date
timestamp[ns]date 2023-05-05 00:00:00
2025-07-11 00:00:00
| arxiv_id
stringlengths 10
10
| title
stringlengths 8
202
| authors
listlengths 1
942
| github
stringlengths 0
116
| abstract
stringlengths 165
1.92k
|
|---|---|---|---|---|---|
2024-01-22T00:00:00 | 2401.10241 | Zero Bubble Pipeline Parallelism | [
"Penghui Qi",
"Xinyi Wan",
"Guangxing Huang",
"Min Lin"
]
| https://github.com/sail-sg/zero-bubble-pipeline-parallelism | Pipeline parallelism is one of the key components for large-scale distributed training, yet its efficiency suffers from pipeline bubbles which were deemed inevitable. In this work, we introduce a scheduling strategy that, to our knowledge, is the first to successfully achieve zero pipeline bubbles under synchronous training semantics. The key idea behind this improvement is to split the backward computation into two parts, one that computes gradient for the input and another that computes for the parameters. Based on this idea, we handcraft novel pipeline schedules that significantly outperform the baseline methods. We further develop an algorithm that automatically finds an optimal schedule based on specific model configuration and memory limit. Additionally, to truly achieve zero bubble, we introduce a novel technique to bypass synchronizations during the optimizer step. Experimental evaluations show that our method outperforms the 1F1B schedule up to 23% in throughput under a similar memory limit. This number can be further pushed to 31% when the memory constraint is relaxed. We believe our results mark a major step forward in harnessing the true potential of pipeline parallelism. We open sourced our implementation based on the popular Megatron-LM repository on https://github.com/sail-sg/zero-bubble-pipeline-parallelism. |
2024-01-22T00:00:00 | 2401.10838 | Rambler: Supporting Writing With Speech via LLM-Assisted Gist Manipulation | [
"Susan Lin",
"Jeremy Warner",
"J. D. Zamfirescu-Pereira",
"Matthew G. Lee",
"Sauhard Jain",
"Michael Xuelin Huang",
"Piyawat Lertvittayakumjorn",
"Shanqing Cai",
"Shumin Zhai",
"Björn Hartmann",
"Can Liu"
]
| Dictation enables efficient text input on mobile devices. However, writing with speech can produce disfluent, wordy, and incoherent text and thus requires heavy post-processing. This paper presents Rambler, an LLM-powered graphical user interface that supports gist-level manipulation of dictated text with two main sets of functions: gist extraction and macro revision. Gist extraction generates keywords and summaries as anchors to support the review and interaction with spoken text. LLM-assisted macro revisions allow users to respeak, split, merge and transform dictated text without specifying precise editing locations. Together they pave the way for interactive dictation and revision that help close gaps between spontaneous spoken words and well-structured writing. In a comparative study with 12 participants performing verbal composition tasks, Rambler outperformed the baseline of a speech-to-text editor + ChatGPT, as it better facilitates iterative revisions with enhanced user control over the content while supporting surprisingly diverse user strategies. |
|
2024-01-22T00:00:00 | 2401.10831 | Understanding Video Transformers via Universal Concept Discovery | [
"Matthew Kowal",
"Achal Dave",
"Rares Ambrus",
"Adrien Gaidon",
"Konstantinos G. Derpanis",
"Pavel Tokmakov"
]
| This paper studies the problem of concept-based interpretability of transformer representations for videos. Concretely, we seek to explain the decision-making process of video transformers based on high-level, spatiotemporal concepts that are automatically discovered. Prior research on concept-based interpretability has concentrated solely on image-level tasks. Comparatively, video models deal with the added temporal dimension, increasing complexity and posing challenges in identifying dynamic concepts over time. In this work, we systematically address these challenges by introducing the first Video Transformer Concept Discovery (VTCD) algorithm. To this end, we propose an efficient approach for unsupervised identification of units of video transformer representations - concepts, and ranking their importance to the output of a model. The resulting concepts are highly interpretable, revealing spatio-temporal reasoning mechanisms and object-centric representations in unstructured video models. Performing this analysis jointly over a diverse set of supervised and self-supervised representations, we discover that some of these mechanism are universal in video transformers. Finally, we demonstrate that VTCDcan be used to improve model performance for fine-grained tasks. |
|
2024-01-23T00:00:00 | 2401.12070 | Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text | [
"Abhimanyu Hans",
"Avi Schwarzschild",
"Valeriia Cherepanova",
"Hamid Kazemi",
"Aniruddha Saha",
"Micah Goldblum",
"Jonas Geiping",
"Tom Goldstein"
]
| Detecting text generated by modern large language models is thought to be hard, as both LLMs and humans can exhibit a wide range of complex behaviors. However, we find that a score based on contrasting two closely related language models is highly accurate at separating human-generated and machine-generated text. Based on this mechanism, we propose a novel LLM detector that only requires simple calculations using a pair of pre-trained LLMs. The method, called Binoculars, achieves state-of-the-art accuracy without any training data. It is capable of spotting machine text from a range of modern LLMs without any model-specific modifications. We comprehensively evaluate Binoculars on a number of text sources and in varied situations. Over a wide range of document types, Binoculars detects over 90% of generated samples from ChatGPT (and other LLMs) at a false positive rate of 0.01%, despite not being trained on any ChatGPT data. |
|
2024-01-23T00:00:00 | 2401.11944 | CMMMU: A Chinese Massive Multi-discipline Multimodal Understanding Benchmark | [
"Ge Zhang",
"Xinrun Du",
"Bei Chen",
"Yiming Liang",
"Tongxu Luo",
"Tianyu Zheng",
"Kang Zhu",
"Yuyang Cheng",
"Chunpu Xu",
"Shuyue Guo",
"Haoran Zhang",
"Xingwei Qu",
"Junjie Wang",
"Ruibin Yuan",
"Yizhi Li",
"Zekun Wang",
"Yudong Liu",
"Yu-Hsuan Tsai",
"Fengji Zhang",
"Chenghua Lin",
"Wenhao Huang",
"Wenhu Chen",
"Jie Fu"
]
| As the capabilities of large multimodal models (LMMs) continue to advance, evaluating the performance of LMMs emerges as an increasing need. Additionally, there is an even larger gap in evaluating the advanced knowledge and reasoning abilities of LMMs in non-English contexts such as Chinese. We introduce CMMMU, a new Chinese Massive Multi-discipline Multimodal Understanding benchmark designed to evaluate LMMs on tasks demanding college-level subject knowledge and deliberate reasoning in a Chinese context. CMMMU is inspired by and strictly follows the annotation and analysis pattern of MMMU. CMMMU includes 12k manually collected multimodal questions from college exams, quizzes, and textbooks, covering six core disciplines: Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering, like its companion, MMMU. These questions span 30 subjects and comprise 39 highly heterogeneous image types, such as charts, diagrams, maps, tables, music sheets, and chemical structures. CMMMU focuses on complex perception and reasoning with domain-specific knowledge in the Chinese context. We evaluate 11 open-source LLMs and one proprietary GPT-4V(ision). Even GPT-4V only achieves accuracies of 42%, indicating a large space for improvement. CMMMU will boost the community to build the next-generation LMMs towards expert artificial intelligence and promote the democratization of LMMs by providing diverse language contexts. |
|
2024-01-23T00:00:00 | 2401.11708 | Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs | [
"Ling Yang",
"Zhaochen Yu",
"Chenlin Meng",
"Minkai Xu",
"Stefano Ermon",
"Bin Cui"
]
| https://github.com/YangLing0818/RPG-DiffusionMaster | Diffusion models have exhibit exceptional performance in text-to-image generation and editing. However, existing methods often face challenges when handling complex text prompts that involve multiple objects with multiple attributes and relationships. In this paper, we propose a brand new training-free text-to-image generation/editing framework, namely Recaption, Plan and Generate (RPG), harnessing the powerful chain-of-thought reasoning ability of multimodal LLMs to enhance the compositionality of text-to-image diffusion models. Our approach employs the MLLM as a global planner to decompose the process of generating complex images into multiple simpler generation tasks within subregions. We propose complementary regional diffusion to enable region-wise compositional generation. Furthermore, we integrate text-guided image generation and editing within the proposed RPG in a closed-loop fashion, thereby enhancing generalization ability. Extensive experiments demonstrate our RPG outperforms state-of-the-art text-to-image diffusion models, including DALL-E 3 and SDXL, particularly in multi-category object composition and text-image semantic alignment. Notably, our RPG framework exhibits wide compatibility with various MLLM architectures (e.g., MiniGPT-4) and diffusion backbones (e.g., ControlNet). Our code is available at: https://github.com/YangLing0818/RPG-DiffusionMaster |
2024-01-23T00:00:00 | 2401.12208 | CheXagent: Towards a Foundation Model for Chest X-Ray Interpretation | [
"Zhihong Chen",
"Maya Varma",
"Jean-Benoit Delbrouck",
"Magdalini Paschali",
"Louis Blankemeier",
"Dave Van Veen",
"Jeya Maria Jose Valanarasu",
"Alaa Youssef",
"Joseph Paul Cohen",
"Eduardo Pontes Reis",
"Emily B. Tsai",
"Andrew Johnston",
"Cameron Olsen",
"Tanishq Mathew Abraham",
"Sergios Gatidis",
"Akshay S. Chaudhari",
"Curtis Langlotz"
]
| Chest X-rays (CXRs) are the most frequently performed imaging test in clinical practice. Recent advances in the development of vision-language foundation models (FMs) give rise to the possibility of performing automated CXR interpretation, which can assist physicians with clinical decision-making and improve patient outcomes. However, developing FMs that can accurately interpret CXRs is challenging due to the (1) limited availability of large-scale vision-language datasets in the medical image domain, (2) lack of vision and language encoders that can capture the complexities of medical data, and (3) absence of evaluation frameworks for benchmarking the abilities of FMs on CXR interpretation. In this work, we address these challenges by first introducing CheXinstruct - a large-scale instruction-tuning dataset curated from 28 publicly-available datasets. We then present CheXagent - an instruction-tuned FM capable of analyzing and summarizing CXRs. To build CheXagent, we design a clinical large language model (LLM) for parsing radiology reports, a vision encoder for representing CXR images, and a network to bridge the vision and language modalities. Finally, we introduce CheXbench - a novel benchmark designed to systematically evaluate FMs across 8 clinically-relevant CXR interpretation tasks. Extensive quantitative evaluations and qualitative reviews with five expert radiologists demonstrate that CheXagent outperforms previously-developed general- and medical-domain FMs on CheXbench tasks. Furthermore, in an effort to improve model transparency, we perform a fairness evaluation across factors of sex, race and age to highlight potential performance disparities. Our project is at https://stanford-aimi.github.io/chexagent.html. |
|
2024-01-23T00:00:00 | 2401.12168 | SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities | [
"Boyuan Chen",
"Zhuo Xu",
"Sean Kirmani",
"Brian Ichter",
"Danny Driess",
"Pete Florence",
"Dorsa Sadigh",
"Leonidas Guibas",
"Fei Xia"
]
| Understanding and reasoning about spatial relationships is a fundamental capability for Visual Question Answering (VQA) and robotics. While Vision Language Models (VLM) have demonstrated remarkable performance in certain VQA benchmarks, they still lack capabilities in 3D spatial reasoning, such as recognizing quantitative relationships of physical objects like distances or size differences. We hypothesize that VLMs' limited spatial reasoning capability is due to the lack of 3D spatial knowledge in training data and aim to solve this problem by training VLMs with Internet-scale spatial reasoning data. To this end, we present a system to facilitate this approach. We first develop an automatic 3D spatial VQA data generation framework that scales up to 2 billion VQA examples on 10 million real-world images. We then investigate various factors in the training recipe, including data quality, training pipeline, and VLM architecture. Our work features the first internet-scale 3D spatial reasoning dataset in metric space. By training a VLM on such data, we significantly enhance its ability on both qualitative and quantitative spatial VQA. Finally, we demonstrate that this VLM unlocks novel downstream applications in chain-of-thought spatial reasoning and robotics due to its quantitative estimation capability. Project website: https://spatial-vlm.github.io/ |
|
2024-01-23T00:00:00 | 2401.12202 | OK-Robot: What Really Matters in Integrating Open-Knowledge Models for Robotics | [
"Peiqi Liu",
"Yaswanth Orru",
"Chris Paxton",
"Nur Muhammad Mahi Shafiullah",
"Lerrel Pinto"
]
| Remarkable progress has been made in recent years in the fields of vision, language, and robotics. We now have vision models capable of recognizing objects based on language queries, navigation systems that can effectively control mobile systems, and grasping models that can handle a wide range of objects. Despite these advancements, general-purpose applications of robotics still lag behind, even though they rely on these fundamental capabilities of recognition, navigation, and grasping. In this paper, we adopt a systems-first approach to develop a new Open Knowledge-based robotics framework called OK-Robot. By combining Vision-Language Models (VLMs) for object detection, navigation primitives for movement, and grasping primitives for object manipulation, OK-Robot offers a integrated solution for pick-and-drop operations without requiring any training. To evaluate its performance, we run OK-Robot in 10 real-world home environments. The results demonstrate that OK-Robot achieves a 58.5% success rate in open-ended pick-and-drop tasks, representing a new state-of-the-art in Open Vocabulary Mobile Manipulation (OVMM) with nearly 1.8x the performance of prior work. On cleaner, uncluttered environments, OK-Robot's performance increases to 82%. However, the most important insight gained from OK-Robot is the critical role of nuanced details when combining Open Knowledge systems like VLMs with robotic modules. Videos of our experiments are available on our website: https://ok-robot.github.io |
|
2024-01-23T00:00:00 | 2401.12187 | WARM: On the Benefits of Weight Averaged Reward Models | [
"Alexandre Ramé",
"Nino Vieillard",
"Léonard Hussenot",
"Robert Dadashi",
"Geoffrey Cideron",
"Olivier Bachem",
"Johan Ferret"
]
| Aligning large language models (LLMs) with human preferences through reinforcement learning (RLHF) can lead to reward hacking, where LLMs exploit failures in the reward model (RM) to achieve seemingly high rewards without meeting the underlying objectives. We identify two primary challenges when designing RMs to mitigate reward hacking: distribution shifts during the RL process and inconsistencies in human preferences. As a solution, we propose Weight Averaged Reward Models (WARM), first fine-tuning multiple RMs, then averaging them in the weight space. This strategy follows the observation that fine-tuned weights remain linearly mode connected when sharing the same pre-training. By averaging weights, WARM improves efficiency compared to the traditional ensembling of predictions, while improving reliability under distribution shifts and robustness to preference inconsistencies. Our experiments on summarization tasks, using best-of-N and RL methods, shows that WARM improves the overall quality and alignment of LLM predictions; for example, a policy RL fine-tuned with WARM has a 79.4% win rate against a policy RL fine-tuned with a single RM. |
|
2024-01-23T00:00:00 | 2401.11739 | EmerDiff: Emerging Pixel-level Semantic Knowledge in Diffusion Models | [
"Koichi Namekata",
"Amirmojtaba Sabour",
"Sanja Fidler",
"Seung Wook Kim"
]
| Diffusion models have recently received increasing research attention for their remarkable transfer abilities in semantic segmentation tasks. However, generating fine-grained segmentation masks with diffusion models often requires additional training on annotated datasets, leaving it unclear to what extent pre-trained diffusion models alone understand the semantic relations of their generated images. To address this question, we leverage the semantic knowledge extracted from Stable Diffusion (SD) and aim to develop an image segmentor capable of generating fine-grained segmentation maps without any additional training. The primary difficulty stems from the fact that semantically meaningful feature maps typically exist only in the spatially lower-dimensional layers, which poses a challenge in directly extracting pixel-level semantic relations from these feature maps. To overcome this issue, our framework identifies semantic correspondences between image pixels and spatial locations of low-dimensional feature maps by exploiting SD's generation process and utilizes them for constructing image-resolution segmentation maps. In extensive experiments, the produced segmentation maps are demonstrated to be well delineated and capture detailed parts of the images, indicating the existence of highly accurate pixel-level semantic knowledge in diffusion models. |
|
2024-01-23T00:00:00 | 2401.11067 | Make-A-Shape: a Ten-Million-scale 3D Shape Model | [
"Ka-Hei Hui",
"Aditya Sanghi",
"Arianna Rampini",
"Kamal Rahimi Malekshan",
"Zhengzhe Liu",
"Hooman Shayani",
"Chi-Wing Fu"
]
| Significant progress has been made in training large generative models for natural language and images. Yet, the advancement of 3D generative models is hindered by their substantial resource demands for training, along with inefficient, non-compact, and less expressive representations. This paper introduces Make-A-Shape, a new 3D generative model designed for efficient training on a vast scale, capable of utilizing 10 millions publicly-available shapes. Technical-wise, we first innovate a wavelet-tree representation to compactly encode shapes by formulating the subband coefficient filtering scheme to efficiently exploit coefficient relations. We then make the representation generatable by a diffusion model by devising the subband coefficients packing scheme to layout the representation in a low-resolution grid. Further, we derive the subband adaptive training strategy to train our model to effectively learn to generate coarse and detail wavelet coefficients. Last, we extend our framework to be controlled by additional input conditions to enable it to generate shapes from assorted modalities, e.g., single/multi-view images, point clouds, and low-resolution voxels. In our extensive set of experiments, we demonstrate various applications, such as unconditional generation, shape completion, and conditional generation on a wide range of modalities. Our approach not only surpasses the state of the art in delivering high-quality results but also efficiently generates shapes within a few seconds, often achieving this in just 2 seconds for most conditions. |
