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检索条件"任意字段=2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020"

共 11281 条记录，以下是101-110 订阅

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Attention-Propagation Network for Egocentric Heatmap to 3D Pose Lifting

Attention-Propagation Network for Egocentric Heatmap to 3D P...

引用

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

作者： Kang, Taeho Lee, Youngki Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9798350353013;9798350353006

We present EgoTAP, a heatmap-to-3D pose lifting method for highly accurate stereo egocentric 3D pose estimation. Severe self-occlusion and out-of-view limbs in egocentric camera views make accurate pose estimation a challenging problem. To address the challenge, prior methods employ joint heatmaps-probabilistic 2D representations of the body pose, but heatmap-to-3D pose conversion still remains an inaccurate process. We propose a novel heatmap-to-3D lifting method composed of the Grid ViT Encoder and the Propagation Network. The Grid ViT Encoder summarizes joint heatmaps into effective feature embedding using self-attention. Then, the Propagation Network estimates the 3D pose by utilizing skeletal information to better estimate the position of obscure joints. Our method significantly outperforms the previous state-of-the-art qualitatively and quantitatively demonstrated by a 23.9% reduction of error in an MPJPE metric. Our source code is available on GitHub (1).

关键词： 3D pose estimation egocentric vision stereo vision

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MAFA: Managing False Negatives for vision-Language Pre-training

MAFA: Managing False Negatives for Vision-Language Pre-train...

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

作者： Byun, Jaeseok Kim, Dohoon Moon, Taesup Seoul Natl Univ Dept ECE Seoul South Korea Seoul Natl Univ Dept ASRI INMC IPAI AIIS Seoul South Korea

ISBN: (纸本)9798350353006

We consider a critical issue of false negatives in vision-Language Pre-training (VLP), a challenge that arises from the inherent many-to-many correspondence of image-text pairs in large-scale web-crawled datasets. The presence of false negatives can impede achieving optimal performance and even lead to a significant performance drop. To address this challenge, we propose MAFA (MAnaging FAlse negatives), which consists of two pivotal components building upon the recently developed GRouped mIni-baTch sampling (GRIT) strategy: 1) an efficient connection mining process that identifies and converts false negatives into positives, and 2) label smoothing for the image-text contrastive (ITC) loss. Our comprehensive experiments verify the effectiveness of MAFA across multiple downstream tasks, emphasizing the crucial role of addressing false negatives in VLP, potentially even surpassing the importance of addressing false positives. In addition, the compatibility of MAFA with the recent BLIP-family model is also demonstrated. Code is available at https://***/jaeseokbyun/MAFA.

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Exploring vision Transformers for 3D Human Motion-Language Models with Motion Patches

Exploring Vision Transformers for 3D Human Motion-Language M...

引用

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

作者： Yu, Qing Tanaka, Mikihiro Fujiwara, Kent LY Corp Tokyo Japan

ISBN: (纸本)9798350353013;9798350353006

To build a cross-modal latent space between 3D human motion and language, acquiring large-scale and high-quality human motion data is crucial. However, unlike the abundance of image data, the scarcity of motion data has limited the performance of existing motion-language models. To counter this, we introduce "motion patches", a new representation of motion sequences, and propose using vision Transformers (ViT) as motion encoders via transfer learning, aiming to extract useful knowledge from the image domain and apply it to the motion domain. These motion patches, created by dividing and sorting skeleton joints based on body parts in motion sequences, are robust to varying skeleton structures, and can be regarded as color image patches in ViT. We find that transfer learning with pre-trained weights of ViT obtained through training with 2D image data can boost the performance of motion analysis, presenting a promising direction for addressing the issue of limited motion data. Our extensive experiments show that the proposed motion patches, used jointly with ViT, achieve state-of-the-art performance in the benchmarks of text-to-motion retrieval, and other novel challenging tasks, such as cross-skeleton recognition, zero-shot motion classification, and human interaction recognition, which are currently impeded by the lack of data.

