Object detection performance, as measured on the canonical PASCAL VOC dataset, has
plateaued in the last few years. The best-performing methods are complex ensemble …

Deep ConvNets have shown great performance for single-label image classification (eg
ImageNet), but it is necessary to move beyond the single-label classification task because …

A novel deep learning framework is proposed for lesion segmentation and classification.
The proposed technique incorporates two primary phases. For lesion segmentation, Mask …

This paper presents a CLIP-based unsupervised learning method for annotation-free multi-
label image classification, including three stages: initialization, training, and inference. At the …

Predicting all applicable labels for a given image is known as multi-label classification.
Compared to the standard multi-class case (where each image has only one label), it is …

We present a new dataset with the goal of advancing the state-of-the-art in object
recognition by placing the question of object recognition in the context of the broader …

The long-standing goal of localizing every object in an image remains elusive. Manually
annotating objects is quite expensive despite crowd engineering innovations. Current state …

The task of multi-label image recognition is to predict a set of object labels that present in an
image. As objects normally co-occur in an image, it is desirable to model the label …

Computer vision systems require large amounts of manually annotated data to properly
learn challenging visual concepts. Crowdsourcing platforms offer an inexpensive method to …

We address the problem of efficient end-to-end learning a multi-label Convolutional Neural
Network (CNN) on training images with partial labels. Training a CNN with partial labels …