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2025-07-14 00:00:00
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2024-03-11T00:00:00 | 2403.05034 | CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model | [
"Zhengyi Wang",
"Yikai Wang",
"Yifei Chen",
"Chendong Xiang",
"Shuo Chen",
"Dajiang Yu",
"Chongxuan Li",
"Hang Su",
"Jun Zhu"
]
| https://github.com/thu-ml/CRM | Feed-forward 3D generative models like the Large Reconstruction Model (LRM) have demonstrated exceptional generation speed. However, the transformer-based methods do not leverage the geometric priors of the triplane component in their architecture, often leading to sub-optimal quality given the limited size of 3D data and slow training. In this work, we present the Convolutional Reconstruction Model (CRM), a high-fidelity feed-forward single image-to-3D generative model. Recognizing the limitations posed by sparse 3D data, we highlight the necessity of integrating geometric priors into network design. CRM builds on the key observation that the visualization of triplane exhibits spatial correspondence of six orthographic images. First, it generates six orthographic view images from a single input image, then feeds these images into a convolutional U-Net, leveraging its strong pixel-level alignment capabilities and significant bandwidth to create a high-resolution triplane. CRM further employs Flexicubes as geometric representation, facilitating direct end-to-end optimization on textured meshes. Overall, our model delivers a high-fidelity textured mesh from an image in just 10 seconds, without any test-time optimization. |
2024-03-11T00:00:00 | 2403.05121 | CogView3: Finer and Faster Text-to-Image Generation via Relay Diffusion | [
"Wendi Zheng",
"Jiayan Teng",
"Zhuoyi Yang",
"Weihan Wang",
"Jidong Chen",
"Xiaotao Gu",
"Yuxiao Dong",
"Ming Ding",
"Jie Tang"
]
| Recent advancements in text-to-image generative systems have been largely driven by diffusion models. However, single-stage text-to-image diffusion models still face challenges, in terms of computational efficiency and the refinement of image details. To tackle the issue, we propose CogView3, an innovative cascaded framework that enhances the performance of text-to-image diffusion. CogView3 is the first model implementing relay diffusion in the realm of text-to-image generation, executing the task by first creating low-resolution images and subsequently applying relay-based super-resolution. This methodology not only results in competitive text-to-image outputs but also greatly reduces both training and inference costs. Our experimental results demonstrate that CogView3 outperforms SDXL, the current state-of-the-art open-source text-to-image diffusion model, by 77.0\% in human evaluations, all while requiring only about 1/2 of the inference time. The distilled variant of CogView3 achieves comparable performance while only utilizing 1/10 of the inference time by SDXL. |
|
2024-03-11T00:00:00 | 2403.05530 | Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context | [
"Machel Reid",
"Nikolay Savinov",
"Denis Teplyashin",
"Dmitry Lepikhin",
"Timothy Lillicrap",
"Jean-baptiste Alayrac",
"Radu Soricut",
"Angeliki Lazaridou",
"Orhan Firat",
"Julian Schrittwieser",
"Ioannis Antonoglou",
"Rohan Anil",
"Sebastian Borgeaud",
"Andrew Dai",
"Katie Millican",
"Ethan Dyer",
"Mia Glaese",
"Thibault Sottiaux",
"Benjamin Lee",
"Fabio Viola",
"Malcolm Reynolds",
"Yuanzhong Xu",
"James Molloy",
"Jilin Chen",
"Michael Isard",
"Paul Barham",
"Tom Hennigan",
"Ross McIlroy",
"Melvin Johnson",
"Johan Schalkwyk",
"Eli Collins",
"Eliza Rutherford",
"Erica Moreira",
"Kareem Ayoub",
"Megha Goel",
"Clemens Meyer",
"Gregory Thornton",
"Zhen Yang",
"Henryk Michalewski",
"Zaheer Abbas",
"Nathan Schucher",
"Ankesh Anand",
"Richard Ives",
"James Keeling",
"Karel Lenc",
"Salem Haykal",
"Siamak Shakeri",
"Pranav Shyam",
"Aakanksha Chowdhery",
"Roman Ring",
"Stephen Spencer",
"Eren Sezener",
"Luke Vilnis",
"Oscar Chang",
"Nobuyuki Morioka",
"George Tucker",
"Ce Zheng",
"Oliver Woodman",
"Nithya Attaluri",
"Tomas Kocisky",
"Evgenii Eltyshev",
"Xi Chen",
"Timothy Chung",
"Vittorio Selo",
"Siddhartha Brahma",
"Petko Georgiev",
"Ambrose Slone",
"Zhenkai Zhu",
"James Lottes",
"Siyuan Qiao",
"Ben Caine",
"Sebastian Riedel",
"Alex Tomala",
"Martin Chadwick",
"Juliette Love",
"Peter Choy",
"Sid Mittal",
"Neil Houlsby",
"Yunhao Tang",
"Matthew Lamm",
"Libin Bai",
"Qiao Zhang",
"Luheng He",
"Yong Cheng",
"Peter Humphreys",
"Yujia Li",
"Sergey Brin",
"Albin Cassirer",
"Yingjie Miao",
"Lukas Zilka",
"Taylor Tobin",
"Kelvin Xu",
"Lev Proleev",
"Daniel Sohn",
"Alberto Magni",
"Lisa Anne Hendricks",
"Isabel Gao",
"Santiago Ontañón",
"Oskar Bunyan",
"Nathan Byrd",
"Abhanshu Sharma",
"Biao Zhang",
"Mario Pinto",
"Rishika Sinha",
"Harsh Mehta",
"Dawei Jia",
"Sergi Caelles",
"Albert Webson",
"Alex Morris",
"Becca Roelofs",
"Yifan Ding",
"Robin Strudel",
"Xuehan Xiong",
"Marvin Ritter",
"Mostafa Dehghani",
"Rahma Chaabouni",
"Abhijit Karmarkar",
"Guangda Lai",
"Fabian Mentzer",
"Bibo Xu",
"YaGuang Li",
"Yujing Zhang",
"Tom Le Paine",
"Alex Goldin",
"Behnam Neyshabur",
"Kate Baumli",
"Anselm Levskaya",
"Michael Laskin",
"Wenhao Jia",
"Jack W. Rae",
"Kefan Xiao",
"Antoine He",
"Skye Giordano",
"Lakshman Yagati",
"Jean-Baptiste Lespiau",
"Paul Natsev",
"Sanjay Ganapathy",
"Fangyu Liu",
"Danilo Martins",
"Nanxin Chen",
"Yunhan Xu",
"Megan Barnes",
"Rhys May",
"Arpi Vezer",
"Junhyuk Oh",
"Ken Franko",
"Sophie Bridgers",
"Ruizhe Zhao",
"Boxi Wu",
"Basil Mustafa",
"Sean Sechrist",
"Emilio Parisotto",
"Thanumalayan Sankaranarayana Pillai",
"Chris Larkin",
"Chenjie Gu",
"Christina Sorokin",
"Maxim Krikun",
"Alexey Guseynov",
"Jessica Landon",
"Romina Datta",
"Alexander Pritzel",
"Phoebe Thacker",
"Fan Yang",
"Kevin Hui",
"Anja Hauth",
"Chih-Kuan Yeh",
"David Barker",
"Justin Mao-Jones",
"Sophia Austin",
"Hannah Sheahan",
"Parker Schuh",
"James Svensson",
"Rohan Jain",
"Vinay Ramasesh",
"Anton Briukhov",
"Da-Woon Chung",
"Tamara von Glehn",
"Christina Butterfield",
"Priya Jhakra",
"Matthew Wiethoff",
"Justin Frye",
"Jordan Grimstad",
"Beer Changpinyo",
"Charline Le Lan",
"Anna Bortsova",
"Yonghui Wu",
"Paul Voigtlaender",
"Tara Sainath",
"Charlotte Smith",
"Will Hawkins",
"Kris Cao",
"James Besley",
"Srivatsan Srinivasan",
"Mark Omernick",
"Colin Gaffney",
"Gabriela Surita",
"Ryan Burnell",
"Bogdan Damoc",
"Junwhan Ahn",
"Andrew Brock",
"Mantas Pajarskas",
"Anastasia Petrushkina",
"Seb Noury",
"Lorenzo Blanco",
"Kevin Swersky",
"Arun Ahuja",
"Thi Avrahami",
"Vedant Misra",
"Raoul de Liedekerke",
"Mariko Iinuma",
"Alex Polozov",
"Sarah York",
"George van den Driessche",
"Paul Michel",
"Justin Chiu",
"Rory Blevins",
"Zach Gleicher",
"Adrià Recasens",
"Alban Rrustemi",
"Elena Gribovskaya",
"Aurko Roy",
"Wiktor Gworek",
"Séb Arnold",
"Lisa Lee",
"James Lee-Thorp",
"Marcello Maggioni",
"Enrique Piqueras",
"Kartikeya Badola",
"Sharad Vikram",
"Lucas Gonzalez",
"Anirudh Baddepudi",
"Evan Senter",
"Jacob Devlin",
"James Qin",
"Michael Azzam",
"Maja Trebacz",
"Martin Polacek",
"Kashyap Krishnakumar",
"Shuo-yiin Chang",
"Matthew Tung",
"Ivo Penchev",
"Rishabh Joshi",
"Kate Olszewska",
"Carrie Muir",
"Mateo Wirth",
"Ale Jakse Hartman",
"Josh Newlan",
"Sheleem Kashem",
"Vijay Bolina",
"Elahe Dabir",
"Joost van Amersfoort",
"Zafarali Ahmed",
"James Cobon-Kerr",
"Aishwarya Kamath",
"Arnar Mar Hrafnkelsson",
"Le Hou",
"Ian Mackinnon",
"Alexandre Frechette",
"Eric Noland",
"Xiance Si",
"Emanuel Taropa",
"Dong Li",
"Phil Crone",
"Anmol Gulati",
"Sébastien Cevey",
"Jonas Adler",
"Ada Ma",
"David Silver",
"Simon Tokumine",
"Richard Powell",
"Stephan Lee",
"Michael Chang",
"Samer Hassan",
"Diana Mincu",
"Antoine Yang",
"Nir Levine",
"Jenny Brennan",
"Mingqiu Wang",
"Sarah Hodkinson",
"Jeffrey Zhao",
"Josh Lipschultz",
"Aedan Pope",
"Michael B. Chang",
"Cheng Li",
"Laurent El Shafey",
"Michela Paganini",
"Sholto Douglas",
"Bernd Bohnet",
"Fabio Pardo",
"Seth Odoom",
"Mihaela Rosca",
"Cicero Nogueira dos Santos",
"Kedar Soparkar",
"Arthur Guez",
"Tom Hudson",
"Steven Hansen",
"Chulayuth Asawaroengchai",
"Ravi Addanki",
"Tianhe Yu",
"Wojciech Stokowiec",
"Mina Khan",
"Justin Gilmer",
"Jaehoon Lee",
"Carrie Grimes Bostock",
"Keran Rong",
"Jonathan Caton",
"Pedram Pejman",
"Filip Pavetic",
"Geoff Brown",
"Vivek Sharma",
"Mario Lučić",
"Rajkumar Samuel",
"Josip Djolonga",
"Amol Mandhane",
"Lars Lowe Sjösund",
"Elena Buchatskaya",
"Elspeth White",
"Natalie Clay",
"Jiepu Jiang",
"Hyeontaek Lim",
"Ross Hemsley",
"Jane Labanowski",
"Nicola De Cao",
"David Steiner",
"Sayed Hadi Hashemi",
"Jacob Austin",
"Anita Gergely",
"Tim Blyth",
"Joe Stanton",
"Kaushik Shivakumar",
"Aditya Siddhant",
"Anders Andreassen",
"Carlos Araya",
"Nikhil Sethi",
"Rakesh Shivanna",
"Steven Hand",
"Ankur Bapna",
"Ali Khodaei",
"Antoine Miech",
"Garrett Tanzer",
"Andy Swing",
"Shantanu Thakoor",
"Zhufeng Pan",
"Zachary Nado",
"Stephanie Winkler",
"Dian Yu",
"Mohammad Saleh",
"Loren Maggiore",
"Iain Barr",
"Minh Giang",
"Thais Kagohara",
"Ivo Danihelka",
"Amit Marathe",
"Vladimir Feinberg",
"Mohamed Elhawaty",
"Nimesh Ghelani",
"Dan Horgan",
"Helen Miller",
"Lexi Walker",
"Richard Tanburn",
"Mukarram Tariq",
"Disha Shrivastava",
"Fei Xia",
"Chung-Cheng Chiu",
"Zoe Ashwood",
"Khuslen Baatarsukh",
"Sina Samangooei",
"Fred Alcober",
"Axel Stjerngren",
"Paul Komarek",
"Katerina Tsihlas",
"Anudhyan Boral",
"Ramona Comanescu",
"Jeremy Chen",
"Ruibo Liu",
"Dawn Bloxwich",
"Charlie Chen",
"Yanhua Sun",
"Fangxiaoyu Feng",
"Matthew Mauger",
"Xerxes Dotiwalla",
"Vincent Hellendoorn",
"Michael Sharman",
"Ivy Zheng",
"Krishna Haridasan",
"Gabe Barth-Maron",
"Craig Swanson",
"Dominika Rogozińska",
"Alek Andreev",
"Paul Kishan Rubenstein",
"Ruoxin Sang",
"Dan Hurt",
"Gamaleldin Elsayed",
"Renshen Wang",
"Dave Lacey",
"Anastasija Ilić",
"Yao Zhao",
"Lora Aroyo",
"Chimezie Iwuanyanwu",
"Vitaly Nikolaev",
"Balaji Lakshminarayanan",
"Sadegh Jazayeri",
"Raphaël Lopez Kaufman",
"Mani Varadarajan",
"Chetan Tekur",
"Doug Fritz",
"Misha Khalman",
"David Reitter",
"Kingshuk Dasgupta",
"Shourya Sarcar",
"Tina Ornduff",
"Javier Snaider",
"Fantine Huot",
"Johnson Jia",
"Rupert Kemp",
"Nejc Trdin",
"Anitha Vijayakumar",
"Lucy Kim",
"Christof Angermueller",
"Li Lao",
"Tianqi Liu",
"Haibin Zhang",
"David Engel",
"Somer Greene",
"Anaïs White",
"Jessica Austin",
"Lilly Taylor",
"Shereen Ashraf",
"Dangyi Liu",
"Maria Georgaki",
"Irene Cai",
"Yana Kulizhskaya",
"Sonam Goenka",
"Brennan Saeta",
"Kiran Vodrahalli",
"Christian Frank",
"Dario de Cesare",
"Brona Robenek",
"Harry Richardson",
"Mahmoud Alnahlawi",
"Christopher Yew",
"Priya Ponnapalli",
"Marco Tagliasacchi",
"Alex Korchemniy",
"Yelin Kim",
"Dinghua Li",
"Bill Rosgen",
"Zoe Ashwood",
"Kyle Levin",
"Jeremy Wiesner",
"Praseem Banzal",
"Praveen Srinivasan",
"Hongkun Yu",
"Çağlar Ünlü",
"David Reid",
"Zora Tung",
"Daniel Finchelstein",
"Ravin Kumar",
"Andre Elisseeff",
"Jin Huang",
"Ming Zhang",
"Rui Zhu",
"Ricardo Aguilar",
"Mai Giménez",
"Jiawei Xia",
"Olivier Dousse",
"Willi Gierke",
"Soheil Hassas Yeganeh",
"Damion Yates",
"Komal Jalan",
"Lu Li",
"Eri Latorre-Chimoto",
"Duc Dung Nguyen",
"Ken Durden",
"Praveen Kallakuri",
"Yaxin Liu",
"Matthew Johnson",
"Tomy Tsai",
"Alice Talbert",
"Jasmine Liu",
"Alexander Neitz",
"Chen Elkind",
"Marco Selvi",
"Mimi Jasarevic",
"Livio Baldini Soares",
"Albert Cui",
"Pidong Wang",
"Alek Wenjiao Wang",
"Xinyu Ye",
"Krystal Kallarackal",
"Lucia Loher",
"Hoi Lam",
"Josef Broder",
"Dan Holtmann-Rice",
"Nina Martin",
"Bramandia Ramadhana",
"Daniel Toyama",
"Mrinal Shukla",
"Sujoy Basu",
"Abhi Mohan",
"Nick Fernando",
"Noah Fiedel",
"Kim Paterson",
"Hui Li",
"Ankush Garg",
"Jane Park",
"DongHyun Choi",
"Diane Wu",
"Sankalp Singh",
"Zhishuai Zhang",
"Amir Globerson",
"Lily Yu",
"John Carpenter",
"Félix de Chaumont Quitry",
"Carey Radebaugh",
"Chu-Cheng Lin",
"Alex Tudor",
"Prakash Shroff",
"Drew Garmon",
"Dayou Du",
"Neera Vats",
"Han Lu",
"Shariq Iqbal",
"Alex Yakubovich",
"Nilesh Tripuraneni",
"James Manyika",
"Haroon Qureshi",
"Nan Hua",
"Christel Ngani",
"Maria Abi Raad",
"Hannah Forbes",
"Anna Bulanova",
"Jeff Stanway",
"Mukund Sundararajan",
"Victor Ungureanu",
"Colton Bishop",
"Yunjie Li",
"Balaji Venkatraman",
"Bo Li",
"Chloe Thornton",
"Salvatore Scellato",
"Nishesh Gupta",
"Yicheng Wang",
"Ian Tenney",
"Xihui Wu",
"Ashish Shenoy",
"Gabriel Carvajal",
"Diana Gage Wright",
"Ben Bariach",
"Zhuyun Xiao",
"Peter Hawkins",
"Sid Dalmia",
"Clement Farabet",
"Pedro Valenzuela",
"Quan Yuan",
"Chris Welty",
"Ananth Agarwal",
"Mia Chen",
"Wooyeol Kim",
"Brice Hulse",
"Nandita Dukkipati",
"Adam Paszke",
"Andrew Bolt",
"Elnaz Davoodi",
"Kiam Choo",
"Jennifer Beattie",
"Jennifer Prendki",
"Harsha Vashisht",
"Rebeca Santamaria-Fernandez",
"Luis C. Cobo",
"Jarek Wilkiewicz",
"David Madras",
"Ali Elqursh",
"Grant Uy",
"Kevin Ramirez",
"Matt Harvey",
"Tyler Liechty",
"Heiga Zen",
"Jeff Seibert",
"Clara Huiyi Hu",
"Mohamed Elhawaty",
"Andrey Khorlin",
"Maigo Le",
"Asaf Aharoni",
"Megan Li",
"Lily Wang",
"Sandeep Kumar",
"Alejandro Lince",
"Norman Casagrande",
"Jay Hoover",
"Dalia El Badawy",
"David Soergel",
"Denis Vnukov",
"Matt Miecnikowski",
"Jiri Simsa",
"Anna Koop",
"Praveen Kumar",
"Thibault Sellam",
"Daniel Vlasic",
"Samira Daruki",
"Nir Shabat",
"John Zhang",
"Guolong Su",
"Jiageng Zhang",
"Jeremiah Liu",
"Yi Sun",
"Evan Palmer",
"Alireza Ghaffarkhah",
"Xi Xiong",
"Victor Cotruta",
"Michael Fink",
"Lucas Dixon",
"Ashwin Sreevatsa",
"Adrian Goedeckemeyer",
"Alek Dimitriev",
"Mohsen Jafari",
"Remi Crocker",
"Nicholas FitzGerald",
"Aviral Kumar",
"Sanjay Ghemawat",
"Ivan Philips",
"Frederick Liu",
"Yannie Liang",
"Rachel Sterneck",
"Alena Repina",
"Marcus Wu",
"Laura Knight",
"Marin Georgiev",
"Hyo Lee",
"Harry Askham",
"Abhishek Chakladar",
"Annie Louis",
"Carl Crous",
"Hardie Cate",
"Dessie Petrova",
"Michael Quinn",
"Denese Owusu-Afriyie",
"Achintya Singhal",
"Nan Wei",
"Solomon Kim",
"Damien Vincent",
"Milad Nasr",
"Christopher A. Choquette-Choo",
"Reiko Tojo",
"Shawn Lu",
"Diego de Las Casas",
"Yuchung Cheng",
"Tolga Bolukbasi",
"Katherine Lee",
"Saaber Fatehi",
"Rajagopal Ananthanarayanan",
"Miteyan Patel",
"Charbel Kaed",
"Jing Li",
"Jakub Sygnowski",
"Shreyas Rammohan Belle",
"Zhe Chen",
"Jaclyn Konzelmann",
"Siim Põder",
"Roopal Garg",
"Vinod Koverkathu",
"Adam Brown",
"Chris Dyer",
"Rosanne Liu",
"Azade Nova",
"Jun Xu",
"Slav Petrov",
"Demis Hassabis",
"Koray Kavukcuoglu",
"Jeffrey Dean",
"Oriol Vinyals"
]
| In this report, we present the latest model of the Gemini family, Gemini 1.5 Pro, a highly compute-efficient multimodal mixture-of-experts model capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. Gemini 1.5 Pro achieves near-perfect recall on long-context retrieval tasks across modalities, improves the state-of-the-art in long-document QA, long-video QA and long-context ASR, and matches or surpasses Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5 Pro's long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 2.1 (200k) and GPT-4 Turbo (128k). Finally, we highlight surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person who learned from the same content. |
|
2024-03-11T00:00:00 | 2403.05185 | Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks | [
"Marco De Nadai",
"Francesco Fabbri",
"Paul Gigioli",
"Alice Wang",
"Ang Li",
"Fabrizio Silvestri",
"Laura Kim",
"Shawn Lin",
"Vladan Radosavljevic",
"Sandeep Ghael",
"David Nyhan",
"Hugues Bouchard",
"Mounia Lalmas-Roelleke",
"Andreas Damianou"
]
| In the ever-evolving digital audio landscape, Spotify, well-known for its music and talk content, has recently introduced audiobooks to its vast user base. While promising, this move presents significant challenges for personalized recommendations. Unlike music and podcasts, audiobooks, initially available for a fee, cannot be easily skimmed before purchase, posing higher stakes for the relevance of recommendations. Furthermore, introducing a new content type into an existing platform confronts extreme data sparsity, as most users are unfamiliar with this new content type. Lastly, recommending content to millions of users requires the model to react fast and be scalable. To address these challenges, we leverage podcast and music user preferences and introduce 2T-HGNN, a scalable recommendation system comprising Heterogeneous Graph Neural Networks (HGNNs) and a Two Tower (2T) model. This novel approach uncovers nuanced item relationships while ensuring low latency and complexity. We decouple users from the HGNN graph and propose an innovative multi-link neighbor sampler. These choices, together with the 2T component, significantly reduce the complexity of the HGNN model. Empirical evaluations involving millions of users show significant improvement in the quality of personalized recommendations, resulting in a +46% increase in new audiobooks start rate and a +23% boost in streaming rates. Intriguingly, our model's impact extends beyond audiobooks, benefiting established products like podcasts. |
|
2024-03-12T00:00:00 | 2403.06634 | Stealing Part of a Production Language Model | [
"Nicholas Carlini",
"Daniel Paleka",
"Krishnamurthy Dj Dvijotham",
"Thomas Steinke",
"Jonathan Hayase",
"A. Feder Cooper",
"Katherine Lee",
"Matthew Jagielski",
"Milad Nasr",
"Arthur Conmy",
"Eric Wallace",
"David Rolnick",
"Florian Tramèr"
]
| We introduce the first model-stealing attack that extracts precise, nontrivial information from black-box production language models like OpenAI's ChatGPT or Google's PaLM-2. Specifically, our attack recovers the embedding projection layer (up to symmetries) of a transformer model, given typical API access. For under \20 USD, our attack extracts the entire projection matrix of OpenAI's Ada and Babbage language models. We thereby confirm, for the first time, that these black-box models have a hidden dimension of 1024 and 2048, respectively. We also recover the exact hidden dimension size of the gpt-3.5-turbo model, and estimate it would cost under 2,000 in queries to recover the entire projection matrix. We conclude with potential defenses and mitigations, and discuss the implications of possible future work that could extend our attack. |
|
2024-03-12T00:00:00 | 2403.06504 | Adding NVMe SSDs to Enable and Accelerate 100B Model Fine-tuning on a Single GPU | [
"Changyue Liao",
"Mo Sun",
"Zihan Yang",
"Kaiqi Chen",
"Binhang Yuan",
"Fei Wu",
"Zeke Wang"
]
| Recent advances in large language models have brought immense value to the world, with their superior capabilities stemming from the massive number of parameters they utilize. However, even the GPUs with the highest memory capacities, currently peaking at 80GB, are far from sufficient to accommodate these vast parameters and their associated optimizer states when conducting stochastic gradient descent-based optimization. One approach to hosting such huge models is to aggregate device memory from many GPUs. However, this approach introduces prohibitive costs for most academic researchers, who always have a limited budget for many high-end GPU servers. In this paper, we focus on huge model fine-tuning on a single, even low-end, GPU in a commodity server, which is accessible to most AI researchers. In such a scenario, the state-of-the-art work ZeRO-Infinity suffers from two severe issues when running in a commodity server: 1) low GPU utilization due to inefficient swapping, and 2) limited trainable model size due to CPU memory capacity. The underlying reason is that ZeRO-Infinity is optimized for running on high-end GPU servers. To this end, we present Fuyou, a low-cost training framework that enables efficient 100B huge model fine-tuning on a low-end server with a low-end GPU and limited CPU memory capacity. The key idea is to add the SSD-CPU communication as an optimization dimension and thus carefully co-optimize computation and data swapping from a systematic approach to maximize GPU utilization. The experimental results show that 1) Fuyou is able to fine-tune 175B GPT-3 on a consumer GPU RTX 4090 with high GPU utilization, while ZeRO-Infinity fails to fine-tune; and 2) when training a small GPT-3 13B model, Fuyou achieves 156 TFLOPS on an RTX 4090 GPU while ZeRO-Infinity only achieves 45 TFLOPS. |
|
2024-03-12T00:00:00 | 2403.06977 | VideoMamba: State Space Model for Efficient Video Understanding | [
"Kunchang Li",
"Xinhao Li",
"Yi Wang",
"Yinan He",
"Yali Wang",
"Limin Wang",
"Yu Qiao"
]