|
2024-01-23T00:00:00 | 2401.11053 | StreamVoice: Streamable Context-Aware Language Modeling for Real-time Zero-Shot Voice Conversion | [
"Zhichao Wang",
"Yuanzhe Chen",
"Xinsheng Wang",
"Zhuo Chen",
"Lei Xie",
"Yuping Wang",
"Yuxuan Wang"
]
| Recent language model (LM) advancements have showcased impressive zero-shot voice conversion (VC) performance. However, existing LM-based VC models usually apply offline conversion from source semantics to acoustic features, demanding the complete source speech, and limiting their deployment to real-time applications. In this paper, we introduce StreamVoice, a novel streaming LM-based model for zero-shot VC, facilitating real-time conversion given arbitrary speaker prompts and source speech. Specifically, to enable streaming capability, StreamVoice employs a fully causal context-aware LM with a temporal-independent acoustic predictor, while alternately processing semantic and acoustic features at each time step of autoregression which eliminates the dependence on complete source speech. To address the potential performance degradation from the incomplete context in streaming processing, we enhance the context-awareness of the LM through two strategies: 1) teacher-guided context foresight, using a teacher model to summarize the present and future semantic context during training to guide the model's forecasting for missing context; 2) semantic masking strategy, promoting acoustic prediction from preceding corrupted semantic and acoustic input, enhancing context-learning ability. Notably, StreamVoice is the first LM-based streaming zero-shot VC model without any future look-ahead. Experimental results demonstrate StreamVoice's streaming conversion capability while maintaining zero-shot performance comparable to non-streaming VC systems. |
|
2024-01-23T00:00:00 | 2401.11605 | Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers | [
"Katherine Crowson",
"Stefan Andreas Baumann",
"Alex Birch",
"Tanishq Mathew Abraham",
"Daniel Z. Kaplan",
"Enrico Shippole"
]
| We present the Hourglass Diffusion Transformer (HDiT), an image generative model that exhibits linear scaling with pixel count, supporting training at high-resolution (e.g. 1024 times 1024) directly in pixel-space. Building on the Transformer architecture, which is known to scale to billions of parameters, it bridges the gap between the efficiency of convolutional U-Nets and the scalability of Transformers. HDiT trains successfully without typical high-resolution training techniques such as multiscale architectures, latent autoencoders or self-conditioning. We demonstrate that HDiT performs competitively with existing models on ImageNet 256^2, and sets a new state-of-the-art for diffusion models on FFHQ-1024^2. |
|
2024-01-23T00:00:00 | 2401.12175 | Single-View 3D Human Digitalization with Large Reconstruction Models | [
"Zhenzhen Weng",
"Jingyuan Liu",
"Hao Tan",
"Zhan Xu",
"Yang Zhou",
"Serena Yeung-Levy",
"Jimei Yang"
]
| In this paper, we introduce Human-LRM, a single-stage feed-forward Large Reconstruction Model designed to predict human Neural Radiance Fields (NeRF) from a single image. Our approach demonstrates remarkable adaptability in training using extensive datasets containing 3D scans and multi-view capture. Furthermore, to enhance the model's applicability for in-the-wild scenarios especially with occlusions, we propose a novel strategy that distills multi-view reconstruction into single-view via a conditional triplane diffusion model. This generative extension addresses the inherent variations in human body shapes when observed from a single view, and makes it possible to reconstruct the full body human from an occluded image. Through extensive experiments, we show that Human-LRM surpasses previous methods by a significant margin on several benchmarks. |
|
2024-01-23T00:00:00 | 2401.12179 | DITTO: Diffusion Inference-Time T-Optimization for Music Generation | [
"Zachary Novack",
"Julian McAuley",
"Taylor Berg-Kirkpatrick",
"Nicholas J. Bryan"
]
| We propose Diffusion Inference-Time T-Optimization (DITTO), a general-purpose frame-work for controlling pre-trained text-to-music diffusion models at inference-time via optimizing initial noise latents. Our method can be used to optimize through any differentiable feature matching loss to achieve a target (stylized) output and leverages gradient checkpointing for memory efficiency. We demonstrate a surprisingly wide-range of applications for music generation including inpainting, outpainting, and looping as well as intensity, melody, and musical structure control - all without ever fine-tuning the underlying model. When we compare our approach against related training, guidance, and optimization-based methods, we find DITTO achieves state-of-the-art performance on nearly all tasks, including outperforming comparable approaches on controllability, audio quality, and computational efficiency, thus opening the door for high-quality, flexible, training-free control of diffusion models. Sound examples can be found at https://DITTO-Music.github.io/web/. |
|
2024-01-23T00:00:00 | 2401.11002 | Fast Registration of Photorealistic Avatars for VR Facial Animation | [
"Chaitanya Patel",
"Shaojie Bai",
"Te-Li Wang",
"Jason Saragih",
"Shih-En Wei"
]
| Virtual Reality (VR) bares promise of social interactions that can feel more immersive than other media. Key to this is the ability to accurately animate a photorealistic avatar of one's likeness while wearing a VR headset. Although high quality registration of person-specific avatars to headset-mounted camera (HMC) images is possible in an offline setting, the performance of generic realtime models are significantly degraded. Online registration is also challenging due to oblique camera views and differences in modality. In this work, we first show that the domain gap between the avatar and headset-camera images is one of the primary sources of difficulty, where a transformer-based architecture achieves high accuracy on domain-consistent data, but degrades when the domain-gap is re-introduced. Building on this finding, we develop a system design that decouples the problem into two parts: 1) an iterative refinement module that takes in-domain inputs, and 2) a generic avatar-guided image-to-image style transfer module that is conditioned on current estimation of expression and head pose. These two modules reinforce each other, as image style transfer becomes easier when close-to-ground-truth examples are shown, and better domain-gap removal helps registration. Our system produces high-quality results efficiently, obviating the need for costly offline registration to generate personalized labels. We validate the accuracy and efficiency of our approach through extensive experiments on a commodity headset, demonstrating significant improvements over direct regression methods as well as offline registration. |
|
2024-01-23T00:00:00 | 2401.11078 | UltrAvatar: A Realistic Animatable 3D Avatar Diffusion Model with Authenticity Guided Textures | [
"Mingyuan Zhou",
"Rakib Hyder",
"Ziwei Xuan",
"Guojun Qi"
]
| Recent advances in 3D avatar generation have gained significant attentions. These breakthroughs aim to produce more realistic animatable avatars, narrowing the gap between virtual and real-world experiences. Most of existing works employ Score Distillation Sampling (SDS) loss, combined with a differentiable renderer and text condition, to guide a diffusion model in generating 3D avatars. However, SDS often generates oversmoothed results with few facial details, thereby lacking the diversity compared with ancestral sampling. On the other hand, other works generate 3D avatar from a single image, where the challenges of unwanted lighting effects, perspective views, and inferior image quality make them difficult to reliably reconstruct the 3D face meshes with the aligned complete textures. In this paper, we propose a novel 3D avatar generation approach termed UltrAvatar with enhanced fidelity of geometry, and superior quality of physically based rendering (PBR) textures without unwanted lighting. To this end, the proposed approach presents a diffuse color extraction model and an authenticity guided texture diffusion model. The former removes the unwanted lighting effects to reveal true diffuse colors so that the generated avatars can be rendered under various lighting conditions. The latter follows two gradient-based guidances for generating PBR textures to render diverse face-identity features and details better aligning with 3D mesh geometry. We demonstrate the effectiveness and robustness of the proposed method, outperforming the state-of-the-art methods by a large margin in the experiments. |
|
2024-01-23T00:00:00 | 2401.11985 | Scaling Face Interaction Graph Networks to Real World Scenes | [
"Tatiana Lopez-Guevara",
"Yulia Rubanova",
"William F. Whitney",
"Tobias Pfaff",
"Kimberly Stachenfeld",
"Kelsey R. Allen"
]
| Accurately simulating real world object dynamics is essential for various applications such as robotics, engineering, graphics, and design. To better capture complex real dynamics such as contact and friction, learned simulators based on graph networks have recently shown great promise. However, applying these learned simulators to real scenes comes with two major challenges: first, scaling learned simulators to handle the complexity of real world scenes which can involve hundreds of objects each with complicated 3D shapes, and second, handling inputs from perception rather than 3D state information. Here we introduce a method which substantially reduces the memory required to run graph-based learned simulators. Based on this memory-efficient simulation model, we then present a perceptual interface in the form of editable NeRFs which can convert real-world scenes into a structured representation that can be processed by graph network simulator. We show that our method uses substantially less memory than previous graph-based simulators while retaining their accuracy, and that the simulators learned in synthetic environments can be applied to real world scenes captured from multiple camera angles. This paves the way for expanding the application of learned simulators to settings where only perceptual information is available at inference time. |
|
2024-01-24T00:00:00 | 2401.12503 | Small Language Model Meets with Reinforced Vision Vocabulary | [
"Haoran Wei",
"Lingyu Kong",
"Jinyue Chen",
"Liang Zhao",
"Zheng Ge",
"En Yu",
"Jianjian Sun",
"Chunrui Han",
"Xiangyu Zhang"
]
| Playing Large Vision Language Models (LVLMs) in 2023 is trendy among the AI community. However, the relatively large number of parameters (more than 7B) of popular LVLMs makes it difficult to train and deploy on consumer GPUs, discouraging many researchers with limited resources. Imagine how cool it would be to experience all the features of current LVLMs on an old GTX1080ti (our only game card). Accordingly, we present Vary-toy in this report, a small-size Vary along with Qwen-1.8B as the base ``large'' language model. In Vary-toy, we introduce an improved vision vocabulary, allowing the model to not only possess all features of Vary but also gather more generality. Specifically, we replace negative samples of natural images with positive sample data driven by object detection in the procedure of generating vision vocabulary, more sufficiently utilizing the capacity of the vocabulary network and enabling it to efficiently encode visual information corresponding to natural objects. For experiments, Vary-toy can achieve 65.6% ANLS on DocVQA, 59.1% accuracy on ChartQA, 88.1% accuracy on RefCOCO, and 29% on MMVet. The code will be publicly available on the homepage. |
|
2024-01-24T00:00:00 | 2401.12474 | Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-Alignment | [
"Keming Lu",
"Bowen Yu",
"Chang Zhou",
"Jingren Zhou"
]
| https://github.com/OFA-Sys/Ditto | Considerable efforts have been invested in augmenting the role-playing proficiency of open-source large language models (LLMs) by emulating proprietary counterparts. Nevertheless, we posit that LLMs inherently harbor role-play capabilities, owing to the extensive knowledge of characters and potential dialogues ingrained in their vast training corpora. Thus, in this study, we introduce Ditto, a self-alignment method for role-play. Ditto capitalizes on character knowledge, encouraging an instruction-following LLM to simulate role-play dialogues as a variant of reading comprehension. This method creates a role-play training set comprising 4,000 characters, surpassing the scale of currently available datasets by tenfold regarding the number of roles. Subsequently, we fine-tune the LLM using this self-generated dataset to augment its role-playing capabilities. Upon evaluating our meticulously constructed and reproducible role-play benchmark and the roleplay subset of MT-Bench, Ditto, in various parameter scales, consistently maintains a consistent role identity and provides accurate role-specific knowledge in multi-turn role-play conversations. Notably, it outperforms all open-source role-play baselines, showcasing performance levels comparable to advanced proprietary chatbots. Furthermore, we present the first comprehensive cross-supervision alignment experiment in the role-play domain, revealing that the intrinsic capabilities of LLMs confine the knowledge within role-play. Meanwhile, the role-play styles can be easily acquired with the guidance of smaller models. We open-source related resources at https://github.com/OFA-Sys/Ditto. |
2024-01-24T00:00:00 | 2401.12945 | Lumiere: A Space-Time Diffusion Model for Video Generation | [
"Omer Bar-Tal",
"Hila Chefer",
"Omer Tov",
"Charles Herrmann",
"Roni Paiss",
"Shiran Zada",
"Ariel Ephrat",
"Junhwa Hur",
"Yuanzhen Li",
"Tomer Michaeli",
"Oliver Wang",
"Deqing Sun",
"Tali Dekel",
"Inbar Mosseri"
]
| We introduce Lumiere -- a text-to-video diffusion model designed for synthesizing videos that portray realistic, diverse and coherent motion -- a pivotal challenge in video synthesis. To this end, we introduce a Space-Time U-Net architecture that generates the entire temporal duration of the video at once, through a single pass in the model. This is in contrast to existing video models which synthesize distant keyframes followed by temporal super-resolution -- an approach that inherently makes global temporal consistency difficult to achieve. By deploying both spatial and (importantly) temporal down- and up-sampling and leveraging a pre-trained text-to-image diffusion model, our model learns to directly generate a full-frame-rate, low-resolution video by processing it in multiple space-time scales. We demonstrate state-of-the-art text-to-video generation results, and show that our design easily facilitates a wide range of content creation tasks and video editing applications, including image-to-video, video inpainting, and stylized generation. |
|
2024-01-24T00:00:00 | 2401.12954 | Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding | [
"Mirac Suzgun",
"Adam Tauman Kalai"
]
| We introduce meta-prompting, an effective scaffolding technique designed to enhance the functionality of language models (LMs). This approach transforms a single LM into a multi-faceted conductor, adept at managing and integrating multiple independent LM queries. By employing high-level instructions, meta-prompting guides the LM to break down complex tasks into smaller, more manageable subtasks. These subtasks are then handled by distinct "expert" instances of the same LM, each operating under specific, tailored instructions. Central to this process is the LM itself, in its role as the conductor, which ensures seamless communication and effective integration of the outputs from these expert models. It additionally employs its inherent critical thinking and robust verification processes to refine and authenticate the end result. This collaborative prompting approach empowers a single LM to simultaneously act as a comprehensive orchestrator and a panel of diverse experts, significantly enhancing its performance across a wide array of tasks. The zero-shot, task-agnostic nature of meta-prompting greatly simplifies user interaction by obviating the need for detailed, task-specific instructions. Furthermore, our research demonstrates the seamless integration of external tools, such as a Python interpreter, into the meta-prompting framework, thereby broadening its applicability and utility. Through rigorous experimentation with GPT-4, we establish the superiority of meta-prompting over conventional scaffolding methods: When averaged across all tasks, including the Game of 24, Checkmate-in-One, and Python Programming Puzzles, meta-prompting, augmented with a Python interpreter functionality, surpasses standard prompting by 17.1%, expert (dynamic) prompting by 17.3%, and multipersona prompting by 15.2%. |
|
2024-01-24T00:00:00 | 2401.12963 | AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents | [
"Michael Ahn",
"Debidatta Dwibedi",
"Chelsea Finn",
"Montse Gonzalez Arenas",
"Keerthana Gopalakrishnan",
"Karol Hausman",
"Brian Ichter",
"Alex Irpan",
"Nikhil Joshi",
"Ryan Julian",
"Sean Kirmani",
"Isabel Leal",
"Edward Lee",
"Sergey Levine",
"Yao Lu",
"Isabel Leal",
"Sharath Maddineni",
"Kanishka Rao",
"Dorsa Sadigh",
"Pannag Sanketi",
"Pierre Sermanet",
"Quan Vuong",
"Stefan Welker",
"Fei Xia",
"Ted Xiao",
"Peng Xu",
"Steve Xu",
"Zhuo Xu"
]
| Foundation models that incorporate language, vision, and more recently actions have revolutionized the ability to harness internet scale data to reason about useful tasks. However, one of the key challenges of training embodied foundation models is the lack of data grounded in the physical world. In this paper, we propose AutoRT, a system that leverages existing foundation models to scale up the deployment of operational robots in completely unseen scenarios with minimal human supervision. AutoRT leverages vision-language models (VLMs) for scene understanding and grounding, and further uses large language models (LLMs) for proposing diverse and novel instructions to be performed by a fleet of robots. Guiding data collection by tapping into the knowledge of foundation models enables AutoRT to effectively reason about autonomy tradeoffs and safety while significantly scaling up data collection for robot learning. We demonstrate AutoRT proposing instructions to over 20 robots across multiple buildings and collecting 77k real robot episodes via both teleoperation and autonomous robot policies. We experimentally show that such "in-the-wild" data collected by AutoRT is significantly more diverse, and that AutoRT's use of LLMs allows for instruction following data collection robots that can align to human preferences. |
