The sample selection approach is popular in learning with noisy labels. The state-of-the-art
methods train two deep networks simultaneously for sample selection, which aims to employ …

We study the challenging incremental few-shot object detection (iFSD) setting. Recently,
hypernetwork-based approaches have been studied in the context of continuous and …

Test-time task adaptation in few-shot learning aims to adapt a pre-trained task-agnostic
model for capturing task-specific knowledge of the test task, rely only on few-labeled support …

Robust few-shot learning (RFSL), which aims to address noisy labels in few-shot learning,
has recently gained considerable attention. Existing RFSL methods are based on the …

Visual object tracking is a key component to many egocentric vision problems. However, the
full spectrum of challenges of egocentric tracking faced by an embodied AI is …

Few-shot learning (FSL) that aims to recognize novel classes with few labeled samples is
troubled by its data scarcity. Though recent works tackle FSL with data augmentation-based …

The sample selection approach is very popular in learning with noisy labels. As deep
networks “learn pattern first”, prior methods built on sample selection share a similar training …

Point-wise supervision is widely adopted in computer vision tasks such as crowd counting
and human pose estimation. In practice, the noise in point annotations may affect the …

Mainstream few-shot segmentation methods meet performance bottleneck due to the data
scarcity of novel classes with insufficient intra-class variations, which results in a biased …

Real-world large-scale datasets are both noisily labeled and class-imbalanced. The issues
seriously hurt the generalization of trained models. It is hence significant to address the …