Exabytes of data are generated daily by humans, leading to the growing needs for new
efforts in dealing with the grand challenges for multi-label learning brought by big data. For …

In a typical multi-label setting, a picture contains on average few positive labels, and many
negative ones. This positive-negative imbalance dominates the optimization process, and …

This paper presents a simple and effective approach to solving the multi-label classification
problem. The proposed approach leverages Transformer decoders to query the existence of …

Multi-label image recognition is a challenging computer vision task of practical use.
Progresses in this area, however, are often characterized by complicated methods, heavy …

Solving multi-label recognition (MLR) for images in the low-label regime is a challenging
task with many real-world applications. Recent work learns an alignment between textual …

Object detection (OD) is a crucial computer vision task that has seen the development of
many algorithms and models over the years. While the performance of current OD models …

Recognizing multiple labels of images is a practical and challenging task, and significant
progress has been made by searching semantic-aware regions and modeling label …

In this paper, we introduce ML-Decoder, a new attention-based classification head. ML-
Decoder predicts the existence of class labels via queries, and enables better utilization of …

Recognizing multiple labels of an image is a practical yet challenging task, and remarkable
progress has been achieved by searching for semantic regions and exploiting label …

In a typical multi-label setting, a picture contains on average few positive labels, and many
negative ones. This positive-negative imbalance dominates the optimization process, and …