|
2024-01-24T00:00:00 | 2401.12244 | Large-scale Reinforcement Learning for Diffusion Models | [
"Yinan Zhang",
"Eric Tzeng",
"Yilun Du",
"Dmitry Kislyuk"
]
| Text-to-image diffusion models are a class of deep generative models that have demonstrated an impressive capacity for high-quality image generation. However, these models are susceptible to implicit biases that arise from web-scale text-image training pairs and may inaccurately model aspects of images we care about. This can result in suboptimal samples, model bias, and images that do not align with human ethics and preferences. In this paper, we present an effective scalable algorithm to improve diffusion models using Reinforcement Learning (RL) across a diverse set of reward functions, such as human preference, compositionality, and fairness over millions of images. We illustrate how our approach substantially outperforms existing methods for aligning diffusion models with human preferences. We further illustrate how this substantially improves pretrained Stable Diffusion (SD) models, generating samples that are preferred by humans 80.3% of the time over those from the base SD model while simultaneously improving both the composition and diversity of generated samples. |
|
2024-01-24T00:00:00 | 2401.12789 | Multilingual and Fully Non-Autoregressive ASR with Large Language Model Fusion: A Comprehensive Study | [
"W. Ronny Huang",
"Cyril Allauzen",
"Tongzhou Chen",
"Kilol Gupta",
"Ke Hu",
"James Qin",
"Yu Zhang",
"Yongqiang Wang",
"Shuo-Yiin Chang",
"Tara N. Sainath"
]
| In the era of large models, the autoregressive nature of decoding often results in latency serving as a significant bottleneck. We propose a non-autoregressive LM-fused ASR system that effectively leverages the parallelization capabilities of accelerator hardware. Our approach combines the Universal Speech Model (USM) and the PaLM 2 language model in per-segment scoring mode, achieving an average relative WER improvement across all languages of 10.8% on FLEURS and 3.6% on YouTube captioning. Furthermore, our comprehensive ablation study analyzes key parameters such as LLM size, context length, vocabulary size, fusion methodology. For instance, we explore the impact of LLM size ranging from 128M to 340B parameters on ASR performance. This study provides valuable insights into the factors influencing the effectiveness of practical large-scale LM-fused speech recognition systems. |
|
2024-01-24T00:00:00 | 2401.12522 | BiTA: Bi-Directional Tuning for Lossless Acceleration in Large Language Models | [
"Feng Lin",
"Hanling Yi",
"Hongbin Li",
"Yifan Yang",
"Xiaotian Yu",
"Guangming Lu",
"Rong Xiao"
]
| Large language models (LLMs) commonly employ autoregressive generation during inference, leading to high memory bandwidth demand and consequently extended latency. To mitigate this inefficiency, we present Bi-directional Tuning for lossless Acceleration (BiTA), an innovative method expediting LLMs via streamlined semi-autoregressive generation and draft verification. Inspired by the concept of prompt tuning, we enhance LLMs with a parameter-efficient design called bi-directional tuning for the capability in semi-autoregressive generation. Employing efficient tree-based decoding, the models perform draft candidate generation and verification in parallel, ensuring outputs identical to their autoregressive counterparts under greedy sampling. BiTA serves as a lightweight plug-in module, seamlessly boosting the inference efficiency of existing LLMs without requiring additional assistance models or incurring significant extra memory costs. Applying the proposed BiTA, LLaMA-2-70B-Chat achieves a 2.7times speedup on the MT-Bench benchmark. Extensive experiments confirm our method surpasses state-of-the-art acceleration techniques. |
|
2024-01-24T00:00:00 | 2401.12246 | Orion-14B: Open-source Multilingual Large Language Models | [
"Du Chen",
"Yi Huang",
"Xiaopu Li",
"Yongqiang Li",
"Yongqiang Liu",
"Haihui Pan",
"Leichao Xu",
"Dacheng Zhang",
"Zhipeng Zhang",
"Kun Han"
]
| https://github.com/OrionStarAI/Orion | In this study, we introduce Orion-14B, a collection of multilingual large language models with 14 billion parameters. We utilize a data scheduling approach to train a foundational model on a diverse corpus of 2.5 trillion tokens, sourced from texts in English, Chinese, Japanese, Korean, and other languages. Additionally, we fine-tuned a series of models tailored for conversational applications and other specific use cases. Our evaluation results demonstrate that Orion-14B achieves state-of-the-art performance across a broad spectrum of tasks. We make the Orion-14B model family and its associated code publicly accessible https://github.com/OrionStarAI/Orion, aiming to inspire future research and practical applications in the field. |
2024-01-24T00:00:00 | 2401.12979 | GALA: Generating Animatable Layered Assets from a Single Scan | [
"Taeksoo Kim",
"Byungjun Kim",
"Shunsuke Saito",
"Hanbyul Joo"
]
| We present GALA, a framework that takes as input a single-layer clothed 3D human mesh and decomposes it into complete multi-layered 3D assets. The outputs can then be combined with other assets to create novel clothed human avatars with any pose. Existing reconstruction approaches often treat clothed humans as a single-layer of geometry and overlook the inherent compositionality of humans with hairstyles, clothing, and accessories, thereby limiting the utility of the meshes for downstream applications. Decomposing a single-layer mesh into separate layers is a challenging task because it requires the synthesis of plausible geometry and texture for the severely occluded regions. Moreover, even with successful decomposition, meshes are not normalized in terms of poses and body shapes, failing coherent composition with novel identities and poses. To address these challenges, we propose to leverage the general knowledge of a pretrained 2D diffusion model as geometry and appearance prior for humans and other assets. We first separate the input mesh using the 3D surface segmentation extracted from multi-view 2D segmentations. Then we synthesize the missing geometry of different layers in both posed and canonical spaces using a novel pose-guided Score Distillation Sampling (SDS) loss. Once we complete inpainting high-fidelity 3D geometry, we also apply the same SDS loss to its texture to obtain the complete appearance including the initially occluded regions. Through a series of decomposition steps, we obtain multiple layers of 3D assets in a shared canonical space normalized in terms of poses and human shapes, hence supporting effortless composition to novel identities and reanimation with novel poses. Our experiments demonstrate the effectiveness of our approach for decomposition, canonicalization, and composition tasks compared to existing solutions. |
|
2024-01-25T00:00:00 | 2401.13388 | UNIMO-G: Unified Image Generation through Multimodal Conditional Diffusion | [
"Wei Li",
"Xue Xu",
"Jiachen Liu",
"Xinyan Xiao"
]
| Existing text-to-image diffusion models primarily generate images from text prompts. However, the inherent conciseness of textual descriptions poses challenges in faithfully synthesizing images with intricate details, such as specific entities or scenes. This paper presents UNIMO-G, a simple multimodal conditional diffusion framework that operates on multimodal prompts with interleaved textual and visual inputs, which demonstrates a unified ability for both text-driven and subject-driven image generation. UNIMO-G comprises two core components: a Multimodal Large Language Model (MLLM) for encoding multimodal prompts, and a conditional denoising diffusion network for generating images based on the encoded multimodal input. We leverage a two-stage training strategy to effectively train the framework: firstly pre-training on large-scale text-image pairs to develop conditional image generation capabilities, and then instruction tuning with multimodal prompts to achieve unified image generation proficiency. A well-designed data processing pipeline involving language grounding and image segmentation is employed to construct multi-modal prompts. UNIMO-G excels in both text-to-image generation and zero-shot subject-driven synthesis, and is notably effective in generating high-fidelity images from complex multimodal prompts involving multiple image entities. |
|
2024-01-25T00:00:00 | 2401.13660 | MambaByte: Token-free Selective State Space Model | [
"Junxiong Wang",
"Tushaar Gangavarapu",
"Jing Nathan Yan",
"Alexander M Rush"
]
| Token-free language models learn directly from raw bytes and remove the bias of subword tokenization. Operating on bytes, however, results in significantly longer sequences, and standard autoregressive Transformers scale poorly in such settings. We experiment with MambaByte, a token-free adaptation of the Mamba state space model, trained autoregressively on byte sequences. Our experiments indicate the computational efficiency of MambaByte compared to other byte-level models. We also find MambaByte to be competitive with and even outperform state-of-the-art subword Transformers. Furthermore, owing to linear scaling in length, MambaByte benefits from fast inference compared to Transformers. Our findings establish the viability of MambaByte in enabling token-free language modeling. |
|
2024-01-25T00:00:00 | 2401.13627 | Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild | [
"Fanghua Yu",
"Jinjin Gu",
"Zheyuan Li",
"Jinfan Hu",
"Xiangtao Kong",
"Xintao Wang",
"Jingwen He",
"Yu Qiao",
"Chao Dong"
]
| We introduce SUPIR (Scaling-UP Image Restoration), a groundbreaking image restoration method that harnesses generative prior and the power of model scaling up. Leveraging multi-modal techniques and advanced generative prior, SUPIR marks a significant advance in intelligent and realistic image restoration. As a pivotal catalyst within SUPIR, model scaling dramatically enhances its capabilities and demonstrates new potential for image restoration. We collect a dataset comprising 20 million high-resolution, high-quality images for model training, each enriched with descriptive text annotations. SUPIR provides the capability to restore images guided by textual prompts, broadening its application scope and potential. Moreover, we introduce negative-quality prompts to further improve perceptual quality. We also develop a restoration-guided sampling method to suppress the fidelity issue encountered in generative-based restoration. Experiments demonstrate SUPIR's exceptional restoration effects and its novel capacity to manipulate restoration through textual prompts. |
|
2024-01-25T00:00:00 | 2401.13601 | MM-LLMs: Recent Advances in MultiModal Large Language Models | [
"Duzhen Zhang",
"Yahan Yu",
"Chenxing Li",
"Jiahua Dong",
"Dan Su",
"Chenhui Chu",
"Dong Yu"
]
| In the past year, MultiModal Large Language Models (MM-LLMs) have undergone substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs via cost-effective training strategies. The resulting models not only preserve the inherent reasoning and decision-making capabilities of LLMs but also empower a diverse range of MM tasks. In this paper, we provide a comprehensive survey aimed at facilitating further research of MM-LLMs. Specifically, we first outline general design formulations for model architecture and training pipeline. Subsequently, we provide brief introductions of 26 existing MM-LLMs, each characterized by its specific formulations. Additionally, we review the performance of MM-LLMs on mainstream benchmarks and summarize key training recipes to enhance the potency of MM-LLMs. Lastly, we explore promising directions for MM-LLMs while concurrently maintaining a real-time tracking website for the latest developments in the field. We hope that this survey contributes to the ongoing advancement of the MM-LLMs domain. |
|
2024-01-25T00:00:00 | 2401.13311 | ConTextual: Evaluating Context-Sensitive Text-Rich Visual Reasoning in Large Multimodal Models | [
"Rohan Wadhawan",
"Hritik Bansal",
"Kai-Wei Chang",
"Nanyun Peng"
]
| Recent advancements in AI have led to the development of large multimodal models (LMMs) capable of processing complex tasks involving joint reasoning over text and visual content in the image (e.g., navigating maps in public places). This paper introduces ConTextual, a novel benchmark comprising instructions designed explicitly to evaluate LMMs' ability to perform context-sensitive text-rich visual reasoning. ConTextual emphasizes diverse real-world scenarios (e.g., time-reading, navigation, shopping and more) demanding a deeper understanding of the interactions between textual and visual elements. Our findings reveal a significant performance gap of 30.8% between the best-performing LMM, GPT-4V(ision), and human capabilities using human evaluation indicating substantial room for improvement in context-sensitive text-rich visual reasoning. Notably, while GPT-4V excelled in abstract categories like meme and quote interpretation, its overall performance still lagged behind humans. In addition to human evaluations, we also employed automatic evaluation metrics using GPT-4, uncovering similar trends in performance disparities. We also perform a fine-grained evaluation across diverse visual contexts and provide qualitative analysis which provides a robust framework for future advancements in the LMM design. https://con-textual.github.io/ |
|
2024-01-25T00:00:00 | 2401.13303 | MaLA-500: Massive Language Adaptation of Large Language Models | [
"Peiqin Lin",
"Shaoxiong Ji",
"Jörg Tiedemann",
"André F. T. Martins",
"Hinrich Schütze"
]
| Large language models have advanced the state of the art in natural language processing. However, their predominant design for English or a limited set of languages creates a substantial gap in their effectiveness for low-resource languages. To bridge this gap, we introduce MaLA-500, a novel large language model designed to cover an extensive range of 534 languages. To train MaLA-500, we employ vocabulary extension and continued pretraining on LLaMA 2 with Glot500-c. Our experiments on SIB-200 show that MaLA-500 achieves state-of-the-art in-context learning results. We release MaLA-500 at https://huggingface.co/MaLA-LM |
|
2024-01-25T00:00:00 | 2401.13160 | SpacTor-T5: Pre-training T5 Models with Span Corruption and Replaced Token Detection | [
"Ke Ye",
"Heinrich Jiang",
"Afshin Rostamizadeh",
"Ayan Chakrabarti",
"Giulia DeSalvo",
"Jean-François Kagy",
"Lazaros Karydas",
"Gui Citovsky",
"Sanjiv Kumar"
]
| Pre-training large language models is known to be extremely resource intensive and often times inefficient, under-utilizing the information encapsulated in the training text sequences. In this paper, we present SpacTor, a new training procedure consisting of (1) a hybrid objective combining span corruption (SC) and token replacement detection (RTD), and (2) a two-stage curriculum that optimizes the hybrid objective over the initial tau iterations, then transitions to standard SC loss. We show empirically that the effectiveness of the hybrid objective is tied to the two-stage pre-training schedule, and provide extensive analysis on why this is the case. In our experiments with encoder-decoder architectures (T5) on a variety of NLP tasks, SpacTor-T5 yields the same downstream performance as standard SC pre-training, while enabling a 50% reduction in pre-training iterations and 40% reduction in total FLOPs. Alternatively, given the same amount of computing budget, we find that SpacTor results in significantly improved downstream benchmark performance. |
|
2024-01-26T00:00:00 | 2401.14019 | Unitxt: Flexible, Shareable and Reusable Data Preparation and Evaluation for Generative AI | [
"Elron Bandel",
"Yotam Perlitz",
"Elad Venezian",
"Roni Friedman-Melamed",
"Ofir Arviv",
"Matan Orbach",
"Shachar Don-Yehyia",
"Dafna Sheinwald",
"Ariel Gera",
"Leshem Choshen",
"Michal Shmueli-Scheuer",
"Yoav Katz"
]
| https://github.com/IBM/unitxt! | In the dynamic landscape of generative NLP, traditional text processing pipelines limit research flexibility and reproducibility, as they are tailored to specific dataset, task, and model combinations. The escalating complexity, involving system prompts, model-specific formats, instructions, and more, calls for a shift to a structured, modular, and customizable solution. Addressing this need, we present Unitxt, an innovative library for customizable textual data preparation and evaluation tailored to generative language models. Unitxt natively integrates with common libraries like HuggingFace and LM-eval-harness and deconstructs processing flows into modular components, enabling easy customization and sharing between practitioners. These components encompass model-specific formats, task prompts, and many other comprehensive dataset processing definitions. The Unitxt-Catalog centralizes these components, fostering collaboration and exploration in modern textual data workflows. Beyond being a tool, Unitxt is a community-driven platform, empowering users to build, share, and advance their pipelines collaboratively. Join the Unitxt community at https://github.com/IBM/unitxt! |
2024-01-26T00:00:00 | 2401.14404 | Deconstructing Denoising Diffusion Models for Self-Supervised Learning | [
"Xinlei Chen",
"Zhuang Liu",
"Saining Xie",
"Kaiming He"
]
| In this study, we examine the representation learning abilities of Denoising Diffusion Models (DDM) that were originally purposed for image generation. Our philosophy is to deconstruct a DDM, gradually transforming it into a classical Denoising Autoencoder (DAE). This deconstructive procedure allows us to explore how various components of modern DDMs influence self-supervised representation learning. We observe that only a very few modern components are critical for learning good representations, while many others are nonessential. Our study ultimately arrives at an approach that is highly simplified and to a large extent resembles a classical DAE. We hope our study will rekindle interest in a family of classical methods within the realm of modern self-supervised learning. |
|
2024-01-26T00:00:00 | 2401.13919 | WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models | [
"Hongliang He",
"Wenlin Yao",
"Kaixin Ma",
"Wenhao Yu",
"Yong Dai",
"Hongming Zhang",
"Zhenzhong Lan",
"Dong Yu"
]
