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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"

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MovieChat: From Dense Token to Sparse Memory for Long Video Understanding

MovieChat: From Dense Token to Sparse Memory for Long Video ...

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

作者： Song, Enxin Chai, Wenhao Wang, Guanhong Zhang, Yucheng Zhou, Haoyang Wu, Feiyang Chi, Haozhe Guo, Xun Ye, Tian Zhang, Yanting Lu, Yan Hwang, Jenq-Neng Wang, Gaoang Zhejiang Univ Hangzhou Peoples R China Univ Washington Seattle WA 98195 USA Microsoft Res Asia Florence Italy Hong Kong Univ Sci & Technol GZ Hong Kong Peoples R China Donghua Univ Shanghai Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9798350353006

Recently, integrating video foundation models and large language models to build a video understanding system can overcome the limitations of specific pre-defined vision tasks. Yet, existing systems can only handle videos with very few frames. For long videos, the computation complexity, memory cost, and long-term temporal connection impose additional challenges. Taking advantage of the Atkinson-Shiffrin memory model, with tokens in Transformers being employed as the carriers of memory in combination with our specially designed memory mechanism, we propose the MovieChat to overcome these challenges. MovieChat achieves state-of-the-art performance in long video understanding, along with the released MovieChat-1K bench-mark with 1K long video and 14K manual annotations for validation of the effectiveness of our method. The code, models and data can be found in https://***/MovieChat.

关键词： computer simulation languages

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Rethinking Transformers Pre-training for Multi-Spectral Satellite Imagery

Rethinking Transformers Pre-training for Multi-Spectral Sate...

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

作者： Noman, Mubashir Naseer, Muzammal Cholakkal, Hisham Anwar, Rao Muhammad Khan, Salman Khan, Fahad Shahbaz Mohamed bin Zayed Univ AI Abu Dhabi U Arab Emirates Australian Natl Univ Canberra ACT Australia Linkoping Univ Linkoping Sweden

ISBN: (纸本)9798350353006

Recent advances in unsupervised learning have demonstrated the ability of large vision models to achieve promising results on downstream tasks by pre-training on large amount of unlabelled data. Such pre-training techniques have also been explored recently in the remote sensing domain due to the availability of large amount of unlabelled data. Different from standard natural image datasets, remote sensing data is acquired from various sensor technologies and exhibit diverse range of scale variations as well as modalities. Existing satellite image pre-training methods either ignore the scale information present in the remote sensing imagery or restrict themselves to use only a single type of data modality. In this paper, we re-visit transformers pre-training and leverage multi-scale information that is effectively utilized with multiple modalities. Our proposed approach, named SatMAE++, performs multiscale pre-training and utilizes convolution based upsampling blocks to reconstruct the image at higher scales making it extensible to include more scales. Compared to existing works, the proposed SatMAE++ with multi-scale pre-training is equally effective for both optical as well as multi-spectral imagery. Extensive experiments on six datasets reveal the merits of proposed contributions, leading to state-of-the-art performance on all datasets. SatMAE++ achieves mean average precision (mAP) gain of 2.5% for multi-label classification task on BigEarthNet dataset. Our code and pre-trained models are available at https://***/techmn/satmae_pp.

关键词： multi-scale mask auto encoder multi-spectral imagery remote sensing scene classification self-supervised learning vision Transformers

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Pre-trained Model Guided Fine-Tuning for Zero-Shot Adversarial Robustness

Pre-trained Model Guided Fine-Tuning for Zero-Shot Adversari...

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

作者： Wang, Sibo Zhang, Jie Yuan, Zheng Shan, Shiguang Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9798350353006

Large-scale pre-trained vision-language models like CLIP have demonstrated impressive performance across various tasks, and exhibit remarkable zero-shot generalization capability, while they are also vulnerable to imperceptible adversarial examples. Existing works typically employ adversarial training (fine-tuning) as a defense method against adversarial examples. However, direct application to the CLIP model may result in overfitting, compromising the model's capacity for generalization. In this paper, we propose Pre-trained Model Guided Adversarial Fine-Tuning (PMG-AFT) method, which leverages supervision from the original pre-trained model by carefully designing an auxiliary branch, to enhance the model's zero-shot adversarial robustness. Specifically, PMG-AFT minimizes the distance between the features of adversarial examples in the target model and those in the pre-trained model, aiming to preserve the generalization features already captured by the pre-trained model. Extensive Experiments on 15 zero-shot datasets demonstrate that PMG-AFT significantly outper-forms the state-of-the-art method, improving the top-1 robust accuracy by an average of 4.99%. Furthermore, our approach consistently improves clean accuracy by an aver-age of 8.72%. Our code is available at here.(1)

关键词： Adversarial Robustness Large-scale vision-language models Zero-Shot

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Multiview Aerial Visual recognition (MAVREC): Can Multi-view Improve Aerial Visual Perception?

Multiview Aerial Visual Recognition (MAVREC): Can Multi-view...

