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InternVL: Scaling up vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks

InternVL: Scaling up Vision Foundation Models and Aligning f...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Chen, Zhe Wu, Jiannan Wang, Wenhai Su, Weijie Chen, Guo Xing, Sen Zhong, Muyan Zhang, Qinglong Zhu, Xizhou Lu, Lewei Li, Bin Luo, Ping Lu, Tong Qiao, Yu Dai, Jifeng Shanghai AI Lab OpenGVLab Shanghai Peoples R China Nanjing Univ Nanjing Peoples R China Univ Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Tsinghua Univ Beijing Peoples R China Univ Sci & Technol China Hefei Peoples R China SenseTime Res Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350353006

The exponential growth of large language models (LLMs) has opened up numerous possibilities for multi-modal AGI systems. However, the progress in vision and vision-language foundation models, which are also critical elements of multi-modal AGI, has not kept pace with LLMs. In this work, we design a large-scale vision-language foundation model (InternVL), which scales up the vision foundation model to 6 billion parameters and progressively aligns it with the LLM, using web-scale image-text data from various sources. This model can be broadly applied to and achieve state-of-the-art performance on 32 generic visual-linguistic benchmarks including visual perception tasks such as image-level or pixel-level recognition, vision-language tasks such as zero-shot image/video classification, zero-shot image/video-text retrieval, and link with LLMs to create multi-modal dialogue systems. It has powerful visual capabilities and can be a good alternative to the ViT-22B. We hope that our research could contribute to the development of multi-modal large models.

关键词： multi-modal vision foundation model vision-language model

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Leveraging Frame Affinity for sRGB-to-RAWVideo De-rendering

Leveraging Frame Affinity for sRGB-to-RAWVideo De-rendering

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Chen Han, Wencheng Zhou, Yang Shen, Jianbing Xu, Cheng-zhong Liu, Wentao SenseTime Res Hong Kong Peoples R China Tetras AI Singapore Singapore Univ Macau CIS SKL IOTSC Taipa Macao Peoples R China

ISBN: (纸本)9798350353006

Unprocessed RAW video has shown distinct advantages over sRGB video in video editing and computer vision tasks. However, capturing RAW video is challenging due to limitations in bandwidth and storage. Various methods have been proposed to address similar issues in single image RAW capture through de-rendering. These methods utilize both the metadata and the sRGB image to perform sRGB-to-RAWde-rendering and recover high-quality single-frame RAW data. However, metadata-based methods always require additional computation for online metadata generation, imposing severe burden on mobile camera device for high frame rate RAW video capture. To address this issue, we propose a framework that utilizes frame affinity to achieve high-quality sRGB-to-RAW video reconstruction. Our approach consists of two main steps. The first step, temporal affinity prior extraction, uses motion information between adjacent frames to obtain a reference RAW image. The second step, spatial feature fusion and mapping, learns a pixel-level mapping function using scene-specific and position-specific features provided by the previous frame. Our method can be easily applied to current mobile camera equipment without complicated adaptations or added burden. To demonstrate the effectiveness of our approach, we introduce the first RAW Video De-rendering Benchmark. In this benchmark, our method outperforms state-of-the-art RAW image reconstruction methods, even without image-level metadata.

关键词： Rendering (computer graphics)

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DocRes: A Generalist Model Toward Unifying Document Image Restoration Tasks

DocRes: A Generalist Model Toward Unifying Document Image Re...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Jiaxin Peng, Dezhi Liu, Chongyu Zhang, Peirong Jin, Lianwen South China Univ Technol Guangzhou Peoples R China INTSIG SCUT Joint Lab Document Anal & Recognit Beijing Peoples R China