| The advancement of large language models (LLMs) leads to a new era marked by the development of autonomous applications in the real world, which drives innovation in the creation of advanced web-based agents. Existing web agents typically only handle one input modality and are evaluated only in simplified web simulators or static web snapshots, greatly limiting their applicability in real-world scenarios. To bridge this gap, we introduce WebVoyager, an innovative Large Multimodal Model (LMM) powered web agent that can complete user instructions end-to-end by interacting with real-world websites. Moreover, we propose a new evaluation protocol for web agents to address the challenges of automatic evaluation of open-ended web agent tasks, leveraging the robust multimodal comprehension capabilities of GPT-4V. We create a new benchmark by gathering real-world tasks from 15 widely used websites to evaluate our agents. We show that WebVoyager achieves a 55.7% task success rate, significantly surpassing the performance of both GPT-4 (All Tools) and the WebVoyager (text-only) setups, underscoring the exceptional capability of WebVoyager in practical applications. We found that our proposed automatic evaluation achieves 85.3% agreement with human judgment, paving the way for further development of web agents in a real-world setting. |
|
2024-01-26T00:00:00 | 2401.14066 | CreativeSynth: Creative Blending and Synthesis of Visual Arts based on Multimodal Diffusion | [
"Nisha Huang",
"Weiming Dong",
"Yuxin Zhang",
"Fan Tang",
"Ronghui Li",
"Chongyang Ma",
"Xiu Li",
"Changsheng Xu"
]
| Large-scale text-to-image generative models have made impressive strides, showcasing their ability to synthesize a vast array of high-quality images. However, adapting these models for artistic image editing presents two significant challenges. Firstly, users struggle to craft textual prompts that meticulously detail visual elements of the input image. Secondly, prevalent models, when effecting modifications in specific zones, frequently disrupt the overall artistic style, complicating the attainment of cohesive and aesthetically unified artworks. To surmount these obstacles, we build the innovative unified framework CreativeSynth, which is based on a diffusion model with the ability to coordinate multimodal inputs and multitask in the field of artistic image generation. By integrating multimodal features with customized attention mechanisms, CreativeSynth facilitates the importation of real-world semantic content into the domain of art through inversion and real-time style transfer. This allows for the precise manipulation of image style and content while maintaining the integrity of the original model parameters. Rigorous qualitative and quantitative evaluations underscore that CreativeSynth excels in enhancing artistic images' fidelity and preserves their innate aesthetic essence. By bridging the gap between generative models and artistic finesse, CreativeSynth becomes a custom digital palette. |
|
2024-01-26T00:00:00 | 2401.13974 | BootPIG: Bootstrapping Zero-shot Personalized Image Generation Capabilities in Pretrained Diffusion Models | [
"Senthil Purushwalkam",
"Akash Gokul",
"Shafiq Joty",
"Nikhil Naik"
]
| Recent text-to-image generation models have demonstrated incredible success in generating images that faithfully follow input prompts. However, the requirement of using words to describe a desired concept provides limited control over the appearance of the generated concepts. In this work, we address this shortcoming by proposing an approach to enable personalization capabilities in existing text-to-image diffusion models. We propose a novel architecture (BootPIG) that allows a user to provide reference images of an object in order to guide the appearance of a concept in the generated images. The proposed BootPIG architecture makes minimal modifications to a pretrained text-to-image diffusion model and utilizes a separate UNet model to steer the generations toward the desired appearance. We introduce a training procedure that allows us to bootstrap personalization capabilities in the BootPIG architecture using data generated from pretrained text-to-image models, LLM chat agents, and image segmentation models. In contrast to existing methods that require several days of pretraining, the BootPIG architecture can be trained in approximately 1 hour. Experiments on the DreamBooth dataset demonstrate that BootPIG outperforms existing zero-shot methods while being comparable with test-time finetuning approaches. Through a user study, we validate the preference for BootPIG generations over existing methods both in maintaining fidelity to the reference object's appearance and aligning with textual prompts. |
|
2024-01-26T00:00:00 | 2401.14196 | DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence | [
"Daya Guo",
"Qihao Zhu",
"Dejian Yang",
"Zhenda Xie",
"Kai Dong",
"Wentao Zhang",
"Guanting Chen",
"Xiao Bi",
"Y. Wu",
"Y. K. Li",
"Fuli Luo",
"Yingfei Xiong",
"Wenfeng Liang"
]
| https://github.com/deepseek-ai/DeepSeek-Coder | The rapid development of large language models has revolutionized code intelligence in software development. However, the predominance of closed-source models has restricted extensive research and development. To address this, we introduce the DeepSeek-Coder series, a range of open-source code models with sizes from 1.3B to 33B, trained from scratch on 2 trillion tokens. These models are pre-trained on a high-quality project-level code corpus and employ a fill-in-the-blank task with a 16K window to enhance code generation and infilling. Our extensive evaluations demonstrate that DeepSeek-Coder not only achieves state-of-the-art performance among open-source code models across multiple benchmarks but also surpasses existing closed-source models like Codex and GPT-3.5. Furthermore, DeepSeek-Coder models are under a permissive license that allows for both research and unrestricted commercial use. |
2024-01-26T00:00:00 | 2401.14112 | FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design | [
"Haojun Xia",
"Zhen Zheng",
"Xiaoxia Wu",
"Shiyang Chen",
"Zhewei Yao",
"Stephen Youn",
"Arash Bakhtiari",
"Michael Wyatt",
"Donglin Zhuang",
"Zhongzhu Zhou",
"Olatunji Ruwase",
"Yuxiong He",
"Shuaiwen Leon Song"
]
| Six-bit quantization (FP6) can effectively reduce the size of large language models (LLMs) and preserve the model quality consistently across varied applications. However, existing systems do not provide Tensor Core support for FP6 quantization and struggle to achieve practical performance improvements during LLM inference. It is challenging to support FP6 quantization on GPUs due to (1) unfriendly memory access of model weights with irregular bit-width and (2) high runtime overhead of weight de-quantization. To address these problems, we propose TC-FPx, the first full-stack GPU kernel design scheme with unified Tensor Core support of float-point weights for various quantization bit-width. We integrate TC-FPx kernel into an existing inference system, providing new end-to-end support (called FP6-LLM) for quantized LLM inference, where better trade-offs between inference cost and model quality are achieved. Experiments show that FP6-LLM enables the inference of LLaMA-70b using only a single GPU, achieving 1.69x-2.65x higher normalized inference throughput than the FP16 baseline. The source code will be publicly available soon. |
|
2024-01-26T00:00:00 | 2401.14257 | Sketch2NeRF: Multi-view Sketch-guided Text-to-3D Generation | [
"Minglin Chen",
"Longguang Wang",
"Weihao Yuan",
"Yukun Wang",
"Zhe Sheng",
"Yisheng He",
"Zilong Dong",
"Liefeng Bo",
"Yulan Guo"
]
| Recently, text-to-3D approaches have achieved high-fidelity 3D content generation using text description. However, the generated objects are stochastic and lack fine-grained control. Sketches provide a cheap approach to introduce such fine-grained control. Nevertheless, it is challenging to achieve flexible control from these sketches due to their abstraction and ambiguity. In this paper, we present a multi-view sketch-guided text-to-3D generation framework (namely, Sketch2NeRF) to add sketch control to 3D generation. Specifically, our method leverages pretrained 2D diffusion models (e.g., Stable Diffusion and ControlNet) to supervise the optimization of a 3D scene represented by a neural radiance field (NeRF). We propose a novel synchronized generation and reconstruction method to effectively optimize the NeRF. In the experiments, we collected two kinds of multi-view sketch datasets to evaluate the proposed method. We demonstrate that our method can synthesize 3D consistent contents with fine-grained sketch control while being high-fidelity to text prompts. Extensive results show that our method achieves state-of-the-art performance in terms of sketch similarity and text alignment. |
|
2024-01-26T00:00:00 | 2401.14391 | Rethinking Patch Dependence for Masked Autoencoders | [
"Letian Fu",
"Long Lian",
"Renhao Wang",
"Baifeng Shi",
"Xudong Wang",
"Adam Yala",
"Trevor Darrell",
"Alexei A. Efros",
"Ken Goldberg"
]
| In this work, we re-examine inter-patch dependencies in the decoding mechanism of masked autoencoders (MAE). We decompose this decoding mechanism for masked patch reconstruction in MAE into self-attention and cross-attention. Our investigations suggest that self-attention between mask patches is not essential for learning good representations. To this end, we propose a novel pretraining framework: Cross-Attention Masked Autoencoders (CrossMAE). CrossMAE's decoder leverages only cross-attention between masked and visible tokens, with no degradation in downstream performance. This design also enables decoding only a small subset of mask tokens, boosting efficiency. Furthermore, each decoder block can now leverage different encoder features, resulting in improved representation learning. CrossMAE matches MAE in performance with 2.5 to 3.7times less decoding compute. It also surpasses MAE on ImageNet classification and COCO instance segmentation under the same compute. Code and models: https://crossmae.github.io |
|
2024-01-26T00:00:00 | 2401.13795 | Diffuse to Choose: Enriching Image Conditioned Inpainting in Latent Diffusion Models for Virtual Try-All | [
"Mehmet Saygin Seyfioglu",
"Karim Bouyarmane",
"Suren Kumar",
"Amir Tavanaei",
"Ismail B. Tutar"
]
| As online shopping is growing, the ability for buyers to virtually visualize products in their settings-a phenomenon we define as "Virtual Try-All"-has become crucial. Recent diffusion models inherently contain a world model, rendering them suitable for this task within an inpainting context. However, traditional image-conditioned diffusion models often fail to capture the fine-grained details of products. In contrast, personalization-driven models such as DreamPaint are good at preserving the item's details but they are not optimized for real-time applications. We present "Diffuse to Choose," a novel diffusion-based image-conditioned inpainting model that efficiently balances fast inference with the retention of high-fidelity details in a given reference item while ensuring accurate semantic manipulations in the given scene content. Our approach is based on incorporating fine-grained features from the reference image directly into the latent feature maps of the main diffusion model, alongside with a perceptual loss to further preserve the reference item's details. We conduct extensive testing on both in-house and publicly available datasets, and show that Diffuse to Choose is superior to existing zero-shot diffusion inpainting methods as well as few-shot diffusion personalization algorithms like DreamPaint. |
|
2024-01-26T00:00:00 | 2401.14398 | pix2gestalt: Amodal Segmentation by Synthesizing Wholes | [
"Ege Ozguroglu",
"Ruoshi Liu",
"Dídac Surís",
"Dian Chen",
"Achal Dave",
"Pavel Tokmakov",
"Carl Vondrick"
]
| We introduce pix2gestalt, a framework for zero-shot amodal segmentation, which learns to estimate the shape and appearance of whole objects that are only partially visible behind occlusions. By capitalizing on large-scale diffusion models and transferring their representations to this task, we learn a conditional diffusion model for reconstructing whole objects in challenging zero-shot cases, including examples that break natural and physical priors, such as art. As training data, we use a synthetically curated dataset containing occluded objects paired with their whole counterparts. Experiments show that our approach outperforms supervised baselines on established benchmarks. Our model can furthermore be used to significantly improve the performance of existing object recognition and 3D reconstruction methods in the presence of occlusions. |
|
2024-01-26T00:00:00 | 2401.14405 | Multimodal Pathway: Improve Transformers with Irrelevant Data from Other Modalities | [
"Yiyuan Zhang",
"Xiaohan Ding",
"Kaixiong Gong",
"Yixiao Ge",
"Ying Shan",
"Xiangyu Yue"
]
| https://github.com/AILab-CVC/M2PT | We propose to improve transformers of a specific modality with irrelevant data from other modalities, e.g., improve an ImageNet model with audio or point cloud datasets. We would like to highlight that the data samples of the target modality are irrelevant to the other modalities, which distinguishes our method from other works utilizing paired (e.g., CLIP) or interleaved data of different modalities. We propose a methodology named Multimodal Pathway - given a target modality and a transformer designed for it, we use an auxiliary transformer trained with data of another modality and construct pathways to connect components of the two models so that data of the target modality can be processed by both models. In this way, we utilize the universal sequence-to-sequence modeling abilities of transformers obtained from two modalities. As a concrete implementation, we use a modality-specific tokenizer and task-specific head as usual but utilize the transformer blocks of the auxiliary model via a proposed method named Cross-Modal Re-parameterization, which exploits the auxiliary weights without any inference costs. On the image, point cloud, video, and audio recognition tasks, we observe significant and consistent performance improvements with irrelevant data from other modalities. The code and models are available at https://github.com/AILab-CVC/M2PT. |
2024-01-26T00:00:00 | 2401.14403 | Adaptive Mobile Manipulation for Articulated Objects In the Open World | [
"Haoyu Xiong",
"Russell Mendonca",
"Kenneth Shaw",
"Deepak Pathak"
]
| Deploying robots in open-ended unstructured environments such as homes has been a long-standing research problem. However, robots are often studied only in closed-off lab settings, and prior mobile manipulation work is restricted to pick-move-place, which is arguably just the tip of the iceberg in this area. In this paper, we introduce Open-World Mobile Manipulation System, a full-stack approach to tackle realistic articulated object operation, e.g. real-world doors, cabinets, drawers, and refrigerators in open-ended unstructured environments. The robot utilizes an adaptive learning framework to initially learns from a small set of data through behavior cloning, followed by learning from online practice on novel objects that fall outside the training distribution. We also develop a low-cost mobile manipulation hardware platform capable of safe and autonomous online adaptation in unstructured environments with a cost of around 20,000 USD. In our experiments we utilize 20 articulate objects across 4 buildings in the CMU campus. With less than an hour of online learning for each object, the system is able to increase success rate from 50% of BC pre-training to 95% using online adaptation. Video results at https://open-world-mobilemanip.github.io/ |
|
2024-01-26T00:00:00 | 2401.14367 | Genie: Achieving Human Parity in Content-Grounded Datasets Generation | [
"Asaf Yehudai",
"Boaz Carmeli",
"Yosi Mass",
"Ofir Arviv",
"Nathaniel Mills",
"Assaf Toledo",
"Eyal Shnarch",
"Leshem Choshen"
]
| The lack of high-quality data for content-grounded generation tasks has been identified as a major obstacle to advancing these tasks. To address this gap, we propose Genie, a novel method for automatically generating high-quality content-grounded data. It consists of three stages: (a) Content Preparation, (b) Generation: creating task-specific examples from the content (e.g., question-answer pairs or summaries). (c) Filtering mechanism aiming to ensure the quality and faithfulness of the generated data. We showcase this methodology by generating three large-scale synthetic data, making wishes, for Long-Form Question-Answering (LFQA), summarization, and information extraction. In a human evaluation, our generated data was found to be natural and of high quality. Furthermore, we compare models trained on our data with models trained on human-written data -- ELI5 and ASQA for LFQA and CNN-DailyMail for Summarization. We show that our models are on par with or outperforming models trained on human-generated data and consistently outperforming them in faithfulness. Finally, we applied our method to create LFQA data within the medical domain and compared a model trained on it with models trained on other domains. |
|
2024-01-29T00:00:00 | 2401.15071 | From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities | [
"Chaochao Lu",
"Chen Qian",
"Guodong Zheng",
"Hongxing Fan",
"Hongzhi Gao",
"Jie Zhang",
"Jing Shao",
"Jingyi Deng",
"Jinlan Fu",
"Kexin Huang",
"Kunchang Li",
"Lijun Li",
"Limin Wang",
"Lu Sheng",
"Meiqi Chen",
"Ming Zhang",
"Qibing Ren",
"Sirui Chen",
"Tao Gui",
"Wanli Ouyang",
"Yali Wang",
"Yan Teng",
"Yaru Wang",
"Yi Wang",
"Yinan He",
"Yingchun Wang",
"Yixu Wang",
"Yongting Zhang",
"Yu Qiao",
"Yujiong Shen",
"Yurong Mou",
"Yuxi Chen",
"Zaibin Zhang",
"Zhelun Shi",
"Zhenfei Yin",
"Zhipin Wang"
]
| Multi-modal Large Language Models (MLLMs) have shown impressive abilities in generating reasonable responses with respect to multi-modal contents. However, there is still a wide gap between the performance of recent MLLM-based applications and the expectation of the broad public, even though the most powerful OpenAI's GPT-4 and Google's Gemini have been deployed. This paper strives to enhance understanding of the gap through the lens of a qualitative study on the generalizability, trustworthiness, and causal reasoning capabilities of recent proprietary and open-source MLLMs across four modalities: ie, text, code, image, and video, ultimately aiming to improve the transparency of MLLMs. We believe these properties are several representative factors that define the reliability of MLLMs, in supporting various downstream applications. To be specific, we evaluate the closed-source GPT-4 and Gemini and 6 open-source LLMs and MLLMs. Overall we evaluate 230 manually designed cases, where the qualitative results are then summarized into 12 scores (ie, 4 modalities times 3 properties). In total, we uncover 14 empirical findings that are useful to understand the capabilities and limitations of both proprietary and open-source MLLMs, towards more reliable downstream multi-modal applications. |
|
2024-01-29T00:00:00 | 2401.15024 | SliceGPT: Compress Large Language Models by Deleting Rows and Columns | [
"Saleh Ashkboos",
"Maximilian L. Croci",
"Marcelo Gennari do Nascimento",
"Torsten Hoefler",
"James Hensman"
]
