Results 231 to 240 of about 4,294,534 (298)

Semantically-aware Spatio-temporal Reasoning Agent for Vision-and-Language Navigation in Continuous Environments

International Conference on Pattern Recognition, 2021
This paper presents a novel approach for the Vision-and-Language Navigation (VLN) task in continuous 3D environments, which requires an autonomous agent to follow natural language instructions in unseen environments.
Muhammad Zubair Irshad, Niluthpol Chowdhury Mithun, Zachary Seymour, Han-Pang Chiu, S. Samarasekera, Rakesh Kumar   +5 more
semanticscholar   +1 more source

Open-o3 Video: Grounded Video Reasoning with Explicit Spatio-Temporal Evidence

arXiv.org
Most video reasoning models only generate textual reasoning traces without indicating when and where key evidence appears. Recent models such as OpenAI-o3 have sparked wide interest in evidence-centered reasoning for images, yet extending this ability to
Jiahao Meng, Xiangtai Li, Haocheng Wang, Yue Tan, Tao Zhang, Lingdong Kong, Yunhai Tong, An-Ran Wang, Zhiyang Teng, Yujing Wang, Zhuo-Chen Wang   +10 more
semanticscholar   +1 more source

Large Language Models Can Learn Temporal Reasoning

Annual Meeting of the Association for Computational Linguistics
While large language models (LLMs) have demonstrated remarkable reasoning capabilities, they are not without their flaws and inaccuracies. Recent studies have introduced various methods to mitigate these limitations.
Siheng Xiong, Ali Payani, R. Kompella, F. Fekri   +3 more
semanticscholar   +1 more source

Momentor: Advancing Video Large Language Model with Fine-Grained Temporal Reasoning

International Conference on Machine Learning
Large Language Models (LLMs) demonstrate remarkable proficiency in comprehending and handling text-based tasks. Many efforts are being made to transfer these attributes to video modality, which are termed Video-LLMs. However, existing Video-LLMs can only
Long Qian, Juncheng Li, Yu Wu, Yaobo Ye, Hao Fei, Tat-Seng Chua, Yueting Zhuang, Siliang Tang   +7 more
semanticscholar   +1 more source

Temporal Knowledge Question Answering via Abstract Reasoning Induction

Annual Meeting of the Association for Computational Linguistics, 2023
In this study, we address the challenge of enhancing temporal knowledge reasoning in Large Language Models (LLMs). LLMs often struggle with this task, leading to the generation of inaccurate or misleading responses.
Ziyang Chen, Dongfang Li, Xiang Zhao, Baotian Hu, Min Zhang   +4 more
semanticscholar   +1 more source

Temporal and hypothetical reasoning as a support for qualitative reasoning

Proceedings Fourth International Conference on Tools with Artificial Intelligence TAI '92, 2003
To deal with complex problems, many kinds of reasoning and representation techniques are needed. The cooperation between different reasoning models constitutes one of the objectives of second-generation systems. The need to mix temporal and hypothetical reasoning for qualitative simulation is highlighted.
Corinne Loesel, François Charpillet, Jean Paul Haton   +2 more
openaire   +1 more source

A rule- and query-guided reinforcement learning for extrapolation reasoning in temporal knowledge graphs

Neural Networks
Extrapolation reasoning in temporal knowledge graphs (TKGs) aims at predicting future facts based on historical data, and finds extensive application in diverse real-world scenarios.
Tingxuan Chen, Liu Yang, Zidong Wang, Jun Long   +3 more
semanticscholar   +1 more source

Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning

International Conference on Learning Representations
Large language models (LLMs) have showcased remarkable reasoning capabilities, yet they remain susceptible to errors, particularly in temporal reasoning tasks involving complex temporal logic.
Bahare Fatemi, Mehran Kazemi, Anton Tsitsulin, Karishma Malkan, Jinyeong Yim, John Palowitch, Sungyong Seo, Jonathan J. Halcrow, Bryan Perozzi   +8 more
semanticscholar   +1 more source

TOMATO: Assessing Visual Temporal Reasoning Capabilities in Multimodal Foundation Models

arXiv.org
Existing benchmarks often highlight the remarkable performance achieved by state-of-the-art Multimodal Foundation Models (MFMs) in leveraging temporal context for video understanding.
Ziyao Shangguan, Chuhan Li, Yuxuan Ding, Yanan Zheng, Yilun Zhao, Tesca Fitzgerald, Arman Cohan   +6 more
semanticscholar   +1 more source

VideoINSTA: Zero-shot Long Video Understanding via Informative Spatial-Temporal Reasoning with LLMs

Conference on Empirical Methods in Natural Language Processing
In the video-language domain, recent works in leveraging zero-shot Large Language Model-based reasoning for video understanding have become competitive challengers to previous end-to-end models.
Ruotong Liao, M. Erler, Huiyu Wang, Guangyao Zhai, Gengyuan Zhang, Yunpu Ma, Volker Tresp   +6 more
semanticscholar   +1 more source