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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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vision-and-Language Navigation via Causal Learning

Vision-and-Language Navigation via Causal Learning

引用

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

作者： Wang, Liuyi He, Zongtao Dang, Ronghao Shen, Mengjiao Liu, Chengju Chen, Qijun Tongji Univ Sch Elect & Informat Engn Shanghai Peoples R China

ISBN: (纸本)9798350353006

In the pursuit of robust and generalizable environment perception and language understanding, the ubiquitous challenge of dataset bias continues to plague vision-and-language navigation (VLN) agents, hindering their perfor-mance in unseen environments. This paper introduces the generalized cross-modal causal transformer (GOAT), a pioneering solution rooted in the paradigm of causal inference. By delving into both observable and unobservable confounders within vision, language, and history, we propose the back-door and front-door adjustment causal learning (BACL and FACL) modules to promote unbiased learning by comprehensively mitigating potential spurious correlations. Additionally, to capture global confounder features, we propose a cross-modal feature pooling (CFP) module supervised by contrastive learning, which is also shown to be effective in improving cross-modal representations during pre-training. Extensive experiments across multiple VLN datasets (R2R, REVERIE, RxR, and SOON) under-score the superiority of our proposed method over previous state-of-the-art approaches. Code is available at https://***/CrystalSixone/VLN-GOAT.

关键词： causal learning cross-modal fusion embodied AI vision-and-language vision-and-language navigation

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Token Transformation Matters: Towards Faithful Post-hoc Explanation for vision Transformer

Token Transformation Matters: Towards Faithful Post-hoc Expl...

引用

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

作者： Wu, Junyi Duan, Bin Kang, Weitai Tang, Hao Yan, Yan IIT Dept Comp Sci Chicago IL 60616 USA Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA

ISBN: (纸本)9798350353006

While Transformers have rapidly gained popularity in various computer vision applications, post-hoc explanations of their internal mechanisms remain largely unexplored. vision Transformers extract visual information by representing image regions as transformed tokens and integrating them via attention weights. However, existing post-hoc explanation methods merely consider these attention weights, neglecting crucial information from the transformed tokens, which fails to accurately illustrate the rationales behind the models' predictions. To incorporate the influence of token transformation into interpretation, we propose TokenTM, a novel post-hoc explanation method that utilizes our introduced measurement of token transformation effects. Specifically, we quantify token transformation effects by measuring changes in token lengths and correlations in their directions pre- and post-transformation. Moreover, we develop initialization and aggregation rules to integrate both attention weights and token transformation effects across all layers, capturing holistic token contributions throughout the model. Experimental results on segmentation and perturbation tests demonstrate the superiority of our proposed TokenTM compared to state-of-the-art vision Transformer explanation methods.

关键词： Explainability Transformer

来源：评论

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SkipPLUS: Skip the First Few Layers to Better Explain vision Transformers

SkipPLUS: Skip the First Few Layers to Better Explain Vision...

引用

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

作者： Mehri, Faridoun Fayyaz, Mohsen Baghshah, Mahdieh Soleymani Pilehvar, Mohammad Taher Sharif Univ Technol Tehran Iran Univ Tehran Tehran Iran Cardiff Univ Cardiff Wales

ISBN: (纸本)9798350365474

Despite their remarkable performance, the explainability of vision Transformers (ViTs) remains a challenge. While forward attention-based token attribution techniques have become popular in text processing, their suitability for ViTs hasn't been extensively explored. In this paper, we compare these methods against state-of-the-art input attribution methods from the vision literature, revealing their limitations due to improper aggregation of information across layers. To address this, we introduce two general techniques, PLUS and SkipPLUS, that can be composed with any input attribution method to more effectively aggregate information across layers while handling noisy layers. Through comprehensive and quantitative evaluations of faithfulness and human interpretability on a variety of ViT architectures and datasets, we demonstrate the effectiveness of PLUS and SkipPLUS, establishing a new state-of-the-art in white-box token attribution. We conclude with a comparative analysis highlighting the strengths and weaknesses of the best versions of all the studied methods. The code used in this paper is freely available at https://***/NightMachinery/SkipPLUS-cvpr-2024.

关键词： Explainable AI Forward Attention-Based Token Attribution Interpretability Neural Network Visualization vision Transformers White-Box Input Attribution Methods xAI

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DrivingGaussian: Composite Gaussian Splatting for Surrounding Dynamic Autonomous Driving Scenes

DrivingGaussian: Composite Gaussian Splatting for Surroundin...

