Multi-label image recognition has attracted considerable research attention and achieved
great success in recent years. Capturing label correlations is an effective manner to advance …

With the growing amount of multimodal data, cross-modal retrieval has attracted more and
more attention and become a hot research topic. To date, most of the existing techniques …

An automatic vision-based sewer inspection plays a key role of sewage system in a modern
city. Recent advances focus on utilizing a deep learning model to realize the sewer …

Modern visual recognition models often display overconfidence due to their reliance on
complex deep neural networks and one-hot target supervision, resulting in unreliable …

Partial Multi-label Learning (PML) addresses the scenario where each instance is assigned
with multiple candidate labels, while only a subset of the labels are relevant. This task is very …

In computer vision, multi-label classification, including zero-shot multi-label classification are
important tasks with many real-world applications. In this paper, we propose a novel …

Recently, as an effective way of learning latent representations, contrastive learning has
been increasingly popular and successful in various domains. The success of contrastive …

Multi-Label Image Classification (MLIC) is a fundamental yet challenging task which aims to
recognize multiple labels from given images. The key to solve MLIC lies in how to accurately …

Weakly supervised group activity recognition deals with the dependence on individual-level
annotations during understanding scenes involving multiple individuals, which is a …

Audio-Visual Segmentation (AVS) aims to extract the sounding object from a video frame,
which is represented by a pixel-wise segmentation mask for application scenarios such as …