforming state-of-the-art methods.

Few-shot learning has attracted considerable attention in the field of hyperspectral image (HSI) classification due to its suitability in addressing the challenges encountered in numerous real-world scenarios. However, the scarcity of labeled samples poses a significant challenge in learning informative and discriminative features, limiting the potential for achieving higher accuracy. In this paper, we propose a contextual information aggregation module (CIAM) as part of the feature extraction network for few-shot hyperspectral image classification which can aggregate more spatial-spectral information for each pixel from the neighbored pixels. Meanwhile, supervised contrastive learning is introduced to learn more discriminative representations for addressing specific challenges of high inter-class similarity and large intra-class variance in hyperspectral images. Extensive experiments on two benchmark datasets show that our proposed method achieves the state-of-the-art results.

Few-shot learning (FSL) aims to generalize from few labeled samples. Recently, metric-based methods have achieved surprising classification performance on many FSL benchmarks. However, those methods ignore the impact of noise, making the few-shot learning still tricky. In this work, we identify that noise suppression is important to improve the performance of FSL algorithms. Hence, we proposed a novel attention-based contrastive learning model with discrete cosine transform input (ACL-DCT), which can suppress the noise in input images, image labels, and learned features, respectively. ACL-DCT takes the transformed frequency domain representations by DCT as input and removes the high-frequency part to suppress the input noise. Besides, an attention-based alignment of the feature maps and a supervised contrastive loss are used to mitigate the feature and label noise. We evaluate our ACL-DCT by comparing previous methods on two widely used datasets for few-shot classification (i.e., miniImageNet and CUB). The results indicate that our proposed method outperforms the state-of-the-art methods.

Few-shot segmentation aims at segmenting target object areas with only a few labeled samples. Previous methods extract class-specific prototypes to guide segmentation. How-ever, using one or more prototypes to represent the whole object inevitably drops vital spatial information, ignoring many details in original images. To address the issue, we propose a Dual-Attention Network (DANet) for few-shot segmentation. Firstly, a light-dense attention module is proposed to set up pixel-wise relations between feature pairs at different levels to activate object regions, which can leverage semantic information in a coarse-to-fine manner. Secondly, in contrast to the previous prototype-based methods that offer a holistic representation for each object class, we propose a prototypical channel attention module which incorporates channel interdependencies to enhance the discriminative capacity of features. The extensive experiments on two benchmarks show that our approach outperforms the state-of-the-arts in most cases.

Deep learning has achieved impressive results on Hyperspectral image (HSI) classification, which generally requires sufficient training samples and a huge number of parameters. However, it is challenging to label HSIs, and likely only a few samples are available in practice. Learning a large number of parameters by the model is also resource-intensive. This paper proposes an HSI classification model that achieves promising classification performance with fewer parameters in few-shot settings. The proposed model adopts the residual 3D-CNN as feature extraction network, and contrastive learning is introduced to learn more discriminative representations for HSIs which can conquer the obstacles from HSIs’ high inter-class similarity and large intra-class variance. The proposed few-shot contrastive learning HSI classification model is tested on five popular HSI datasets and outperforms the state-of-the-art models.