| https://github.com/microsoft/TransformerCompression | Large language models have become the cornerstone of natural language processing, but their use comes with substantial costs in terms of compute and memory resources. Sparsification provides a solution to alleviate these resource constraints, and recent works have shown that trained models can be sparsified post-hoc. Existing sparsification techniques face challenges as they need additional data structures and offer constrained speedup with current hardware. In this paper we present SliceGPT, a new post-training sparsification scheme which replaces each weight matrix with a smaller (dense) matrix, reducing the embedding dimension of the network. Through extensive experimentation, we show that SliceGPT can remove up to 25% of the model parameters (including embeddings) for LLAMA2-70B, OPT 66B and Phi-2 models while maintaining 99%, 99% and 90% zero-shot task performance of the dense model respectively. Our sliced models run on fewer GPUs and run faster without any additional code optimization: on 24GB consumer GPUs we reduce the total compute for inference on LLAMA2-70B to 64% of that of the dense model; on 40GB A100 GPUs we reduce it to 66%. We offer a new insight, computational invariance in transformer networks, which enables SliceGPT and we hope it will inspire and enable future avenues to reduce memory and computation demands for pre-trained models. Code is available at: https://github.com/microsoft/TransformerCompression |
2024-01-29T00:00:00 | 2401.14953 | Learning Universal Predictors | [
"Jordi Grau-Moya",
"Tim Genewein",
"Marcus Hutter",
"Laurent Orseau",
"Grégoire Delétang",
"Elliot Catt",
"Anian Ruoss",
"Li Kevin Wenliang",
"Christopher Mattern",
"Matthew Aitchison",
"Joel Veness"
]
| Meta-learning has emerged as a powerful approach to train neural networks to learn new tasks quickly from limited data. Broad exposure to different tasks leads to versatile representations enabling general problem solving. But, what are the limits of meta-learning? In this work, we explore the potential of amortizing the most powerful universal predictor, namely Solomonoff Induction (SI), into neural networks via leveraging meta-learning to its limits. We use Universal Turing Machines (UTMs) to generate training data used to expose networks to a broad range of patterns. We provide theoretical analysis of the UTM data generation processes and meta-training protocols. We conduct comprehensive experiments with neural architectures (e.g. LSTMs, Transformers) and algorithmic data generators of varying complexity and universality. Our results suggest that UTM data is a valuable resource for meta-learning, and that it can be used to train neural networks capable of learning universal prediction strategies. |
|
2024-01-29T00:00:00 | 2401.14688 | Taiyi-Diffusion-XL: Advancing Bilingual Text-to-Image Generation with Large Vision-Language Model Support | [
"Xiaojun Wu",
"Dixiang Zhang",
"Ruyi Gan",
"Junyu Lu",
"Ziwei Wu",
"Renliang Sun",
"Jiaxing Zhang",
"Pingjian Zhang",
"Yan Song"
]
| Recent advancements in text-to-image models have significantly enhanced image generation capabilities, yet a notable gap of open-source models persists in bilingual or Chinese language support. To address this need, we present Taiyi-Diffusion-XL, a new Chinese and English bilingual text-to-image model which is developed by extending the capabilities of CLIP and Stable-Diffusion-XL through a process of bilingual continuous pre-training. This approach includes the efficient expansion of vocabulary by integrating the most frequently used Chinese characters into CLIP's tokenizer and embedding layers, coupled with an absolute position encoding expansion. Additionally, we enrich text prompts by large vision-language model, leading to better images captions and possess higher visual quality. These enhancements are subsequently applied to downstream text-to-image models. Our empirical results indicate that the developed CLIP model excels in bilingual image-text retrieval.Furthermore, the bilingual image generation capabilities of Taiyi-Diffusion-XL surpass previous models. This research leads to the development and open-sourcing of the Taiyi-Diffusion-XL model, representing a notable advancement in the field of image generation, particularly for Chinese language applications. This contribution is a step forward in addressing the need for more diverse language support in multimodal research. The model and demonstration are made publicly available at https://huggingface.co/IDEA-CCNL/Taiyi-Stable-Diffusion-XL-3.5B/{this https URL}, fostering further research and collaboration in this domain. |
|
2024-01-29T00:00:00 | 2401.14673 | Generative Expressive Robot Behaviors using Large Language Models | [
"Karthik Mahadevan",
"Jonathan Chien",
"Noah Brown",
"Zhuo Xu",
"Carolina Parada",
"Fei Xia",
"Andy Zeng",
"Leila Takayama",
"Dorsa Sadigh"
]
| People employ expressive behaviors to effectively communicate and coordinate their actions with others, such as nodding to acknowledge a person glancing at them or saying "excuse me" to pass people in a busy corridor. We would like robots to also demonstrate expressive behaviors in human-robot interaction. Prior work proposes rule-based methods that struggle to scale to new communication modalities or social situations, while data-driven methods require specialized datasets for each social situation the robot is used in. We propose to leverage the rich social context available from large language models (LLMs) and their ability to generate motion based on instructions or user preferences, to generate expressive robot motion that is adaptable and composable, building upon each other. Our approach utilizes few-shot chain-of-thought prompting to translate human language instructions into parametrized control code using the robot's available and learned skills. Through user studies and simulation experiments, we demonstrate that our approach produces behaviors that users found to be competent and easy to understand. Supplementary material can be found at https://generative-expressive-motion.github.io/. |
|
2024-01-29T00:00:00 | 2401.14828 | TIP-Editor: An Accurate 3D Editor Following Both Text-Prompts And Image-Prompts | [
"Jingyu Zhuang",
"Di Kang",
"Yan-Pei Cao",
"Guanbin Li",
"Liang Lin",
"Ying Shan"
]
| Text-driven 3D scene editing has gained significant attention owing to its convenience and user-friendliness. However, existing methods still lack accurate control of the specified appearance and location of the editing result due to the inherent limitations of the text description. To this end, we propose a 3D scene editing framework, TIPEditor, that accepts both text and image prompts and a 3D bounding box to specify the editing region. With the image prompt, users can conveniently specify the detailed appearance/style of the target content in complement to the text description, enabling accurate control of the appearance. Specifically, TIP-Editor employs a stepwise 2D personalization strategy to better learn the representation of the existing scene and the reference image, in which a localization loss is proposed to encourage correct object placement as specified by the bounding box. Additionally, TIPEditor utilizes explicit and flexible 3D Gaussian splatting as the 3D representation to facilitate local editing while keeping the background unchanged. Extensive experiments have demonstrated that TIP-Editor conducts accurate editing following the text and image prompts in the specified bounding box region, consistently outperforming the baselines in editing quality, and the alignment to the prompts, qualitatively and quantitatively. |
|
2024-01-29T00:00:00 | 2401.15077 | EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty | [
"Yuhui Li",
"Fangyun Wei",
"Chao Zhang",
"Hongyang Zhang"
]
| Auto-regressive decoding makes the inference of Large Language Models (LLMs) time-consuming. We propose a simple framework, EAGLE (Extrapolation Algorithm for Greater Language-model Efficiency), for lossless acceleration. Unlike traditional speculative sampling methods, EAGLE operates the drafting process auto-regressively at the more regular (second-top-layer) feature level and addresses the sampling uncertainty issues in the next-feature prediction problems by integrating tokens from one time step ahead. The acceleration provided by EAGLE is lossless: it involves no fine-tuning of the target LLM, and the generated text maintains the same distribution as that of vanilla auto-regressive decoding. As of the submission of this paper, EAGLE is the fastest known framework within the speculative sampling family. On MT-bench, EAGLE is 3x faster than vanilla decoding, 2x faster than Lookahead, and 1.6x faster than Medusa. Using gpt-fast, EAGLE attains on average 160 tokens/s with LLaMA2-Chat 13B on a single RTX 3090 GPU, compared to 24 tokens/s of Huggingface's implementations. |
|
2024-01-30T00:00:00 | 2401.15947 | MoE-LLaVA: Mixture of Experts for Large Vision-Language Models | [
"Bin Lin",
"Zhenyu Tang",
"Yang Ye",
"Jiaxi Cui",
"Bin Zhu",
"Peng Jin",
"Junwu Zhang",
"Munan Ning",
"Li Yuan"
]
| https://github.com/PKU-YuanGroup/MoE-LLaVA | For Large Vision-Language Models (LVLMs), scaling the model can effectively improve performance. However, expanding model parameters significantly increases the training and inferring costs, as all model parameters are activated for each token in the calculation. In this work, we propose a novel training strategy MoE-tuning for LVLMs, which can constructing a sparse model with an outrageous number of parameter but a constant computational cost, and effectively addresses the performance degradation typically associated with multi-modal learning and model sparsity. Furthermore, we present the MoE-LLaVA framework, a MoE-based sparse LVLM architecture. This framework uniquely activates only the top-k experts through routers during deployment, keeping the remaining experts inactive. Our extensive experiments highlight the excellent capabilities of MoE-LLaVA in visual understanding and its potential to reduce hallucinations in model outputs. Remarkably, with just 3 billion sparsely activated parameters, MoE-LLaVA demonstrates performance comparable to the LLaVA-1.5-7B on various visual understanding datasets and even surpasses the LLaVA-1.5-13B in object hallucination benchmarks. Through MoE-LLaVA, we aim to establish a baseline for sparse LVLMs and provide valuable insights for future research in developing more efficient and effective multi-modal learning systems. Code is released at https://github.com/PKU-YuanGroup/MoE-LLaVA. |
2024-01-30T00:00:00 | 2401.15977 | Motion-I2V: Consistent and Controllable Image-to-Video Generation with Explicit Motion Modeling | [
"Xiaoyu Shi",
"Zhaoyang Huang",
"Fu-Yun Wang",
"Weikang Bian",
"Dasong Li",
"Yi Zhang",
"Manyuan Zhang",
"Ka Chun Cheung",
"Simon See",
"Hongwei Qin",
"Jifeng Da",
"Hongsheng Li"
]
| We introduce Motion-I2V, a novel framework for consistent and controllable image-to-video generation (I2V). In contrast to previous methods that directly learn the complicated image-to-video mapping, Motion-I2V factorizes I2V into two stages with explicit motion modeling. For the first stage, we propose a diffusion-based motion field predictor, which focuses on deducing the trajectories of the reference image's pixels. For the second stage, we propose motion-augmented temporal attention to enhance the limited 1-D temporal attention in video latent diffusion models. This module can effectively propagate reference image's feature to synthesized frames with the guidance of predicted trajectories from the first stage. Compared with existing methods, Motion-I2V can generate more consistent videos even at the presence of large motion and viewpoint variation. By training a sparse trajectory ControlNet for the first stage, Motion-I2V can support users to precisely control motion trajectories and motion regions with sparse trajectory and region annotations. This offers more controllability of the I2V process than solely relying on textual instructions. Additionally, Motion-I2V's second stage naturally supports zero-shot video-to-video translation. Both qualitative and quantitative comparisons demonstrate the advantages of Motion-I2V over prior approaches in consistent and controllable image-to-video generation. |
|
2024-01-30T00:00:00 | 2401.16158 | Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception | [
"Junyang Wang",
"Haiyang Xu",
"Jiabo Ye",
"Ming Yan",
"Weizhou Shen",
"Ji Zhang",
"Fei Huang",
"Jitao Sang"
]
| https://github.com/X-PLUG/MobileAgent | Mobile device agent based on Multimodal Large Language Models (MLLM) is becoming a popular application. In this paper, we introduce Mobile-Agent, an autonomous multi-modal mobile device agent. Mobile-Agent first leverages visual perception tools to accurately identify and locate both the visual and textual elements within the app's front-end interface. Based on the perceived vision context, it then autonomously plans and decomposes the complex operation task, and navigates the mobile Apps through operations step by step. Different from previous solutions that rely on XML files of Apps or mobile system metadata, Mobile-Agent allows for greater adaptability across diverse mobile operating environments in a vision-centric way, thereby eliminating the necessity for system-specific customizations. To assess the performance of Mobile-Agent, we introduced Mobile-Eval, a benchmark for evaluating mobile device operations. Based on Mobile-Eval, we conducted a comprehensive evaluation of Mobile-Agent. The experimental results indicate that Mobile-Agent achieved remarkable accuracy and completion rates. Even with challenging instructions, such as multi-app operations, Mobile-Agent can still complete the requirements. Code and model will be open-sourced at https://github.com/X-PLUG/MobileAgent. |
2024-01-30T00:00:00 | 2401.16420 | InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model | [
"Xiaoyi Dong",
"Pan Zhang",
"Yuhang Zang",
"Yuhang Cao",
"Bin Wang",
"Linke Ouyang",
"Xilin Wei",
"Songyang Zhang",
"Haodong Duan",
"Maosong Cao",
"Wenwei Zhang",
"Yining Li",
"Hang Yan",
"Yang Gao",
"Xinyue Zhang",
"Wei Li",
"Jingwen Li",
"Kai Chen",
"Conghui He",
"Xingcheng Zhang",
"Yu Qiao",
"Dahua Lin",
"Jiaqi Wang"
]
| https://github.com/InternLM/InternLM-XComposer | We introduce InternLM-XComposer2, a cutting-edge vision-language model excelling in free-form text-image composition and comprehension. This model goes beyond conventional vision-language understanding, adeptly crafting interleaved text-image content from diverse inputs like outlines, detailed textual specifications, and reference images, enabling highly customizable content creation. InternLM-XComposer2 proposes a Partial LoRA (PLoRA) approach that applies additional LoRA parameters exclusively to image tokens to preserve the integrity of pre-trained language knowledge, striking a balance between precise vision understanding and text composition with literary talent. Experimental results demonstrate the superiority of InternLM-XComposer2 based on InternLM2-7B in producing high-quality long-text multi-modal content and its exceptional vision-language understanding performance across various benchmarks, where it not only significantly outperforms existing multimodal models but also matches or even surpasses GPT-4V and Gemini Pro in certain assessments. This highlights its remarkable proficiency in the realm of multimodal understanding. The InternLM-XComposer2 model series with 7B parameters are publicly available at https://github.com/InternLM/InternLM-XComposer. |
2024-01-30T00:00:00 | 2401.16380 | Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling | [
"Pratyush Maini",
"Skyler Seto",
"He Bai",
"David Grangier",
"Yizhe Zhang",
"Navdeep Jaitly"
]
| Large language models are trained on massive scrapes of the web, which are often unstructured, noisy, and poorly phrased. Current scaling laws show that learning from such data requires an abundance of both compute and data, which grows with the size of the model being trained. This is infeasible both because of the large compute costs and duration associated with pre-training, and the impending scarcity of high-quality data on the web. In this work, we propose Web Rephrase Augmented Pre-training (WRAP) that uses an off-the-shelf instruction-tuned model prompted to paraphrase documents on the web in specific styles such as "like Wikipedia" or in "question-answer format" to jointly pre-train LLMs on real and synthetic rephrases. First, we show that using WRAP on the C4 dataset, which is naturally noisy, speeds up pre-training by sim3x. At the same pre-training compute budget, it improves perplexity by more than 10% on average across different subsets of the Pile, and improves zero-shot question answer accuracy across 13 tasks by more than 2%. Second, we investigate the impact of the re-phrasing style on the performance of the model, offering insights into how the composition of the training data can impact the performance of LLMs in OOD settings. Our gains are attributed to the fact that re-phrased synthetic data has higher utility than just real data because it (i) incorporates style diversity that closely reflects downstream evaluation style, and (ii) has higher 'quality' than web-scraped data. |
|
2024-01-30T00:00:00 | 2401.15914 | Overcoming the Pitfalls of Vision-Language Model Finetuning for OOD Generalization | [
"Yuhang Zang",
"Hanlin Goh",
"Josh Susskind",
"Chen Huang"
]
| Existing vision-language models exhibit strong generalization on a variety of visual domains and tasks. However, such models mainly perform zero-shot recognition in a closed-set manner, and thus struggle to handle open-domain visual concepts by design. There are recent finetuning methods, such as prompt learning, that not only study the discrimination between in-distribution (ID) and out-of-distribution (OOD) samples, but also show some improvements in both ID and OOD accuracies. In this paper, we first demonstrate that vision-language models, after long enough finetuning but without proper regularization, tend to overfit the known classes in the given dataset, with degraded performance on unknown classes. Then we propose a novel approach OGEN to address this pitfall, with the main focus on improving the OOD GENeralization of finetuned models. Specifically, a class-conditional feature generator is introduced to synthesize OOD features using just the class name of any unknown class. Such synthesized features will provide useful knowledge about unknowns and help regularize the decision boundary between ID and OOD data when optimized jointly. Equally important is our adaptive self-distillation mechanism to regularize our feature generation model during joint optimization, i.e., adaptively transferring knowledge between model states to further prevent overfitting. Experiments validate that our method yields convincing gains in OOD generalization performance in different settings. |
|
2024-01-30T00:00:00 | 2401.15975 | StableIdentity: Inserting Anybody into Anywhere at First Sight | [
"Qinghe Wang",
"Xu Jia",
"Xiaomin Li",
"Taiqing Li",
"Liqian Ma",
"Yunzhi Zhuge",
"Huchuan Lu"
]
