Human trajectory prediction task aims to analyze human future movements given their past
status, which is a crucial step for many autonomous systems such as self-driving cars and …

In this paper, we propose a genuine group-level contrastive visual representation learning
method whose linear evaluation performance on ImageNet surpasses the vanilla supervised …

Novelty detection is a fundamental task of machine learning which aims to detect abnormal
(ie out-of-distribution (OOD)) samples. Since diffusion models have recently emerged as the …

Due to privacy, security, and costly labeling of images, unsupervised semantic segmentation
with very few samples has become a promising direction, but still remains unexplored. This …

Predicting the risk of in-hospital mortality from electronic health records (EHRs) has received
considerable attention. Such predictions will provide early warning of a patient's health …

Due to the difficulty of annotating the 3D LiDAR data of autonomous driving, an efficient
unsupervised 3D representation learning method is important. In this paper, we design the …

Contrastive learning, a discriminative self-learning framework, is one of the most popular
representation learning methods which has a wide range of application scenarios. Although …

Unsupervised semantic segmentation (USS) aims at partitioning an image into semantically
meaningful segments by learning from a collection of unlabeled images. The effectiveness …

The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the
European Conference on Machine Learning and Knowledge Discovery in Databases …

Removing supervision in semantic segmentation is still tricky. Current approaches can deal
with common categorical patterns yet resort to multi-stage architectures. We design a novel …