| https://github.com/OpenGVLab/VideoMamba | Addressing the dual challenges of local redundancy and global dependencies in video understanding, this work innovatively adapts the Mamba to the video domain. The proposed VideoMamba overcomes the limitations of existing 3D convolution neural networks and video transformers. Its linear-complexity operator enables efficient long-term modeling, which is crucial for high-resolution long video understanding. Extensive evaluations reveal VideoMamba's four core abilities: (1) Scalability in the visual domain without extensive dataset pretraining, thanks to a novel self-distillation technique; (2) Sensitivity for recognizing short-term actions even with fine-grained motion differences; (3) Superiority in long-term video understanding, showcasing significant advancements over traditional feature-based models; and (4) Compatibility with other modalities, demonstrating robustness in multi-modal contexts. Through these distinct advantages, VideoMamba sets a new benchmark for video understanding, offering a scalable and efficient solution for comprehensive video understanding. All the code and models are available at https://github.com/OpenGVLab/VideoMamba. |
2024-03-12T00:00:00 | 2403.05812 | Algorithmic progress in language models | [
"Anson Ho",
"Tamay Besiroglu",
"Ege Erdil",
"David Owen",
"Robi Rahman",
"Zifan Carl Guo",
"David Atkinson",
"Neil Thompson",
"Jaime Sevilla"
]
| We investigate the rate at which algorithms for pre-training language models have improved since the advent of deep learning. Using a dataset of over 200 language model evaluations on Wikitext and Penn Treebank spanning 2012-2023, we find that the compute required to reach a set performance threshold has halved approximately every 8 months, with a 95% confidence interval of around 5 to 14 months, substantially faster than hardware gains per Moore's Law. We estimate augmented scaling laws, which enable us to quantify algorithmic progress and determine the relative contributions of scaling models versus innovations in training algorithms. Despite the rapid pace of algorithmic progress and the development of new architectures such as the transformer, our analysis reveals that the increase in compute made an even larger contribution to overall performance improvements over this time period. Though limited by noisy benchmark data, our analysis quantifies the rapid progress in language modeling, shedding light on the relative contributions from compute and algorithms. |
|
2024-03-12T00:00:00 | 2403.06738 | V3D: Video Diffusion Models are Effective 3D Generators | [
"Zilong Chen",
"Yikai Wang",
"Feng Wang",
"Zhengyi Wang",
"Huaping Liu"
]
| https://github.com/heheyas/V3D | Automatic 3D generation has recently attracted widespread attention. Recent methods have greatly accelerated the generation speed, but usually produce less-detailed objects due to limited model capacity or 3D data. Motivated by recent advancements in video diffusion models, we introduce V3D, which leverages the world simulation capacity of pre-trained video diffusion models to facilitate 3D generation. To fully unleash the potential of video diffusion to perceive the 3D world, we further introduce geometrical consistency prior and extend the video diffusion model to a multi-view consistent 3D generator. Benefiting from this, the state-of-the-art video diffusion model could be fine-tuned to generate 360degree orbit frames surrounding an object given a single image. With our tailored reconstruction pipelines, we can generate high-quality meshes or 3D Gaussians within 3 minutes. Furthermore, our method can be extended to scene-level novel view synthesis, achieving precise control over the camera path with sparse input views. Extensive experiments demonstrate the superior performance of the proposed approach, especially in terms of generation quality and multi-view consistency. Our code is available at https://github.com/heheyas/V3D |
2024-03-12T00:00:00 | 2403.06775 | FaceChain-SuDe: Building Derived Class to Inherit Category Attributes for One-shot Subject-Driven Generation | [
"Pengchong Qiao",
"Lei Shang",
"Chang Liu",
"Baigui Sun",
"Xiangyang Ji",
"Jie Chen"
]
| https://github.com/modelscope/facechain | Subject-driven generation has garnered significant interest recently due to its ability to personalize text-to-image generation. Typical works focus on learning the new subject's private attributes. However, an important fact has not been taken seriously that a subject is not an isolated new concept but should be a specialization of a certain category in the pre-trained model. This results in the subject failing to comprehensively inherit the attributes in its category, causing poor attribute-related generations. In this paper, motivated by object-oriented programming, we model the subject as a derived class whose base class is its semantic category. This modeling enables the subject to inherit public attributes from its category while learning its private attributes from the user-provided example. Specifically, we propose a plug-and-play method, Subject-Derived regularization (SuDe). It constructs the base-derived class modeling by constraining the subject-driven generated images to semantically belong to the subject's category. Extensive experiments under three baselines and two backbones on various subjects show that our SuDe enables imaginative attribute-related generations while maintaining subject fidelity. Codes will be open sourced soon at FaceChain (https://github.com/modelscope/facechain). |
2024-03-12T00:00:00 | 2403.06098 | VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video Diffusion Models | [
"Wenhao Wang",
"Yi Yang"
]
| The arrival of Sora marks a new era for text-to-video diffusion models, bringing significant advancements in video generation and potential applications. However, Sora, as well as other text-to-video diffusion models, highly relies on the prompts, and there is no publicly available dataset featuring a study of text-to-video prompts. In this paper, we introduce VidProM, the first large-scale dataset comprising 1.67 million unique text-to-video prompts from real users. Additionally, the dataset includes 6.69 million videos generated by four state-of-the-art diffusion models and some related data. We initially demonstrate the curation of this large-scale dataset, which is a time-consuming and costly process. Subsequently, we show how the proposed VidProM differs from DiffusionDB, a large-scale prompt-gallery dataset for image generation. Based on the analysis of these prompts, we identify the necessity for a new prompt dataset specifically designed for text-to-video generation and gain insights into the preferences of real users when creating videos. Our large-scale and diverse dataset also inspires many exciting new research areas. For instance, to develop better, more efficient, and safer text-to-video diffusion models, we suggest exploring text-to-video prompt engineering, efficient video generation, and video copy detection for diffusion models. We make the collected dataset VidProM publicly available at GitHub and Hugging Face under the CC-BY- NC 4.0 License. |
|
2024-03-12T00:00:00 | 2403.06807 | Multistep Consistency Models | [
"Jonathan Heek",
"Emiel Hoogeboom",
"Tim Salimans"
]
| Diffusion models are relatively easy to train but require many steps to generate samples. Consistency models are far more difficult to train, but generate samples in a single step. In this paper we propose Multistep Consistency Models: A unification between Consistency Models (Song et al., 2023) and TRACT (Berthelot et al., 2023) that can interpolate between a consistency model and a diffusion model: a trade-off between sampling speed and sampling quality. Specifically, a 1-step consistency model is a conventional consistency model whereas we show that a infty-step consistency model is a diffusion model. Multistep Consistency Models work really well in practice. By increasing the sample budget from a single step to 2-8 steps, we can train models more easily that generate higher quality samples, while retaining much of the sampling speed benefits. Notable results are 1.4 FID on Imagenet 64 in 8 step and 2.1 FID on Imagenet128 in 8 steps with consistency distillation. We also show that our method scales to a text-to-image diffusion model, generating samples that are very close to the quality of the original model. |
|
2024-03-12T00:00:00 | 2403.06764 | An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for Large Vision-Language Models | [
"Liang Chen",
"Haozhe Zhao",
"Tianyu Liu",
"Shuai Bai",
"Junyang Lin",
"Chang Zhou",
"Baobao Chang"
]
| https://github.com/pkunlp-icler/FastV | In this study, we identify the inefficient attention phenomena in Large Vision-Language Models (LVLMs), notably within prominent models like LLaVA-1.5, QwenVL-Chat and Video-LLaVA. We find out that the attention computation over visual tokens is of extreme inefficiency in the deep layers of popular LVLMs, suggesting a need for a sparser approach compared to textual data handling. To this end, we introduce FastV, a versatile plug-and-play method designed to optimize computational efficiency by learning adaptive attention patterns in early layers and pruning visual tokens in subsequent ones. Our evaluations demonstrate FastV's ability to dramatically reduce computational costs (e.g., a 45 reduction in FLOPs for LLaVA-1.5-13B) without sacrificing performance in a wide range of image and video understanding tasks. The computational efficiency and performance trade-off of FastV are highly customizable and pareto-efficient. It can compress the FLOPs of a 13B-parameter model to achieve a lower budget than that of a 7B-parameter model, while still maintaining superior performance. We believe FastV has practical values for deployment of LVLMs in edge devices and commercial models. Code is released at https://github.com/pkunlp-icler/FastV. |
2024-03-13T00:00:00 | 2403.07420 | DragAnything: Motion Control for Anything using Entity Representation | [
"Wejia Wu",
"Zhuang Li",
"Yuchao Gu",
"Rui Zhao",
"Yefei He",
"David Junhao Zhang",
"Mike Zheng Shou",
"Yan Li",
"Tingting Gao",
"Di Zhang"
]
| https://github.com/showlab/DragAnything | We introduce DragAnything, which utilizes a entity representation to achieve motion control for any object in controllable video generation. Comparison to existing motion control methods, DragAnything offers several advantages. Firstly, trajectory-based is more userfriendly for interaction, when acquiring other guidance signals (e.g., masks, depth maps) is labor-intensive. Users only need to draw a line (trajectory) during interaction. Secondly, our entity representation serves as an open-domain embedding capable of representing any object, enabling the control of motion for diverse entities, including background. Lastly, our entity representation allows simultaneous and distinct motion control for multiple objects. Extensive experiments demonstrate that our DragAnything achieves state-of-the-art performance for FVD, FID, and User Study, particularly in terms of object motion control, where our method surpasses the previous methods (e.g., DragNUWA) by 26% in human voting. |
2024-03-13T00:00:00 | 2403.07750 | Synth^2: Boosting Visual-Language Models with Synthetic Captions and Image Embeddings | [
"Sahand Sharifzadeh",
"Christos Kaplanis",
"Shreya Pathak",
"Dharshan Kumaran",
"Anastasija Ilic",
"Jovana Mitrovic",
"Charles Blundell",
"Andrea Banino"
]
| The creation of high-quality human-labeled image-caption datasets presents a significant bottleneck in the development of Visual-Language Models (VLMs). We propose a novel approach that leverages the strengths of Large Language Models (LLMs) and image generation models to create synthetic image-text pairs for efficient and effective VLM training. Our method employs pretraining a text-to-image model to synthesize image embeddings starting from captions generated by an LLM. These synthetic pairs are then used to train a VLM. Extensive experiments demonstrate that the VLM trained with synthetic data exhibits comparable performance on image captioning, while requiring a fraction of the data used by models trained solely on human-annotated data. In particular, we outperform the baseline by 17% through augmentation with a synthetic dataset. Furthermore, we show that synthesizing in the image embedding space is 25% faster than in the pixel space. This research introduces a promising technique for generating large-scale, customizable image datasets, leading to enhanced VLM performance and wider applicability across various domains, all with improved data efficiency and resource utilization. |
|
2024-03-13T00:00:00 | 2403.07816 | Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM | [
"Sainbayar Sukhbaatar",
"Olga Golovneva",
"Vasu Sharma",
"Hu Xu",
"Xi Victoria Lin",
"Baptiste Rozière",
"Jacob Kahn",
"Daniel Li",
"Wen-tau Yih",
"Jason Weston",
"Xian Li"
]
| We investigate efficient methods for training Large Language Models (LLMs) to possess capabilities in multiple specialized domains, such as coding, math reasoning and world knowledge. Our method, named Branch-Train-MiX (BTX), starts from a seed model, which is branched to train experts in embarrassingly parallel fashion with high throughput and reduced communication cost. After individual experts are asynchronously trained, BTX brings together their feedforward parameters as experts in Mixture-of-Expert (MoE) layers and averages the remaining parameters, followed by an MoE-finetuning stage to learn token-level routing. BTX generalizes two special cases, the Branch-Train-Merge method, which does not have the MoE finetuning stage to learn routing, and sparse upcycling, which omits the stage of training experts asynchronously. Compared to alternative approaches, BTX achieves the best accuracy-efficiency tradeoff. |
|
2024-03-13T00:00:00 | 2403.07815 | Chronos: Learning the Language of Time Series | [
"Abdul Fatir Ansari",
"Lorenzo Stella",
"Caner Turkmen",
"Xiyuan Zhang",
"Pedro Mercado",
"Huibin Shen",
"Oleksandr Shchur",
"Syama Sundar Rangapuram",
"Sebastian Pineda Arango",
"Shubham Kapoor",
"Jasper Zschiegner",
"Danielle C. Maddix",
"Michael W. Mahoney",
"Kari Torkkola",
"Andrew Gordon Wilson",
"Michael Bohlke-Schneider",
"Yuyang Wang"
]
| We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models. Chronos tokenizes time series values using scaling and quantization into a fixed vocabulary and trains existing transformer-based language model architectures on these tokenized time series via the cross-entropy loss. We pretrained Chronos models based on the T5 family (ranging from 20M to 710M parameters) on a large collection of publicly available datasets, complemented by a synthetic dataset that we generated via Gaussian processes to improve generalization. In a comprehensive benchmark consisting of 42 datasets, and comprising both classical local models and deep learning methods, we show that Chronos models: (a) significantly outperform other methods on datasets that were part of the training corpus; and (b) have comparable and occasionally superior zero-shot performance on new datasets, relative to methods that were trained specifically on them. Our results demonstrate that Chronos models can leverage time series data from diverse domains to improve zero-shot accuracy on unseen forecasting tasks, positioning pretrained models as a viable tool to greatly simplify forecasting pipelines. |
|
2024-03-13T00:00:00 | 2403.07508 | MoAI: Mixture of All Intelligence for Large Language and Vision Models | [
"Byung-Kwan Lee",
"Beomchan Park",
"Chae Won Kim",
"Yong Man Ro"
]
| The rise of large language models (LLMs) and instruction tuning has led to the current trend of instruction-tuned large language and vision models (LLVMs). This trend involves either meticulously curating numerous instruction tuning datasets tailored to specific objectives or enlarging LLVMs to manage vast amounts of vision language (VL) data. However, current LLVMs have disregarded the detailed and comprehensive real-world scene understanding available from specialized computer vision (CV) models in visual perception tasks such as segmentation, detection, scene graph generation (SGG), and optical character recognition (OCR). Instead, the existing LLVMs rely mainly on the large capacity and emergent capabilities of their LLM backbones. Therefore, we present a new LLVM, Mixture of All Intelligence (MoAI), which leverages auxiliary visual information obtained from the outputs of external segmentation, detection, SGG, and OCR models. MoAI operates through two newly introduced modules: MoAI-Compressor and MoAI-Mixer. After verbalizing the outputs of the external CV models, the MoAI-Compressor aligns and condenses them to efficiently use relevant auxiliary visual information for VL tasks. MoAI-Mixer then blends three types of intelligence (1) visual features, (2) auxiliary features from the external CV models, and (3) language features by utilizing the concept of Mixture of Experts. Through this integration, MoAI significantly outperforms both open-source and closed-source LLVMs in numerous zero-shot VL tasks, particularly those related to real-world scene understanding such as object existence, positions, relations, and OCR without enlarging the model size or curating extra visual instruction tuning datasets. |
|
2024-03-13T00:00:00 | 2403.07128 | FAX: Scalable and Differentiable Federated Primitives in JAX | [
"Keith Rush",
"Zachary Charles",
"Zachary Garrett"
]
| https://github.com/google-research/google-research/tree/master/fax | We present FAX, a JAX-based library designed to support large-scale distributed and federated computations in both data center and cross-device applications. FAX leverages JAX's sharding mechanisms to enable native targeting of TPUs and state-of-the-art JAX runtimes, including Pathways. FAX embeds building blocks for federated computations as primitives in JAX. This enables three key benefits. First, FAX computations can be translated to XLA HLO. Second, FAX provides a full implementation of federated automatic differentiation, greatly simplifying the expression of federated computations. Last, FAX computations can be interpreted out to existing production cross-device federated compute systems. We show that FAX provides an easily programmable, performant, and scalable framework for federated computations in the data center. FAX is available at https://github.com/google-research/google-research/tree/master/fax . |
2024-03-13T00:00:00 | 2403.07563 | Learning Generalizable Feature Fields for Mobile Manipulation | [
"Ri-Zhao Qiu",
"Yafei Hu",
"Ge Yang",
"Yuchen Song",
"Yang Fu",
"Jianglong Ye",
"Jiteng Mu",
"Ruihan Yang",
"Nikolay Atanasov",
"Sebastian Scherer",
"Xiaolong Wang"
]
| An open problem in mobile manipulation is how to represent objects and scenes in a unified manner, so that robots can use it both for navigating in the environment and manipulating objects. The latter requires capturing intricate geometry while understanding fine-grained semantics, whereas the former involves capturing the complexity inherit to an expansive physical scale. In this work, we present GeFF (Generalizable Feature Fields), a scene-level generalizable neural feature field that acts as a unified representation for both navigation and manipulation that performs in real-time. To do so, we treat generative novel view synthesis as a pre-training task, and then align the resulting rich scene priors with natural language via CLIP feature distillation. We demonstrate the effectiveness of this approach by deploying GeFF on a quadrupedal robot equipped with a manipulator. We evaluate GeFF's ability to generalize to open-set objects as well as running time, when performing open-vocabulary mobile manipulation in dynamic scenes. |
|
2024-03-13T00:00:00 | 2403.07487 | Motion Mamba: Efficient and Long Sequence Motion Generation with Hierarchical and Bidirectional Selective SSM | [
"Zeyu Zhang",
"Akide Liu",
"Ian Reid",
"Richard Hartley",
"Bohan Zhuang",
"Hao Tang"
]
| Human motion generation stands as a significant pursuit in generative computer vision, while achieving long-sequence and efficient motion generation remains challenging. Recent advancements in state space models (SSMs), notably Mamba, have showcased considerable promise in long sequence modeling with an efficient hardware-aware design, which appears to be a promising direction to build motion generation model upon it. Nevertheless, adapting SSMs to motion generation faces hurdles since the lack of a specialized design architecture to model motion sequence. To address these challenges, we propose Motion Mamba, a simple and efficient approach that presents the pioneering motion generation model utilized SSMs. Specifically, we design a Hierarchical Temporal Mamba (HTM) block to process temporal data by ensemble varying numbers of isolated SSM modules across a symmetric U-Net architecture aimed at preserving motion consistency between frames. We also design a Bidirectional Spatial Mamba (BSM) block to bidirectionally process latent poses, to enhance accurate motion generation within a temporal frame. Our proposed method achieves up to 50% FID improvement and up to 4 times faster on the HumanML3D and KIT-ML datasets compared to the previous best diffusion-based method, which demonstrates strong capabilities of high-quality long sequence motion modeling and real-time human motion generation. See project website https://steve-zeyu-zhang.github.io/MotionMamba/ |
