1,351 research outputs found

    Learning to Recommend Links using Graph Structure and Node Content

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    International audienceThe link prediction problem for graphs is a binary classification task that estimates the presence or absence of a link between two nodes in the graph. Links absent from the training set, however, cannot be directly considered as the negative examples since they might be present links at test time. Finding a hard decision boundary for link prediction is thus unnatural. This paper formalizes the link prediction problem from the flexible perspective of preference learning: the goal is to learn a preference score between any two nodes---either observed in the network at training time or to appear only later in the test---by using the feature vectors of the nodes and the structure of the graph as side information. Our assumption is that the observed edges, and in general, shortest paths between nodes in the graph, can reinforce an existing similarity between the nodes feature vectors. We propose a model implemented by a simple neural network architecture and an objective function that can be optimized by stochastic gradient descent over appropriate triplets of nodes in the graph. Our first preliminary experiments in small undirected graphs show that our learning algorithm outperforms baselines in real networks and is able to learn the correct distance function in synthetic networks

    CIDEr: Consensus-based Image Description Evaluation

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    Automatically describing an image with a sentence is a long-standing challenge in computer vision and natural language processing. Due to recent progress in object detection, attribute classification, action recognition, etc., there is renewed interest in this area. However, evaluating the quality of descriptions has proven to be challenging. We propose a novel paradigm for evaluating image descriptions that uses human consensus. This paradigm consists of three main parts: a new triplet-based method of collecting human annotations to measure consensus, a new automated metric (CIDEr) that captures consensus, and two new datasets: PASCAL-50S and ABSTRACT-50S that contain 50 sentences describing each image. Our simple metric captures human judgment of consensus better than existing metrics across sentences generated by various sources. We also evaluate five state-of-the-art image description approaches using this new protocol and provide a benchmark for future comparisons. A version of CIDEr named CIDEr-D is available as a part of MS COCO evaluation server to enable systematic evaluation and benchmarking.Comment: To appear in CVPR 201

    Discovery of Precursor LBV Outbursts in Two Recent Optical Transients: The Fitfully Variable Missing Links UGC 2773-OT and SN 2009ip

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    We present progenitor-star detections, light curves, and optical spectra of SN2009ip and the 2009 optical transient in UGC2773 (U2773-OT), which were not genuine SNe. Precursor variability in the decade before outburst indicates that both of the progenitor stars were LBVs. Their pre-outburst light curves resemble the S Doradus phases that preceded giant eruptions of eta Carinae and SN1954J (V12 in NGC2403), with intermediate progenitor luminosities. HST detections a decade before discovery indicate that the SN2009ip and U2773-OT progenitors were supergiants with likely initial masses of 50-80 Msun and \ga20 Msun, respectively. Both outbursts had spectra befitting known LBVs, although in different physical states. SN 2009ip exhibited a hot LBV spectrum with characteristic speeds of 550 km/s, plus faster material up to 5000 km/s, resembling the slow Homunculus and fast blast wave of eta Carinae. U2773-OT shows a forest of narrow absorption and emission lines comparable to that of S Dor in its cool state, plus [CaII] emission and an IR excess indicative of dust, similar to SN2008S and N300-OT. [CaII] emission is probably tied to a dusty pre-outburst environment, and not the outburst mechanism. SN2009ip and U2773-OT may provide a critical link between historical LBV eruptions, while U2773-OT may provide a link between LBVs and SN2008S and N300-OT. Future searches will uncover more examples of precursor LBV variability of this kind, providing key clues that may help unravel the instability driving LBVs.Comment: 18 pages, 13 Figures, accepted AJ. added significant material while revising after referee repor

    LayerDiffusion: Layered Controlled Image Editing with Diffusion Models

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    Text-guided image editing has recently experienced rapid development. However, simultaneously performing multiple editing actions on a single image, such as background replacement and specific subject attribute changes, while maintaining consistency between the subject and the background remains challenging. In this paper, we propose LayerDiffusion, a semantic-based layered controlled image editing method. Our method enables non-rigid editing and attribute modification of specific subjects while preserving their unique characteristics and seamlessly integrating them into new backgrounds. We leverage a large-scale text-to-image model and employ a layered controlled optimization strategy combined with layered diffusion training. During the diffusion process, an iterative guidance strategy is used to generate a final image that aligns with the textual description. Experimental results demonstrate the effectiveness of our method in generating highly coherent images that closely align with the given textual description. The edited images maintain a high similarity to the features of the input image and surpass the performance of current leading image editing methods. LayerDiffusion opens up new possibilities for controllable image editing.Comment: 17 pages, 14 figure
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