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SelfOcc: Self-Supervised vision-Based 3D Occupancy Prediction

SelfOcc: Self-Supervised Vision-Based 3D Occupancy Predictio...

引用

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

作者： Huang, Yuanhui Zheng, Wenzhao Zhang, Borui Zhou, Jie Lu, Jiwen Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Automat Beijing Peoples R China

ISBN: (纸本)9798350353006

3D occupancy prediction is an important task for the robustness of vision-centric autonomous driving, which aims to predict whether each point is occupied in the surrounding 3D space. Existing methods usually require 3D occupancy labels to produce meaningful results. However, it is very laborious to annotate the occupancy status of each voxel. In this paper, we propose SelfOcc to explore a self-supervised way to learn 3D occupancy using only video sequences. We first transform the images into the 3D space (e.g., bird's eye view) to obtain 3D representation of the scene. We directly impose constraints on the 3D representations by treating them as signed distance fields. We can then render 2D images of previous and future frames as self-supervision signals to learn the 3D representations. We propose an MVS-embedded strategy to directly optimize the SDF-induced weights with multiple depth proposals. Our SelfOcc outperforms the previous best method SceneRF by 58.7% using a single frame as input on SemanticKITTI and is the first self-supervised work that produces reasonable 3D occupancy for surround cameras on nuScenes. SelfOcc produces high-quality depth and achieves state-of-the-art results on novel depth synthesis, monocular depth estimation, and surround-view depth estimation on the SemanticKITTI, KITTI-2015, and nuScenes, respectively. Code: https://***/huang-yh/SelfOcc.

关键词： 3D reconstruction autonomous driving NeRF occupancy self-supervised

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HEAL-SWIN: A vision Transformer On The Sphere

HEAL-SWIN: A Vision Transformer On The Sphere

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

作者： Carlsson, Oscar Gerken, Jan E. Linander, Hampus Spiess, Heiner Ohlsson, Fredrik Petersson, Christoffer Persson, Daniel Univ Gothenburg Chalmers Univ Technol Dept Math Sci SE-41296 Gothenburg Sweden Tech Univ Berlin Neural Informat Proc Sci Intelligence DE-10623 Berlin Germany Umea Univ Dept Math & Math Stat Umea degrees SE-90187 Umea Sweden Zenseact SE-41756 Gothenburg Sweden

ISBN: (纸本)9798350353013;9798350353006

High-resolution wide-angle fisheye images are becoming more and more important for robotics applications such as autonomous driving. However, using ordinary convolutional neural networks or vision transformers on this data is problematic due to projection and distortion losses introduced when projecting to a rectangular grid on the plane. We introduce the HEAL-SWIN transformer, which combines the highly uniform Hierarchical Equal Area iso-Latitude Pixelation (HEALPix) grid used in astrophysics and cosmology with the Hierarchical Shifted-Window (SWIN) transformer to yield an efficient and flexible model capable of training on high-resolution, distortion-free spherical data. In HEAL-SWIN, the nested structure of the HEALPix grid is used to perform the patching and windowing operations of the SWIN transformer, enabling the network to process spherical representations with minimal computational overhead. We demonstrate the superior performance of our model on both synthetic and real automotive datasets, as well as a selection of other image datasets, for semantic segmentation, depth regression and classification tasks. Our code is publicly available(1).

关键词： depth estimation fisheye images image classification omni-directional images semantic segmentation spherical grid transformer

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InteractDiffusion: Interaction Control in Text-to-Image Diffusion Models

InteractDiffusion: Interaction Control in Text-to-Image Diff...

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

作者： Hoe, Jiun Tian Jiang, Xudong Chan, Chee Seng Tan, Yap-Peng Hu, Weipeng Nanyang Technol Univ Sch EEE Singapore Singapore Univ Malaya CISiP Kuala Lumpur Malaysia

ISBN: (纸本)9798350353013;9798350353006

Large-scale text-to-image (T2I) diffusion models have showcased incredible capabilities in generating coherent images based on textual descriptions, enabling vast applications in content generation. While recent advancements have introduced control over factors such as object localization, posture, and image contours, a crucial gap remains in our ability to control the interactions between objects in the generated content. Well-controlling interactions in generated images could yield meaningful applications, such as creating realistic scenes with interacting characters. In this work, we study the problems of conditioning T2I diffusion models with Human-Object Interaction (HOI) information, consisting of a triplet label (person, action, object) and corresponding bounding boxes. We propose a pluggable interaction control model, called InteractDiffusion that extends existing pre-trained T2I diffusion models to enable them being better conditioned on interactions. Specifically, we tokenize the HOI information and learn their relationships via interaction embeddings. A conditioning self-attention layer is trained to map HOI tokens to visual tokens, thereby conditioning the visual tokens better in existing T2I diffusion models. Our model attains the ability to control the interaction and location on existing T2I diffusion models, which outperforms existing baselines by a large margin in HOI detection score, as well as fidelity in FID and KID. Project page: https://***/interactdiffusion.