ISBN: (纸本)9798350353006

Document image restoration is a crucial aspect of Document AI systems, as the quality of document images significantly influences the overall performance. Prevailing methods address distinct restoration tasks independently, leading to intricate systems and the incapability to harness the potential synergies of multi-task learning. To overcome this challenge, we propose DocRes, a generalist model that unifies five document image restoration tasks including dewarping, deshadowing, appearance enhancement, deblurring, and binarization. To instruct DocRes to perform various restoration tasks, we propose a novel visual prompt approach called Dynamic Task-Specific Prompt ( DTSPrompt). The DTSPrompt for different tasks comprises distinct prior features, which are additional characteristics extracted from the input image. Beyond its role as a cue for task-specific execution, DTSPrompt can also serve as supplementary information to enhance the model's performance. Moreover, DTSPrompt is more flexible than prior visual prompt approaches as it can be seamlessly applied and adapted to inputs with high and variable resolutions. Experimental results demonstrate that DocRes achieves competitive or superior performance compared to existing state-of-the-art task-specific models. This underscores the potential of DocRes across a broader spectrum of document image restoration tasks. The source code is publicly available at https://***/ZZZHANG-jx/DocRes.

关键词： document analysis and recognition document image enhancement unified model

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Holo-Relighting: Controllable Volumetric Portrait Relighting from a Single Image

Holo-Relighting: Controllable Volumetric Portrait Relighting...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Mei, Yiqun Zeng, Yu Zhang, He Shu, Zhixin Zhang, Xuaner Bi, Sai Zhang, Jianming Jung, HyunJoon Patel, Vishal M. Johns Hopkins Univ Baltimore MD 21218 USA Adobe Inc San Jose CA USA

ISBN: (纸本)9798350353013;9798350353006

At the core of portrait photography is the search for ideal lighting and viewpoint. The process often requires advanced knowledge in photography and an elaborate studio setup. In this work, we propose Holo-Relighting, a volumetric relighting method that is capable of synthesizing novel viewpoints, and novel lighting from a single image. Holo-Relighting leverages the pretrained 3D GAN (EG3D) to reconstruct geometry and appearance from an input portrait as a set of 3D-aware features. We design a relighting module conditioned on a given lighting to process these features, and predict a relit 3D representation in the form of a tri-plane, which can render to an arbitrary viewpoint through volume rendering. Besides viewpoint and lighting control, Holo-Relighting also takes the head pose as a condition to enable head-pose-dependent lighting effects. With these novel designs, Holo-Relighting can generate complex non-Lambertian lighting effects (e.g., specular highlights and cast shadows) without using any explicit physical lighting priors. We train Holo-Relighting with data captured with a light stage, and propose two data-rendering techniques to improve the data quality for training the volumetric relighting system. Through quantitative and qualitative experiments, we demonstrate Holo-Relighting can achieve state-of-the-arts relighting quality with better photorealism, 3D consistency and controllability.

关键词： vision + graphics

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Extend Your Own Correspondences: Unsupervised Distant Point Cloud Registration by Progressive Distance Extension

Extend Your Own Correspondences: Unsupervised Distant Point ...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Liu, Quan Zhul, Hongzi Wang, Zhenxi Zhou, Yunsong Chang, Shan Guo, Minyi Shanghai Jiao Tong Univ Shanghai Peoples R China Donghua Univ Shanghai Peoples R China

ISBN: (纸本)9798350353006

Registration of point clouds collected from a pair of distant vehicles provides a comprehensive and accurate 3D view of the driving scenario, which is vital for driving safety related applications, yet existing literature suffers from the expensive pose label acquisition and the deficiency to generalize to new data distributions. In this paper, we propose EYOC, an unsupervised distant point cloud registration method that adapts to new point cloud distributions on the fly, requiring no global pose labels. The core idea of EYOC is to train a feature extractor in a progressive fashion, where in each round, the feature extractor, trained with near point cloud pairs, can label slightly farther point cloud pairs, enabling self- supervision on such far point cloud pairs. This process continues until the derived extractor can be used to register distant point clouds. Particularly, to enable high-fidelity correspondence label generation, we devise an effective spatial filtering scheme to select the most representative correspondences to register a point cloud pair, and then utilize the aligned point clouds to discover more correct correspondences. Experiments show that EYOC can achieve comparable performance with state-of-the-art supervised methods at a lower training cost. Moreover, it outwits supervised methods regarding generalization performance on new data distributions.

