Despite the recent success of long-tailed object detection, almost all long-tailed object
detectors are developed based on the two-stage paradigm. In practice, one-stage detectors …

In image classification, it is common practice to train deep networks to extract a single
feature vector per input image. Few-shot classification methods also mostly follow this trend …

In graph classification, attention-and pooling-based graph neural networks (GNNs) prevail to
extract the critical features from the input graph and support the prediction. They mostly …

Deep neural networks (DNNs) used for brain–computer interface (BCI) classification are
commonly expected to learn general features when trained across a variety of contexts, such …

Multi-instance learning (MIL) is an effective paradigm for whole-slide pathological images
(WSIs) classification to handle the gigapixel resolution and slide-level label. Prevailing MIL …

Video grounding aims to localize a moment from an untrimmed video for a given textual
query. Existing approaches focus more on the alignment of visual and language stimuli with …

Recommender systems usually amplify the biases in the data. The model learned from
historical interactions with imbalanced item distribution will amplify the imbalance by over …

Recently proposed decoupled training methods emerge as a dominant paradigm for long-
tailed object detection. But they require an extra fine-tuning stage, and the disjointed …

This work studies the bias issue of pseudo-labeling, a natural phenomenon that widely
occurs but often overlooked by prior research. Pseudo-labels are generated when a …

Existing long-tailed recognition methods, aiming to train class-balanced models from long-
tailed data, generally assume the models would be evaluated on the uniform test class …