关键词： Motion Representation Motion-Language Models Text-Motion Retrieval

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eTraM: Event-based Traffic Monitoring Dataset

eTraM: Event-based Traffic Monitoring Dataset

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

作者： Verma, Aayush Atul Chakravarthi, Bharatesh Vaghela, Arpitsinh Wei, Hua Yang, Yezhou Arizona State Univ Tempe AZ 85287 USA

ISBN: (纸本)9798350353006

Event cameras, with their high temporal and dynamic range and minimal memory usage, have found applications in various fields. However, their potential in static traffic monitoring remains largely unexplored. To facilitate this exploration, we present eTraM - a first-of-its-kind, fully event-based traffic monitoring dataset. eTraM offers 10 hr of data from different traffic scenarios in various lighting and weather conditions, providing a comprehensive overview of real-world situations. Providing 2M bounding box annotations, it covers eight distinct classes of traffic participants, ranging from vehicles to pedestrians and micro-mobility. eTraM's utility has been assessed using state-of-the-art methods for traffic participant detection, including RVT, RED, and YOLOv8. We quantitatively evaluate the ability of event-based models to generalize on nighttime and unseen scenes. Our findings substantiate the compelling potential of leveraging event cameras for traffic monitoring, opening new avenues for research and application. eTraM is available at https://***/eTraM.

关键词： DVS Dynamic vision Sensor Event-based Event-based vision Event-camera ITS Neuromorphic

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Neural Refinement for Absolute Pose Regression with Feature Synthesis

Neural Refinement for Absolute Pose Regression with Feature ...

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

作者： Chen, Shuai Bhalgat, Yash Li, Xinghui Bin, Jia-Wang Li, Kejie Wang, Zirui Prisacariu, Victor Adrian Univ Oxford Act Vision Lab Oxford England Univ Oxford Visual Geometry Grp Oxford England

ISBN: (纸本)9798350353006

Absolute Pose Regression (APR) methods use deep neural networks to directly regress camera poses from RGB images. However, the predominant APR architectures only rely on 2D operations during inference, resulting in limited accuracy of pose estimation due to the lack of 3D geometry constraints or priors. In this work, we propose a test-time refinement pipeline that leverages implicit geometric constraints using a robust feature field to enhance the ability of APR methods to use 3D information during inference. We also introduce a novel Neural Feature Synthesizer (NeFeS) model, which encodes 3D geometric features during training and directly renders dense novel view features at test time to refine APR methods. To enhance the robustness of our model, we introduce a feature fusion module and a progressive training strategy. Our proposed method achieves state-of- the-art single-image APR accuracy on indoor and outdoor datasets. Code will be released at https:// ***/ActivevisionLab/NeFeS.

关键词： Feature Distillation Neural Radiance Field Pose Regression Test-time Refinement Visual Re-Localization

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Eclipse: Disambiguating Illumination and Materials using Unintended Shadows

Eclipse: Disambiguating Illumination and Materials using Uni...

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

作者： Verbin, Dor Mildenhall, Ben Hedman, Peter Barron, Jonathan T. Zickler, Todd Srinivasan, Pratul P. Google Res Mountain View CA 94043 USA Harvard Univ Cambridge MA USA

ISBN: (纸本)9798350353013;9798350353006

Decomposing an object's appearance into representations of its materials and the surrounding illumination is difficult, even when the object's 3D shape is known beforehand. This problem is especially challenging for diffuse objects: it is ill-conditioned because diffuse materials severely blur incoming light, and it is ill-posed because diffuse materials under high-frequency lighting can be indistinguishable from shiny materials under low-frequency lighting. We show that it is possible to recover precise materials and illumination-even from diffuse objects-by exploiting unintended shadows, like the ones cast onto an object by the photographer who moves around it. These shadows are a nuisance in most previous inverse rendering pipelines, but here we exploit them as signals that improve conditioning and help resolve material-lighting ambiguities. We present a method based on differentiable Monte Carlo ray tracing that uses images of an object to jointly recover its spatiallyvarying materials, the surrounding illumination environment, and the shapes of the unseen light occluders who inadvertently cast shadows upon it.

关键词： computer Graphics Inverse Rendering Non-line-of-sight Imaging

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Video2Game: Real-time, Interactive, Realistic and Browser-Compatible Environment from a Single Video

Video2Game: Real-time, Interactive, Realistic and Browser-Co...

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

作者： Xia, Hongchi Lin, Zhi-Hao Ma, Wei-Chiu Wang, Shenlong Univ Illinois Champaign IL 61820 USA Shanghai Jiao Tong Univ Shanghai Peoples R China Cornell Univ Ithaca NY USA

ISBN: (纸本)9798350353013;9798350353006

Creating high-quality and interactive virtual environments, such as games and simulators, often involves complex and costly manual modeling processes. In this paper, we present Video2Game, a novel approach that automatically converts videos of real-world scenes into realistic and interactive game environments. At the heart of our system are three core components: (i) a neural radiance fields (NeRF) module that effectively captures the geometry and visual appearance of the scene;(ii) a mesh module that distills the knowledge from NeRF for faster rendering;and (iii) a physics module that models the interactions and physical dynamics among the objects. By following the carefully designed pipeline, one can construct an interactable and actionable digital replica of the real world. We benchmark our system on both indoor and large-scale outdoor scenes. We show that we can not only produce highly-realistic renderings in real-time, but also build interactive games on top.

