Despite the previous success of object analysis, detecting and segmenting a large number
of object categories with a long-tailed data distribution remains a challenging problem and is …

Existing video-audio understanding models are trained and evaluated in an intra-domain
setting, facing performance degeneration in real-world applications where multiple domains …

Long-tailed learning has attracted much attention recently, with the goal of improving
generalisation for tail classes. Most existing works use supervised learning without …

We address the overlooked unbiasedness in existing long-tailed classification methods: we
find that their overall improvement is mostly attributed to the biased preference of" tail" over" …

In the real world, large-scale datasets for visual recognition typically exhibit a long-tailed
distribution, where only a few classes contain adequate samples but the others have (much) …

Major advancements have been made in the field of object detection and segmentation
recently. However, when it comes to rare categories, the state-of-the-art methods fail to …

Abstract Knowledge distillation (KD) addresses model compression by distilling knowledge
from a large model (teacher) to a smaller one (student). The existing distillation approaches …

Large-scale object detection and instance segmentation face a severe data imbalance. The
finer-grained object classes become, the less frequent they appear in our datasets …

Graph Convolutional Network (GCN) is an emerging technique for information retrieval (IR)
applications. While GCN assumes the homophily property of a graph, real-world graphs are …

Human trajectory Prediction (HTP) in complex social environments plays a crucial and
fundamental role in artificial intelligence systems. Conventional methods make use of both …