引用

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

作者： Zhou, Xiaoyu Lin, Zhiwei Shan, Xiaojun Wang, Yongtao Sun, Deqing Yang, Ming-Hsuan Peking Univ Wangxuan Inst Comp Technol Beijing Peoples R China Google Res Mountain View CA USA Univ Calif Merced Merced CA USA

ISBN: (纸本)9798350353006

We present DrivingGaussian, an efficient and effective framework for surrounding dynamic autonomous driving scenes. For complex scenes with moving objects, we first sequentially and progressively model the static background of the entire scene with incremental static 3D Gaussians. We then leverage a composite dynamic Gaussian graph to handle multiple moving objects, individually reconstructing each object and restoring their accurate positions and occlusion relationships within the scene. We further use a LiDAR prior for Gaussian Splatting to reconstruct scenes with greater details and maintain panoramic consistency. DrivingGaussian outperforms existing methods in dynamic driving scene reconstruction and enables photorealistic surround-view synthesis with high-fidelity and multi-camera consistency. Our project page is at: https://***/VDIGPKU/DrivingGaussian.

关键词： 3D Reconstruction 3D vision Autonomous Driving

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From Correspondences to Pose: Non-minimal Certifiably Optimal Relative Pose without Disambiguation

From Correspondences to Pose: Non-minimal Certifiably Optima...

引用

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

作者： Tirado-Garin, Javier Civera, Javier Univ Zaragoza I3A Zaragoza Spain

ISBN: (纸本)9798350353013;9798350353006

Estimating the relative camera pose from n >= 5 correspondences between two calibrated views is a fundamental task in computer vision. This process typically involves two stages: 1) estimating the essential matrix between the views, and 2) disambiguating among the four candidate relative poses that satisfy the epipolar geometry. In this paper, we demonstrate a novel approach that, for the first time, bypasses the second stage. Specifically, we show that it is possible to directly estimate the correct relative camera pose from correspondences without needing a post-processing step to enforce the cheirality constraint on the correspondences. Building on recent advances in certifiable non-minimal optimization, we frame the relative pose estimation as a Quadratically Constrained Quadratic Program (QCQP). By applying the appropriate constraints, we ensure the estimation of a camera pose that corresponds to a valid 3D geometry and that is globally optimal when certified. We validate our method through exhaustive synthetic and real-world experiments, confirming the efficacy, efficiency and accuracy of the proposed approach. Code is available at https://***/javrtg/C2P.

关键词： computer vision convex optimization epipolar geometry essential matrix non-minimal solver relative pose semidefinite programming

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Hyper-MD: Mesh Denoising with Customized Parameters Aware of Noise Intensity and Geometric Characteristics

Hyper-MD: Mesh Denoising with Customized Parameters Aware of...

引用

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

作者： Wang, Xingtao Wei, Hongliang Fan, Xiaopeng Zhao, Debin Harbin Inst Technol Harbin Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Mesh denoising (MD) is a critical task in geometry processing, as meshes from scanning or AIGC techniques are susceptible to noise contamination. The challenge of MD lies in the diverse nature of mesh facets in terms of geometric characteristics and noise distributions. Despite recent advancements in deep learning-based MD methods, existing MD networks typically neglect the consideration of geometric characteristics and noise distributions. In this paper, we propose Hyper-MD, a hyper-network-based approach that addresses this limitation by dynamically customizing denoising parameters for each facet based on its noise intensity and geometric characteristics. Specifically, HyperMD is composed of a hyper-network and an MD network. For each noisy facet, the hyper-network takes two angles as input to customize parameters for the MD network. These two angles are specially defined to reveal the noise intensity and geometric characteristics of the current facet, respectively. The MD network receives a facet patch as input, and outputs the denoised normal using the customized parameters. Experimental results on synthetic and real-scanned meshes demonstrate that Hyper-MD outperforms state-of-the-art mesh denoising methods.

关键词： computer graphics hyper-network Mesh denoising

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GreedyViG: Dynamic Axial Graph Construction for Efficient vision GNNs

GreedyViG: Dynamic Axial Graph Construction for Efficient Vi...