|
2024-03-14T00:00:00 | 2403.08295 | Gemma: Open Models Based on Gemini Research and Technology | [
"Gemma Team",
"Thomas Mesnard",
"Cassidy Hardin",
"Robert Dadashi",
"Surya Bhupatiraju",
"Shreya Pathak",
"Laurent Sifre",
"Morgane Rivière",
"Mihir Sanjay Kale",
"Juliette Love",
"Pouya Tafti",
"Léonard Hussenot",
"Aakanksha Chowdhery",
"Adam Roberts",
"Aditya Barua",
"Alex Botev",
"Alex Castro-Ros",
"Ambrose Slone",
"Amélie Héliou",
"Andrea Tacchetti",
"Anna Bulanova",
"Antonia Paterson",
"Beth Tsai",
"Bobak Shahriari",
"Charline Le Lan",
"Christopher A. Choquette-Choo",
"Clément Crepy",
"Daniel Cer",
"Daphne Ippolito",
"David Reid",
"Elena Buchatskaya",
"Eric Ni",
"Eric Noland",
"Geng Yan",
"George Tucker",
"George-Christian Muraru",
"Grigory Rozhdestvenskiy",
"Henryk Michalewski",
"Ian Tenney",
"Ivan Grishchenko",
"Jacob Austin",
"James Keeling",
"Jane Labanowski",
"Jean-Baptiste Lespiau",
"Jeff Stanway",
"Jenny Brennan",
"Jeremy Chen",
"Johan Ferret",
"Justin Chiu",
"Justin Mao-Jones",
"Katherine Lee",
"Kathy Yu",
"Katie Millican",
"Lars Lowe Sjoesund",
"Lisa Lee",
"Lucas Dixon",
"Machel Reid",
"Maciej Mikuła",
"Mateo Wirth",
"Michael Sharman",
"Nikolai Chinaev",
"Nithum Thain",
"Olivier Bachem",
"Oscar Chang",
"Oscar Wahltinez",
"Paige Bailey",
"Paul Michel",
"Petko Yotov",
"Pier Giuseppe Sessa",
"Rahma Chaabouni",
"Ramona Comanescu",
"Reena Jana",
"Rohan Anil",
"Ross McIlroy",
"Ruibo Liu",
"Ryan Mullins",
"Samuel L Smith",
"Sebastian Borgeaud",
"Sertan Girgin",
"Sholto Douglas",
"Shree Pandya",
"Siamak Shakeri",
"Soham De",
"Ted Klimenko",
"Tom Hennigan",
"Vlad Feinberg",
"Wojciech Stokowiec",
"Yu-hui Chen",
"Zafarali Ahmed",
"Zhitao Gong",
"Tris Warkentin",
"Ludovic Peran",
"Minh Giang",
"Clément Farabet",
"Oriol Vinyals",
"Jeff Dean",
"Koray Kavukcuoglu",
"Demis Hassabis",
"Zoubin Ghahramani",
"Douglas Eck",
"Joelle Barral",
"Fernando Pereira",
"Eli Collins",
"Armand Joulin",
"Noah Fiedel",
"Evan Senter",
"Alek Andreev",
"Kathleen Kenealy"
]
| This work introduces Gemma, a family of lightweight, state-of-the art open models built from the research and technology used to create Gemini models. Gemma models demonstrate strong performance across academic benchmarks for language understanding, reasoning, and safety. We release two sizes of models (2 billion and 7 billion parameters), and provide both pretrained and fine-tuned checkpoints. Gemma outperforms similarly sized open models on 11 out of 18 text-based tasks, and we present comprehensive evaluations of safety and responsibility aspects of the models, alongside a detailed description of model development. We believe the responsible release of LLMs is critical for improving the safety of frontier models, and for enabling the next wave of LLM innovations. |
|
2024-03-14T00:00:00 | 2403.08764 | VLOGGER: Multimodal Diffusion for Embodied Avatar Synthesis | [
"Enric Corona",
"Andrei Zanfir",
"Eduard Gabriel Bazavan",
"Nikos Kolotouros",
"Thiemo Alldieck",
"Cristian Sminchisescu"
]
| We propose VLOGGER, a method for audio-driven human video generation from a single input image of a person, which builds on the success of recent generative diffusion models. Our method consists of 1) a stochastic human-to-3d-motion diffusion model, and 2) a novel diffusion-based architecture that augments text-to-image models with both spatial and temporal controls. This supports the generation of high quality video of variable length, easily controllable through high-level representations of human faces and bodies. In contrast to previous work, our method does not require training for each person, does not rely on face detection and cropping, generates the complete image (not just the face or the lips), and considers a broad spectrum of scenarios (e.g. visible torso or diverse subject identities) that are critical to correctly synthesize humans who communicate. We also curate MENTOR, a new and diverse dataset with 3d pose and expression annotations, one order of magnitude larger than previous ones (800,000 identities) and with dynamic gestures, on which we train and ablate our main technical contributions. VLOGGER outperforms state-of-the-art methods in three public benchmarks, considering image quality, identity preservation and temporal consistency while also generating upper-body gestures. We analyze the performance of VLOGGER with respect to multiple diversity metrics, showing that our architectural choices and the use of MENTOR benefit training a fair and unbiased model at scale. Finally we show applications in video editing and personalization. |
|
2024-03-14T00:00:00 | 2403.08268 | Follow-Your-Click: Open-domain Regional Image Animation via Short Prompts | [
"Yue Ma",
"Yingqing He",
"Hongfa Wang",
"Andong Wang",
"Chenyang Qi",
"Chengfei Cai",
"Xiu Li",
"Zhifeng Li",
"Heung-Yeung Shum",
"Wei Liu",
"Qifeng Chen"
]
| Despite recent advances in image-to-video generation, better controllability and local animation are less explored. Most existing image-to-video methods are not locally aware and tend to move the entire scene. However, human artists may need to control the movement of different objects or regions. Additionally, current I2V methods require users not only to describe the target motion but also to provide redundant detailed descriptions of frame contents. These two issues hinder the practical utilization of current I2V tools. In this paper, we propose a practical framework, named Follow-Your-Click, to achieve image animation with a simple user click (for specifying what to move) and a short motion prompt (for specifying how to move). Technically, we propose the first-frame masking strategy, which significantly improves the video generation quality, and a motion-augmented module equipped with a short motion prompt dataset to improve the short prompt following abilities of our model. To further control the motion speed, we propose flow-based motion magnitude control to control the speed of target movement more precisely. Our framework has simpler yet precise user control and better generation performance than previous methods. Extensive experiments compared with 7 baselines, including both commercial tools and research methods on 8 metrics, suggest the superiority of our approach. Project Page: https://follow-your-click.github.io/ |
|
2024-03-14T00:00:00 | 2403.08763 | Simple and Scalable Strategies to Continually Pre-train Large Language Models | [
"Adam Ibrahim",
"Benjamin Thérien",
"Kshitij Gupta",
"Mats L. Richter",
"Quentin Anthony",
"Timothée Lesort",
"Eugene Belilovsky",
"Irina Rish"
]
| Large language models (LLMs) are routinely pre-trained on billions of tokens, only to start the process over again once new data becomes available. A much more efficient solution is to continually pre-train these models, saving significant compute compared to re-training. However, the distribution shift induced by new data typically results in degraded performance on previous data or poor adaptation to the new data. In this work, we show that a simple and scalable combination of learning rate (LR) re-warming, LR re-decaying, and replay of previous data is sufficient to match the performance of fully re-training from scratch on all available data, as measured by final loss and language model (LM) evaluation benchmarks. Specifically, we show this for a weak but realistic distribution shift between two commonly used LLM pre-training datasets (EnglishrightarrowEnglish) and a stronger distribution shift (EnglishrightarrowGerman) at the 405M parameter model scale with large dataset sizes (hundreds of billions of tokens). Selecting the weak but realistic shift for larger-scale experiments, we also find that our continual learning strategies match the re-training baseline for a 10B parameter LLM. Our results demonstrate that LLMs can be successfully updated via simple and scalable continual learning strategies, matching the re-training baseline using only a fraction of the compute. Finally, inspired by previous work, we propose alternatives to the cosine learning rate schedule that help circumvent forgetting induced by LR re-warming and that are not bound to a fixed token budget. |
|
2024-03-14T00:00:00 | 2403.07918 | On the Societal Impact of Open Foundation Models | [
"Sayash Kapoor",
"Rishi Bommasani",
"Kevin Klyman",
"Shayne Longpre",
"Ashwin Ramaswami",
"Peter Cihon",
"Aspen Hopkins",
"Kevin Bankston",
"Stella Biderman",
"Miranda Bogen",
"Rumman Chowdhury",
"Alex Engler",
"Peter Henderson",
"Yacine Jernite",
"Seth Lazar",
"Stefano Maffulli",
"Alondra Nelson",
"Joelle Pineau",
"Aviya Skowron",
"Dawn Song",
"Victor Storchan",
"Daniel Zhang",
"Daniel E. Ho",
"Percy Liang",
"Arvind Narayanan"
]
| Foundation models are powerful technologies: how they are released publicly directly shapes their societal impact. In this position paper, we focus on open foundation models, defined here as those with broadly available model weights (e.g. Llama 2, Stable Diffusion XL). We identify five distinctive properties (e.g. greater customizability, poor monitoring) of open foundation models that lead to both their benefits and risks. Open foundation models present significant benefits, with some caveats, that span innovation, competition, the distribution of decision-making power, and transparency. To understand their risks of misuse, we design a risk assessment framework for analyzing their marginal risk. Across several misuse vectors (e.g. cyberattacks, bioweapons), we find that current research is insufficient to effectively characterize the marginal risk of open foundation models relative to pre-existing technologies. The framework helps explain why the marginal risk is low in some cases, clarifies disagreements about misuse risks by revealing that past work has focused on different subsets of the framework with different assumptions, and articulates a way forward for more constructive debate. Overall, our work helps support a more grounded assessment of the societal impact of open foundation models by outlining what research is needed to empirically validate their theoretical benefits and risks. |
|
2024-03-14T00:00:00 | 2403.08715 | SOTOPIA-π: Interactive Learning of Socially Intelligent Language Agents | [
"Ruiyi Wang",
"Haofei Yu",
"Wenxin Zhang",
"Zhengyang Qi",
"Maarten Sap",
"Graham Neubig",
"Yonatan Bisk",
"Hao Zhu"
]
| Humans learn social skills through both imitation and social interaction. This social learning process is largely understudied by existing research on building language agents. Motivated by this gap, we propose an interactive learning method, SOTOPIA-pi, improving the social intelligence of language agents. This method leverages behavior cloning and self-reinforcement training on filtered social interaction data according to large language model (LLM) ratings. We show that our training method allows a 7B LLM to reach the social goal completion ability of an expert model (GPT-4-based agent), while improving the safety of language agents and maintaining general QA ability on the MMLU benchmark. We also find that this training paradigm uncovers some difficulties in LLM-based evaluation of social intelligence: LLM-based evaluators overestimate the abilities of the language agents trained specifically for social interaction. |
|
2024-03-14T00:00:00 | 2403.08551 | GaussianImage: 1000 FPS Image Representation and Compression by 2D Gaussian Splatting | [
"Xinjie Zhang",
"Xingtong Ge",
"Tongda Xu",
"Dailan He",
"Yan Wang",
"Hongwei Qin",
"Guo Lu",
"Jing Geng",
"Jun Zhang"
]
| Implicit neural representations (INRs) recently achieved great success in image representation and compression, offering high visual quality and fast rendering speeds with 10-1000 FPS, assuming sufficient GPU resources are available. However, this requirement often hinders their use on low-end devices with limited memory. In response, we propose a groundbreaking paradigm of image representation and compression by 2D Gaussian Splatting, named GaussianImage. We first introduce 2D Gaussian to represent the image, where each Gaussian has 8 parameters including position, covariance and color. Subsequently, we unveil a novel rendering algorithm based on accumulated summation. Remarkably, our method with a minimum of 3times lower GPU memory usage and 5times faster fitting time not only rivals INRs (e.g., WIRE, I-NGP) in representation performance, but also delivers a faster rendering speed of 1500-2000 FPS regardless of parameter size. Furthermore, we integrate existing vector quantization technique to build an image codec. Experimental results demonstrate that our codec attains rate-distortion performance comparable to compression-based INRs such as COIN and COIN++, while facilitating decoding speeds of approximately 1000 FPS. Additionally, preliminary proof of concept shows that our codec surpasses COIN and COIN++ in performance when using partial bits-back coding. |
|
2024-03-14T00:00:00 | 2403.08540 | Language models scale reliably with over-training and on downstream tasks | [
"Samir Yitzhak Gadre",
"Georgios Smyrnis",
"Vaishaal Shankar",
"Suchin Gururangan",
"Mitchell Wortsman",
"Rulin Shao",
"Jean Mercat",
"Alex Fang",
"Jeffrey Li",
"Sedrick Keh",
"Rui Xin",
"Marianna Nezhurina",
"Igor Vasiljevic",
"Jenia Jitsev",
"Alexandros G. Dimakis",
"Gabriel Ilharco",
"Shuran Song",
"Thomas Kollar",
"Yair Carmon",
"Achal Dave",
"Reinhard Heckel",
"Niklas Muennighoff",
"Ludwig Schmidt"
]
| https://github.com/mlfoundations/scaling | Scaling laws are useful guides for developing language models, but there are still gaps between current scaling studies and how language models are ultimately trained and evaluated. For instance, scaling is usually studied in the compute-optimal training regime (i.e., "Chinchilla optimal" regime); however, in practice, models are often over-trained to reduce inference costs. Moreover, scaling laws mostly predict loss on next-token prediction, but ultimately models are compared based on downstream task performance. In this paper, we address both shortcomings. To do so, we create a testbed of 104 models with 0.011B to 6.9B parameters trained with various numbers of tokens on three data distributions. First, we investigate scaling in the over-trained regime. We fit scaling laws that extrapolate in both the number of model parameters and the ratio of training tokens to parameters. This enables us to predict the validation loss of a 1.4B parameter, 900B token run (i.e., 32times over-trained) and a 6.9B parameter, 138B token runx2014each from experiments that take 300times less compute. Second, we relate the perplexity of a language model to its downstream task performance via a power law. We use this law to predict top-1 error averaged over downstream tasks for the two aforementioned models using experiments that take 20times less compute. Our experiments are available at https://github.com/mlfoundations/scaling. |
2024-03-14T00:00:00 | 2403.08629 | Scaling Up Dynamic Human-Scene Interaction Modeling | [
"Nan Jiang",
"Zhiyuan Zhang",
"Hongjie Li",
"Xiaoxuan Ma",
"Zan Wang",
"Yixin Chen",
"Tengyu Liu",
"Yixin Zhu",
"Siyuan Huang"
]
| Confronting the challenges of data scarcity and advanced motion synthesis in human-scene interaction modeling, we introduce the TRUMANS dataset alongside a novel HSI motion synthesis method. TRUMANS stands as the most comprehensive motion-captured HSI dataset currently available, encompassing over 15 hours of human interactions across 100 indoor scenes. It intricately captures whole-body human motions and part-level object dynamics, focusing on the realism of contact. This dataset is further scaled up by transforming physical environments into exact virtual models and applying extensive augmentations to appearance and motion for both humans and objects while maintaining interaction fidelity. Utilizing TRUMANS, we devise a diffusion-based autoregressive model that efficiently generates HSI sequences of any length, taking into account both scene context and intended actions. In experiments, our approach shows remarkable zero-shot generalizability on a range of 3D scene datasets (e.g., PROX, Replica, ScanNet, ScanNet++), producing motions that closely mimic original motion-captured sequences, as confirmed by quantitative experiments and human studies. |
|
2024-03-15T00:00:00 | 2403.09611 | MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training | [
"Brandon McKinzie",
"Zhe Gan",
"Jean-Philippe Fauconnier",
"Sam Dodge",
"Bowen Zhang",
"Philipp Dufter",
"Dhruti Shah",
"Xianzhi Du",
"Futang Peng",
"Floris Weers",
"Anton Belyi",
"Haotian Zhang",
"Karanjeet Singh",
"Doug Kang",
"Hongyu Hè",
"Max Schwarzer",
"Tom Gunter",
"Xiang Kong",
"Aonan Zhang",
"Jianyu Wang",
"Chong Wang",
"Nan Du",
"Tao Lei",
"Sam Wiseman",
"Guoli Yin",
"Mark Lee",
"Zirui Wang",
"Ruoming Pang",
"Peter Grasch",
"Alexander Toshev",
"Yinfei Yang"
]
| In this work, we discuss building performant Multimodal Large Language Models (MLLMs). In particular, we study the importance of various architecture components and data choices. Through careful and comprehensive ablations of the image encoder, the vision language connector, and various pre-training data choices, we identified several crucial design lessons. For example, we demonstrate that for large-scale multimodal pre-training using a careful mix of image-caption, interleaved image-text, and text-only data is crucial for achieving state-of-the-art (SOTA) few-shot results across multiple benchmarks, compared to other published pre-training results. Further, we show that the image encoder together with image resolution and the image token count has substantial impact, while the vision-language connector design is of comparatively negligible importance. By scaling up the presented recipe, we build MM1, a family of multimodal models up to 30B parameters, consisting of both dense models and mixture-of-experts (MoE) variants, that are SOTA in pre-training metrics and achieve competitive performance after supervised fine-tuning on a range of established multimodal benchmarks. Thanks to large-scale pre-training, MM1 enjoys appealing properties such as enhanced in-context learning, and multi-image reasoning, enabling few-shot chain-of-thought prompting. |
|
2024-03-15T00:00:00 | 2403.09334 | Video Editing via Factorized Diffusion Distillation | [
"Uriel Singer",
"Amit Zohar",
"Yuval Kirstain",
"Shelly Sheynin",
"Adam Polyak",
"Devi Parikh",
"Yaniv Taigman"
]
| We introduce Emu Video Edit (EVE), a model that establishes a new state-of-the art in video editing without relying on any supervised video editing data. To develop EVE we separately train an image editing adapter and a video generation adapter, and attach both to the same text-to-image model. Then, to align the adapters towards video editing we introduce a new unsupervised distillation procedure, Factorized Diffusion Distillation. This procedure distills knowledge from one or more teachers simultaneously, without any supervised data. We utilize this procedure to teach EVE to edit videos by jointly distilling knowledge to (i) precisely edit each individual frame from the image editing adapter, and (ii) ensure temporal consistency among the edited frames using the video generation adapter. Finally, to demonstrate the potential of our approach in unlocking other capabilities, we align additional combinations of adapters |
|
2024-03-15T00:00:00 | 2403.09530 | VisionGPT-3D: A Generalized Multimodal Agent for Enhanced 3D Vision Understanding | [
"Chris Kelly",
"Luhui Hu",
"Jiayin Hu",
"Yu Tian",
"Deshun Yang",
"Bang Yang",
"Cindy Yang",
"Zihao Li",
"Zaoshan Huang",
"Yuexian Zou"
]
