Image saliency detection has recently achieved great success due to the development of deep convolutional neural networks . However, most existing salient object methods cannot identify individual instances in detected region In this paper, we present a instance segmentation method that produces map with distinct labels for an input image. Our consists three primary steps, i.e. , inference, con...

In instance-based learning the classification of a novel instance relies upon experience given in the form of similar instances whose labels are already known. Each of these instances can hence be seen as an individual piece of evidence. In this paper, we elaborate on issues concerning the representation and combination of such pieces of evidence. Particularly, we argue that the information pro...

We propose a novel approach to identify one of the most significant dermoscopic criteria in the diagnosis of Cutaneous Melanoma: the Blue-whitish structure. In this paper, we achieve this goal in a Multiple Instance Learning framework using only image-level labels of whether the feature is present or not. As the output, we predict the image classification label and as well localize the feature ...

We consider the task of fine-grained sentiment analysis from the perspective of multiple instance learning (MIL). Our neural model is trained on document sentiment labels, and learns to predict the sentiment of text segments, i.e. sentences or elementary discourse units (EDUs), without segment-level supervision. We introduce an attention-based polarity scoring method for identifying positive an...

Multiple Instance Learning (MIL) provides a framework for training a discriminative classifier from data with ambiguous labels. This framework is well suited for the task of learning object classifiers from weakly labeled image data, where only the presence of an object in an image is known, but not its location. Some recent work has explored the application of MIL algorithms to the tasks of im...

In this paper, we consider a new low-quality label learning problem: learning time series detection models from temporally imprecise labels. In this problem, the data consist of a set of input time series, and supervision is provided by a sequence of noisy time stamps corresponding to the occurrence of positive class events. Such temporally imprecise labels commonly occur in areas like mobile h...

MOTIVATION Calmodulin (CaM) is a ubiquitously conserved protein that acts as a calcium sensor, and interacts with a large number of proteins. Detection of CaM binding proteins and their interaction sites experimentally requires a significant effort, so accurate methods for their prediction are important. RESULTS We present a novel algorithm (MI-1 SVM) for binding site prediction and evaluate ...