4 accepted papers
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 degr
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 informa…
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 detai…
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…