| The evolution of text to visual components facilitates people's daily lives, such as generating image, videos from text and identifying the desired elements within the images. Computer vision models involving the multimodal abilities in the previous days are focused on image detection, classification based on well-defined objects. Large language models (LLMs) introduces the transformation from nature language to visual objects, which present the visual layout for text contexts. OpenAI GPT-4 has emerged as the pinnacle in LLMs, while the computer vision (CV) domain boasts a plethora of state-of-the-art (SOTA) models and algorithms to convert 2D images to their 3D representations. However, the mismatching between the algorithms with the problem could lead to undesired results. In response to this challenge, we propose an unified VisionGPT-3D framework to consolidate the state-of-the-art vision models, thereby facilitating the development of vision-oriented AI. VisionGPT-3D provides a versatile multimodal framework building upon the strengths of multimodal foundation models. It seamlessly integrates various SOTA vision models and brings the automation in the selection of SOTA vision models, identifies the suitable 3D mesh creation algorithms corresponding to 2D depth maps analysis, generates optimal results based on diverse multimodal inputs such as text prompts. Keywords: VisionGPT-3D, 3D vision understanding, Multimodal agent |
|
2024-03-15T00:00:00 | 2403.09622 | Glyph-ByT5: A Customized Text Encoder for Accurate Visual Text Rendering | [
"Zeyu Liu",
"Weicong Liang",
"Zhanhao Liang",
"Chong Luo",
"Ji Li",
"Gao Huang",
"Yuhui Yuan"
]
| Visual text rendering poses a fundamental challenge for contemporary text-to-image generation models, with the core problem lying in text encoder deficiencies. To achieve accurate text rendering, we identify two crucial requirements for text encoders: character awareness and alignment with glyphs. Our solution involves crafting a series of customized text encoder, Glyph-ByT5, by fine-tuning the character-aware ByT5 encoder using a meticulously curated paired glyph-text dataset. We present an effective method for integrating Glyph-ByT5 with SDXL, resulting in the creation of the Glyph-SDXL model for design image generation. This significantly enhances text rendering accuracy, improving it from less than 20% to nearly 90% on our design image benchmark. Noteworthy is Glyph-SDXL's newfound ability for text paragraph rendering, achieving high spelling accuracy for tens to hundreds of characters with automated multi-line layouts. Finally, through fine-tuning Glyph-SDXL with a small set of high-quality, photorealistic images featuring visual text, we showcase a substantial improvement in scene text rendering capabilities in open-domain real images. These compelling outcomes aim to encourage further exploration in designing customized text encoders for diverse and challenging tasks. |
|
2024-03-15T00:00:00 | 2403.08773 | Veagle: Advancements in Multimodal Representation Learning | [
"Rajat Chawla",
"Arkajit Datta",
"Tushar Verma",
"Adarsh Jha",
"Anmol Gautam",
"Ayush Vatsal",
"Sukrit Chaterjee",
"Mukunda NS",
"Ishaan Bhola"
]
| Lately, researchers in artificial intelligence have been really interested in how language and vision come together, giving rise to the development of multimodal models that aim to seamlessly integrate textual and visual information. Multimodal models, an extension of Large Language Models (LLMs), have exhibited remarkable capabilities in addressing a diverse array of tasks, ranging from image captioning and visual question answering (VQA) to visual grounding. While these models have showcased significant advancements, challenges persist in accurately interpreting images and answering the question, a common occurrence in real-world scenarios. This paper introduces a novel approach to enhance the multimodal capabilities of existing models. In response to the limitations observed in current Vision Language Models (VLMs) and Multimodal Large Language Models (MLLMs), our proposed model Veagle, incorporates a unique mechanism inspired by the successes and insights of previous works. Veagle leverages a dynamic mechanism to project encoded visual information directly into the language model. This dynamic approach allows for a more nuanced understanding of intricate details present in visual contexts. To validate the effectiveness of Veagle, we conduct comprehensive experiments on benchmark datasets, emphasizing tasks such as visual question answering and image understanding. Our results indicate a improvement of 5-6 \% in performance, with Veagle outperforming existing models by a notable margin. The outcomes underscore the model's versatility and applicability beyond traditional benchmarks. |
|
2024-03-15T00:00:00 | 2403.09626 | Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding | [
"Guo Chen",
"Yifei Huang",
"Jilan Xu",
"Baoqi Pei",
"Zhe Chen",
"Zhiqi Li",
"Jiahao Wang",
"Kunchang Li",
"Tong Lu",
"Limin Wang"
]
| https://github.com/OpenGVLab/video-mamba-suite | Understanding videos is one of the fundamental directions in computer vision research, with extensive efforts dedicated to exploring various architectures such as RNN, 3D CNN, and Transformers. The newly proposed architecture of state space model, e.g., Mamba, shows promising traits to extend its success in long sequence modeling to video modeling. To assess whether Mamba can be a viable alternative to Transformers in the video understanding domain, in this work, we conduct a comprehensive set of studies, probing different roles Mamba can play in modeling videos, while investigating diverse tasks where Mamba could exhibit superiority. We categorize Mamba into four roles for modeling videos, deriving a Video Mamba Suite composed of 14 models/modules, and evaluating them on 12 video understanding tasks. Our extensive experiments reveal the strong potential of Mamba on both video-only and video-language tasks while showing promising efficiency-performance trade-offs. We hope this work could provide valuable data points and insights for future research on video understanding. Code is public: https://github.com/OpenGVLab/video-mamba-suite. |
2024-03-15T00:00:00 | 2403.09338 | LocalMamba: Visual State Space Model with Windowed Selective Scan | [
"Tao Huang",
"Xiaohuan Pei",
"Shan You",
"Fei Wang",
"Chen Qian",
"Chang Xu"
]
| https://github.com/hunto/LocalMamba | Recent advancements in state space models, notably Mamba, have demonstrated significant progress in modeling long sequences for tasks like language understanding. Yet, their application in vision tasks has not markedly surpassed the performance of traditional Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). This paper posits that the key to enhancing Vision Mamba (ViM) lies in optimizing scan directions for sequence modeling. Traditional ViM approaches, which flatten spatial tokens, overlook the preservation of local 2D dependencies, thereby elongating the distance between adjacent tokens. We introduce a novel local scanning strategy that divides images into distinct windows, effectively capturing local dependencies while maintaining a global perspective. Additionally, acknowledging the varying preferences for scan patterns across different network layers, we propose a dynamic method to independently search for the optimal scan choices for each layer, substantially improving performance. Extensive experiments across both plain and hierarchical models underscore our approach's superiority in effectively capturing image representations. For example, our model significantly outperforms Vim-Ti by 3.1% on ImageNet with the same 1.5G FLOPs. Code is available at: https://github.com/hunto/LocalMamba. |
2024-03-15T00:00:00 | 2403.09029 | Unlocking the conversion of Web Screenshots into HTML Code with the WebSight Dataset | [
"Hugo Laurençon",
"Léo Tronchon",
"Victor Sanh"
]
| Using vision-language models (VLMs) in web development presents a promising strategy to increase efficiency and unblock no-code solutions: by providing a screenshot or a sketch of a UI, a VLM could generate the code to reproduce it, for instance in a language like HTML. Despite the advancements in VLMs for various tasks, the specific challenge of converting a screenshot into a corresponding HTML has been minimally explored. We posit that this is mainly due to the absence of a suitable, high-quality dataset. This work introduces WebSight, a synthetic dataset consisting of 2 million pairs of HTML codes and their corresponding screenshots. We fine-tune a foundational VLM on our dataset and show proficiency in converting webpage screenshots to functional HTML code. To accelerate the research in this area, we open-source WebSight. |
|
2024-03-15T00:00:00 | 2403.09347 | BurstAttention: An Efficient Distributed Attention Framework for Extremely Long Sequences | [
"Sun Ao",
"Weilin Zhao",
"Xu Han",
"Cheng Yang",
"Zhiyuan Liu",
"Chuan Shi",
"Maosong Sun",
"Shengnan Wang",
"Teng Su"
]
| Effective attention modules have played a crucial role in the success of Transformer-based large language models (LLMs), but the quadratic time and memory complexities of these attention modules also pose a challenge when processing long sequences. One potential solution for the long sequence problem is to utilize distributed clusters to parallelize the computation of attention modules across multiple devices (e.g., GPUs). However, adopting a distributed approach inevitably introduces extra memory overheads to store local attention results and incurs additional communication costs to aggregate local results into global ones. In this paper, we propose a distributed attention framework named ``BurstAttention'' to optimize memory access and communication operations at both the global cluster and local device levels. In our experiments, we compare BurstAttention with other competitive distributed attention solutions for long sequence processing. The experimental results under different length settings demonstrate that BurstAttention offers significant advantages for processing long sequences compared with these competitive baselines, reducing 40% communication overheads and achieving 2 X speedup during training 32K sequence length on 8 X A100. |
|
2024-03-15T00:00:00 | 2403.09333 | Griffon v2: Advancing Multimodal Perception with High-Resolution Scaling and Visual-Language Co-Referring | [
"Yufei Zhan",
"Yousong Zhu",
"Hongyin Zhao",
"Fan Yang",
"Ming Tang",
"Jinqiao Wang"
]
| https://github.com/jefferyZhan/Griffon | Large Vision Language Models have achieved fine-grained object perception, but the limitation of image resolution remains a significant obstacle to surpass the performance of task-specific experts in complex and dense scenarios. Such limitation further restricts the model's potential to achieve nuanced visual and language referring in domains such as GUI Agents, Counting and \etc. To address this issue, we introduce a unified high-resolution generalist model, Griffon v2, enabling flexible object referring with visual and textual prompts. To efficiently scaling up image resolution, we design a simple and lightweight down-sampling projector to overcome the input tokens constraint in Large Language Models. This design inherently preserves the complete contexts and fine details, and significantly improves multimodal perception ability especially for small objects. Building upon this, we further equip the model with visual-language co-referring capabilities through a plug-and-play visual tokenizer. It enables user-friendly interaction with flexible target images, free-form texts and even coordinates. Experiments demonstrate that Griffon v2 can localize any objects of interest with visual and textual referring, achieve state-of-the-art performance on REC, phrase grounding, and REG tasks, and outperform expert models in object detection and object counting. Data, codes and models will be released at https://github.com/jefferyZhan/Griffon. |
2024-03-15T00:00:00 | 2403.09394 | GiT: Towards Generalist Vision Transformer through Universal Language Interface | [
"Haiyang Wang",
"Hao Tang",
"Li Jiang",
"Shaoshuai Shi",
"Muhammad Ferjad Naeem",
"Hongsheng Li",
"Bernt Schiele",
"Liwei Wang"
]
| https://github.com/Haiyang-W/GiT | This paper proposes a simple, yet effective framework, called GiT, simultaneously applicable for various vision tasks only with a vanilla ViT. Motivated by the universality of the Multi-layer Transformer architecture (e.g, GPT) widely used in large language models (LLMs), we seek to broaden its scope to serve as a powerful vision foundation model (VFM). However, unlike language modeling, visual tasks typically require specific modules, such as bounding box heads for detection and pixel decoders for segmentation, greatly hindering the application of powerful multi-layer transformers in the vision domain. To solve this, we design a universal language interface that empowers the successful auto-regressive decoding to adeptly unify various visual tasks, from image-level understanding (e.g., captioning), over sparse perception (e.g., detection), to dense prediction (e.g., segmentation). Based on the above designs, the entire model is composed solely of a ViT, without any specific additions, offering a remarkable architectural simplification. GiT is a multi-task visual model, jointly trained across five representative benchmarks without task-specific fine-tuning. Interestingly, our GiT builds a new benchmark in generalist performance, and fosters mutual enhancement across tasks, leading to significant improvements compared to isolated training. This reflects a similar impact observed in LLMs. Further enriching training with 27 datasets, GiT achieves strong zero-shot results over various tasks. Due to its simple design, this paradigm holds promise for narrowing the architectural gap between vision and language. Code and models will be available at https://github.com/Haiyang-W/GiT. |
2024-03-15T00:00:00 | 2403.09629 | Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking | [
"Eric Zelikman",
"Georges Harik",
"Yijia Shao",
"Varuna Jayasiri",
"Nick Haber",
"Noah D. Goodman"
]
| When writing and talking, people sometimes pause to think. Although reasoning-focused works have often framed reasoning as a method of answering questions or completing agentic tasks, reasoning is implicit in almost all written text. For example, this applies to the steps not stated between the lines of a proof or to the theory of mind underlying a conversation. In the Self-Taught Reasoner (STaR, Zelikman et al. 2022), useful thinking is learned by inferring rationales from few-shot examples in question-answering and learning from those that lead to a correct answer. This is a highly constrained setting -- ideally, a language model could instead learn to infer unstated rationales in arbitrary text. We present Quiet-STaR, a generalization of STaR in which LMs learn to generate rationales at each token to explain future text, improving their predictions. We address key challenges, including 1) the computational cost of generating continuations, 2) the fact that the LM does not initially know how to generate or use internal thoughts, and 3) the need to predict beyond individual next tokens. To resolve these, we propose a tokenwise parallel sampling algorithm, using learnable tokens indicating a thought's start and end, and an extended teacher-forcing technique. Encouragingly, generated rationales disproportionately help model difficult-to-predict tokens and improve the LM's ability to directly answer difficult questions. In particular, after continued pretraining of an LM on a corpus of internet text with Quiet-STaR, we find zero-shot improvements on GSM8K (5.9%rightarrow10.9%) and CommonsenseQA (36.3%rightarrow47.2%) and observe a perplexity improvement of difficult tokens in natural text. Crucially, these improvements require no fine-tuning on these tasks. Quiet-STaR marks a step towards LMs that can learn to reason in a more general and scalable way. |
|
2024-03-15T00:00:00 | 2403.09055 | StreamMultiDiffusion: Real-Time Interactive Generation with Region-Based Semantic Control | [
"Jaerin Lee",
"Daniel Sungho Jung",
"Kanggeon Lee",
"Kyoung Mu Lee"
]
| https://github.com/ironjr/StreamMultiDiffusion | The enormous success of diffusion models in text-to-image synthesis has made them promising candidates for the next generation of end-user applications for image generation and editing. Previous works have focused on improving the usability of diffusion models by reducing the inference time or increasing user interactivity by allowing new, fine-grained controls such as region-based text prompts. However, we empirically find that integrating both branches of works is nontrivial, limiting the potential of diffusion models. To solve this incompatibility, we present StreamMultiDiffusion, the first real-time region-based text-to-image generation framework. By stabilizing fast inference techniques and restructuring the model into a newly proposed multi-prompt stream batch architecture, we achieve times 10 faster panorama generation than existing solutions, and the generation speed of 1.57 FPS in region-based text-to-image synthesis on a single RTX 2080 Ti GPU. Our solution opens up a new paradigm for interactive image generation named semantic palette, where high-quality images are generated in real-time from given multiple hand-drawn regions, encoding prescribed semantic meanings (e.g., eagle, girl). Our code and demo application are available at https://github.com/ironjr/StreamMultiDiffusion. |
2024-03-15T00:00:00 | 2403.09631 | 3D-VLA: A 3D Vision-Language-Action Generative World Model | [
"Haoyu Zhen",
"Xiaowen Qiu",
"Peihao Chen",
"Jincheng Yang",
"Xin Yan",
"Yilun Du",
"Yining Hong",
"Chuang Gan"
]
| https://github.com/UMass-Foundation-Model/3D-VLA | Recent vision-language-action (VLA) models rely on 2D inputs, lacking integration with the broader realm of the 3D physical world. Furthermore, they perform action prediction by learning a direct mapping from perception to action, neglecting the vast dynamics of the world and the relations between actions and dynamics. In contrast, human beings are endowed with world models that depict imagination about future scenarios to plan actions accordingly. To this end, we propose 3D-VLA by introducing a new family of embodied foundation models that seamlessly link 3D perception, reasoning, and action through a generative world model. Specifically, 3D-VLA is built on top of a 3D-based large language model (LLM), and a set of interaction tokens is introduced to engage with the embodied environment. Furthermore, to inject generation abilities into the model, we train a series of embodied diffusion models and align them into the LLM for predicting the goal images and point clouds. To train our 3D-VLA, we curate a large-scale 3D embodied instruction dataset by extracting vast 3D-related information from existing robotics datasets. Our experiments on held-in datasets demonstrate that 3D-VLA significantly improves the reasoning, multimodal generation, and planning capabilities in embodied environments, showcasing its potential in real-world applications. |
2024-03-18T00:00:00 | 2403.10493 | MusicHiFi: Fast High-Fidelity Stereo Vocoding | [
"Ge Zhu",
"Juan-Pablo Caceres",
"Zhiyao Duan",
"Nicholas J. Bryan"
]
| Diffusion-based audio and music generation models commonly generate music by constructing an image representation of audio (e.g., a mel-spectrogram) and then converting it to audio using a phase reconstruction model or vocoder. Typical vocoders, however, produce monophonic audio at lower resolutions (e.g., 16-24 kHz), which limits their effectiveness. We propose MusicHiFi -- an efficient high-fidelity stereophonic vocoder. Our method employs a cascade of three generative adversarial networks (GANs) that convert low-resolution mel-spectrograms to audio, upsamples to high-resolution audio via bandwidth expansion, and upmixes to stereophonic audio. Compared to previous work, we propose 1) a unified GAN-based generator and discriminator architecture and training procedure for each stage of our cascade, 2) a new fast, near downsampling-compatible bandwidth extension module, and 3) a new fast downmix-compatible mono-to-stereo upmixer that ensures the preservation of monophonic content in the output. We evaluate our approach using both objective and subjective listening tests and find our approach yields comparable or better audio quality, better spatialization control, and significantly faster inference speed compared to past work. Sound examples are at https://MusicHiFi.github.io/web/. |
|
2024-03-18T00:00:00 | 2403.09981 | Controllable Text-to-3D Generation via Surface-Aligned Gaussian Splatting | [
"Zhiqi Li",
"Yiming Chen",
"Lingzhe Zhao",
"Peidong Liu"
]