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

作者： Dutta, Aritra Das, Srijan Nielsen, Jacob Chakraborty, Rajatshubhra Shah, Mubarak UCF AI Initiat Orlando FL 32816 USA UNC Charlotte Charlotte NC USA Univ So Denmark IMADA Odense Denmark UCF CRCV Orlando FL USA

ISBN: (纸本)9798350353006

Despite the commercial abundance of UAVs, aerial data acquisition remains challenging, and the existing Asia and North America-centric open-source UAV datasets are small-scale or low-resolution and lack diversity in scene contextuality. Additionally, the color content of the scenes, solar zenith angle, and population density of different geographies influence the data diversity. These factors conjointly render suboptimal aerial-visual perception of the deep neural network (DNN) models trained primarily on the ground view data, including the open-world foundational models. To pave the way for a transformative era of aerial detection, we present Multiview Aerial Visual recognition or MAVREC, a video dataset where we record synchronized scenes from different perspectives - ground camera and drone-mounted camera. MAVREC consists of around 2.5 hours of industry-standard 2.7K resolution video sequences, more than 0.5 million frames, and 1.1 million annotated bounding boxes. This makes MAVREC the largest ground and aerial view dataset, and the fourth largest among all drone-based datasets across all modalities and tasks. Through our extensive benchmarking on MAVREC, we recognize that augmenting object detectors with ground view images from the corresponding geographical location is a superior pre-training strategy for aerial detection. Building on this strategy, we benchmark MAVREC with a curriculum-based semi-supervised object detection approach that leverages labeled (ground and aerial) and unlabeled (only aerial) images to enhance aerial detection.

关键词： Aerial Dataset Baselines and Benchmarking Drone Dataset Multiview Dataset Object Detection Semi-supervised Learning

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WorDepth: Variational Language Prior for Monocular Depth Estimation

WorDepth: Variational Language Prior for Monocular Depth Est...

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

作者： Zeng, Ziyao Wang, Daniel Yang, Fengyu Park, Hyoungseob Wong, Alex Soatto, Stefano Lao, Dong Yale Univ New Haven CT 06511 USA Univ Calif Los Angeles Los Angeles CA USA

ISBN: (纸本)9798350353006

Three-dimensional (3D) reconstruction from a single image is an ill-posed problem with inherent ambiguities, i.e. scale. Predicting a 3D scene from text description(s) is similarly ill-posed, i.e. spatial arrangements of objects described. We investigate the question of whether two inherently ambiguous modalities can be used in conjunction to produce metric-scaled reconstructions. To test this, we focus on monocular depth estimation, the problem of predicting a dense depth map from a single image, but with an additional text caption describing the scene. To this end, we begin by encoding the text caption as a mean and standard deviation;using a variational framework, we learn the distribution of the plausible metric reconstructions of 3D scenes corresponding to the text captions as a prior. To "select" a specific reconstruction or depth map, we encode the given image through a conditional sampler that samples from the latent space of the variational text encoder, which is then decoded to the output depth map. Our approach is trained alternatingly between the text and image branches: in one optimization step, we predict the mean and standard deviation from the text description and sample from a standard Gaussian, and in the other, we sample using a (image) conditional sampler. Once trained, we directly predict depth from the encoded text using the conditional sampler. We demonstrate our approach on indoor (NYUv2) and out-door (KITTI) scenarios, where we show that language can consistently improve performance in both. Code: https: //***/Adonis-galaxy/WorDepth.

关键词： Monocular Depth Estimation Variational Model vision-Language Model

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Towards Long-Form Video Understanding

Towards Long-Form Video Understanding

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

作者： Wu, Chao-Yuan Krahenbuhl, Philipp Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781665445092

Our world offers a never-ending stream of visual stimuli, yet today's vision systems only accurately recognize patterns within a few seconds. These systems understand the present, but fail to contextualize it in past or future events. In this paper, we study long-form video understanding. We introduce a framework for modeling long-form videos and develop evaluation protocols on large-scale datasets. We show that existing state-of-the-art short-term models are limited for long-form tasks. A novel object-centric transformer-based video recognition architecture performs significantly better on 7 diverse tasks. It also outperforms comparable state-of-the-art on the AVA dataset.

关键词： Visualization computer vision Protocols Computational modeling Machine vision computer architecture Benchmark testing

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RGBD Objects in the Wild: Scaling Real-World 3D Object Learning from RGB-D Videos

RGBD Objects in the Wild: Scaling Real-World 3D Object Learn...

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

作者： Xia, Hongchi Fu, Yang Liu, Sifei Wang, Xiaolong Shanghai Jiao Tong Univ Shanghai Peoples R China Univ Calif San Diego San Diego CA USA NVIDIA Santa Clara CA USA

ISBN: (纸本)9798350353006

We introduce a new RGB-D object dataset captured in the wild called WildRGB-D. Unlike most existing real-world object-centric datasets which only come with RGB capturing, the direct capture of the depth channel allows better 3D annotations and broader downstream applications. WildRGB-D comprises large-scale category-level RGB-D object videos, which are taken using an iPhone to go around the objects in 360 degrees. It contains around 8500 recorded objects and nearly 20000 RGB-D videos across 46 common object categories. These videos are taken with diverse cluttered backgrounds with three setups to cover as many real-world scenarios as possible: (i) a single object in one video;(ii) multiple objects in one video;and (iii) an object with a static hand in one video. The dataset is annotated with object masks, real-world scale camera poses, and reconstructed aggregated point clouds from RGBD videos. We benchmark four tasks with WildRGB-D including novel view synthesis, camera pose estimation, object 6d pose estimation, and object surface reconstruction. Our experiments show that the large-scale capture of RGB-D objects provides a large potential to advance 3D object learning. Our project page is https://***/.