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

作者： Munir, Mustafa Avery, William Rahman, Md Mostafijur Marculescu, Radu Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9798350353013;9798350353006

vision graph neural networks (ViG) offer a new avenue for exploration in computer vision. A major bottleneck in ViGs is the inefficient k-nearest neighbor (KNN) operation used for graph construction. To solve this issue, we propose a new method for designing ViGs, Dynamic Axial Graph Construction (DAGC), which is more efficient than KNN as it limits the number of considered graph connections made within an image. Additionally, we propose a novel CNN-GNN architecture, GreedyViG, which uses DAGC. Extensive experiments show that GreedyViG beats existing ViG, CNN, and ViT architectures in terms of accuracy, GMACs, and parameters on image classification, object detection, instance segmentation, and semantic segmentation tasks. Our smallest model, GreedyViG-S, achieves 81.1% top-1 accuracy on ImageNet-1K, 2.9% higher than vision GNN and 2.2% higher than vision HyperGraph Neural Network (ViHGNN), with less GMACs and a similar number of parameters. Our largest model, GreedyViG-B obtains 83.9% top-1 accuracy, 0.2% higher than vision GNN, with a 66.6% decrease in parameters and a 69% decrease in GMACs. GreedyViG-B also obtains the same accuracy as ViHGNN with a 67.3% decrease in parameters and a 71.3% decrease in GMACs. Our work shows that hybrid CNN-GNN architectures not only provide a new avenue for de-signing efficient models, but that they can also exceed the performance of current state-of-the-art models(1).

关键词： Deep Learning Efficient computer vision Graph Neural Networks

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Towards 3D vision with Low-Cost Single-Photon Cameras

Towards 3D Vision with Low-Cost Single-Photon Cameras

引用

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

作者： Mu, Fangzhou Sifferman, Carter Jungerman, Sacha Li, Yiquan Han, Mark Gleicher, Michael Gupta, Mohit Li, Yin Univ Wisconsin Madison WI 53706 USA

ISBN: (纸本)9798350353013;9798350353006

We present a method for reconstructing 3D shape of arbitrary Lambertian objects based on measurements by miniature, energy-efficient, low-cost single-photon cameras. These cameras, operating as time resolved image sensors, illuminate the scene with a very fast pulse of diffuse light and record the shape of that pulse as it returns back from the scene at a high temporal resolution. We propose to model this image formation process, account for its non-idealities, and adapt neural rendering to reconstruct 3D geometry from a set of spatially distributed sensors with known poses. We show that our approach can successfully recover complex 3D shapes from simulated data. We further demonstrate 3D object reconstruction from real-world captures, utilizing measurements from a commodity proximity sensor. Our work draws a connection between image-based modeling and active range scanning, and offers a step towards 3D vision with single-photon cameras. Our project webpage is at https://***/ towards_3d_vision/.

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Action Scene Graphs for Long-Form Understanding of Egocentric Videos

Action Scene Graphs for Long-Form Understanding of Egocentri...

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

作者： Rodin, Ivan Furnari, Antonino Min, Kyle Tripathi, Subarna Farinella, Giovanni Maria Univ Catania Catania Italy Intel Labs Hillsboro OR USA

ISBN: (纸本)9798350353006

We present Egocentric Action Scene Graphs (EASGs), a new representation for long-form understanding of egocentric videos. EASGs extend standard manually-annotated representations of egocentric videos, such as verb-noun action labels, by providing a temporally evolving graph-based description of the actions performed by the camera wearer, including interacted objects, their relationships, and how actions unfold in time. Through a novel annotation procedure, we extend the Ego4D dataset adding manually labeled Egocentric Action Scene Graphs which offer a rich set of annotations for long-from egocentric video understanding. We hence define the EASG generation task and provide a baseline approach, establishing preliminary benchmarks. Experiments on two downstream tasks, action anticipation and activity summarization, highlight the effectiveness of EASGs for long-form egocentric video understanding. We will release the dataset and code to replicate experiments and annotations 1 1 The code is available at https://***/fpv-iplab/EASG.

关键词： egocentric vision long-form video understanding scene graphs

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De-Diffusion Makes Text a Strong Cross-Modal Interface

De-Diffusion Makes Text a Strong Cross-Modal Interface

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

作者： Wei, Chen Liu, Chenxi Qi, Siyuan Zhang, Zhishuai Yuille, Alan Yu, Jiahui Google DeepMind London England Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9798350353006

We demonstrate text as a strong cross-modal interface. Rather than relying on deep embeddings to connect image and language as the interface representation, our approach represents an image as text, from which we enjoy the interpretability and flexibility inherent to natural language. We employ an autoencoder that uses a pre-trained text-to-image diffusion model for decoding. The encoder is trained to transform an input image into text, which is then fed into the fixed text- to-image diffusion decoder to reconstruct the original input - a process we term De- Diffusion. Experiments validate both the precision and comprehensiveness of De-Diffusion text representing images, such that it can be readily ingested by off-the-shelf text-to-image tools and LLMs for diverse multi-modal tasks. For example, a single De-Diffusion model can generalize to provide transferable prompts for different text-to-image tools, and also achieves a new state of the art on open-ended vision-language tasks by simply prompting large language models with few-shot examples. Project page: ***.

关键词： Diffusion Generative Model vision and Language

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