| https://github.com/WU-CVGL/MVControl-threestudio | While text-to-3D and image-to-3D generation tasks have received considerable attention, one important but under-explored field between them is controllable text-to-3D generation, which we mainly focus on in this work. To address this task, 1) we introduce Multi-view ControlNet (MVControl), a novel neural network architecture designed to enhance existing pre-trained multi-view diffusion models by integrating additional input conditions, such as edge, depth, normal, and scribble maps. Our innovation lies in the introduction of a conditioning module that controls the base diffusion model using both local and global embeddings, which are computed from the input condition images and camera poses. Once trained, MVControl is able to offer 3D diffusion guidance for optimization-based 3D generation. And, 2) we propose an efficient multi-stage 3D generation pipeline that leverages the benefits of recent large reconstruction models and score distillation algorithm. Building upon our MVControl architecture, we employ a unique hybrid diffusion guidance method to direct the optimization process. In pursuit of efficiency, we adopt 3D Gaussians as our representation instead of the commonly used implicit representations. We also pioneer the use of SuGaR, a hybrid representation that binds Gaussians to mesh triangle faces. This approach alleviates the issue of poor geometry in 3D Gaussians and enables the direct sculpting of fine-grained geometry on the mesh. Extensive experiments demonstrate that our method achieves robust generalization and enables the controllable generation of high-quality 3D content. |
2024-03-18T00:00:00 | 2403.09919 | Recurrent Drafter for Fast Speculative Decoding in Large Language Models | [
"Aonan Zhang",
"Chong Wang",
"Yi Wang",
"Xuanyu Zhang",
"Yunfei Cheng"
]
| In this paper, we introduce an improved approach of speculative decoding aimed at enhancing the efficiency of serving large language models. Our method capitalizes on the strengths of two established techniques: the classic two-model speculative decoding approach, and the more recent single-model approach, Medusa. Drawing inspiration from Medusa, our approach adopts a single-model strategy for speculative decoding. However, our method distinguishes itself by employing a single, lightweight draft head with a recurrent dependency design, akin in essence to the small, draft model uses in classic speculative decoding, but without the complexities of the full transformer architecture. And because of the recurrent dependency, we can use beam search to swiftly filter out undesired candidates with the draft head. The outcome is a method that combines the simplicity of single-model design and avoids the need to create a data-dependent tree attention structure only for inference in Medusa. We empirically demonstrate the effectiveness of the proposed method on several popular open source language models, along with a comprehensive analysis of the trade-offs involved in adopting this approach. |
|
2024-03-18T00:00:00 | 2403.09704 | Alignment Studio: Aligning Large Language Models to Particular Contextual Regulations | [
"Swapnaja Achintalwar",
"Ioana Baldini",
"Djallel Bouneffouf",
"Joan Byamugisha",
"Maria Chang",
"Pierre Dognin",
"Eitan Farchi",
"Ndivhuwo Makondo",
"Aleksandra Mojsilovic",
"Manish Nagireddy",
"Karthikeyan Natesan Ramamurthy",
"Inkit Padhi",
"Orna Raz",
"Jesus Rios",
"Prasanna Sattigeri",
"Moninder Singh",
"Siphiwe Thwala",
"Rosario A. Uceda-Sosa",
"Kush R. Varshney"
]
| The alignment of large language models is usually done by model providers to add or control behaviors that are common or universally understood across use cases and contexts. In contrast, in this article, we present an approach and architecture that empowers application developers to tune a model to their particular values, social norms, laws and other regulations, and orchestrate between potentially conflicting requirements in context. We lay out three main components of such an Alignment Studio architecture: Framers, Instructors, and Auditors that work in concert to control the behavior of a language model. We illustrate this approach with a running example of aligning a company's internal-facing enterprise chatbot to its business conduct guidelines. |
|
2024-03-18T00:00:00 | 2403.10395 | Isotropic3D: Image-to-3D Generation Based on a Single CLIP Embedding | [
"Pengkun Liu",
"Yikai Wang",
"Fuchun Sun",
"Jiafang Li",
"Hang Xiao",
"Hongxiang Xue",
"Xinzhou Wang"
]
| https://github.com/pkunliu/Isotropic3D | Encouraged by the growing availability of pre-trained 2D diffusion models, image-to-3D generation by leveraging Score Distillation Sampling (SDS) is making remarkable progress. Most existing methods combine novel-view lifting from 2D diffusion models which usually take the reference image as a condition while applying hard L2 image supervision at the reference view. Yet heavily adhering to the image is prone to corrupting the inductive knowledge of the 2D diffusion model leading to flat or distorted 3D generation frequently. In this work, we reexamine image-to-3D in a novel perspective and present Isotropic3D, an image-to-3D generation pipeline that takes only an image CLIP embedding as input. Isotropic3D allows the optimization to be isotropic w.r.t. the azimuth angle by solely resting on the SDS loss. The core of our framework lies in a two-stage diffusion model fine-tuning. Firstly, we fine-tune a text-to-3D diffusion model by substituting its text encoder with an image encoder, by which the model preliminarily acquires image-to-image capabilities. Secondly, we perform fine-tuning using our Explicit Multi-view Attention (EMA) which combines noisy multi-view images with the noise-free reference image as an explicit condition. CLIP embedding is sent to the diffusion model throughout the whole process while reference images are discarded once after fine-tuning. As a result, with a single image CLIP embedding, Isotropic3D is capable of generating multi-view mutually consistent images and also a 3D model with more symmetrical and neat content, well-proportioned geometry, rich colored texture, and less distortion compared with existing image-to-3D methods while still preserving the similarity to the reference image to a large extent. The project page is available at https://isotropic3d.github.io/. The code and models are available at https://github.com/pkunliu/Isotropic3D. |
2024-03-18T00:00:00 | 2403.10517 | VideoAgent: Long-form Video Understanding with Large Language Model as Agent | [
"Xiaohan Wang",
"Yuhui Zhang",
"Orr Zohar",
"Serena Yeung-Levy"
]
| Long-form video understanding represents a significant challenge within computer vision, demanding a model capable of reasoning over long multi-modal sequences. Motivated by the human cognitive process for long-form video understanding, we emphasize interactive reasoning and planning over the ability to process lengthy visual inputs. We introduce a novel agent-based system, VideoAgent, that employs a large language model as a central agent to iteratively identify and compile crucial information to answer a question, with vision-language foundation models serving as tools to translate and retrieve visual information. Evaluated on the challenging EgoSchema and NExT-QA benchmarks, VideoAgent achieves 54.1% and 71.3% zero-shot accuracy with only 8.4 and 8.2 frames used on average. These results demonstrate superior effectiveness and efficiency of our method over the current state-of-the-art methods, highlighting the potential of agent-based approaches in advancing long-form video understanding. |
|
2024-03-18T00:00:00 | 2403.09977 | EfficientVMamba: Atrous Selective Scan for Light Weight Visual Mamba | [
"Xiaohuan Pei",
"Tao Huang",
"Chang Xu"
]
| https://github.com/TerryPei/EfficientVMamba | Prior efforts in light-weight model development mainly centered on CNN and Transformer-based designs yet faced persistent challenges. CNNs adept at local feature extraction compromise resolution while Transformers offer global reach but escalate computational demands O(N^2). This ongoing trade-off between accuracy and efficiency remains a significant hurdle. Recently, state space models (SSMs), such as Mamba, have shown outstanding performance and competitiveness in various tasks such as language modeling and computer vision, while reducing the time complexity of global information extraction to O(N). Inspired by this, this work proposes to explore the potential of visual state space models in light-weight model design and introduce a novel efficient model variant dubbed EfficientVMamba. Concretely, our EfficientVMamba integrates a atrous-based selective scan approach by efficient skip sampling, constituting building blocks designed to harness both global and local representational features. Additionally, we investigate the integration between SSM blocks and convolutions, and introduce an efficient visual state space block combined with an additional convolution branch, which further elevate the model performance. Experimental results show that, EfficientVMamba scales down the computational complexity while yields competitive results across a variety of vision tasks. For example, our EfficientVMamba-S with 1.3G FLOPs improves Vim-Ti with 1.5G FLOPs by a large margin of 5.6% accuracy on ImageNet. Code is available at: https://github.com/TerryPei/EfficientVMamba. |
2024-03-18T00:00:00 | 2403.10301 | Uni-SMART: Universal Science Multimodal Analysis and Research Transformer | [
"Hengxing Cai",
"Xiaochen Cai",
"Shuwen Yang",
"Jiankun Wang",
"Lin Yao",
"Zhifeng Gao",
"Junhan Chang",
"Sihang Li",
"Mingjun Xu",
"Changxin Wang",
"Hongshuai Wang",
"Yongge Li",
"Mujie Lin",
"Yaqi Li",
"Yuqi Yin",
"Linfeng Zhang",
"Guolin Ke"
]
| In scientific research and its application, scientific literature analysis is crucial as it allows researchers to build on the work of others. However, the fast growth of scientific knowledge has led to a massive increase in scholarly articles, making in-depth literature analysis increasingly challenging and time-consuming. The emergence of Large Language Models (LLMs) has offered a new way to address this challenge. Known for their strong abilities in summarizing texts, LLMs are seen as a potential tool to improve the analysis of scientific literature. However, existing LLMs have their own limits. Scientific literature often includes a wide range of multimodal elements, such as molecular structure, tables, and charts, which are hard for text-focused LLMs to understand and analyze. This issue points to the urgent need for new solutions that can fully understand and analyze multimodal content in scientific literature. To answer this demand, we present Uni-SMART (Universal Science Multimodal Analysis and Research Transformer), an innovative model designed for in-depth understanding of multimodal scientific literature. Through rigorous quantitative evaluation across several domains, Uni-SMART demonstrates superior performance over leading text-focused LLMs. Furthermore, our exploration extends to practical applications, including patent infringement detection and nuanced analysis of charts. These applications not only highlight Uni-SMART's adaptability but also its potential to revolutionize how we interact with scientific literature. |
|
2024-03-18T00:00:00 | 2403.10131 | RAFT: Adapting Language Model to Domain Specific RAG | [
"Tianjun Zhang",
"Shishir G. Patil",
"Naman Jain",
"Sheng Shen",
"Matei Zaharia",
"Ion Stoica",
"Joseph E. Gonzalez"
]
| https://github.com/ShishirPatil/gorilla | Pretraining Large Language Models (LLMs) on large corpora of textual data is now a standard paradigm. When using these LLMs for many downstream applications, it is common to additionally bake in new knowledge (e.g., time-critical news, or private domain knowledge) into the pretrained model either through RAG-based-prompting, or fine-tuning. However, the optimal methodology for the model to gain such new knowledge remains an open question. In this paper, we present Retrieval Augmented FineTuning (RAFT), a training recipe that improves the model's ability to answer questions in a "open-book" in-domain settings. In RAFT, given a question, and a set of retrieved documents, we train the model to ignore those documents that don't help in answering the question, which we call, distractor documents. RAFT accomplishes this by citing verbatim the right sequence from the relevant document that would help answer the question. This coupled with RAFT's chain-of-thought-style response helps improve the model's ability to reason. In domain-specific RAG, RAFT consistently improves the model's performance across PubMed, HotpotQA, and Gorilla datasets, presenting a post-training recipe to improve pre-trained LLMs to in-domain RAG. RAFT's code and demo are open-sourced at github.com/ShishirPatil/gorilla. |
2024-03-18T00:00:00 | 2403.10425 | NeuFlow: Real-time, High-accuracy Optical Flow Estimation on Robots Using Edge Devices | [
"Zhiyong Zhang",
"Huaizu Jiang",
"Hanumant Singh"
]
| https://github.com/neufieldrobotics/NeuFlow | Real-time high-accuracy optical flow estimation is a crucial component in various applications, including localization and mapping in robotics, object tracking, and activity recognition in computer vision. While recent learning-based optical flow methods have achieved high accuracy, they often come with heavy computation costs. In this paper, we propose a highly efficient optical flow architecture, called NeuFlow, that addresses both high accuracy and computational cost concerns. The architecture follows a global-to-local scheme. Given the features of the input images extracted at different spatial resolutions, global matching is employed to estimate an initial optical flow on the 1/16 resolution, capturing large displacement, which is then refined on the 1/8 resolution with lightweight CNN layers for better accuracy. We evaluate our approach on Jetson Orin Nano and RTX 2080 to demonstrate efficiency improvements across different computing platforms. We achieve a notable 10x-80x speedup compared to several state-of-the-art methods, while maintaining comparable accuracy. Our approach achieves around 30 FPS on edge computing platforms, which represents a significant breakthrough in deploying complex computer vision tasks such as SLAM on small robots like drones. The full training and evaluation code is available at https://github.com/neufieldrobotics/NeuFlow. |
2024-03-18T00:00:00 | 2403.10242 | FDGaussian: Fast Gaussian Splatting from Single Image via Geometric-aware Diffusion Model | [
"Qijun Feng",
"Zhen Xing",
"Zuxuan Wu",
"Yu-Gang Jiang"
]
| Reconstructing detailed 3D objects from single-view images remains a challenging task due to the limited information available. In this paper, we introduce FDGaussian, a novel two-stage framework for single-image 3D reconstruction. Recent methods typically utilize pre-trained 2D diffusion models to generate plausible novel views from the input image, yet they encounter issues with either multi-view inconsistency or lack of geometric fidelity. To overcome these challenges, we propose an orthogonal plane decomposition mechanism to extract 3D geometric features from the 2D input, enabling the generation of consistent multi-view images. Moreover, we further accelerate the state-of-the-art Gaussian Splatting incorporating epipolar attention to fuse images from different viewpoints. We demonstrate that FDGaussian generates images with high consistency across different views and reconstructs high-quality 3D objects, both qualitatively and quantitatively. More examples can be found at our website https://qjfeng.net/FDGaussian/. |
|
2024-03-19T00:00:00 | 2403.12015 | Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation | [
"Axel Sauer",
"Frederic Boesel",
"Tim Dockhorn",
"Andreas Blattmann",
"Patrick Esser",
"Robin Rombach"
]
| Diffusion models are the main driver of progress in image and video synthesis, but suffer from slow inference speed. Distillation methods, like the recently introduced adversarial diffusion distillation (ADD) aim to shift the model from many-shot to single-step inference, albeit at the cost of expensive and difficult optimization due to its reliance on a fixed pretrained DINOv2 discriminator. We introduce Latent Adversarial Diffusion Distillation (LADD), a novel distillation approach overcoming the limitations of ADD. In contrast to pixel-based ADD, LADD utilizes generative features from pretrained latent diffusion models. This approach simplifies training and enhances performance, enabling high-resolution multi-aspect ratio image synthesis. We apply LADD to Stable Diffusion 3 (8B) to obtain SD3-Turbo, a fast model that matches the performance of state-of-the-art text-to-image generators using only four unguided sampling steps. Moreover, we systematically investigate its scaling behavior and demonstrate LADD's effectiveness in various applications such as image editing and inpainting. |
|
2024-03-19T00:00:00 | 2403.12008 | SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image using Latent Video Diffusion | [
"Vikram Voleti",
"Chun-Han Yao",
"Mark Boss",
"Adam Letts",
"David Pankratz",
"Dmitry Tochilkin",
"Christian Laforte",
"Robin Rombach",
"Varun Jampani"
]
| We present Stable Video 3D (SV3D) -- a latent video diffusion model for high-resolution, image-to-multi-view generation of orbital videos around a 3D object. Recent work on 3D generation propose techniques to adapt 2D generative models for novel view synthesis (NVS) and 3D optimization. However, these methods have several disadvantages due to either limited views or inconsistent NVS, thereby affecting the performance of 3D object generation. In this work, we propose SV3D that adapts image-to-video diffusion model for novel multi-view synthesis and 3D generation, thereby leveraging the generalization and multi-view consistency of the video models, while further adding explicit camera control for NVS. We also propose improved 3D optimization techniques to use SV3D and its NVS outputs for image-to-3D generation. Extensive experimental results on multiple datasets with 2D and 3D metrics as well as user study demonstrate SV3D's state-of-the-art performance on NVS as well as 3D reconstruction compared to prior works. |
|
2024-03-19T00:00:00 | 2403.11781 | Infinite-ID: Identity-preserved Personalization via ID-semantics Decoupling Paradigm | [
"Yi Wu",
"Ziqiang Li",
"Heliang Zheng",
"Chaoyue Wang",
"Bin Li"
]
| Drawing on recent advancements in diffusion models for text-to-image generation, identity-preserved personalization has made significant progress in accurately capturing specific identities with just a single reference image. However, existing methods primarily integrate reference images within the text embedding space, leading to a complex entanglement of image and text information, which poses challenges for preserving both identity fidelity and semantic consistency. To tackle this challenge, we propose Infinite-ID, an ID-semantics decoupling paradigm for identity-preserved personalization. Specifically, we introduce identity-enhanced training, incorporating an additional image cross-attention module to capture sufficient ID information while deactivating the original text cross-attention module of the diffusion model. This ensures that the image stream faithfully represents the identity provided by the reference image while mitigating interference from textual input. Additionally, we introduce a feature interaction mechanism that combines a mixed attention module with an AdaIN-mean operation to seamlessly merge the two streams. This mechanism not only enhances the fidelity of identity and semantic consistency but also enables convenient control over the styles of the generated images. Extensive experimental results on both raw photo generation and style image generation demonstrate the superior performance of our proposed method. |
|
2024-03-19T00:00:00 | 2403.10615 | LightIt: Illumination Modeling and Control for Diffusion Models | [
"Peter Kocsis",
"Julien Philip",
"Kalyan Sunkavalli",
"Matthias Nießner",
"Yannick Hold-Geoffroy"
]
| We introduce LightIt, a method for explicit illumination control for image generation. Recent generative methods lack lighting control, which is crucial to numerous artistic aspects of image generation such as setting the overall mood or cinematic appearance. To overcome these limitations, we propose to condition the generation on shading and normal maps. We model the lighting with single bounce shading, which includes cast shadows. We first train a shading estimation module to generate a dataset of real-world images and shading pairs. Then, we train a control network using the estimated shading and normals as input. Our method demonstrates high-quality image generation and lighting control in numerous scenes. Additionally, we use our generated dataset to train an identity-preserving relighting model, conditioned on an image and a target shading. Our method is the first that enables the generation of images with controllable, consistent lighting and performs on par with specialized relighting state-of-the-art methods. |
