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pro vyhledávání: '"Erdenee, Enkhbayar"'
Autor:
Han, Seungju, Kim, Beomsu, Yoo, Jin Yong, Seo, Seokjun, Kim, Sangbum, Erdenee, Enkhbayar, Chang, Buru
In this paper, we consider mimicking fictional characters as a promising direction for building engaging conversation models. To this end, we present a new practical task where only a few utterances of each fictional character are available to genera
Externí odkaz:
http://arxiv.org/abs/2204.10825
Exemplar-based generative models for open-domain conversation produce responses based on the exemplars provided by the retriever, taking advantage of generative models and retrieval models. However, they often ignore the retrieved exemplars while gen
Externí odkaz:
http://arxiv.org/abs/2112.06723
Despite the remarkable performance of large-scale generative models in open-domain conversation, they are known to be less practical for building real-time conversation systems due to high latency. On the other hand, retrieval models could return res
Externí odkaz:
http://arxiv.org/abs/2108.12582
This paper presents a robust multi-class multi-object tracking (MCMOT) formulated by a Bayesian filtering framework. Multi-object tracking for unlimited object classes is conducted by combining detection responses and changing point detection (CPD) a
Externí odkaz:
http://arxiv.org/abs/1608.08434
Publikováno v:
In Cognitive Systems Research October 2017 45:109-123
Akademický článek
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