1,351 research outputs found
Learning to Recommend Links using Graph Structure and Node Content
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
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
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
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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