In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Ex Input:
Semantic segmentation requires a detailed labeling of image pixels by object category. Information derived from local image patches is necessary to describe the detailed shape of individual objects. However, this information is ambiguous and can result in noisy labels. Global inference of image content can instead capture the general semantic concepts present. We advocate that holistic inference of image concepts provides valuable information for detailed pixel labeling. We propose a generic framework to leverage holistic information in the form of a LabelBank for pixellevel segmentation. We show the ability of our framework to improve semantic segmentation performance in a variety of settings. We learn models for extracting a holistic LabelBank from visual cues, attributes, and/or textual descriptions. We demonstrate improvements in semantic segmentation accuracy on standard datasets across a range of state-of-the-art segmentation architectures and holistic inference approaches.

Ex Output:
LabelBank: Revisiting Global Perspectives for Semantic Segmentation


Ex Input:
Robust belief revision methods are crucial in streaming data situations for updating existing knowledge (or beliefs) with new incoming evidence. Bayes conditioning is the primary mechanism in use for belief revision in data fusion systems that use probabilistic inference. However, traditional conditioning methods face several challenges due to inherent data/source imperfections in big-data environments that harness soft (i.e., human or human-based) sources in addition to hard (i.e., physicsbased) sensors. The objective of this paper is to investigate the most natural extension of Bayes conditioning that is suitable for evidence updating in the presence of such uncertainties. By viewing the evidence updating process as a thought experiment, an elegant strategy is derived for robust evidence updating in the presence of extreme uncertainties that are characteristic of big-data environments. In particular, utilizing the Fagin-Halpern conditional notions, a natural extension to Bayes conditioning is derived for evidence that takes the form of a general belief function. The presented work differs fundamentally from the Conditional Update Equation (CUE) and authors own extensions of it. An overview of this development is provided via illustrative examples. Furthermore, insights into parameter selection under various fusion contexts are also provided.

Ex Output:
Evidence Updating for Stream-Processing in Big-Data: Robust Conditioning in Soft and Hard Data Fusion Environments


Ex Input:
Retrieving spoken content with spoken queries, or query-byexample spoken term detection (STD), is attractive because it makes possible the matching of signals directly on the acoustic level without transcribing them into text. Here, we propose an end-to-end query-by-example STD model based on an attention-based multi-hop network, whose input is a spoken query and an audio segment containing several utterances; the output states whether the audio segment includes the query. The model can be trained in either a supervised scenario using labeled data, or in an unsupervised fashion. In the supervised scenario, we find that the attention mechanism and multiple hops improve performance, and that the attention weights indicate the time span of the detected terms. In the unsupervised setting, the model mimics the behavior of the existing query-by-example STD system, yielding performance comparable to the existing system but with a lower search time complexity.

Ex Output:

QUERY-BY-EXAMPLE SPOKEN TERM DETECTION USING ATTENTION-BASED MULTI-HOP NETWORKS