关键词： 3D vision Autonomous Driving Point Cloud Registration Unsupervised Learning

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3D Feature Tracking via Event Camera

3D Feature Tracking via Event Camera

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Li, Siqi Zhou, Zhikuan Xue, Zhou Li, Yipeng Du, Shaoyi Gao, Yue Tsinghua Univ Sch Software BNRist THUIBCS Beijing Peoples R China Li Auto Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Natl Engn Res Ctr Visual Informat & Applicat Natl Key Lab Human Machine Hybrid Augmented Intel Xian Peoples R China

ISBN: (纸本)9798350353006

This paper presents the first 3D feature tracking method with the corresponding dataset. Our proposed method takes event streams from stereo event cameras as input to predict 3D trajectories of the target features with high-speed motion. To achieve this, our method leverages a joint framework to predict the 2D feature motion offsets and the 3D feature spatial position simultaneously. A motion compensation module is leveraged to overcome the feature deformation. A patch matching module based on bipolarity hypergraph modeling is proposed to robustly estimate the feature spatial position. Meanwhile, we collect the first 3D feature tracking dataset with high-speed moving objects and ground truth 3D feature trajectories at 250 FPS, named E-3DTrack, which can be used as the first high-speed 3D feature tracking benchmark. Our code and dataset could be found at: https:// github. com/ lisiqi19971013/E-3DTrack.

关键词： 3D vision Dataset Event Camera Feature Tracking

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Segment Any Event Streams via Weighted Adaptation of Pivotal Tokens

Segment Any Event Streams via Weighted Adaptation of Pivotal...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Chen, Zhiwen Zhu, Zhiyu Zhang, Yifan Hou, Junhui Shi, Guangming Wu, Jinjian Xidian Univ Xian Shaanxi Peoples R China City Univ Hong Kong Hong Kong Peoples R China Pazhou Lab Guangzhou Peoples R China

ISBN: (纸本)9798350353013;9798350353006

In this paper, we delve into the nuanced challenge of tailoring the Segment Anything Models (SAMs) for integration with event data, with the overarching objective of attaining robust and universal object segmentation within the event-centric domain. One pivotal issue at the heart of this endeavor is the precise alignment and calibration of embeddings derived from event-centric data such that they harmoniously coincide with those originating from RGB imagery. Capitalizing on the vast repositories of datasets with paired events and RGB images, our proposition is to harness and extrapolate the profound knowledge encapsulated within the pretrained SAM framework. As a cornerstone to achieving this, we introduce a multi-scale feature distillation methodology. This methodology rigorously optimizes the alignment of token embeddings originating from event data with their RGB image counterparts, thereby preserving and enhancing the robustness of the overall architecture. Considering the distinct significance that token embeddings from intermediate layers hold for higher-level embeddings, our strategy is centered on accurately calibrating the pivotal token embeddings. This targeted calibration is aimed at effectively managing the discrepancies in high-level embeddings originating from both the event and image domains. Extensive experiments on different datasets demonstrate the effectiveness of the proposed distillation method. Code in https://***/happychenpipi/EventSAM.

关键词： cross-modal knowledge distillation event-based vision segmentation

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LAMP: Learn A Motion pattern for Few-Shot Video Generation

LAMP: Learn A Motion Pattern for Few-Shot Video Generation

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wu, Ruiqi Chen, Liangyu Yang, Tong Guo, Chunle Li, Chongyi Zhang, Xiangyu Nankai Univ CS VCIP Tianjin Peoples R China NKIARI Shenzhen Futian Peoples R China MEGVII Technol Beijing Peoples R China

ISBN: (纸本)9798350353013;9798350353006

In this paper, we present a few-shot text-to-video framework, LAMP, which enables a text-to-image diffusion model to Learn A specific Motion pattern with 8 16 videos on a single GPU. Unlike existing methods, which require a large number of training resources or learn motions that are precisely aligned with template videos, it achieves a trade-off between the degree of generation freedom and the resource costs for model training. Specifically, we design a motion-content decoupled pipeline that uses an off-the-shelf text-to-image model for content generation so that our tuned video diffusion model mainly focuses on motion learning. The well-developed text-to-image techniques can provide visually pleasing and diverse content as generation conditions, which highly improves video quality and generation freedom. To capture the features of temporal dimension, we expand the pre-trained 2D convolution layers of the T2I model to our novel temporal-spatial motion learning layers and modify the attention blocks to the temporal level. Additionally, we develop an effective inference trick, shared-noise sampling, which can improve the stability of videos without computational costs. Our method can also be flexibly applied to other tasks, e.g. real-world image animation and video editing. Extensive experiments demonstrate that LAMP can effectively learn the motion pattern on limited data and generate high-quality videos. The code and models are available at https://rqwu. ***/projects/LAMP.

