Multi-modality (MM) image fusion aims to render fused images that maintain the merits of
different modalities, eg, functional highlight and detailed textures. To tackle the challenge in …

Multi-label image classification is a prediction task that aims to identify more than one label
from a given image. This paper considers the semantic consistency of the latent space …

Multi-label image classification is a fundamental yet challenging task in computer vision that
aims to identify multiple objects from a given image. Recent studies on this task mainly focus …

Multilabel image recognition (MLR) aims to annotate an image with comprehensive labels
and suffers from object occlusion or small object sizes within images. Although the existing …

Multimodal affective computing (MAC) has garnered increasing attention due to its broad
applications in analyzing human behaviors and intentions, especially in text-dominated …

This paper develops the MUFIN technique for extreme classification (XC) tasks with millions
of labels where datapoints and labels are endowed with visual and textual descriptors …

Anomaly detection plays a critical role in ensuring safe, smooth, and efficient operation of
machinery and equipment in industrial environments. With the wide deployment of …

Recently many multi-label image recognition (MLR) works have made significant progress
by introducing pre-trained object detection models to generate lots of proposals or utilizing …

Personalized outfit compatibility learning is an emerging yet challenging task. Most of the
existing methods focus on general outfit compatibility learning. Although a few works have …

Learning from online data with noisy web labels is gaining more attention due to the
increasing cost of fully annotated datasets in large-scale multi-label classification tasks …