|
2024-03-19T00:00:00 | 2403.11481 | VideoAgent: A Memory-augmented Multimodal Agent for Video Understanding | [
"Yue Fan",
"Xiaojian Ma",
"Rujie Wu",
"Yuntao Du",
"Jiaqi Li",
"Zhi Gao",
"Qing Li"
]
| We explore how reconciling several foundation models (large language models and vision-language models) with a novel unified memory mechanism could tackle the challenging video understanding problem, especially capturing the long-term temporal relations in lengthy videos. In particular, the proposed multimodal agent VideoAgent: 1) constructs a structured memory to store both the generic temporal event descriptions and object-centric tracking states of the video; 2) given an input task query, it employs tools including video segment localization and object memory querying along with other visual foundation models to interactively solve the task, utilizing the zero-shot tool-use ability of LLMs. VideoAgent demonstrates impressive performances on several long-horizon video understanding benchmarks, an average increase of 6.6% on NExT-QA and 26.0% on EgoSchema over baselines, closing the gap between open-sourced models and private counterparts including Gemini 1.5 Pro. |
|
2024-03-19T00:00:00 | 2403.11207 | MindEye2: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Data | [
"Paul S. Scotti",
"Mihir Tripathy",
"Cesar Kadir Torrico Villanueva",
"Reese Kneeland",
"Tong Chen",
"Ashutosh Narang",
"Charan Santhirasegaran",
"Jonathan Xu",
"Thomas Naselaris",
"Kenneth A. Norman",
"Tanishq Mathew Abraham"
]
| https://github.com/MedARC-AI/MindEyeV2 | Reconstructions of visual perception from brain activity have improved tremendously, but the practical utility of such methods has been limited. This is because such models are trained independently per subject where each subject requires dozens of hours of expensive fMRI training data to attain high-quality results. The present work showcases high-quality reconstructions using only 1 hour of fMRI training data. We pretrain our model across 7 subjects and then fine-tune on minimal data from a new subject. Our novel functional alignment procedure linearly maps all brain data to a shared-subject latent space, followed by a shared non-linear mapping to CLIP image space. We then map from CLIP space to pixel space by fine-tuning Stable Diffusion XL to accept CLIP latents as inputs instead of text. This approach improves out-of-subject generalization with limited training data and also attains state-of-the-art image retrieval and reconstruction metrics compared to single-subject approaches. MindEye2 demonstrates how accurate reconstructions of perception are possible from a single visit to the MRI facility. All code is available on GitHub. |
2024-03-19T00:00:00 | 2403.12032 | Generic 3D Diffusion Adapter Using Controlled Multi-View Editing | [
"Hansheng Chen",
"Ruoxi Shi",
"Yulin Liu",
"Bokui Shen",
"Jiayuan Gu",
"Gordon Wetzstein",
"Hao Su",
"Leonidas Guibas"
]
| https://github.com/Lakonik/MVEdit | Open-domain 3D object synthesis has been lagging behind image synthesis due to limited data and higher computational complexity. To bridge this gap, recent works have investigated multi-view diffusion but often fall short in either 3D consistency, visual quality, or efficiency. This paper proposes MVEdit, which functions as a 3D counterpart of SDEdit, employing ancestral sampling to jointly denoise multi-view images and output high-quality textured meshes. Built on off-the-shelf 2D diffusion models, MVEdit achieves 3D consistency through a training-free 3D Adapter, which lifts the 2D views of the last timestep into a coherent 3D representation, then conditions the 2D views of the next timestep using rendered views, without uncompromising visual quality. With an inference time of only 2-5 minutes, this framework achieves better trade-off between quality and speed than score distillation. MVEdit is highly versatile and extendable, with a wide range of applications including text/image-to-3D generation, 3D-to-3D editing, and high-quality texture synthesis. In particular, evaluations demonstrate state-of-the-art performance in both image-to-3D and text-guided texture generation tasks. Additionally, we introduce a method for fine-tuning 2D latent diffusion models on small 3D datasets with limited resources, enabling fast low-resolution text-to-3D initialization. |
2024-03-19T00:00:00 | 2403.12019 | LN3Diff: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation | [
"Yushi Lan",
"Fangzhou Hong",
"Shuai Yang",
"Shangchen Zhou",
"Xuyi Meng",
"Bo Dai",
"Xingang Pan",
"Chen Change Loy"
]
| https://github.com/NIRVANALAN/LN3Diff | The field of neural rendering has witnessed significant progress with advancements in generative models and differentiable rendering techniques. Though 2D diffusion has achieved success, a unified 3D diffusion pipeline remains unsettled. This paper introduces a novel framework called LN3Diff to address this gap and enable fast, high-quality, and generic conditional 3D generation. Our approach harnesses a 3D-aware architecture and variational autoencoder (VAE) to encode the input image into a structured, compact, and 3D latent space. The latent is decoded by a transformer-based decoder into a high-capacity 3D neural field. Through training a diffusion model on this 3D-aware latent space, our method achieves state-of-the-art performance on ShapeNet for 3D generation and demonstrates superior performance in monocular 3D reconstruction and conditional 3D generation across various datasets. Moreover, it surpasses existing 3D diffusion methods in terms of inference speed, requiring no per-instance optimization. Our proposed LN3Diff presents a significant advancement in 3D generative modeling and holds promise for various applications in 3D vision and graphics tasks. |
2024-03-19T00:00:00 | 2403.11901 | Larimar: Large Language Models with Episodic Memory Control | [
"Payel Das",
"Subhajit Chaudhury",
"Elliot Nelson",
"Igor Melnyk",
"Sarath Swaminathan",
"Sihui Dai",
"Aurélie Lozano",
"Georgios Kollias",
"Vijil Chenthamarakshan",
"Jiří",
"Navrátil",
"Soham Dan",
"Pin-Yu Chen"
]
| Efficient and accurate updating of knowledge stored in Large Language Models (LLMs) is one of the most pressing research challenges today. This paper presents Larimar - a novel, brain-inspired architecture for enhancing LLMs with a distributed episodic memory. Larimar's memory allows for dynamic, one-shot updates of knowledge without the need for computationally expensive re-training or fine-tuning. Experimental results on multiple fact editing benchmarks demonstrate that Larimar attains accuracy comparable to most competitive baselines, even in the challenging sequential editing setup, but also excels in speed - yielding speed-ups of 4-10x depending on the base LLM - as well as flexibility due to the proposed architecture being simple, LLM-agnostic, and hence general. We further provide mechanisms for selective fact forgetting and input context length generalization with Larimar and show their effectiveness. |
|
2024-03-19T00:00:00 | 2403.11703 | LLaVA-UHD: an LMM Perceiving Any Aspect Ratio and High-Resolution Images | [
"Ruyi Xu",
"Yuan Yao",
"Zonghao Guo",
"Junbo Cui",
"Zanlin Ni",
"Chunjiang Ge",
"Tat-Seng Chua",
"Zhiyuan Liu",
"Maosong Sun",
"Gao Huang"
]
| https://github.com/thunlp/LLaVA-UHD | Visual encoding constitutes the basis of large multimodal models (LMMs) in understanding the visual world. Conventional LMMs process images in fixed sizes and limited resolutions, while recent explorations in this direction are limited in adaptivity, efficiency, and even correctness. In this work, we first take GPT-4V and LLaVA-1.5 as representative examples and expose systematic flaws rooted in their visual encoding strategy. To address the challenges, we present LLaVA-UHD, a large multimodal model that can efficiently perceive images in any aspect ratio and high resolution. LLaVA-UHD includes three key components: (1) An image modularization strategy that divides native-resolution images into smaller variable-sized slices for efficient and extensible encoding, (2) a compression module that further condenses image tokens from visual encoders, and (3) a spatial schema to organize slice tokens for LLMs. Comprehensive experiments show that LLaVA-UHD outperforms established LMMs trained with 2-3 orders of magnitude more data on 9 benchmarks. Notably, our model built on LLaVA-1.5 336x336 supports 6 times larger (i.e., 672x1088) resolution images using only 94% inference computation, and achieves 6.4 accuracy improvement on TextVQA. Moreover, the model can be efficiently trained in academic settings, within 23 hours on 8 A100 GPUs (vs. 26 hours of LLaVA-1.5). We make the data and code publicly available at https://github.com/thunlp/LLaVA-UHD. |
2024-03-19T00:00:00 | 2403.10704 | PERL: Parameter Efficient Reinforcement Learning from Human Feedback | [
"Hakim Sidahmed",
"Samrat Phatale",
"Alex Hutcheson",
"Zhuonan Lin",
"Zhang Chen",
"Zac Yu",
"Jarvis Jin",
"Roman Komarytsia",
"Christiane Ahlheim",
"Yonghao Zhu",
"Simral Chaudhary",
"Bowen Li",
"Saravanan Ganesh",
"Bill Byrne",
"Jessica Hoffmann",
"Hassan Mansoor",
"Wei Li",
"Abhinav Rastogi",
"Lucas Dixon"
]
| Reinforcement Learning from Human Feedback (RLHF) has proven to be a strong method to align Pretrained Large Language Models (LLMs) with human preferences. But training models with RLHF is computationally expensive, and an overall complex process. In this work, we study RLHF where the underlying models are trained using the parameter efficient method of Low-Rank Adaptation (LoRA) introduced by Hu et al. [2021]. We investigate the setup of "Parameter Efficient Reinforcement Learning" (PERL), in which we perform reward model training and reinforcement learning using LoRA. We compare PERL to conventional fine-tuning (full-tuning) across various configurations for 7 benchmarks, including 2 novel datasets, of reward modeling and reinforcement learning. We find that PERL performs on par with the conventional RLHF setting, while training faster, and with less memory. This enables the high performance of RLHF, while reducing the computational burden that limits its adoption as an alignment technique for Large Language Models. We also release 2 novel thumbs up/down preference datasets: "Taskmaster Coffee", and "Taskmaster Ticketing" to promote research around RLHF. |
|
2024-03-19T00:00:00 | 2403.10616 | DiPaCo: Distributed Path Composition | [
"Arthur Douillard",
"Qixuan Feng",
"Andrei A. Rusu",
"Adhiguna Kuncoro",
"Yani Donchev",
"Rachita Chhaparia",
"Ionel Gog",
"Marc'Aurelio Ranzato",
"Jiajun Shen",
"Arthur Szlam"
]
| Progress in machine learning (ML) has been fueled by scaling neural network models. This scaling has been enabled by ever more heroic feats of engineering, necessary for accommodating ML approaches that require high bandwidth communication between devices working in parallel. In this work, we propose a co-designed modular architecture and training approach for ML models, dubbed DIstributed PAth COmposition (DiPaCo). During training, DiPaCo distributes computation by paths through a set of shared modules. Together with a Local-SGD inspired optimization (DiLoCo) that keeps modules in sync with drastically reduced communication, Our approach facilitates training across poorly connected and heterogeneous workers, with a design that ensures robustness to worker failures and preemptions. At inference time, only a single path needs to be executed for each input, without the need for any model compression. We consider this approach as a first prototype towards a new paradigm of large-scale learning, one that is less synchronous and more modular. Our experiments on the widely used C4 benchmark show that, for the same amount of training steps but less wall-clock time, DiPaCo exceeds the performance of a 1 billion-parameter dense transformer language model by choosing one of 256 possible paths, each with a size of 150 million parameters. |
|
2024-03-19T00:00:00 | 2403.12034 | VFusion3D: Learning Scalable 3D Generative Models from Video Diffusion Models | [
"Junlin Han",
"Filippos Kokkinos",
"Philip Torr"
]
| This paper presents a novel paradigm for building scalable 3D generative models utilizing pre-trained video diffusion models. The primary obstacle in developing foundation 3D generative models is the limited availability of 3D data. Unlike images, texts, or videos, 3D data are not readily accessible and are difficult to acquire. This results in a significant disparity in scale compared to the vast quantities of other types of data. To address this issue, we propose using a video diffusion model, trained with extensive volumes of text, images, and videos, as a knowledge source for 3D data. By unlocking its multi-view generative capabilities through fine-tuning, we generate a large-scale synthetic multi-view dataset to train a feed-forward 3D generative model. The proposed model, VFusion3D, trained on nearly 3M synthetic multi-view data, can generate a 3D asset from a single image in seconds and achieves superior performance when compared to current SOTA feed-forward 3D generative models, with users preferring our results over 70% of the time. |
|
2024-03-20T00:00:00 | 2403.12706 | AnimateDiff-Lightning: Cross-Model Diffusion Distillation | [
"Shanchuan Lin",
"Xiao Yang"
]
| We present AnimateDiff-Lightning for lightning-fast video generation. Our model uses progressive adversarial diffusion distillation to achieve new state-of-the-art in few-step video generation. We discuss our modifications to adapt it for the video modality. Furthermore, we propose to simultaneously distill the probability flow of multiple base diffusion models, resulting in a single distilled motion module with broader style compatibility. We are pleased to release our distilled AnimateDiff-Lightning model for the community's use. |
|
2024-03-20T00:00:00 | 2403.12409 | ComboVerse: Compositional 3D Assets Creation Using Spatially-Aware Diffusion Guidance | [
"Yongwei Chen",
"Tengfei Wang",
"Tong Wu",
"Xingang Pan",
"Kui Jia",
"Ziwei Liu"
]
| Generating high-quality 3D assets from a given image is highly desirable in various applications such as AR/VR. Recent advances in single-image 3D generation explore feed-forward models that learn to infer the 3D model of an object without optimization. Though promising results have been achieved in single object generation, these methods often struggle to model complex 3D assets that inherently contain multiple objects. In this work, we present ComboVerse, a 3D generation framework that produces high-quality 3D assets with complex compositions by learning to combine multiple models. 1) We first perform an in-depth analysis of this ``multi-object gap'' from both model and data perspectives. 2) Next, with reconstructed 3D models of different objects, we seek to adjust their sizes, rotation angles, and locations to create a 3D asset that matches the given image. 3) To automate this process, we apply spatially-aware score distillation sampling (SSDS) from pretrained diffusion models to guide the positioning of objects. Our proposed framework emphasizes spatial alignment of objects, compared with standard score distillation sampling, and thus achieves more accurate results. Extensive experiments validate ComboVerse achieves clear improvements over existing methods in generating compositional 3D assets. |
|
2024-03-20T00:00:00 | 2403.12963 | FouriScale: A Frequency Perspective on Training-Free High-Resolution Image Synthesis | [
"Linjiang Huang",
"Rongyao Fang",
"Aiping Zhang",
"Guanglu Song",
"Si Liu",
"Yu Liu",
"Hongsheng Li"
]
| https://github.com/LeonHLJ/FouriScale | In this study, we delve into the generation of high-resolution images from pre-trained diffusion models, addressing persistent challenges, such as repetitive patterns and structural distortions, that emerge when models are applied beyond their trained resolutions. To address this issue, we introduce an innovative, training-free approach FouriScale from the perspective of frequency domain analysis. We replace the original convolutional layers in pre-trained diffusion models by incorporating a dilation technique along with a low-pass operation, intending to achieve structural consistency and scale consistency across resolutions, respectively. Further enhanced by a padding-then-crop strategy, our method can flexibly handle text-to-image generation of various aspect ratios. By using the FouriScale as guidance, our method successfully balances the structural integrity and fidelity of generated images, achieving an astonishing capacity of arbitrary-size, high-resolution, and high-quality generation. With its simplicity and compatibility, our method can provide valuable insights for future explorations into the synthesis of ultra-high-resolution images. The code will be released at https://github.com/LeonHLJ/FouriScale. |
2024-03-20T00:00:00 | 2403.12596 | Chart-based Reasoning: Transferring Capabilities from LLMs to VLMs | [
"Victor Carbune",
"Hassan Mansoor",
"Fangyu Liu",
"Rahul Aralikatte",
"Gilles Baechler",
"Jindong Chen",
"Abhanshu Sharma"
]
| Vision-language models (VLMs) are achieving increasingly strong performance on multimodal tasks. However, reasoning capabilities remain limited particularly for smaller VLMs, while those of large-language models (LLMs) have seen numerous improvements. We propose a technique to transfer capabilities from LLMs to VLMs. On the recently introduced ChartQA, our method obtains state-of-the-art performance when applied on the PaLI3-5B VLM by chen2023pali3, while also enabling much better performance on PlotQA and FigureQA. We first improve the chart representation by continuing the pre-training stage using an improved version of the chart-to-table translation task by liu2023deplot. We then propose constructing a 20x larger dataset than the original training set. To improve general reasoning capabilities and improve numerical operations, we synthesize reasoning traces using the table representation of charts. Lastly, our model is fine-tuned using the multitask loss introduced by hsieh2023distilling. Our variant ChartPaLI-5B outperforms even 10x larger models such as PaLIX-55B without using an upstream OCR system, while keeping inference time constant compared to the PaLI3-5B baseline. When rationales are further refined with a simple program-of-thought prompt chen2023program, our model outperforms the recently introduced Gemini Ultra and GPT-4V. |
|
2024-03-20T00:00:00 | 2403.12957 | GVGEN: Text-to-3D Generation with Volumetric Representation | [
"Xianglong He",
"Junyi Chen",
"Sida Peng",
"Di Huang",
"Yangguang Li",
"Xiaoshui Huang",
"Chun Yuan",
"Wanli Ouyang",
"Tong He"
]
| https://github.com/GVGEN/GVGEN | In recent years, 3D Gaussian splatting has emerged as a powerful technique for 3D reconstruction and generation, known for its fast and high-quality rendering capabilities. To address these shortcomings, this paper introduces a novel diffusion-based framework, GVGEN, designed to efficiently generate 3D Gaussian representations from text input. We propose two innovative techniques:(1) Structured Volumetric Representation. We first arrange disorganized 3D Gaussian points as a structured form GaussianVolume. This transformation allows the capture of intricate texture details within a volume composed of a fixed number of Gaussians. To better optimize the representation of these details, we propose a unique pruning and densifying method named the Candidate Pool Strategy, enhancing detail fidelity through selective optimization. (2) Coarse-to-fine Generation Pipeline. To simplify the generation of GaussianVolume and empower the model to generate instances with detailed 3D geometry, we propose a coarse-to-fine pipeline. It initially constructs a basic geometric structure, followed by the prediction of complete Gaussian attributes. Our framework, GVGEN, demonstrates superior performance in qualitative and quantitative assessments compared to existing 3D generation methods. Simultaneously, it maintains a fast generation speed (sim7 seconds), effectively striking a balance between quality and efficiency. |
2024-03-20T00:00:00 | 2403.12173 | TnT-LLM: Text Mining at Scale with Large Language Models | [
"Mengting Wan",
"Tara Safavi",
"Sujay Kumar Jauhar",
"Yujin Kim",
"Scott Counts",
"Jennifer Neville",
"Siddharth Suri",
"Chirag Shah",
"Ryen W White",
"Longqi Yang",
"Reid Andersen",
"Georg Buscher",
"Dhruv Joshi",
"Nagu Rangan"
]
