Recognition (object detection, categorization)

BoxeR: Box-Attention for 2D and 3D Transformers

In this paper, we propose a simple attention mechanism, we call box-attention. It enables spatial interaction between grid features, as sampled from boxes of interest, and improves the learning capability of transformers for several vision tasks. …

Human-Object Interaction Detection via Weak Supervision

The goal of this paper is Human-object Interaction (HO-I) detection. HO-I detection aims to find interacting human-objects regions and classify their interaction from an image. Researchers obtain significant improvement in recent years by relying on …

Seminar Learning for Click-Level Weakly Supervised Semantic Segmentation

Annotation burden has become one of the biggest barriers to semantic segmentation. Approaches based on click-level annotations have therefore attracted increasing attention due to their superior trade-off between supervision and annotation cost. In …

Sparse-Shot Learning With Exclusive Cross-Entropy for Extremely Many Localisations

Object localisation, in the context of regular images, often depicts objects like people or cars. In these images, there is typically a relatively small number of objects per class, which usually is manageable to annotate. However, outside the …

Memory Attention Networks for Skeleton-Based Action Recognition

Skeleton-based action recognition has been extensively studied, but it remains an unsolved problem because of the complex variations of skeleton joints in 3-D spatiotemporal space. To handle this issue, we propose a newly temporal-then-spatial …

The Hateful Memes Challenge: Competition Report

Machine learning and artificial intelligence play an ever more crucial role in mitigating important societal problems, such as the prevalence of hate speech. We describe the Hateful Memes Challenge competition, held at NeurIPS 2020, focusing on …

A Bit More Bayesian: Domain-Invariant Learning with Uncertainty

Domain generalization is challenging due to the domain shift and the uncertainty caused by the inaccessibility of target domain data. In this pa- per, we address both challenges with a probabilis- tic framework based on variational Bayesian in- …