Search Results for author: Qichen Fu

Found 8 papers, 2 papers with code

Superposition Prompting: Improving and Accelerating Retrieval-Augmented Generation

no code implementations10 Apr 2024 Thomas Merth, Qichen Fu, Mohammad Rastegari, Mahyar Najibi

Despite the successes of large language models (LLMs), they exhibit significant drawbacks, particularly when processing long contexts.

Question Answering Retrieval

Speculative Streaming: Fast LLM Inference without Auxiliary Models

no code implementations16 Feb 2024 Nikhil Bhendawade, Irina Belousova, Qichen Fu, Henry Mason, Mohammad Rastegari, Mahyar Najibi

Speculative decoding is a prominent technique to speed up the inference of a large target language model based on predictions of an auxiliary draft model.

Language Modelling

FastSR-NeRF: Improving NeRF Efficiency on Consumer Devices with A Simple Super-Resolution Pipeline

no code implementations15 Dec 2023 Chien-Yu Lin, Qichen Fu, Thomas Merth, Karren Yang, Anurag Ranjan

Compared to existing NeRF+SR methods, our pipeline mitigates the SR computing overhead and can be trained up to 23x faster, making it feasible to run on consumer devices such as the Apple MacBook.

Knowledge Distillation Super-Resolution

eDKM: An Efficient and Accurate Train-time Weight Clustering for Large Language Models

no code implementations2 Sep 2023 Minsik Cho, Keivan A. Vahid, Qichen Fu, Saurabh Adya, Carlo C Del Mundo, Mohammad Rastegari, Devang Naik, Peter Zatloukal

Since Large Language Models or LLMs have demonstrated high-quality performance on many complex language tasks, there is a great interest in bringing these LLMs to mobile devices for faster responses and better privacy protection.

Clustering Quantization

Domain Adaptive Hand Keypoint and Pixel Localization in the Wild

no code implementations16 Mar 2022 Takehiko Ohkawa, Yu-Jhe Li, Qichen Fu, Ryosuke Furuta, Kris M. Kitani, Yoichi Sato

We aim to improve the performance of regressing hand keypoints and segmenting pixel-level hand masks under new imaging conditions (e. g., outdoors) when we only have labeled images taken under very different conditions (e. g., indoors).

Domain Adaptation Knowledge Distillation

Sequential Voting with Relational Box Fields for Active Object Detection

1 code implementation CVPR 2022 Qichen Fu, Xingyu Liu, Kris M. Kitani

While our voting function is able to improve the bounding box of the active object, one round of voting is typically not enough to accurately localize the active object.

Active Object Detection Imitation Learning +4

Ego4D: Around the World in 3,000 Hours of Egocentric Video

7 code implementations CVPR 2022 Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, Miguel Martin, Tushar Nagarajan, Ilija Radosavovic, Santhosh Kumar Ramakrishnan, Fiona Ryan, Jayant Sharma, Michael Wray, Mengmeng Xu, Eric Zhongcong Xu, Chen Zhao, Siddhant Bansal, Dhruv Batra, Vincent Cartillier, Sean Crane, Tien Do, Morrie Doulaty, Akshay Erapalli, Christoph Feichtenhofer, Adriano Fragomeni, Qichen Fu, Abrham Gebreselasie, Cristina Gonzalez, James Hillis, Xuhua Huang, Yifei HUANG, Wenqi Jia, Weslie Khoo, Jachym Kolar, Satwik Kottur, Anurag Kumar, Federico Landini, Chao Li, Yanghao Li, Zhenqiang Li, Karttikeya Mangalam, Raghava Modhugu, Jonathan Munro, Tullie Murrell, Takumi Nishiyasu, Will Price, Paola Ruiz Puentes, Merey Ramazanova, Leda Sari, Kiran Somasundaram, Audrey Southerland, Yusuke Sugano, Ruijie Tao, Minh Vo, Yuchen Wang, Xindi Wu, Takuma Yagi, Ziwei Zhao, Yunyi Zhu, Pablo Arbelaez, David Crandall, Dima Damen, Giovanni Maria Farinella, Christian Fuegen, Bernard Ghanem, Vamsi Krishna Ithapu, C. V. Jawahar, Hanbyul Joo, Kris Kitani, Haizhou Li, Richard Newcombe, Aude Oliva, Hyun Soo Park, James M. Rehg, Yoichi Sato, Jianbo Shi, Mike Zheng Shou, Antonio Torralba, Lorenzo Torresani, Mingfei Yan, Jitendra Malik

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite.

De-identification Ethics

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