关键词： computer vision

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SlowFormer: Adversarial Attack on Compute and Energy Consumption of Efficient vision Transformers

SlowFormer: Adversarial Attack on Compute and Energy Consump...

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

作者： Navaneet, K. L. Koohpayegani, Soroush Abbasi Sleiman, Essam Pirsiavash, Hamed Univ Calif Davis Davis CA 95616 USA Harvard Univ Cambridge MA 02138 USA

ISBN: (纸本)9798350353006

Recently, there has been a lot of progress in reducing the computation of deep models at inference time. These methods can reduce both the computational needs and power usage of deep models. Some of these approaches adaptively scale the compute based on the input instance. We show that such models can be vulnerable to a universal adversarial patch attack, where the attacker optimizes for a patch that when pasted on any image, can increase the compute and power consumption of the model. We run experiments with three different efficient vision transformer methods showing that in some cases, the attacker can increase the computation to the maximum possible level by simply pasting a patch that occupies only 8% of the image area. We also show that a standard adversarial training defense method can reduce some of the attack's success. We believe adaptive efficient methods will be necessary in the future to lower the power usage of expensive deep models, so we hope our paper encourages the community to study the robustness of these methods and develop better defense methods for the proposed attack. Code is available at: https://***/UCDvision/SlowFormer

关键词： Adversarial attack computation attack efficient inference efficient vision transformers energy attack

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Do vision and Language Encoders Represent the World Similarly?

Do Vision and Language Encoders Represent the World Similarl...

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

作者： Maniparambil, Mayug Akshulakov, Raiymbek Djilali, Yasser Abdelaziz Dahou Seddik, Mohamed El Amine Narayan, Sanath Mangalam, Karttikeya O'Connor, Noel E. Dublin City Univ ML Labs Dublin Ireland Univ Calif Berkeley Berkeley CA 94720 USA Technol Innovat Inst Dublin Ireland

ISBN: (纸本)9798350353006

Aligned text-image encoders such as CLIP have become the de-facto model for vision-language tasks. Furthermore, modality-specific encoders achieve impressive performances in their respective domains. This raises a central question: does an alignment exist between uni-modal vision and language encoders since they fundamentally represent the same physical world? Analyzing the latent spaces structure of vision and language models on image-caption benchmarks using the Centered Kernel Alignment (CKA), we find that the representation spaces of unaligned and aligned encoders are semantically similar. In the absence of statistical similarity in aligned encoders like CLIP, we show that a possible matching of unaligned encoders exists without any training. We frame this as a seeded graph-matching problem exploiting the semantic similarity between graphs and propose two methods - a Fast Quadratic Assignment Problem optimization, and a novel localized CKA metric-based matching/retrieval. We demonstrate the effectiveness of this on several downstream tasks including cross-lingual, cross-domain caption matching and image classification. Code available at ***/mayug/0-shot-llm-vision.

关键词： CLIP Unified Representations vision Language Zero-shot

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Unsupervised Video Domain Adaptation with Masked Pre-Training and Collaborative Self-Training

Unsupervised Video Domain Adaptation with Masked Pre-Trainin...

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

作者： Reddy, Arun Paul, William Rivera, Corban Shah, Ketul de Melo, Celso M. Chellappa, Rama Johns Hopkins Univ Baltimore MD 21218 USA Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD USA DEVCOM US Army Res Lab Aberdeen Proving Ground MD USA

ISBN: (纸本)9798350353006

In this work, we tackle the problem of unsupervised domain adaptation (UDA) for video action recognition. Our approach, which we call UNITE, uses an image teacher model to adapt a video student model to the target domain. UNITE first employs self-supervised pre-training to promote discriminative feature learning on target domain videos using a teacher-guided masked distillation objective. We then perform self-training on masked target data, using the video student model and image teacher model together to generate improved pseudolabels for unlabeled target videos. Our self-training process successfully leverages the strengths of both models to achieve strong transfer performance across domains. We evaluate our approach on multiple video domain adaptation benchmarks and observe significant improvements upon previously reported results.

关键词： action recognition domain adaptation masked modeling vuda

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