| Recent advances in large pretrained text-to-image models have shown unprecedented capabilities for high-quality human-centric generation, however, customizing face identity is still an intractable problem. Existing methods cannot ensure stable identity preservation and flexible editability, even with several images for each subject during training. In this work, we propose StableIdentity, which allows identity-consistent recontextualization with just one face image. More specifically, we employ a face encoder with an identity prior to encode the input face, and then land the face representation into a space with an editable prior, which is constructed from celeb names. By incorporating identity prior and editability prior, the learned identity can be injected anywhere with various contexts. In addition, we design a masked two-phase diffusion loss to boost the pixel-level perception of the input face and maintain the diversity of generation. Extensive experiments demonstrate our method outperforms previous customization methods. In addition, the learned identity can be flexibly combined with the off-the-shelf modules such as ControlNet. Notably, to the best knowledge, we are the first to directly inject the identity learned from a single image into video/3D generation without finetuning. We believe that the proposed StableIdentity is an important step to unify image, video, and 3D customized generation models. |
|
2024-01-30T00:00:00 | 2401.16013 | SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning | [
"Jianlan Luo",
"Zheyuan Hu",
"Charles Xu",
"You Liang Tan",
"Jacob Berg",
"Archit Sharma",
"Stefan Schaal",
"Chelsea Finn",
"Abhishek Gupta",
"Sergey Levine"
]
| In recent years, significant progress has been made in the field of robotic reinforcement learning (RL), enabling methods that handle complex image observations, train in the real world, and incorporate auxiliary data, such as demonstrations and prior experience. However, despite these advances, robotic RL remains hard to use. It is acknowledged among practitioners that the particular implementation details of these algorithms are often just as important (if not more so) for performance as the choice of algorithm. We posit that a significant challenge to widespread adoption of robotic RL, as well as further development of robotic RL methods, is the comparative inaccessibility of such methods. To address this challenge, we developed a carefully implemented library containing a sample efficient off-policy deep RL method, together with methods for computing rewards and resetting the environment, a high-quality controller for a widely-adopted robot, and a number of challenging example tasks. We provide this library as a resource for the community, describe its design choices, and present experimental results. Perhaps surprisingly, we find that our implementation can achieve very efficient learning, acquiring policies for PCB board assembly, cable routing, and object relocation between 25 to 50 minutes of training per policy on average, improving over state-of-the-art results reported for similar tasks in the literature. These policies achieve perfect or near-perfect success rates, extreme robustness even under perturbations, and exhibit emergent recovery and correction behaviors. We hope that these promising results and our high-quality open-source implementation will provide a tool for the robotics community to facilitate further developments in robotic RL. Our code, documentation, and videos can be found at https://serl-robot.github.io/ |
|
2024-01-30T00:00:00 | 2401.15687 | Media2Face: Co-speech Facial Animation Generation With Multi-Modality Guidance | [
"Qingcheng Zhao",
"Pengyu Long",
"Qixuan Zhang",
"Dafei Qin",
"Han Liang",
"Longwen Zhang",
"Yingliang Zhang",
"Jingyi Yu",
"Lan Xu"
]
| The synthesis of 3D facial animations from speech has garnered considerable attention. Due to the scarcity of high-quality 4D facial data and well-annotated abundant multi-modality labels, previous methods often suffer from limited realism and a lack of lexible conditioning. We address this challenge through a trilogy. We first introduce Generalized Neural Parametric Facial Asset (GNPFA), an efficient variational auto-encoder mapping facial geometry and images to a highly generalized expression latent space, decoupling expressions and identities. Then, we utilize GNPFA to extract high-quality expressions and accurate head poses from a large array of videos. This presents the M2F-D dataset, a large, diverse, and scan-level co-speech 3D facial animation dataset with well-annotated emotional and style labels. Finally, we propose Media2Face, a diffusion model in GNPFA latent space for co-speech facial animation generation, accepting rich multi-modality guidances from audio, text, and image. Extensive experiments demonstrate that our model not only achieves high fidelity in facial animation synthesis but also broadens the scope of expressiveness and style adaptability in 3D facial animation. |
|
2024-01-30T00:00:00 | 2401.15708 | Object-Driven One-Shot Fine-tuning of Text-to-Image Diffusion with Prototypical Embedding | [
"Jianxiang Lu",
"Cong Xie",
"Hui Guo"
]
| As large-scale text-to-image generation models have made remarkable progress in the field of text-to-image generation, many fine-tuning methods have been proposed. However, these models often struggle with novel objects, especially with one-shot scenarios. Our proposed method aims to address the challenges of generalizability and fidelity in an object-driven way, using only a single input image and the object-specific regions of interest. To improve generalizability and mitigate overfitting, in our paradigm, a prototypical embedding is initialized based on the object's appearance and its class, before fine-tuning the diffusion model. And during fine-tuning, we propose a class-characterizing regularization to preserve prior knowledge of object classes. To further improve fidelity, we introduce object-specific loss, which can also use to implant multiple objects. Overall, our proposed object-driven method for implanting new objects can integrate seamlessly with existing concepts as well as with high fidelity and generalization. Our method outperforms several existing works. The code will be released. |
|
2024-01-30T00:00:00 | 2401.15688 | Divide and Conquer: Language Models can Plan and Self-Correct for Compositional Text-to-Image Generation | [
"Zhenyu Wang",
"Enze Xie",
"Aoxue Li",
"Zhongdao Wang",
"Xihui Liu",
"Zhenguo Li"
]
| Despite significant advancements in text-to-image models for generating high-quality images, these methods still struggle to ensure the controllability of text prompts over images in the context of complex text prompts, especially when it comes to retaining object attributes and relationships. In this paper, we propose CompAgent, a training-free approach for compositional text-to-image generation, with a large language model (LLM) agent as its core. The fundamental idea underlying CompAgent is premised on a divide-and-conquer methodology. Given a complex text prompt containing multiple concepts including objects, attributes, and relationships, the LLM agent initially decomposes it, which entails the extraction of individual objects, their associated attributes, and the prediction of a coherent scene layout. These individual objects can then be independently conquered. Subsequently, the agent performs reasoning by analyzing the text, plans and employs the tools to compose these isolated objects. The verification and human feedback mechanism is finally incorporated into our agent to further correct the potential attribute errors and refine the generated images. Guided by the LLM agent, we propose a tuning-free multi-concept customization model and a layout-to-image generation model as the tools for concept composition, and a local image editing method as the tool to interact with the agent for verification. The scene layout controls the image generation process among these tools to prevent confusion among multiple objects. Extensive experiments demonstrate the superiority of our approach for compositional text-to-image generation: CompAgent achieves more than 10\% improvement on T2I-CompBench, a comprehensive benchmark for open-world compositional T2I generation. The extension to various related tasks also illustrates the flexibility of our CompAgent for potential applications. |
|
2024-01-31T00:00:00 | 2401.17268 | Weaver: Foundation Models for Creative Writing | [
"Tiannan Wang",
"Jiamin Chen",
"Qingrui Jia",
"Shuai Wang",
"Ruoyu Fang",
"Huilin Wang",
"Zhaowei Gao",
"Chunzhao Xie",
"Chuou Xu",
"Jihong Dai",
"Yibin Liu",
"Jialong Wu",
"Shengwei Ding",
"Long Li",
"Zhiwei Huang",
"Xinle Deng",
"Teng Yu",
"Gangan Ma",
"Han Xiao",
"Zixin Chen",
"Danjun Xiang",
"Yunxia Wang",
"Yuanyuan Zhu",
"Yi Xiao",
"Jing Wang",
"Yiru Wang",
"Siran Ding",
"Jiayang Huang",
"Jiayi Xu",
"Yilihamu Tayier",
"Zhenyu Hu",
"Yuan Gao",
"Chengfeng Zheng",
"Yueshu Ye",
"Yihang Li",
"Lei Wan",
"Xinyue Jiang",
"Yujie Wang",
"Siyu Cheng",
"Zhule Song",
"Xiangru Tang",
"Xiaohua Xu",
"Ningyu Zhang",
"Huajun Chen",
"Yuchen Eleanor Jiang",
"Wangchunshu Zhou"
]
| This work introduces Weaver, our first family of large language models (LLMs) dedicated to content creation. Weaver is pre-trained on a carefully selected corpus that focuses on improving the writing capabilities of large language models. We then fine-tune Weaver for creative and professional writing purposes and align it to the preference of professional writers using a suit of novel methods for instruction data synthesis and LLM alignment, making it able to produce more human-like texts and follow more diverse instructions for content creation. The Weaver family consists of models of Weaver Mini (1.8B), Weaver Base (6B), Weaver Pro (14B), and Weaver Ultra (34B) sizes, suitable for different applications and can be dynamically dispatched by a routing agent according to query complexity to balance response quality and computation cost. Evaluation on a carefully curated benchmark for assessing the writing capabilities of LLMs shows Weaver models of all sizes outperform generalist LLMs several times larger than them. Notably, our most-capable Weaver Ultra model surpasses GPT-4, a state-of-the-art generalist LLM, on various writing scenarios, demonstrating the advantage of training specialized LLMs for writing purposes. Moreover, Weaver natively supports retrieval-augmented generation (RAG) and function calling (tool usage). We present various use cases of these abilities for improving AI-assisted writing systems, including integration of external knowledge bases, tools, or APIs, and providing personalized writing assistance. Furthermore, we discuss and summarize a guideline and best practices for pre-training and fine-tuning domain-specific LLMs. |
|
2024-01-31T00:00:00 | 2401.17256 | Weak-to-Strong Jailbreaking on Large Language Models | [
"Xuandong Zhao",
"Xianjun Yang",
"Tianyu Pang",
"Chao Du",
"Lei Li",
"Yu-Xiang Wang",
"William Yang Wang"
]
| https://github.com/XuandongZhao/weak-to-strong | Although significant efforts have been dedicated to aligning large language models (LLMs), red-teaming reports suggest that these carefully aligned LLMs could still be jailbroken through adversarial prompts, tuning, or decoding. Upon examining the jailbreaking vulnerability of aligned LLMs, we observe that the decoding distributions of jailbroken and aligned models differ only in the initial generations. This observation motivates us to propose the weak-to-strong jailbreaking attack, where adversaries can utilize smaller unsafe/aligned LLMs (e.g., 7B) to guide jailbreaking against significantly larger aligned LLMs (e.g., 70B). To jailbreak, one only needs to additionally decode two smaller LLMs once, which involves minimal computation and latency compared to decoding the larger LLMs. The efficacy of this attack is demonstrated through experiments conducted on five models from three different organizations. Our study reveals a previously unnoticed yet efficient way of jailbreaking, exposing an urgent safety issue that needs to be considered when aligning LLMs. As an initial attempt, we propose a defense strategy to protect against such attacks, but creating more advanced defenses remains challenging. The code for replicating the method is available at https://github.com/XuandongZhao/weak-to-strong |
2024-01-31T00:00:00 | 2401.16818 | H2O-Danube-1.8B Technical Report | [
"Philipp Singer",
"Pascal Pfeiffer",
"Yauhen Babakhin",
"Maximilian Jeblick",
"Nischay Dhankhar",
"Gabor Fodor",
"Sri Satish Ambati"
]
| We present H2O-Danube-1.8B, a 1.8B language model trained on 1T tokens following the core principles of LLama 2 and Mistral. We leverage and refine various techniques for pre-training large language models. Although our model is trained on significantly fewer total tokens compared to reference models of similar size, it exhibits highly competitive metrics across a multitude of benchmarks. We additionally release a chat model trained with supervised fine-tuning followed by direct preference optimization. We make H2O-Danube-1.8B openly available under Apache 2.0 license further democratizing LLMs to a wider audience economically. |
|
2024-01-31T00:00:00 | 2401.17270 | YOLO-World: Real-Time Open-Vocabulary Object Detection | [
"Tianheng Cheng",
"Lin Song",
"Yixiao Ge",
"Wenyu Liu",
"Xinggang Wang",
"Ying Shan"
]
| https://github.com/AILab-CVC/YOLO-World | The You Only Look Once (YOLO) series of detectors have established themselves as efficient and practical tools. However, their reliance on predefined and trained object categories limits their applicability in open scenarios. Addressing this limitation, we introduce YOLO-World, an innovative approach that enhances YOLO with open-vocabulary detection capabilities through vision-language modeling and pre-training on large-scale datasets. Specifically, we propose a new Re-parameterizable Vision-Language Path Aggregation Network (RepVL-PAN) and region-text contrastive loss to facilitate the interaction between visual and linguistic information. Our method excels in detecting a wide range of objects in a zero-shot manner with high efficiency. On the challenging LVIS dataset, YOLO-World achieves 35.4 AP with 52.0 FPS on V100, which outperforms many state-of-the-art methods in terms of both accuracy and speed. Furthermore, the fine-tuned YOLO-World achieves remarkable performance on several downstream tasks, including object detection and open-vocabulary instance segmentation. |
2024-01-31T00:00:00 | 2401.17221 | MouSi: Poly-Visual-Expert Vision-Language Models | [
"Xiaoran Fan",
"Tao Ji",
"Changhao Jiang",
"Shuo Li",
"Senjie Jin",
"Sirui Song",
"Junke Wang",
"Boyang Hong",
"Lu Chen",
"Guodong Zheng",
"Ming Zhang",
"Caishuang Huang",
"Rui Zheng",
"Zhiheng Xi",
"Yuhao Zhou",
"Shihan Dou",
"Junjie Ye",
"Hang Yan",
"Tao Gui",
"Qi Zhang",
"Xipeng Qiu",
"Xuanjing Huang",
"Zuxuan Wu",
"Yu-Gang Jiang"
]
| Current large vision-language models (VLMs) often encounter challenges such as insufficient capabilities of a single visual component and excessively long visual tokens. These issues can limit the model's effectiveness in accurately interpreting complex visual information and over-lengthy contextual information. Addressing these challenges is crucial for enhancing the performance and applicability of VLMs. This paper proposes the use of ensemble experts technique to synergizes the capabilities of individual visual encoders, including those skilled in image-text matching, OCR, image segmentation, etc. This technique introduces a fusion network to unify the processing of outputs from different visual experts, while bridging the gap between image encoders and pre-trained LLMs. In addition, we explore different positional encoding schemes to alleviate the waste of positional encoding caused by lengthy image feature sequences, effectively addressing the issue of position overflow and length limitations. For instance, in our implementation, this technique significantly reduces the positional occupancy in models like SAM, from a substantial 4096 to a more efficient and manageable 64 or even down to 1. Experimental results demonstrate that VLMs with multiple experts exhibit consistently superior performance over isolated visual encoders and mark a significant performance boost as more experts are integrated. We have open-sourced the training code used in this report. All of these resources can be found on our project website. |
|
2024-01-31T00:00:00 | 2401.17181 | Transfer Learning for Text Diffusion Models | [
"Kehang Han",
"Kathleen Kenealy",
"Aditya Barua",
"Noah Fiedel",
"Noah Constant"
]
| In this report, we explore the potential for text diffusion to replace autoregressive (AR) decoding for the training and deployment of large language models (LLMs). We are particularly interested to see whether pretrained AR models can be transformed into text diffusion models through a lightweight adaptation procedure we call ``AR2Diff''. We begin by establishing a strong baseline setup for training text diffusion models. Comparing across multiple architectures and pretraining objectives, we find that training a decoder-only model with a prefix LM objective is best or near-best across several tasks. Building on this finding, we test various transfer learning setups for text diffusion models. On machine translation, we find that text diffusion underperforms the standard AR approach. However, on code synthesis and extractive QA, we find diffusion models trained from scratch outperform AR models in many cases. We also observe quality gains from AR2Diff -- adapting AR models to use diffusion decoding. These results are promising given that text diffusion is relatively underexplored and can be significantly faster than AR decoding for long text generation. |
|
2024-01-31T00:00:00 | 2401.16467 | ReGAL: Refactoring Programs to Discover Generalizable Abstractions | [
"Elias Stengel-Eskin",
"Archiki Prasad",
"Mohit Bansal"
]
| While large language models (LLMs) are increasingly being used for program synthesis, they lack the global view needed to develop useful abstractions; they generally predict programs one at a time, often repeating the same functionality. Generating redundant code from scratch is both inefficient and error-prone. To address this, we propose Refactoring for Generalizable Abstraction Learning (ReGAL), a gradient-free method for learning a library of reusable functions via code refactorization, i.e. restructuring code without changing its execution output. ReGAL learns from a small set of existing programs, iteratively verifying and refining its abstractions via execution. We find that the shared function libraries discovered by ReGAL make programs easier to predict across diverse domains. On three datasets (LOGO graphics generation, Date reasoning, and TextCraft, a Minecraft-based text game), both open-source and proprietary LLMs improve in accuracy when predicting programs with ReGAL functions. For CodeLlama-13B, ReGAL results in absolute accuracy increases of 11.5% on graphics, 26.1% on date understanding, and 8.1% on TextCraft, outperforming GPT-3.5 in two of three domains. Our analysis reveals ReGAL's abstractions encapsulate frequently-used subroutines as well as environment dynamics. |