关键词： Image coding

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DUSt3R: Geometric 3D vision Made Easy

DUSt3R: Geometric 3D Vision Made Easy

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wang, Shuzhe Leroyt, Vincent Cabon, Yohann Chidlovskii, Boris Revaud, Jerome Aalto Univ Espoo Finland Naver Labs Europe Meylan France

ISBN: (纸本)9798350353006

Multi-view stereo reconstruction (MVS) in the wild re-quires to first estimate the camera intrinsic and extrinsic parameters. These are usually tedious and cumbersome to obtain, yet they are mandatory to triangulate corresponding pixels in 3D space, which is at the core of all best performing MVS algorithms. In this work, we take an opposite stance and introduce DUSt3R, a radically novel paradigm for Dense and Unconstrained Stereo 3D Reconstruction of arbitrary image collections, operating without prior information about camera calibration nor viewpoint poses. We cast the pairwise reconstruction problem as a regression of pointmaps, relaxing the hard constraints of usual projective camera models. We show that this formulation smoothly unifies the monocular and binocular reconstruction cases. In the case where more than two images are provided, we further propose a simple yet effective global alignment strategy that expresses all pairwise pointmaps in a common reference frame. We base our network architecture on standard Transformer encoders and decoders, allowing us to leverage powerful pretrained models. Our formulation directly provides a 3D model of the scene as well as depth information, but interestingly, we can seamlessly recover from it, pixel matches, focal lengths, relative and absolute cameras. Extensive experiments on all these tasks showcase how DUSt3R effectively unifies various 3D vision tasks, setting new performance records on monocular & multi-view depth estimation as well as relative pose estimation. In summary, DUSt3R makes many geometric 3D vision tasks easy. Code and mod-els at https://***/naver/dust3r.

关键词： 3D reconstruction camera calibration foundation model monocular depth multi-view depth multi-view pose estimation visual localization

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AHIVE: Anatomy-aware Hierarchical vision Encoding for Interactive Radiology Report Retrieval

AHIVE: Anatomy-aware Hierarchical Vision Encoding for Intera...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yan, Sixing Cheung, William K. Tsang, Ivor W. Chiu, Keith Tong, Terence M. Cheung, Ka Chun Seel, Simon Hong Kong Baptist Univ Hong Kong Peoples R China Agcy Sci Technol & Res CFAR Singapore Singapore Agcy Sci Technol & Res IHPC Singapore Singapore Nanyang Technol Univ SCSE Singapore Singapore Univ Technol Sydney AAII Sydney NSW Australia Queen Elizabeth Hosp Hong Kong Peoples R China Kwong Wah Hosp Hong Kong Peoples R China Tuen Mun Hosp Hong Kong Peoples R China NVIDIA Corp NVIDIA AI Technol Ctr Santa Clara CA USA

ISBN: (纸本)9798350353006

Automatic radiology report generation using deep learning models has been recently explored and found promising. Neural decoders are commonly used for the report generation, where irrelevant and unfaithful contents are unavoidable. The retrieval-based approach alleviates the limitation by identifying reports which are relevant to the input to assist the generation. To achieve clinically accurate report retrieval, we make reference to clinicians' diagnostic steps of examining a radiology image where anatomical and diagnostic details are typically focused, and propose a novel hierarchical visual concept representation called anatomy-aware hierarchical vision encoding (AHIVE). To learn AHIVE, we first derive a methodology to extract hierarchical diagnostic descriptions from radiology reports and develop a CLIP-based framework for the model training. Also, the hierarchical architecture of AHIVE is designed to support interactive report retrieval so that report revision made at one layer can be propagated to the subsequent ones to trigger other necessary revisions. We conduct extensive experiments and show that AHIVE can outperform the SOTA vision-language retrieval methods in terms of clinical accuracy by a large margin. We provide also a case study to illustrate how it enables interactive report retrieval.

关键词： image-to-text retrieval interactive retrieval system radiology imaging radiology report retrieval

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