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FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space
arXiv.orgWe present evaluation results for FLUX.1 Kontext, a generative flow matching model that unifies image generation and editing. The model generates novel output views by incorporating semantic context from text and image inputs.
Black Forest Labs +20 more
semanticscholar +1 more source
Global matching models of recognition memory: How the models match the data
Psychonomic Bulletin & Review, 1996We present a review of global matching models of recognition memory, describing their theoretical origins and fundamental assumptions, focusing on two defining properties: (1) recognition is based solely on familiarity due to a match of test items to memory at a global level, and (2) multiple cues are combined interactively.
S E, Clark, S D, Gronlund
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Model matching for finite state machines
Proceedings of 1994 33rd IEEE Conference on Decision and Control, 2001The problem of model matching has been previously considered for linear (see \textit{B. Moore} and \textit{L. Silverman} [ibid. 17, 491-497 (1972; Zbl 0263.93033)], \textit{A. S. Morse} [ibid. 18, 346-354 (1973; Zbl 0264.93005)]) and nonlinear (\textit{M. D. Di Benedetto} [ibid. 35, 1351-1355 (1990; Zbl 0734.93039)], \textit{A. Isidori} [ibid.
DI BENEDETTO, MARIA DOMENICA +2 more
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Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2002
A simple MAP model-matching criterion that captures important aspects of recognition in controlled situations is described. A detailed metrical object model is assumed. A probabilistic model of image features is combined with a simple prior on both the pose and the feature interpretations to yield a mixed objective function.
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A simple MAP model-matching criterion that captures important aspects of recognition in controlled situations is described. A detailed metrical object model is assumed. A probabilistic model of image features is combined with a simple prior on both the pose and the feature interpretations to yield a mixed objective function.
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Robust Active Appearance Model Matching
2005A novel robust active appearance model (AAM) matching algorithm is presented. The method consists of two main stages. First, initial residuals are clustered by a non parametric mean shift mode detection step. Second, modes without gross outliers are selected using an objective function.
Reinhard, Beichel +3 more
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FoundationStereo: Zero-Shot Stereo Matching
Computer Vision and Pattern RecognitionTremendous progress has been made in deep stereo matching to excel on benchmark datasets through per-domain fine-tuning. However, achieving strong zero-shot generalization — a hallmark of foundation models in other computer vision tasks — remains ...
Bowen Wen +5 more
semanticscholar +1 more source
Improved Distribution Matching Distillation for Fast Image Synthesis
Neural Information Processing SystemsRecent approaches have shown promises distilling diffusion models into efficient one-step generators. Among them, Distribution Matching Distillation (DMD) produces one-step generators that match their teacher in distribution, without enforcing a one-to ...
Tianwei Yin +6 more
semanticscholar +1 more source
F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching
Annual Meeting of the Association for Computational LinguisticsThis paper introduces F5-TTS, a fully non-autoregressive text-to-speech system based on flow matching with Diffusion Transformer (DiT). Without requiring complex designs such as duration model, text encoder, and phoneme alignment, the text input is ...
Yushen Chen +7 more
semanticscholar +1 more source
Matching Between Photogrammetric Models
The Canadian Surveyor, 1981Solutions for matching between photogrammetric models and adjusting information related to the edges of adjacent DTMs are presented.
B. Shmutter, Y. Doytsher
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Neural Information Processing Systems
Despite Flow Matching and diffusion models having emerged as powerful generative paradigms for continuous variables such as images and videos, their application to high-dimensional discrete data, such as language, is still limited.
Itai Gat +7 more
semanticscholar +1 more source
Despite Flow Matching and diffusion models having emerged as powerful generative paradigms for continuous variables such as images and videos, their application to high-dimensional discrete data, such as language, is still limited.
Itai Gat +7 more
semanticscholar +1 more source