|
2024-01-31T00:00:00 | 2401.16861 | Repositioning the Subject within Image | [
"Yikai Wang",
"Chenjie Cao",
"Qiaole Dong",
"Yifan Li",
"Yanwei Fu"
]
| Current image manipulation primarily centers on static manipulation, such as replacing specific regions within an image or altering its overall style. In this paper, we introduce an innovative dynamic manipulation task, subject repositioning. This task involves relocating a user-specified subject to a desired position while preserving the image's fidelity. Our research reveals that the fundamental sub-tasks of subject repositioning, which include filling the void left by the repositioned subject, reconstructing obscured portions of the subject and blending the subject to be consistent with surrounding areas, can be effectively reformulated as a unified, prompt-guided inpainting task. Consequently, we can employ a single diffusion generative model to address these sub-tasks using various task prompts learned through our proposed task inversion technique. Additionally, we integrate pre-processing and post-processing techniques to further enhance the quality of subject repositioning. These elements together form our SEgment-gEnerate-and-bLEnd (SEELE) framework. To assess SEELE's effectiveness in subject repositioning, we assemble a real-world subject repositioning dataset called ReS. Our results on ReS demonstrate the quality of repositioned image generation. |
|
2024-01-31T00:00:00 | 2401.16677 | T3: Transparent Tracking & Triggering for Fine-grained Overlap of Compute & Collectives | [
"Suchita Pati",
"Shaizeen Aga",
"Mahzabeen Islam",
"Nuwan Jayasena",
"Matthew D. Sinclair"
]
| Large Language Models increasingly rely on distributed techniques for their training and inference. These techniques require communication across devices which can reduce scaling efficiency as the number of devices increases. While some distributed techniques can overlap, and thus, hide this communication with independent computations, techniques such as Tensor Parallelism (TP) inherently serialize communication with model execution. One approach to hide this serialized communication is to interleave it with the producer operation (of the communicated data) in a fine-grained manner. However, this fine-grained interleaving of communication and computation in software can be difficult. Furthermore, as with any concurrent execution, it requires compute and memory resources to be shared between computation and communication, causing resource contention that reduces overlapping efficacy. To overcome these challenges, we propose T3 which applies hardware-software co-design to transparently overlap serialized communication while minimizing resource contention with compute. T3 transparently fuses producer operations with the subsequent communication via a simple configuration of the producer's output address space and requires minor software changes. At the hardware level, T3 adds a lightweight track and trigger mechanism to orchestrate the producer's compute, and communication. It further uses compute-enhanced memories for communication's attendant compute. As a result, T3 reduces resource contention, and efficiently overlaps serialized communication with computation. For important Transformer models like T-NLG, T3 speeds up communication-heavy sublayers by 30% geomean (max 47%) and reduces data movement by 22% geomean (max 36%). Furthermore, T3's benefits persist as models scale: geomean 29% for sublayers in sim500-billion parameter models, PALM and MT-NLG. |
|
2024-01-31T00:00:00 | 2401.17093 | StrokeNUWA: Tokenizing Strokes for Vector Graphic Synthesis | [
"Zecheng Tang",
"Chenfei Wu",
"Zekai Zhang",
"Mingheng Ni",
"Shengming Yin",
"Yu Liu",
"Zhengyuan Yang",
"Lijuan Wang",
"Zicheng Liu",
"Juntao Li",
"Nan Duan"
]
| To leverage LLMs for visual synthesis, traditional methods convert raster image information into discrete grid tokens through specialized visual modules, while disrupting the model's ability to capture the true semantic representation of visual scenes. This paper posits that an alternative representation of images, vector graphics, can effectively surmount this limitation by enabling a more natural and semantically coherent segmentation of the image information. Thus, we introduce StrokeNUWA, a pioneering work exploring a better visual representation ''stroke tokens'' on vector graphics, which is inherently visual semantics rich, naturally compatible with LLMs, and highly compressed. Equipped with stroke tokens, StrokeNUWA can significantly surpass traditional LLM-based and optimization-based methods across various metrics in the vector graphic generation task. Besides, StrokeNUWA achieves up to a 94x speedup in inference over the speed of prior methods with an exceptional SVG code compression ratio of 6.9%. |
|
2024-01-31T00:00:00 | 2401.17053 | BlockFusion: Expandable 3D Scene Generation using Latent Tri-plane Extrapolation | [
"Zhennan Wu",
"Yang Li",
"Han Yan",
"Taizhang Shang",
"Weixuan Sun",
"Senbo Wang",
"Ruikai Cui",
"Weizhe Liu",
"Hiroyuki Sato",
"Hongdong Li",
"Pan Ji"
]
| We present BlockFusion, a diffusion-based model that generates 3D scenes as unit blocks and seamlessly incorporates new blocks to extend the scene. BlockFusion is trained using datasets of 3D blocks that are randomly cropped from complete 3D scene meshes. Through per-block fitting, all training blocks are converted into the hybrid neural fields: with a tri-plane containing the geometry features, followed by a Multi-layer Perceptron (MLP) for decoding the signed distance values. A variational auto-encoder is employed to compress the tri-planes into the latent tri-plane space, on which the denoising diffusion process is performed. Diffusion applied to the latent representations allows for high-quality and diverse 3D scene generation. To expand a scene during generation, one needs only to append empty blocks to overlap with the current scene and extrapolate existing latent tri-planes to populate new blocks. The extrapolation is done by conditioning the generation process with the feature samples from the overlapping tri-planes during the denoising iterations. Latent tri-plane extrapolation produces semantically and geometrically meaningful transitions that harmoniously blend with the existing scene. A 2D layout conditioning mechanism is used to control the placement and arrangement of scene elements. Experimental results indicate that BlockFusion is capable of generating diverse, geometrically consistent and unbounded large 3D scenes with unprecedented high-quality shapes in both indoor and outdoor scenarios. |
|
2024-01-31T00:00:00 | 2401.16468 | High-Quality Image Restoration Following Human Instructions | [
"Marcos V. Conde",
"Gregor Geigle",
"Radu Timofte"
]
| https://github.com/mv-lab/InstructIR | Image restoration is a fundamental problem that involves recovering a high-quality clean image from its degraded observation. All-In-One image restoration models can effectively restore images from various types and levels of degradation using degradation-specific information as prompts to guide the restoration model. In this work, we present the first approach that uses human-written instructions to guide the image restoration model. Given natural language prompts, our model can recover high-quality images from their degraded counterparts, considering multiple degradation types. Our method, InstructIR, achieves state-of-the-art results on several restoration tasks including image denoising, deraining, deblurring, dehazing, and (low-light) image enhancement. InstructIR improves +1dB over previous all-in-one restoration methods. Moreover, our dataset and results represent a novel benchmark for new research on text-guided image restoration and enhancement. Our code, datasets and models are available at: https://github.com/mv-lab/InstructIR |
2024-01-31T00:00:00 | 2401.17264 | Proactive Detection of Voice Cloning with Localized Watermarking | [
"Robin San Roman",
"Pierre Fernandez",
"Alexandre Défossez",
"Teddy Furon",
"Tuan Tran",
"Hady Elsahar"
]
| In the rapidly evolving field of speech generative models, there is a pressing need to ensure audio authenticity against the risks of voice cloning. We present AudioSeal, the first audio watermarking technique designed specifically for localized detection of AI-generated speech. AudioSeal employs a generator/detector architecture trained jointly with a localization loss to enable localized watermark detection up to the sample level, and a novel perceptual loss inspired by auditory masking, that enables AudioSeal to achieve better imperceptibility. AudioSeal achieves state-of-the-art performance in terms of robustness to real life audio manipulations and imperceptibility based on automatic and human evaluation metrics. Additionally, AudioSeal is designed with a fast, single-pass detector, that significantly surpasses existing models in speed - achieving detection up to two orders of magnitude faster, making it ideal for large-scale and real-time applications. |
|
2024-01-31T00:00:00 | 2401.16658 | OWSM v3.1: Better and Faster Open Whisper-Style Speech Models based on E-Branchformer | [
"Yifan Peng",
"Jinchuan Tian",
"William Chen",
"Siddhant Arora",
"Brian Yan",
"Yui Sudo",
"Muhammad Shakeel",
"Kwanghee Choi",
"Jiatong Shi",
"Xuankai Chang",
"Jee-weon Jung",
"Shinji Watanabe"
]
| Recent studies have advocated for fully open foundation models to promote transparency and open science. As an initial step, the Open Whisper-style Speech Model (OWSM) reproduced OpenAI's Whisper using publicly available data and open-source toolkits. With the aim of reproducing Whisper, the previous OWSM v1 through v3 models were still based on Transformer, which might lead to inferior performance compared to other state-of-the-art speech encoders. In this work, we aim to improve the performance and efficiency of OWSM without extra training data. We present E-Branchformer based OWSM v3.1 models at two scales, i.e., 100M and 1B. The 1B model is the largest E-Branchformer based speech model that has been made publicly available. It outperforms the previous OWSM v3 in a vast majority of evaluation benchmarks, while demonstrating up to 25% faster inference speed. We publicly release the data preparation scripts, pre-trained models and training logs. |
|
2024-02-01T00:00:00 | 2401.17377 | Infini-gram: Scaling Unbounded n-gram Language Models to a Trillion Tokens | [
"Jiacheng Liu",
"Sewon Min",
"Luke Zettlemoyer",
"Yejin Choi",
"Hannaneh Hajishirzi"
]
| Are n-gram language models still relevant in this era of neural large language models (LLMs)? Our answer is yes, and we show their values in both text analysis and improving neural LLMs. Yet this necessitates modernizing n-gram models in two aspects. First, we train them at the same data scale as neural LLMs -- 1.4 trillion tokens. This is the largest n-gram model ever built. Second, existing n-gram models use small n which hinders their performance; we instead allow n to be arbitrarily large, by introducing a new infty-gram LM with backoff. Instead of pre-computing n-gram count tables (which would be very expensive), we develop an engine named infini-gram -- powered by suffix arrays -- that can compute infty-gram (as well as n-gram with arbitrary n) probabilities with millisecond-level latency. The infty-gram framework and infini-gram engine enable us to conduct many novel and interesting analyses of human-written and machine-generated text: we find that the infty-gram LM has fairly high accuracy for next-token prediction (47%), and can complement neural LLMs to greatly reduce their language modeling perplexities. When analyzing machine-generated text, we also observe irregularities in the machine--infty-gram agreement level with respect to the suffix length, which indicates deficiencies in neural LLM pretraining and the positional embeddings of Transformers. We open-source our infini-gram engine in the hopes of enabling more study on how to best use verbatim information retrieved from large text corpora. |
|
2024-02-01T00:00:00 | 2401.17574 | Scavenging Hyena: Distilling Transformers into Long Convolution Models | [
"Tokiniaina Raharison Ralambomihanta",
"Shahrad Mohammadzadeh",
"Mohammad Sami Nur Islam",
"Wassim Jabbour",
"Laurence Liang"
]
| The rapid evolution of Large Language Models (LLMs), epitomized by architectures like GPT-4, has reshaped the landscape of natural language processing. This paper introduces a pioneering approach to address the efficiency concerns associated with LLM pre-training, proposing the use of knowledge distillation for cross-architecture transfer. Leveraging insights from the efficient Hyena mechanism, our method replaces attention heads in transformer models by Hyena, offering a cost-effective alternative to traditional pre-training while confronting the challenge of processing long contextual information, inherent in quadratic attention mechanisms. Unlike conventional compression-focused methods, our technique not only enhances inference speed but also surpasses pre-training in terms of both accuracy and efficiency. In the era of evolving LLMs, our work contributes to the pursuit of sustainable AI solutions, striking a balance between computational power and environmental impact. |
|
2024-02-01T00:00:00 | 2401.18058 | LongAlign: A Recipe for Long Context Alignment of Large Language Models | [
"Yushi Bai",
"Xin Lv",
"Jiajie Zhang",
"Yuze He",
"Ji Qi",
"Lei Hou",
"Jie Tang",
"Yuxiao Dong",
"Juanzi Li"
]
| https://github.com/THUDM/LongAlign | Extending large language models to effectively handle long contexts requires instruction fine-tuning on input sequences of similar length. To address this, we present LongAlign -- a recipe of the instruction data, training, and evaluation for long context alignment. First, we construct a long instruction-following dataset using Self-Instruct. To ensure the data diversity, it covers a broad range of tasks from various long context sources. Second, we adopt the packing and sorted batching strategies to speed up supervised fine-tuning on data with varied length distributions. Additionally, we develop a loss weighting method to balance the contribution to the loss across different sequences during packing training. Third, we introduce the LongBench-Chat benchmark for evaluating instruction-following capabilities on queries of 10k-100k in length. Experiments show that LongAlign outperforms existing recipes for LLMs in long context tasks by up to 30\%, while also maintaining their proficiency in handling short, generic tasks. The code, data, and long-aligned models are open-sourced at https://github.com/THUDM/LongAlign. |
2024-02-01T00:00:00 | 2401.17583 | Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion | [
"Tairan He",
"Chong Zhang",
"Wenli Xiao",
"Guanqi He",
"Changliu Liu",
"Guanya Shi"
]
| Legged robots navigating cluttered environments must be jointly agile for efficient task execution and safe to avoid collisions with obstacles or humans. Existing studies either develop conservative controllers (< 1.0 m/s) to ensure safety, or focus on agility without considering potentially fatal collisions. This paper introduces Agile But Safe (ABS), a learning-based control framework that enables agile and collision-free locomotion for quadrupedal robots. ABS involves an agile policy to execute agile motor skills amidst obstacles and a recovery policy to prevent failures, collaboratively achieving high-speed and collision-free navigation. The policy switch in ABS is governed by a learned control-theoretic reach-avoid value network, which also guides the recovery policy as an objective function, thereby safeguarding the robot in a closed loop. The training process involves the learning of the agile policy, the reach-avoid value network, the recovery policy, and an exteroception representation network, all in simulation. These trained modules can be directly deployed in the real world with onboard sensing and computation, leading to high-speed and collision-free navigation in confined indoor and outdoor spaces with both static and dynamic obstacles. |
|
2024-02-01T00:00:00 | 2401.17464 | Efficient Tool Use with Chain-of-Abstraction Reasoning | [
"Silin Gao",
"Jane Dwivedi-Yu",
"Ping Yu",
"Xiaoqing Ellen Tan",
"Ramakanth Pasunuru",
"Olga Golovneva",
"Koustuv Sinha",
"Asli Celikyilmaz",
"Antoine Bosselut",
"Tianlu Wang"
]
| To achieve faithful reasoning that aligns with human expectations, large language models (LLMs) need to ground their reasoning to real-world knowledge (e.g., web facts, math and physical rules). Tools help LLMs access this external knowledge, but there remains challenges for fine-tuning LLM agents (e.g., Toolformer) to invoke tools in multi-step reasoning problems, where inter-connected tool calls require holistic and efficient tool usage planning. In this work, we propose a new method for LLMs to better leverage tools in multi-step reasoning. Our method, Chain-of-Abstraction (CoA), trains LLMs to first decode reasoning chains with abstract placeholders, and then call domain tools to reify each reasoning chain by filling in specific knowledge. This planning with abstract chains enables LLMs to learn more general reasoning strategies, which are robust to shifts of domain knowledge (e.g., math results) relevant to different reasoning questions. It also allows LLMs to perform decoding and calling of external tools in parallel, which avoids the inference delay caused by waiting for tool responses. In mathematical reasoning and Wiki QA domains, we show that our method consistently outperforms previous chain-of-thought and tool-augmented baselines on both in-distribution and out-of-distribution test sets, with an average ~6% absolute QA accuracy improvement. LLM agents trained with our method also show more efficient tool use, with inference speed being on average ~1.4x faster than baseline tool-augmented LLMs. |
|
2024-02-01T00:00:00 | 2401.18059 | RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval | [
"Parth Sarthi",
"Salman Abdullah",
"Aditi Tuli",
"Shubh Khanna",
"Anna Goldie",
"Christopher D. Manning"
]
| Retrieval-augmented language models can better adapt to changes in world state and incorporate long-tail knowledge. However, most existing methods retrieve only short contiguous chunks from a retrieval corpus, limiting holistic understanding of the overall document context. We introduce the novel approach of recursively embedding, clustering, and summarizing chunks of text, constructing a tree with differing levels of summarization from the bottom up. At inference time, our RAPTOR model retrieves from this tree, integrating information across lengthy documents at different levels of abstraction. Controlled experiments show that retrieval with recursive summaries offers significant improvements over traditional retrieval-augmented LMs on several tasks. On question-answering tasks that involve complex, multi-step reasoning, we show state-of-the-art results; for example, by coupling RAPTOR retrieval with the use of GPT-4, we can improve the best performance on the QuALITY benchmark by 20% in absolute accuracy. |
