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pro vyhledávání: '"Qasemi, Ehsan"'
Autor:
Huang, Tenghao, Qasemi, Ehsan, Li, Bangzheng, Wang, He, Brahman, Faeze, Chen, Muhao, Chaturvedi, Snigdha
Storytelling's captivating potential makes it a fascinating research area, with implications for entertainment, education, therapy, and cognitive studies. In this paper, we propose Affective Story Generator (AffGen) for generating interesting narrati
Externí odkaz:
http://arxiv.org/abs/2310.15079
Video Question Answering (VidQA) exhibits remarkable potential in facilitating advanced machine reasoning capabilities within the domains of Intelligent Traffic Monitoring and Intelligent Transportation Systems. Nevertheless, the integration of urban
Externí odkaz:
http://arxiv.org/abs/2307.09636
Humans can infer the affordance of objects by extracting related contextual preconditions for each scenario. For example, upon seeing an image of a broken cup, we can infer that this precondition prevents the cup from being used for drinking. Reasoni
Externí odkaz:
http://arxiv.org/abs/2306.01753
In recent years, vision-language models (VLMs) have shown remarkable performance on visual reasoning tasks (e.g. attributes, location). While such tasks measure the requisite knowledge to ground and reason over a given visual instance, they do not, h
Externí odkaz:
http://arxiv.org/abs/2209.07000
Autor:
Qasemi, Ehsan, Oltramari, Alessandro
Challenges in Intelligent Traffic Monitoring (ITMo) are exacerbated by the large quantity and modalities of data and the need for the utilization of state-of-the-art (SOTA) reasoners. We formulate the problem of ITMo and introduce HANS, a neuro-symbo
Externí odkaz:
http://arxiv.org/abs/2209.00448
Reasoning with preconditions such as "glass can be used for drinking water unless the glass is shattered" remains an open problem for language models. The main challenge lies in the scarcity of preconditions data and the model's lack of support for s
Externí odkaz:
http://arxiv.org/abs/2206.07920
Language models (LMs) show state of the art performance for common sense (CS) question answering, but whether this ability implies a human-level mastery of CS remains an open question. Understanding the limitations and strengths of LMs can help resea
Externí odkaz:
http://arxiv.org/abs/2201.07902
Humans can seamlessly reason with circumstantial preconditions of commonsense knowledge. We understand that a glass is used for drinking water, unless the glass is broken or the water is toxic. Despite state-of-the-art (SOTA) language models' (LMs) i
Externí odkaz:
http://arxiv.org/abs/2104.08712
Commonsense reasoning is an important aspect of building robust AI systems and is receiving significant attention in the natural language understanding, computer vision, and knowledge graphs communities. At present, a number of valuable commonsense k
Externí odkaz:
http://arxiv.org/abs/2006.06114
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