| Transforming unstructured text into structured and meaningful forms, organized by useful category labels, is a fundamental step in text mining for downstream analysis and application. However, most existing methods for producing label taxonomies and building text-based label classifiers still rely heavily on domain expertise and manual curation, making the process expensive and time-consuming. This is particularly challenging when the label space is under-specified and large-scale data annotations are unavailable. In this paper, we address these challenges with Large Language Models (LLMs), whose prompt-based interface facilitates the induction and use of large-scale pseudo labels. We propose TnT-LLM, a two-phase framework that employs LLMs to automate the process of end-to-end label generation and assignment with minimal human effort for any given use-case. In the first phase, we introduce a zero-shot, multi-stage reasoning approach which enables LLMs to produce and refine a label taxonomy iteratively. In the second phase, LLMs are used as data labelers that yield training samples so that lightweight supervised classifiers can be reliably built, deployed, and served at scale. We apply TnT-LLM to the analysis of user intent and conversational domain for Bing Copilot (formerly Bing Chat), an open-domain chat-based search engine. Extensive experiments using both human and automatic evaluation metrics demonstrate that TnT-LLM generates more accurate and relevant label taxonomies when compared against state-of-the-art baselines, and achieves a favorable balance between accuracy and efficiency for classification at scale. We also share our practical experiences and insights on the challenges and opportunities of using LLMs for large-scale text mining in real-world applications. |
|
2024-03-20T00:00:00 | 2403.12943 | Vid2Robot: End-to-end Video-conditioned Policy Learning with Cross-Attention Transformers | [
"Vidhi Jain",
"Maria Attarian",
"Nikhil J Joshi",
"Ayzaan Wahid",
"Danny Driess",
"Quan Vuong",
"Pannag R Sanketi",
"Pierre Sermanet",
"Stefan Welker",
"Christine Chan",
"Igor Gilitschenski",
"Yonatan Bisk",
"Debidatta Dwibedi"
]
| While large-scale robotic systems typically rely on textual instructions for tasks, this work explores a different approach: can robots infer the task directly from observing humans? This shift necessitates the robot's ability to decode human intent and translate it into executable actions within its physical constraints and environment. We introduce Vid2Robot, a novel end-to-end video-based learning framework for robots. Given a video demonstration of a manipulation task and current visual observations, Vid2Robot directly produces robot actions. This is achieved through a unified representation model trained on a large dataset of human video and robot trajectory. The model leverages cross-attention mechanisms to fuse prompt video features to the robot's current state and generate appropriate actions that mimic the observed task. To further improve policy performance, we propose auxiliary contrastive losses that enhance the alignment between human and robot video representations. We evaluate Vid2Robot on real-world robots, demonstrating a 20% improvement in performance compared to other video-conditioned policies when using human demonstration videos. Additionally, our model exhibits emergent capabilities, such as successfully transferring observed motions from one object to another, and long-horizon composition, thus showcasing its potential for real-world applications. Project website: vid2robot.github.io |
|
2024-03-20T00:00:00 | 2403.12365 | GaussianFlow: Splatting Gaussian Dynamics for 4D Content Creation | [
"Quankai Gao",
"Qiangeng Xu",
"Zhe Cao",
"Ben Mildenhall",
"Wenchao Ma",
"Le Chen",
"Danhang Tang",
"Ulrich Neumann"
]
| https://github.com/Zerg-Overmind/GaussianFlow | Creating 4D fields of Gaussian Splatting from images or videos is a challenging task due to its under-constrained nature. While the optimization can draw photometric reference from the input videos or be regulated by generative models, directly supervising Gaussian motions remains underexplored. In this paper, we introduce a novel concept, Gaussian flow, which connects the dynamics of 3D Gaussians and pixel velocities between consecutive frames. The Gaussian flow can be efficiently obtained by splatting Gaussian dynamics into the image space. This differentiable process enables direct dynamic supervision from optical flow. Our method significantly benefits 4D dynamic content generation and 4D novel view synthesis with Gaussian Splatting, especially for contents with rich motions that are hard to be handled by existing methods. The common color drifting issue that happens in 4D generation is also resolved with improved Guassian dynamics. Superior visual quality on extensive experiments demonstrates our method's effectiveness. Quantitative and qualitative evaluations show that our method achieves state-of-the-art results on both tasks of 4D generation and 4D novel view synthesis. Project page: https://zerg-overmind.github.io/GaussianFlow.github.io/ |
2024-03-20T00:00:00 | 2403.12968 | LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression | [
"Zhuoshi Pan",
"Qianhui Wu",
"Huiqiang Jiang",
"Menglin Xia",
"Xufang Luo",
"Jue Zhang",
"Qingwei Lin",
"Victor Rühle",
"Yuqing Yang",
"Chin-Yew Lin",
"H. Vicky Zhao",
"Lili Qiu",
"Dongmei Zhang"
]
| https://github.com/microsoft/LLMLingua | This paper focuses on task-agnostic prompt compression for better generalizability and efficiency. Considering the redundancy in natural language, existing approaches compress prompts by removing tokens or lexical units according to their information entropy obtained from a causal language model such as LLaMa-7B. The challenge is that information entropy may be a suboptimal compression metric: (i) it only leverages unidirectional context and may fail to capture all essential information needed for prompt compression; (ii) it is not aligned with the prompt compression objective. To address these issues, we propose a data distillation procedure to derive knowledge from an LLM to compress prompts without losing crucial information, and meantime, introduce an extractive text compression dataset. We formulate prompt compression as a token classification problem to guarantee the faithfulness of the compressed prompt to the original one, and use a Transformer encoder as the base architecture to capture all essential information for prompt compression from the full bidirectional context. Our approach leads to lower latency by explicitly learning the compression objective with smaller models such as XLM-RoBERTa-large and mBERT. We evaluate our method on both in-domain and out-of-domain datasets, including MeetingBank, LongBench, ZeroScrolls, GSM8K, and BBH. Despite its small size, our model shows significant performance gains over strong baselines and demonstrates robust generalization ability across different LLMs. Additionally, our model is 3x-6x faster than existing prompt compression methods, while accelerating the end-to-end latency by 1.6x-2.9x with compression ratios of 2x-5x. |
2024-03-20T00:00:00 | 2403.12895 | mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understanding | [
"Anwen Hu",
"Haiyang Xu",
"Jiabo Ye",
"Ming Yan",
"Liang Zhang",
"Bo Zhang",
"Chen Li",
"Ji Zhang",
"Qin Jin",
"Fei Huang",
"Jingren Zhou"
]
| https://github.com/X-PLUG/mPLUG-DocOwl/tree/main/DocOwl1.5 | Structure information is critical for understanding the semantics of text-rich images, such as documents, tables, and charts. Existing Multimodal Large Language Models (MLLMs) for Visual Document Understanding are equipped with text recognition ability but lack general structure understanding abilities for text-rich document images. In this work, we emphasize the importance of structure information in Visual Document Understanding and propose the Unified Structure Learning to boost the performance of MLLMs. Our Unified Structure Learning comprises structure-aware parsing tasks and multi-grained text localization tasks across 5 domains: document, webpage, table, chart, and natural image. To better encode structure information, we design a simple and effective vision-to-text module H-Reducer, which can not only maintain the layout information but also reduce the length of visual features by merging horizontal adjacent patches through convolution, enabling the LLM to understand high-resolution images more efficiently. Furthermore, by constructing structure-aware text sequences and multi-grained pairs of texts and bounding boxes for publicly available text-rich images, we build a comprehensive training set DocStruct4M to support structure learning. Finally, we construct a small but high-quality reasoning tuning dataset DocReason25K to trigger the detailed explanation ability in the document domain. Our model DocOwl 1.5 achieves state-of-the-art performance on 10 visual document understanding benchmarks, improving the SOTA performance of MLLMs with a 7B LLM by more than 10 points in 5/10 benchmarks. Our codes, models, and datasets are publicly available at https://github.com/X-PLUG/mPLUG-DocOwl/tree/main/DocOwl1.5. |
2024-03-20T00:00:00 | 2403.12881 | Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models | [
"Zehui Chen",
"Kuikun Liu",
"Qiuchen Wang",
"Wenwei Zhang",
"Jiangning Liu",
"Dahua Lin",
"Kai Chen",
"Feng Zhao"
]
| https://github.com/InternLM/Agent-FLAN | Open-sourced Large Language Models (LLMs) have achieved great success in various NLP tasks, however, they are still far inferior to API-based models when acting as agents. How to integrate agent ability into general LLMs becomes a crucial and urgent problem. This paper first delivers three key observations: (1) the current agent training corpus is entangled with both formats following and agent reasoning, which significantly shifts from the distribution of its pre-training data; (2) LLMs exhibit different learning speeds on the capabilities required by agent tasks; and (3) current approaches have side-effects when improving agent abilities by introducing hallucinations. Based on the above findings, we propose Agent-FLAN to effectively Fine-tune LANguage models for Agents. Through careful decomposition and redesign of the training corpus, Agent-FLAN enables Llama2-7B to outperform prior best works by 3.5\% across various agent evaluation datasets. With comprehensively constructed negative samples, Agent-FLAN greatly alleviates the hallucination issues based on our established evaluation benchmark. Besides, it consistently improves the agent capability of LLMs when scaling model sizes while slightly enhancing the general capability of LLMs. The code will be available at https://github.com/InternLM/Agent-FLAN. |
2024-03-20T00:00:00 | 2403.12906 | TexDreamer: Towards Zero-Shot High-Fidelity 3D Human Texture Generation | [
"Yufei Liu",
"Junwei Zhu",
"Junshu Tang",
"Shijie Zhang",
"Jiangning Zhang",
"Weijian Cao",
"Chengjie Wang",
"Yunsheng Wu",
"Dongjin Huang"
]
| https://github.com/ggxxii/texdreamer | Texturing 3D humans with semantic UV maps remains a challenge due to the difficulty of acquiring reasonably unfolded UV. Despite recent text-to-3D advancements in supervising multi-view renderings using large text-to-image (T2I) models, issues persist with generation speed, text consistency, and texture quality, resulting in data scarcity among existing datasets. We present TexDreamer, the first zero-shot multimodal high-fidelity 3D human texture generation model. Utilizing an efficient texture adaptation finetuning strategy, we adapt large T2I model to a semantic UV structure while preserving its original generalization capability. Leveraging a novel feature translator module, the trained model is capable of generating high-fidelity 3D human textures from either text or image within seconds. Furthermore, we introduce ArTicuLated humAn textureS (ATLAS), the largest high-resolution (1024 X 1024) 3D human texture dataset which contains 50k high-fidelity textures with text descriptions. |
2024-03-20T00:00:00 | 2403.12962 | FRESCO: Spatial-Temporal Correspondence for Zero-Shot Video Translation | [
"Shuai Yang",
"Yifan Zhou",
"Ziwei Liu",
"Chen Change Loy"
]
| The remarkable efficacy of text-to-image diffusion models has motivated extensive exploration of their potential application in video domains. Zero-shot methods seek to extend image diffusion models to videos without necessitating model training. Recent methods mainly focus on incorporating inter-frame correspondence into attention mechanisms. However, the soft constraint imposed on determining where to attend to valid features can sometimes be insufficient, resulting in temporal inconsistency. In this paper, we introduce FRESCO, intra-frame correspondence alongside inter-frame correspondence to establish a more robust spatial-temporal constraint. This enhancement ensures a more consistent transformation of semantically similar content across frames. Beyond mere attention guidance, our approach involves an explicit update of features to achieve high spatial-temporal consistency with the input video, significantly improving the visual coherence of the resulting translated videos. Extensive experiments demonstrate the effectiveness of our proposed framework in producing high-quality, coherent videos, marking a notable improvement over existing zero-shot methods. |
|
2024-03-21T00:00:00 | 2403.13248 | Mora: Enabling Generalist Video Generation via A Multi-Agent Framework | [
"Zhengqing Yuan",
"Ruoxi Chen",
"Zhaoxu Li",
"Haolong Jia",
"Lifang He",
"Chi Wang",
"Lichao Sun"
]
| https://github.com/lichao-sun/Mora | Sora is the first large-scale generalist video generation model that garnered significant attention across society. Since its launch by OpenAI in February 2024, no other video generation models have paralleled {Sora}'s performance or its capacity to support a broad spectrum of video generation tasks. Additionally, there are only a few fully published video generation models, with the majority being closed-source. To address this gap, this paper proposes a new multi-agent framework Mora, which incorporates several advanced visual AI agents to replicate generalist video generation demonstrated by Sora. In particular, Mora can utilize multiple visual agents and successfully mimic Sora's video generation capabilities in various tasks, such as (1) text-to-video generation, (2) text-conditional image-to-video generation, (3) extend generated videos, (4) video-to-video editing, (5) connect videos and (6) simulate digital worlds. Our extensive experimental results show that Mora achieves performance that is proximate to that of Sora in various tasks. However, there exists an obvious performance gap between our work and Sora when assessed holistically. In summary, we hope this project can guide the future trajectory of video generation through collaborative AI agents. |
2024-03-21T00:00:00 | 2403.13064 | SceneScript: Reconstructing Scenes With An Autoregressive Structured Language Model | [
"Armen Avetisyan",
"Christopher Xie",
"Henry Howard-Jenkins",
"Tsun-Yi Yang",
"Samir Aroudj",
"Suvam Patra",
"Fuyang Zhang",
"Duncan Frost",
"Luke Holland",
"Campbell Orme",
"Jakob Engel",
"Edward Miller",
"Richard Newcombe",
"Vasileios Balntas"
]
| We introduce SceneScript, a method that directly produces full scene models as a sequence of structured language commands using an autoregressive, token-based approach. Our proposed scene representation is inspired by recent successes in transformers & LLMs, and departs from more traditional methods which commonly describe scenes as meshes, voxel grids, point clouds or radiance fields. Our method infers the set of structured language commands directly from encoded visual data using a scene language encoder-decoder architecture. To train SceneScript, we generate and release a large-scale synthetic dataset called Aria Synthetic Environments consisting of 100k high-quality in-door scenes, with photorealistic and ground-truth annotated renders of egocentric scene walkthroughs. Our method gives state-of-the art results in architectural layout estimation, and competitive results in 3D object detection. Lastly, we explore an advantage for SceneScript, which is the ability to readily adapt to new commands via simple additions to the structured language, which we illustrate for tasks such as coarse 3D object part reconstruction. |
|
2024-03-21T00:00:00 | 2403.13793 | Evaluating Frontier Models for Dangerous Capabilities | [
"Mary Phuong",
"Matthew Aitchison",
"Elliot Catt",
"Sarah Cogan",
"Alexandre Kaskasoli",
"Victoria Krakovna",
"David Lindner",
"Matthew Rahtz",
"Yannis Assael",
"Sarah Hodkinson",
"Heidi Howard",
"Tom Lieberum",
"Ramana Kumar",
"Maria Abi Raad",
"Albert Webson",
"Lewis Ho",
"Sharon Lin",
"Sebastian Farquhar",
"Marcus Hutter",
"Gregoire Deletang",
"Anian Ruoss",
"Seliem El-Sayed",
"Sasha Brown",
"Anca Dragan",
"Rohin Shah",
"Allan Dafoe",
"Toby Shevlane"
]
| To understand the risks posed by a new AI system, we must understand what it can and cannot do. Building on prior work, we introduce a programme of new "dangerous capability" evaluations and pilot them on Gemini 1.0 models. Our evaluations cover four areas: (1) persuasion and deception; (2) cyber-security; (3) self-proliferation; and (4) self-reasoning. We do not find evidence of strong dangerous capabilities in the models we evaluated, but we flag early warning signs. Our goal is to help advance a rigorous science of dangerous capability evaluation, in preparation for future models. |
|
2024-03-21T00:00:00 | 2403.13787 | RewardBench: Evaluating Reward Models for Language Modeling | [
"Nathan Lambert",
"Valentina Pyatkin",
"Jacob Morrison",
"LJ Miranda",
"Bill Yuchen Lin",
"Khyathi Chandu",
"Nouha Dziri",
"Sachin Kumar",
"Tom Zick",
"Yejin Choi",
"Noah A. Smith",
"Hannaneh Hajishirzi"
]
| https://github.com/allenai/reward-bench | Reward models (RMs) are at the crux of successful RLHF to align pretrained models to human preferences, yet there has been relatively little study that focuses on evaluation of those reward models. Evaluating reward models presents an opportunity to understand the opaque technologies used for alignment of language models and which values are embedded in them. To date, very few descriptors of capabilities, training methods, or open-source reward models exist. In this paper, we present RewardBench, a benchmark dataset and code-base for evaluation, to enhance scientific understanding of reward models. The RewardBench dataset is a collection of prompt-win-lose trios spanning chat, reasoning, and safety, to benchmark how reward models perform on challenging, structured and out-of-distribution queries. We created specific comparison datasets for RMs that have subtle, but verifiable reasons (e.g. bugs, incorrect facts) why one answer should be preferred to another. On the RewardBench leaderboard, we evaluate reward models trained with a variety of methods, such as the direct MLE training of classifiers and the implicit reward modeling of Direct Preference Optimization (DPO), and on a spectrum of datasets. We present many findings on propensity for refusals, reasoning limitations, and instruction following shortcomings of various reward models towards a better understanding of the RLHF process. |
2024-03-21T00:00:00 | 2403.13799 | Reverse Training to Nurse the Reversal Curse | [
"Olga Golovneva",
"Zeyuan Allen-Zhu",
"Jason Weston",
"Sainbayar Sukhbaatar"
]
| Large language models (LLMs) have a surprising failure: when trained on "A has a feature B", they do not generalize to "B is a feature of A", which is termed the Reversal Curse. Even when training with trillions of tokens this issue still appears due to Zipf's law - hence even if we train on the entire internet. This work proposes an alternative training scheme, called reverse training, whereby all words are used twice, doubling the amount of available tokens. The LLM is trained in both forward and reverse directions by reversing the training strings while preserving (i.e., not reversing) chosen substrings, such as entities. We show that data-matched reverse-trained models provide superior performance to standard models on standard tasks, and compute-matched reverse-trained models provide far superior performance on reversal tasks, helping resolve the reversal curse issue. |
|
2024-03-21T00:00:00 | 2403.13535 | IDAdapter: Learning Mixed Features for Tuning-Free Personalization of Text-to-Image Models | [
"Siying Cui",
"Jiankang Deng",
"Jia Guo",
"Xiang An",
"Yongle Zhao",
"Xinyu Wei",
"Ziyong Feng"
]