关键词： computer vision

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CityDreamer: Compositional Generative Model of Unbounded 3D Cities

CityDreamer: Compositional Generative Model of Unbounded 3D ...

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

作者： Xie, Haozhe Chen, Zhaoxi Hong, Fangzhou Liu, Ziwei Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (纸本)9798350353006

3D city generation is a desirable yet challenging task, since humans are more sensitive to structural distortions in urban environments. Additionally, generating 3D cities is more complex than 3D natural scenes since buildings, as objects of the same class, exhibit a wider range of appearances compared to the relatively consistent appearance of objects like trees in natural scenes. To address these challenges, we propose CityDreamer, a compositional generative model designed specifically for unbounded 3D cities. Our key insight is that 3D city generation should be a composition of different types of neural fields: 1) various building instances, and 2) background stuff, such as roads and green lands. Specifically, we adopt the bird's eye view scene representation and employ a volumetric render for both instance-oriented and stuff-oriented neural fields. The generative hash grid and periodic positional embedding are tailored as scene parameterization to suit the distinct characteristics of building instances and background stuff. Furthermore, we contribute a suite of CityGen Datasets, including OSM and GoogleEarth, which comprises a vast amount of real-world city imagery to enhance the realism of the generated 3D cities both in their layouts and appearances. CityDreamer achieves state-of-the-art performance not only in generating realistic 3D cities but also in localized editing within the generated cities.

关键词： 3D vision AIGC City Generation Scene Generation

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Prompt Learning with One-Shot Setting based Feature Space Analysis in vision-and-Language Models

Prompt Learning with One-Shot Setting based Feature Space An...

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

作者： Hirohashi, Yuki Hirakawa, Tsubasa Yamashita, Takayoshi Fujiyoshi, Hironobu OMRON Corp Kyoto Japan Chubu Univ Kasugai Aichi Japan

ISBN: (纸本)9798350365474

By using few-shot data and labels, prompt learning obtains optimal prompts that are capable of achieving high performance on downstream tasks. Existing prompt learning methods generate high-quality prompts that are suitable for downstream tasks but tend to perform poorly in scenarios where only very limited data (e.g., one-shot) is available. We address on this challenging one-shot scenario and propose a novel architecture for prompt learning, called Image-Text Feature Alignment Branch (ITFAB). ITFAB aligns text features closer to the centroids of image features and separates text features with different classes to resolve misalignment in the feature space, thereby facilitating the acquisition of high-quality prompts with very limited data. In one-shot setting, our method outperforms the existing CoOp and CoCoOp methods and in some cases even surpasses CoCoOp's 16-shot performance. Testing on different datasets and domain, show that ITFAB almost matches CoCoOp's effectiveness. It also works with current prompt learning methods like MapLe and PromptSRC, improving their performance in one-shot setting.

关键词： Prompt Learning vision-and-Language Model

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Equivariant Multi-Modality Image Fusion

Equivariant Multi-Modality Image Fusion

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

作者： Zhao, Zixiang Hai, Haowen Zhang, Jiangshe Zhang, Yulun Zhane, Kai Xu, Shuang Chen, Dongdong Timofte, Radu Van Gool, Luc Xi An Jiao Tong Univ Xian Peoples R China Swiss Fed Inst Technol Zurich Switzerland Shanghai Jiao Tong Univ Shanghai Peoples R China Nanjing Univ Nanjing Peoples R China Northwestern Polytech Univ Xian Peoples R China Heriot Watt Univ Edinburgh Midlothian Scotland Univ Wurzburg Wurzburg Germany INSAIT Sofia Bulgaria

ISBN: (纸本)9798350353006

Multi-modality image fusion is a technique that combines information from different sensors or modalities, enabling the fused image to retain complementary features from each modality, such as functional highlights and texture details. However, effective training of such fusion models is challenging due to the scarcity of ground truth fusion data. To tackle this issue, we propose the Equivariant Multi-Modality imAge fusion (EMMA) paradigm for end-to-end self-supervised learning. Our approach is rooted in the prior knowledge that natural imaging responses are equivariant to certain transformations. Consequently, we introduce a novel training paradigm that encompasses a fusion module, a pseudo-sensing module, and an equivariant fusion module. These components enable the net training to follow the principles of the natural sensing-imaging process while satisfying the equivariant imaging prior. Extensive experiments confirm that EMMA yields high-quality fusion results for infraredvisible and medical images, concurrently facilitating downstream multi-modal segmentation and detection tasks. The code is available at https://***/Zhaozixiang1228/MMIF-EMMA.

关键词： image fusion low-level vision

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