Ruiqi Wang

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Ruiqi Wang

Ruiqi Wang

@RuiqisNotes

phd @SFU @nvidia @awscloud @MMLabNTU @AmazonScience

Vancouver, British Columbia शामिल हुए Ocak 2014
60 फ़ॉलोइंग62 फ़ॉलोवर्स
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Ruiqi Wang रीट्वीट किया
田中義弘 | taziku CEO / AI × Creative
1週間分の一人称映像を“人間のように”理解するAI。 Ego-R1は、複雑な行動を「小さなステップ」に分解し、その都度ツールを使って推論する新型フレームワーク。強化学習で訓練されたエージェントが、映像の意味を“考えながら解く”。 プロジェクトページなどは🧵から
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AI Bites | YouTube Channel
Ego-R1, a novel framework for reasoning over ultra-long (i.e.,in days and weeks) egocentric videos, which leverages a structured Chain-of-Tool-Thought (CoTT) process, orchestrated by an Ego-R1 Agent trained via reinforcement learning (RL). Inspired by human problem-solving strategies, CoTT decomposes complex reasoning into modular steps, with the RL agent invoking specific tools, one per step, to iteratively and collaboratively answer sub-question stackling such tasks as temporal retrieval and multi-modal understanding. Paper Title: Ego-R1: Chain-of-Tool-Thought for Ultra-Long Egocentric Video Reasoning Project: egolife-ai.github.io/Ego-R1/ Link: arxiv.org/abs/2506.13654 #AI #video #GenerativeAI #AIイケメン部
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Ruiqi Wang रीट्वीट किया
AK
AK@_akhaliq·
Ego-R1 Chain-of-Tool-Thought for Ultra-Long Egocentric Video Reasoning
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Ruiqi Wang
Ruiqi Wang@RuiqisNotes·
This work was done during my visit at MMlab@NTU! 🇸🇬✨Special thanks to Prof Ziwei Liu @liuziwei7 for the amazing opportunity and to my supervisor Prof Richard Zhang @richardzhangsfu for his unconditional support! 🙏Grateful for such incredible mentorship that made this possible!
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Ruiqi Wang रीट्वीट किया
Arthur Douillard
Arthur Douillard@Ar_Douillard·
Ego-R1: Chain-of-Tool-Thought for Ultra-Long Egocentric Video Reasoning Reasoning over weeks-long video, with a mix of CoT and using tools
Arthur Douillard tweet media
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Ruiqi Wang
Ruiqi Wang@RuiqisNotes·
We introduce the first active learning (AL) framework for high-accuracy instance segmentation of moveable parts from RGB images of real indoor scenes with a dataset contribution. @FenggenYu
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Ruiqi Wang
Ruiqi Wang@RuiqisNotes·
Learning part motions from real images is challenging due to data limitation. Today at #ECCV2024 (10/3, 4:30PM, #41) I will present our work, Active Coarse-to-Fine Segmentation of Moveable Parts from Real Images. @richardzhangsfu
Ruiqi Wang tweet media
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