| Leveraging Stable Diffusion for the generation of personalized portraits has emerged as a powerful and noteworthy tool, enabling users to create high-fidelity, custom character avatars based on their specific prompts. However, existing personalization methods face challenges, including test-time fine-tuning, the requirement of multiple input images, low preservation of identity, and limited diversity in generated outcomes. To overcome these challenges, we introduce IDAdapter, a tuning-free approach that enhances the diversity and identity preservation in personalized image generation from a single face image. IDAdapter integrates a personalized concept into the generation process through a combination of textual and visual injections and a face identity loss. During the training phase, we incorporate mixed features from multiple reference images of a specific identity to enrich identity-related content details, guiding the model to generate images with more diverse styles, expressions, and angles compared to previous works. Extensive evaluations demonstrate the effectiveness of our method, achieving both diversity and identity fidelity in generated images. |
|
2024-03-21T00:00:00 | 2403.13043 | When Do We Not Need Larger Vision Models? | [
"Baifeng Shi",
"Ziyang Wu",
"Maolin Mao",
"Xin Wang",
"Trevor Darrell"
]
| https://github.com/bfshi/scaling_on_scales | Scaling up the size of vision models has been the de facto standard to obtain more powerful visual representations. In this work, we discuss the point beyond which larger vision models are not necessary. First, we demonstrate the power of Scaling on Scales (S^2), whereby a pre-trained and frozen smaller vision model (e.g., ViT-B or ViT-L), run over multiple image scales, can outperform larger models (e.g., ViT-H or ViT-G) on classification, segmentation, depth estimation, Multimodal LLM (MLLM) benchmarks, and robotic manipulation. Notably, S^2 achieves state-of-the-art performance in detailed understanding of MLLM on the V* benchmark, surpassing models such as GPT-4V. We examine the conditions under which S^2 is a preferred scaling approach compared to scaling on model size. While larger models have the advantage of better generalization on hard examples, we show that features of larger vision models can be well approximated by those of multi-scale smaller models. This suggests most, if not all, of the representations learned by current large pre-trained models can also be obtained from multi-scale smaller models. Our results show that a multi-scale smaller model has comparable learning capacity to a larger model, and pre-training smaller models with S^2 can match or even exceed the advantage of larger models. We release a Python package that can apply S^2 on any vision model with one line of code: https://github.com/bfshi/scaling_on_scales. |
2024-03-21T00:00:00 | 2401.13923 | Towards 3D Molecule-Text Interpretation in Language Models | [
"Sihang Li",
"Zhiyuan Liu",
"Yanchen Luo",
"Xiang Wang",
"Xiangnan He",
"Kenji Kawaguchi",
"Tat-Seng Chua",
"Qi Tian"
]
| https://github.com/lsh0520/3D-MoLM | Language Models (LMs) have greatly influenced diverse domains. However, their inherent limitation in comprehending 3D molecular structures has considerably constrained their potential in the biomolecular domain. To bridge this gap, we focus on 3D molecule-text interpretation, and propose 3D-MoLM: 3D-Molecular Language Modeling. Specifically, 3D-MoLM enables an LM to interpret and analyze 3D molecules by equipping the LM with a 3D molecular encoder. This integration is achieved by a 3D molecule-text projector, bridging the 3D molecular encoder's representation space and the LM's input space. Moreover, to enhance 3D-MoLM's ability of cross-modal molecular understanding and instruction following, we meticulously curated a 3D molecule-centric instruction tuning dataset -- 3D-MoIT. Through 3D molecule-text alignment and 3D molecule-centric instruction tuning, 3D-MoLM establishes an integration of 3D molecular encoder and LM. It significantly surpasses existing baselines on downstream tasks, including molecule-text retrieval, molecule captioning, and more challenging open-text molecular QA tasks, especially focusing on 3D-dependent properties. |
2024-03-21T00:00:00 | 2403.13044 | Magic Fixup: Streamlining Photo Editing by Watching Dynamic Videos | [
"Hadi Alzayer",
"Zhihao Xia",
"Xuaner Zhang",
"Eli Shechtman",
"Jia-Bin Huang",
"Michael Gharbi"
]
| We propose a generative model that, given a coarsely edited image, synthesizes a photorealistic output that follows the prescribed layout. Our method transfers fine details from the original image and preserves the identity of its parts. Yet, it adapts it to the lighting and context defined by the new layout. Our key insight is that videos are a powerful source of supervision for this task: objects and camera motions provide many observations of how the world changes with viewpoint, lighting, and physical interactions. We construct an image dataset in which each sample is a pair of source and target frames extracted from the same video at randomly chosen time intervals. We warp the source frame toward the target using two motion models that mimic the expected test-time user edits. We supervise our model to translate the warped image into the ground truth, starting from a pretrained diffusion model. Our model design explicitly enables fine detail transfer from the source frame to the generated image, while closely following the user-specified layout. We show that by using simple segmentations and coarse 2D manipulations, we can synthesize a photorealistic edit faithful to the user's input while addressing second-order effects like harmonizing the lighting and physical interactions between edited objects. |
|
2024-03-21T00:00:00 | 2403.13524 | Compress3D: a Compressed Latent Space for 3D Generation from a Single Image | [
"Bowen Zhang",
"Tianyu Yang",
"Yu Li",
"Lei Zhang",
"Xi Zhao"
]
| 3D generation has witnessed significant advancements, yet efficiently producing high-quality 3D assets from a single image remains challenging. In this paper, we present a triplane autoencoder, which encodes 3D models into a compact triplane latent space to effectively compress both the 3D geometry and texture information. Within the autoencoder framework, we introduce a 3D-aware cross-attention mechanism, which utilizes low-resolution latent representations to query features from a high-resolution 3D feature volume, thereby enhancing the representation capacity of the latent space. Subsequently, we train a diffusion model on this refined latent space. In contrast to solely relying on image embedding for 3D generation, our proposed method advocates for the simultaneous utilization of both image embedding and shape embedding as conditions. Specifically, the shape embedding is estimated via a diffusion prior model conditioned on the image embedding. Through comprehensive experiments, we demonstrate that our method outperforms state-of-the-art algorithms, achieving superior performance while requiring less training data and time. Our approach enables the generation of high-quality 3D assets in merely 7 seconds on a single A100 GPU. |
|
2024-03-21T00:00:00 | 2403.13372 | LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models | [
"Yaowei Zheng",
"Richong Zhang",
"Junhao Zhang",
"Yanhan Ye",
"Zheyan Luo"
]
| https://github.com/hiyouga/LLaMA-Factory | Efficient fine-tuning is vital for adapting large language models (LLMs) to downstream tasks. However, it requires non-trivial efforts to implement these methods on different models. We present LlamaFactory, a unified framework that integrates a suite of cutting-edge efficient training methods. It allows users to flexibly customize the fine-tuning of 100+ LLMs without the need for coding through the built-in web UI LlamaBoard. We empirically validate the efficiency and effectiveness of our framework on language modeling and text generation tasks. It has been released at https://github.com/hiyouga/LLaMA-Factory and already received over 13,000 stars and 1,600 forks. |
2024-03-21T00:00:00 | 2403.13745 | Be-Your-Outpainter: Mastering Video Outpainting through Input-Specific Adaptation | [
"Fu-Yun Wang",
"Xiaoshi Wu",
"Zhaoyang Huang",
"Xiaoyu Shi",
"Dazhong Shen",
"Guanglu Song",
"Yu Liu",
"Hongsheng Li"
]
| Video outpainting is a challenging task, aiming at generating video content outside the viewport of the input video while maintaining inter-frame and intra-frame consistency. Existing methods fall short in either generation quality or flexibility. We introduce MOTIA Mastering Video Outpainting Through Input-Specific Adaptation, a diffusion-based pipeline that leverages both the intrinsic data-specific patterns of the source video and the image/video generative prior for effective outpainting. MOTIA comprises two main phases: input-specific adaptation and pattern-aware outpainting. The input-specific adaptation phase involves conducting efficient and effective pseudo outpainting learning on the single-shot source video. This process encourages the model to identify and learn patterns within the source video, as well as bridging the gap between standard generative processes and outpainting. The subsequent phase, pattern-aware outpainting, is dedicated to the generalization of these learned patterns to generate outpainting outcomes. Additional strategies including spatial-aware insertion and noise travel are proposed to better leverage the diffusion model's generative prior and the acquired video patterns from source videos. Extensive evaluations underscore MOTIA's superiority, outperforming existing state-of-the-art methods in widely recognized benchmarks. Notably, these advancements are achieved without necessitating extensive, task-specific tuning. |
|
2024-03-21T00:00:00 | 2403.13806 | RadSplat: Radiance Field-Informed Gaussian Splatting for Robust Real-Time Rendering with 900+ FPS | [
"Michael Niemeyer",
"Fabian Manhardt",
"Marie-Julie Rakotosaona",
"Michael Oechsle",
"Daniel Duckworth",
"Rama Gosula",
"Keisuke Tateno",
"John Bates",
"Dominik Kaeser",
"Federico Tombari"
]
| Recent advances in view synthesis and real-time rendering have achieved photorealistic quality at impressive rendering speeds. While Radiance Field-based methods achieve state-of-the-art quality in challenging scenarios such as in-the-wild captures and large-scale scenes, they often suffer from excessively high compute requirements linked to volumetric rendering. Gaussian Splatting-based methods, on the other hand, rely on rasterization and naturally achieve real-time rendering but suffer from brittle optimization heuristics that underperform on more challenging scenes. In this work, we present RadSplat, a lightweight method for robust real-time rendering of complex scenes. Our main contributions are threefold. First, we use radiance fields as a prior and supervision signal for optimizing point-based scene representations, leading to improved quality and more robust optimization. Next, we develop a novel pruning technique reducing the overall point count while maintaining high quality, leading to smaller and more compact scene representations with faster inference speeds. Finally, we propose a novel test-time filtering approach that further accelerates rendering and allows to scale to larger, house-sized scenes. We find that our method enables state-of-the-art synthesis of complex captures at 900+ FPS. |
|
2024-03-21T00:00:00 | 2403.13501 | VSTAR: Generative Temporal Nursing for Longer Dynamic Video Synthesis | [
"Yumeng Li",
"William Beluch",
"Margret Keuper",
"Dan Zhang",
"Anna Khoreva"
]
| https://github.com/boschresearch/VSTAR | Despite tremendous progress in the field of text-to-video (T2V) synthesis, open-sourced T2V diffusion models struggle to generate longer videos with dynamically varying and evolving content. They tend to synthesize quasi-static videos, ignoring the necessary visual change-over-time implied in the text prompt. At the same time, scaling these models to enable longer, more dynamic video synthesis often remains computationally intractable. To address this challenge, we introduce the concept of Generative Temporal Nursing (GTN), where we aim to alter the generative process on the fly during inference to improve control over the temporal dynamics and enable generation of longer videos. We propose a method for GTN, dubbed VSTAR, which consists of two key ingredients: 1) Video Synopsis Prompting (VSP) - automatic generation of a video synopsis based on the original single prompt leveraging LLMs, which gives accurate textual guidance to different visual states of longer videos, and 2) Temporal Attention Regularization (TAR) - a regularization technique to refine the temporal attention units of the pre-trained T2V diffusion models, which enables control over the video dynamics. We experimentally showcase the superiority of the proposed approach in generating longer, visually appealing videos over existing open-sourced T2V models. We additionally analyze the temporal attention maps realized with and without VSTAR, demonstrating the importance of applying our method to mitigate neglect of the desired visual change over time. |
2024-03-21T00:00:00 | 2403.13788 | DepthFM: Fast Monocular Depth Estimation with Flow Matching | [
"Ming Gui",
"Johannes S. Fischer",
"Ulrich Prestel",
"Pingchuan Ma",
"Dmytro Kotovenko",
"Olga Grebenkova",
"Stefan Andreas Baumann",
"Vincent Tao Hu",
"Björn Ommer"
]
| Monocular depth estimation is crucial for numerous downstream vision tasks and applications. Current discriminative approaches to this problem are limited due to blurry artifacts, while state-of-the-art generative methods suffer from slow sampling due to their SDE nature. Rather than starting from noise, we seek a direct mapping from input image to depth map. We observe that this can be effectively framed using flow matching, since its straight trajectories through solution space offer efficiency and high quality. Our study demonstrates that a pre-trained image diffusion model can serve as an adequate prior for a flow matching depth model, allowing efficient training on only synthetic data to generalize to real images. We find that an auxiliary surface normals loss further improves the depth estimates. Due to the generative nature of our approach, our model reliably predicts the confidence of its depth estimates. On standard benchmarks of complex natural scenes, our lightweight approach exhibits state-of-the-art performance at favorable low computational cost despite only being trained on little synthetic data. |
|
2024-03-21T00:00:00 | 2403.13802 | ZigMa: Zigzag Mamba Diffusion Model | [
"Vincent Tao Hu",
"Stefan Andreas Baumann",
"Ming Gui",
"Olga Grebenkova",
"Pingchuan Ma",
"Johannes Fischer",
"Bjorn Ommer"
]
| https://github.com/CompVis/zigma | The diffusion model has long been plagued by scalability and quadratic complexity issues, especially within transformer-based structures. In this study, we aim to leverage the long sequence modeling capability of a State-Space Model called Mamba to extend its applicability to visual data generation. Firstly, we identify a critical oversight in most current Mamba-based vision methods, namely the lack of consideration for spatial continuity in the scan scheme of Mamba. Secondly, building upon this insight, we introduce a simple, plug-and-play, zero-parameter method named Zigzag Mamba, which outperforms Mamba-based baselines and demonstrates improved speed and memory utilization compared to transformer-based baselines. Lastly, we integrate Zigzag Mamba with the Stochastic Interpolant framework to investigate the scalability of the model on large-resolution visual datasets, such as FacesHQ 1024times 1024 and UCF101, MultiModal-CelebA-HQ, and MS COCO 256times 256. Code will be released at https://taohu.me/zigma/ |
2024-03-21T00:00:00 | 2403.13447 | HyperLLaVA: Dynamic Visual and Language Expert Tuning for Multimodal Large Language Models | [
"Wenqiao Zhang",
"Tianwei Lin",
"Jiang Liu",
"Fangxun Shu",
"Haoyuan Li",
"Lei Zhang",
"He Wanggui",
"Hao Zhou",
"Zheqi Lv",
"Hao Jiang",
"Juncheng Li",
"Siliang Tang",
"Yueting Zhuang"
]
| https://github.com/DCDmllm/HyperLLaVA | Recent advancements indicate that scaling up Multimodal Large Language Models (MLLMs) effectively enhances performance on downstream multimodal tasks. The prevailing MLLM paradigm, e.g., LLaVA, transforms visual features into text-like tokens using a static vision-language mapper, thereby enabling static LLMs to develop the capability to comprehend visual information through visual instruction tuning. Although promising, the static tuning strategy~The static tuning refers to the trained model with static parameters. that shares the same parameters may constrain performance across different downstream multimodal tasks. In light of this, we introduce HyperLLaVA, which involves adaptive tuning of the projector and LLM parameters, in conjunction with a dynamic visual expert and language expert, respectively. These experts are derived from HyperNetworks, which generates adaptive parameter shifts through visual and language guidance, enabling dynamic projector and LLM modeling in two-stage training. Our experiments demonstrate that our solution significantly surpasses LLaVA on existing MLLM benchmarks, including MME, MMBench, SEED-Bench, and LLaVA-Bench. ~Our project is available on the link https://github.com/DCDmllm/HyperLLaVA. |
2024-03-21T00:00:00 | 2403.13187 | Evolutionary Optimization of Model Merging Recipes | [
"Takuya Akiba",
"Makoto Shing",
"Yujin Tang",
"Qi Sun",
"David Ha"
]
| https://github.com/SakanaAI/evolutionary-model-merge | We present a novel application of evolutionary algorithms to automate the creation of powerful foundation models. While model merging has emerged as a promising approach for LLM development due to its cost-effectiveness, it currently relies on human intuition and domain knowledge, limiting its potential. Here, we propose an evolutionary approach that overcomes this limitation by automatically discovering effective combinations of diverse open-source models, harnessing their collective intelligence without requiring extensive additional training data or compute. Our approach operates in both parameter space and data flow space, allowing for optimization beyond just the weights of the individual models. This approach even facilitates cross-domain merging, generating models like a Japanese LLM with Math reasoning capabilities. Surprisingly, our Japanese Math LLM achieved state-of-the-art performance on a variety of established Japanese LLM benchmarks, even surpassing models with significantly more parameters, despite not being explicitly trained for such tasks. Furthermore, a culturally-aware Japanese VLM generated through our approach demonstrates its effectiveness in describing Japanese culture-specific content, outperforming previous Japanese VLMs. This work not only contributes new state-of-the-art models back to the open-source community, but also introduces a new paradigm for automated model composition, paving the way for exploring alternative, efficient approaches to foundation model development. |
2024-03-22T00:00:00 | 2403.14148 | Efficient Video Diffusion Models via Content-Frame Motion-Latent Decomposition | [
"Sihyun Yu",
"Weili Nie",
"De-An Huang",
"Boyi Li",
"Jinwoo Shin",
"Anima Anandkumar"
]
| Video diffusion models have recently made great progress in generation quality, but are still limited by the high memory and computational requirements. This is because current video diffusion models often attempt to process high-dimensional videos directly. To tackle this issue, we propose content-motion latent diffusion model (CMD), a novel efficient extension of pretrained image diffusion models for video generation. Specifically, we propose an autoencoder that succinctly encodes a video as a combination of a content frame (like an image) and a low-dimensional motion latent representation. The former represents the common content, and the latter represents the underlying motion in the video, respectively. We generate the content frame by fine-tuning a pretrained image diffusion model, and we generate the motion latent representation by training a new lightweight diffusion model. A key innovation here is the design of a compact latent space that can directly utilizes a pretrained image diffusion model, which has not been done in previous latent video diffusion models. This leads to considerably better quality generation and reduced computational costs. For instance, CMD can sample a video 7.7times faster than prior approaches by generating a video of 512times1024 resolution and length 16 in 3.1 seconds. Moreover, CMD achieves an FVD score of 212.7 on WebVid-10M, 27.3% better than the previous state-of-the-art of 292.4. |
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