|
2024-02-01T00:00:00 | 2401.18075 | CARFF: Conditional Auto-encoded Radiance Field for 3D Scene Forecasting | [
"Jiezhi Yang",
"Khushi Desai",
"Charles Packer",
"Harshil Bhatia",
"Nicholas Rhinehart",
"Rowan McAllister",
"Joseph Gonzalez"
]
| We propose CARFF: Conditional Auto-encoded Radiance Field for 3D Scene Forecasting, a method for predicting future 3D scenes given past observations, such as 2D ego-centric images. Our method maps an image to a distribution over plausible 3D latent scene configurations using a probabilistic encoder, and predicts the evolution of the hypothesized scenes through time. Our latent scene representation conditions a global Neural Radiance Field (NeRF) to represent a 3D scene model, which enables explainable predictions and straightforward downstream applications. This approach extends beyond previous neural rendering work by considering complex scenarios of uncertainty in environmental states and dynamics. We employ a two-stage training of Pose-Conditional-VAE and NeRF to learn 3D representations. Additionally, we auto-regressively predict latent scene representations as a partially observable Markov decision process, utilizing a mixture density network. We demonstrate the utility of our method in realistic scenarios using the CARLA driving simulator, where CARFF can be used to enable efficient trajectory and contingency planning in complex multi-agent autonomous driving scenarios involving visual occlusions. |
|
2024-02-01T00:00:00 | 2401.17509 | Anything in Any Scene: Photorealistic Video Object Insertion | [
"Chen Bai",
"Zeman Shao",
"Guoxiang Zhang",
"Di Liang",
"Jie Yang",
"Zhuorui Zhang",
"Yujian Guo",
"Chengzhang Zhong",
"Yiqiao Qiu",
"Zhendong Wang",
"Yichen Guan",
"Xiaoyin Zheng",
"Tao Wang",
"Cheng Lu"
]
| Realistic video simulation has shown significant potential across diverse applications, from virtual reality to film production. This is particularly true for scenarios where capturing videos in real-world settings is either impractical or expensive. Existing approaches in video simulation often fail to accurately model the lighting environment, represent the object geometry, or achieve high levels of photorealism. In this paper, we propose Anything in Any Scene, a novel and generic framework for realistic video simulation that seamlessly inserts any object into an existing dynamic video with a strong emphasis on physical realism. Our proposed general framework encompasses three key processes: 1) integrating a realistic object into a given scene video with proper placement to ensure geometric realism; 2) estimating the sky and environmental lighting distribution and simulating realistic shadows to enhance the light realism; 3) employing a style transfer network that refines the final video output to maximize photorealism. We experimentally demonstrate that Anything in Any Scene framework produces simulated videos of great geometric realism, lighting realism, and photorealism. By significantly mitigating the challenges associated with video data generation, our framework offers an efficient and cost-effective solution for acquiring high-quality videos. Furthermore, its applications extend well beyond video data augmentation, showing promising potential in virtual reality, video editing, and various other video-centric applications. Please check our project website https://anythinginanyscene.github.io for access to our project code and more high-resolution video results. |
|
2024-02-01T00:00:00 | 2401.17807 | Advances in 3D Generation: A Survey | [
"Xiaoyu Li",
"Qi Zhang",
"Di Kang",
"Weihao Cheng",
"Yiming Gao",
"Jingbo Zhang",
"Zhihao Liang",
"Jing Liao",
"Yan-Pei Cao",
"Ying Shan"
]
| Generating 3D models lies at the core of computer graphics and has been the focus of decades of research. With the emergence of advanced neural representations and generative models, the field of 3D content generation is developing rapidly, enabling the creation of increasingly high-quality and diverse 3D models. The rapid growth of this field makes it difficult to stay abreast of all recent developments. In this survey, we aim to introduce the fundamental methodologies of 3D generation methods and establish a structured roadmap, encompassing 3D representation, generation methods, datasets, and corresponding applications. Specifically, we introduce the 3D representations that serve as the backbone for 3D generation. Furthermore, we provide a comprehensive overview of the rapidly growing literature on generation methods, categorized by the type of algorithmic paradigms, including feedforward generation, optimization-based generation, procedural generation, and generative novel view synthesis. Lastly, we discuss available datasets, applications, and open challenges. We hope this survey will help readers explore this exciting topic and foster further advancements in the field of 3D content generation. |
|
2024-02-01T00:00:00 | 2401.17895 | ReplaceAnything3D:Text-Guided 3D Scene Editing with Compositional Neural Radiance Fields | [
"Edward Bartrum",
"Thu Nguyen-Phuoc",
"Chris Xie",
"Zhengqin Li",
"Numair Khan",
"Armen Avetisyan",
"Douglas Lanman",
"Lei Xiao"
]
| We introduce ReplaceAnything3D model (RAM3D), a novel text-guided 3D scene editing method that enables the replacement of specific objects within a scene. Given multi-view images of a scene, a text prompt describing the object to replace, and a text prompt describing the new object, our Erase-and-Replace approach can effectively swap objects in the scene with newly generated content while maintaining 3D consistency across multiple viewpoints. We demonstrate the versatility of ReplaceAnything3D by applying it to various realistic 3D scenes, showcasing results of modified foreground objects that are well-integrated with the rest of the scene without affecting its overall integrity. |
|
2024-02-02T00:00:00 | 2402.00396 | Efficient Exploration for LLMs | [
"Vikranth Dwaracherla",
"Seyed Mohammad Asghari",
"Botao Hao",
"Benjamin Van Roy"
]
| We present evidence of substantial benefit from efficient exploration in gathering human feedback to improve large language models. In our experiments, an agent sequentially generates queries while fitting a reward model to the feedback received. Our best-performing agent generates queries using double Thompson sampling, with uncertainty represented by an epistemic neural network. Our results demonstrate that efficient exploration enables high levels of performance with far fewer queries. Further, both uncertainty estimation and the choice of exploration scheme play critical roles. |
|
2024-02-02T00:00:00 | 2402.00769 | AnimateLCM: Accelerating the Animation of Personalized Diffusion Models and Adapters with Decoupled Consistency Learning | [
"Fu-Yun Wang",
"Zhaoyang Huang",
"Xiaoyu Shi",
"Weikang Bian",
"Guanglu Song",
"Yu Liu",
"Hongsheng Li"
]
| https://github.com/G-U-N/AnimateLCM | Video diffusion models has been gaining increasing attention for its ability to produce videos that are both coherent and of high fidelity. However, the iterative denoising process makes it computationally intensive and time-consuming, thus limiting its applications. Inspired by the Consistency Model (CM) that distills pretrained image diffusion models to accelerate the sampling with minimal steps and its successful extension Latent Consistency Model (LCM) on conditional image generation, we propose AnimateLCM, allowing for high-fidelity video generation within minimal steps. Instead of directly conducting consistency learning on the raw video dataset, we propose a decoupled consistency learning strategy that decouples the distillation of image generation priors and motion generation priors, which improves the training efficiency and enhance the generation visual quality. Additionally, to enable the combination of plug-and-play adapters in stable diffusion community to achieve various functions (e.g., ControlNet for controllable generation). we propose an efficient strategy to adapt existing adapters to our distilled text-conditioned video consistency model or train adapters from scratch without harming the sampling speed. We validate the proposed strategy in image-conditioned video generation and layout-conditioned video generation, all achieving top-performing results. Experimental results validate the effectiveness of our proposed method. Code and weights will be made public. More details are available at https://github.com/G-U-N/AnimateLCM. |
2024-02-02T00:00:00 | 2402.00159 | Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research | [
"Luca Soldaini",
"Rodney Kinney",
"Akshita Bhagia",
"Dustin Schwenk",
"David Atkinson",
"Russell Authur",
"Ben Bogin",
"Khyathi Chandu",
"Jennifer Dumas",
"Yanai Elazar",
"Valentin Hofmann",
"Ananya Harsh Jha",
"Sachin Kumar",
"Li Lucy",
"Xinxi Lyu",
"Nathan Lambert",
"Ian Magnusson",
"Jacob Morrison",
"Niklas Muennighoff",
"Aakanksha Naik",
"Crystal Nam",
"Matthew E. Peters",
"Abhilasha Ravichander",
"Kyle Richardson",
"Zejiang Shen",
"Emma Strubell",
"Nishant Subramani",
"Oyvind Tafjord",
"Pete Walsh",
"Luke Zettlemoyer",
"Noah A. Smith",
"Hannaneh Hajishirzi",
"Iz Beltagy",
"Dirk Groeneveld",
"Jesse Dodge",
"Kyle Lo"
]
| Language models have become a critical technology to tackling a wide range of natural language processing tasks, yet many details about how the best-performing language models were developed are not reported. In particular, information about their pretraining corpora is seldom discussed: commercial language models rarely provide any information about their data; even open models rarely release datasets they are trained on, or an exact recipe to reproduce them. As a result, it is challenging to conduct certain threads of language modeling research, such as understanding how training data impacts model capabilities and shapes their limitations. To facilitate open research on language model pretraining, we release Dolma, a three trillion tokens English corpus, built from a diverse mixture of web content, scientific papers, code, public-domain books, social media, and encyclopedic materials. In addition, we open source our data curation toolkit to enable further experimentation and reproduction of our work. In this report, we document Dolma, including its design principles, details about its construction, and a summary of its contents. We interleave this report with analyses and experimental results from training language models on intermediate states of Dolma to share what we have learned about important data curation practices, including the role of content or quality filters, deduplication, and multi-source mixing. Dolma has been used to train OLMo, a state-of-the-art, open language model and framework designed to build and study the science of language modeling. |
|
2024-02-02T00:00:00 | 2402.00786 | CroissantLLM: A Truly Bilingual French-English Language Model | [
"Manuel Faysse",
"Patrick Fernandes",
"Nuno Guerreiro",
"António Loison",
"Duarte Alves",
"Caio Corro",
"Nicolas Boizard",
"João Alves",
"Ricardo Rei",
"Pedro Martins",
"Antoni Bigata Casademunt",
"François Yvon",
"André Martins",
"Gautier Viaud",
"Céline Hudelot",
"Pierre Colombo"
]
| We introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully open-sourced bilingual model that runs swiftly on consumer-grade local hardware. To that end, we pioneer the approach of training an intrinsically bilingual model with a 1:1 English-to-French pretraining data ratio, a custom tokenizer, and bilingual finetuning datasets. We release the training dataset, notably containing a French split with manually curated, high-quality, and varied data sources. To assess performance outside of English, we craft a novel benchmark, FrenchBench, consisting of an array of classification and generation tasks, covering various orthogonal aspects of model performance in the French Language. Additionally, rooted in transparency and to foster further Large Language Model research, we release codebases, and dozens of checkpoints across various model sizes, training data distributions, and training steps, as well as fine-tuned Chat models, and strong translation models. We evaluate our model through the FMTI framework, and validate 81 % of the transparency criteria, far beyond the scores of even most open initiatives. This work enriches the NLP landscape, breaking away from previous English-centric work in order to strengthen our understanding of multilinguality in language models. |
|
2024-02-02T00:00:00 | 2402.00742 | Transforming and Combining Rewards for Aligning Large Language Models | [
"Zihao Wang",
"Chirag Nagpal",
"Jonathan Berant",
"Jacob Eisenstein",
"Alex D'Amour",
"Sanmi Koyejo",
"Victor Veitch"
]
| A common approach for aligning language models to human preferences is to first learn a reward model from preference data, and then use this reward model to update the language model. We study two closely related problems that arise in this approach. First, any monotone transformation of the reward model preserves preference ranking; is there a choice that is ``better'' than others? Second, we often wish to align language models to multiple properties: how should we combine multiple reward models? Using a probabilistic interpretation of the alignment procedure, we identify a natural choice for transformation for (the common case of) rewards learned from Bradley-Terry preference models. This derived transformation has two important properties. First, it emphasizes improving poorly-performing outputs, rather than outputs that already score well. This mitigates both underfitting (where some prompts are not improved) and reward hacking (where the model learns to exploit misspecification of the reward model). Second, it enables principled aggregation of rewards by linking summation to logical conjunction: the sum of transformed rewards corresponds to the probability that the output is ``good'' in all measured properties, in a sense we make precise. Experiments aligning language models to be both helpful and harmless using RLHF show substantial improvements over the baseline (non-transformed) approach. |
|
2024-02-02T00:00:00 | 2402.00518 | EE-Tuning: An Economical yet Scalable Solution for Tuning Early-Exit Large Language Models | [
"Xuchen Pan",
"Yanxi Chen",
"Yaliang Li",
"Bolin Ding",
"Jingren Zhou"
]
| https://github.com/pan-x-c/EE-LLM | This work introduces EE-Tuning, a lightweight and economical solution to training/tuning early-exit large language models (LLMs). In contrast to the common approach of full-parameter pre-training, EE-Tuning augments any pre-trained (and possibly fine-tuned) standard LLM with additional early-exit layers that are tuned in a parameter-efficient manner, which requires significantly less computational resources and training data. Our implementation of EE-Tuning achieves outstanding training efficiency via extensive performance optimizations, as well as scalability due to its full compatibility with 3D parallelism. Results of systematic experiments validate the efficacy of EE-Tuning, confirming that effective early-exit LLM inference can be achieved with a limited training budget. In hope of making early-exit LLMs accessible to the community, we release the source code of our implementation of EE-Tuning at https://github.com/pan-x-c/EE-LLM. |
2024-02-02T00:00:00 | 2402.00838 | OLMo: Accelerating the Science of Language Models | [
"Dirk Groeneveld",
"Iz Beltagy",
"Pete Walsh",
"Akshita Bhagia",
"Rodney Kinney",
"Oyvind Tafjord",
"Ananya Harsh Jha",
"Hamish Ivison",
"Ian Magnusson",
"Yizhong Wang",
"Shane Arora",
"David Atkinson",
"Russell Authur",
"Khyathi Raghavi Chandu",
"Arman Cohan",
"Jennifer Dumas",
"Yanai Elazar",
"Yuling Gu",
"Jack Hessel",
"Tushar Khot",
"William Merrill",
"Jacob Morrison",
"Niklas Muennighoff",
"Aakanksha Naik",
"Crystal Nam",
"Matthew E. Peters",
"Valentina Pyatkin",
"Abhilasha Ravichander",
"Dustin Schwenk",
"Saurabh Shah",
"Will Smith",
"Emma Strubell",
"Nishant Subramani",
"Mitchell Wortsman",
"Pradeep Dasigi",
"Nathan Lambert",
"Kyle Richardson",
"Luke Zettlemoyer",
"Jesse Dodge",
"Kyle Lo",
"Luca Soldaini",
"Noah A. Smith",
"Hannaneh Hajishirzi"
]
| Language models (LMs) have become ubiquitous in both NLP research and in commercial product offerings. As their commercial importance has surged, the most powerful models have become closed off, gated behind proprietary interfaces, with important details of their training data, architectures, and development undisclosed. Given the importance of these details in scientifically studying these models, including their biases and potential risks, we believe it is essential for the research community to have access to powerful, truly open LMs. To this end, this technical report details the first release of OLMo, a state-of-the-art, truly Open Language Model and its framework to build and study the science of language modeling. Unlike most prior efforts that have only released model weights and inference code, we release OLMo and the whole framework, including training data and training and evaluation code. We hope this release will empower and strengthen the open research community and inspire a new wave of innovation. |
|
2024-02-02T00:00:00 | 2402.00858 | Can Large Language Models Understand Context? | [
"Yilun Zhu",
"Joel Ruben Antony Moniz",
"Shruti Bhargava",
"Jiarui Lu",
"Dhivya Piraviperumal",
"Site Li",
"Yuan Zhang",
"Hong Yu",
"Bo-Hsiang Tseng"
]
| Understanding context is key to understanding human language, an ability which Large Language Models (LLMs) have been increasingly seen to demonstrate to an impressive extent. However, though the evaluation of LLMs encompasses various domains within the realm of Natural Language Processing, limited attention has been paid to probing their linguistic capability of understanding contextual features. This paper introduces a context understanding benchmark by adapting existing datasets to suit the evaluation of generative models. This benchmark comprises of four distinct tasks and nine datasets, all featuring prompts designed to assess the models' ability to understand context. First, we evaluate the performance of LLMs under the in-context learning pretraining scenario. Experimental results indicate that pre-trained dense models struggle with understanding more nuanced contextual features when compared to state-of-the-art fine-tuned models. Second, as LLM compression holds growing significance in both research and real-world applications, we assess the context understanding of quantized models under in-context-learning settings. We find that 3-bit post-training quantization leads to varying degrees of performance reduction on our benchmark. We conduct an extensive analysis of these scenarios to substantiate our experimental results. |
Subsets and Splits