关键词： computer vision conditional image generation diffusion model generative ai human-object interaction image generation

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Taming Self-Training for Open-Vocabulary Object Detection

Taming Self-Training for Open-Vocabulary Object Detection

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

作者： Zhao, Shiyu Schulter, Samuel Zhao, Long Zhang, Zhixing Kumar, Vijay B. G. Suh, Yumin Chandraker, Manmohan Metaxas, Dimitris N. Rutgers State Univ New Brunswick NJ 08901 USA NEC Labs Amer Princeton NJ USA Google Res Mountain View CA USA Univ Calif San Diego San Diego CA USA

ISBN: (纸本)9798350353006

Recent studies have shown promising performance in open-vocabulary object detection (OVD) by utilizing pseudo labels (PLs) from pretrained vision and language models (VLMs). However, teacher-student self-training, a powerful and widely used paradigm to leverage PLs, is rarely explored for OVD. This work identifies two challenges of using self-training in OVD: noisy PLs from VLMs and frequent distribution changes of PLs. To address these challenges, we propose SAS-Det that tames self-training for OVD from two key perspectives. First, we present a split-and-fusion (SAF) head that splits a standard detection into an open-branch and a closed-branch. This design can reduce noisy supervision from pseudo boxes. Moreover, the two branches learn complementary knowledge from different training data, significantly enhancing performance when fused together. Second, in our view, unlike in closed-set tasks, the PL distributions in OVD are solely determined by the teacher model. We introduce a periodic update strategy to decrease the number of updates to the teacher, thereby decreasing the frequency of changes in PL distributions, which stabilizes the training process. Extensive experiments demonstrate SAS-Det is both efficient and effective. SAS-Det outperforms recent models of the same scale by a clear margin and achieves 37.4 AP50 and 29.1 APr on novel categories of the COCO and LVIS benchmarks, respectively. Code is available at https://***/xiaofeng94/SAS-Det.

关键词： open-vocabulary object detection pseudo label self-training vision and language

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On the Effect of Atmospheric Turbulence in the Feature Space of Deep Face recognition

On the Effect of Atmospheric Turbulence in the Feature Space...

引用

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

作者： Robbins, Wes Boult, Terrance Univ Colorado Colorado Springs CO 80907 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

When captured over long distances, image quality is degraded by inconsistent refractive indexes in the atmosphere. This effect, known as Atmospheric Turbulence (AT), leads to lower performance for vision-based biometric systems such as face recognition. To account for AT, the literature has proposed methods to restore face-images from atmospheric turbulence, but has limited success. There is still a need to understand how atmospheric turbulence breaks recognition performance. We offer a first-look in this direction by providing a study on the effect of atmospheric turbulence in the feature space of deep-learning-based face recognition. We present results on recognition performance and feature space transformation caused by a wide range of AT levels. In deep feature space, we find interesting phenomena such as increasing feature magnitudes, which contradicts the expected result from the literature. From our results, we are able to identify an effect that makes face recognition under atmospheric turbulence uniquely difficult, which we call feature defection. In total, our findings suggest several areas of available improvement which can be used as a guideline for further progress in building models that are robust to AT.

关键词： Image quality computer vision Extraterrestrial phenomena Face recognition Atmospheric modeling conferences Computational modeling

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Unknown Prompt, the only Lacuna: Unveiling CLIP's Potential for Open Domain Generalization

Unknown Prompt, the only Lacuna: Unveiling CLIP's Potential ...

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

作者： Singha, Mainak Jha, Ankit Bose, Shirsha Nair, Ashwin Abdar, Moloud Banerjee, Biplab Aisin Corp Kariya Aichi Japan Indian Inst Technol Bombay Maharashtra India Tech Univ Munich Munich Germany IISER Thiruvananthapuram Thiruvananthapuram Kerala India Deakin Univ Geelong Vic Australia

ISBN: (纸本)9798350353006

We delve into Open Domain Generalization (ODG), marked by domain and category shifts between training's labeled source and testing's unlabeled target domains. Existing solutions to ODG face limitations due to constrained generalizations of traditional CNN backbones and errors in detecting target open samples in the absence of prior knowledge. Addressing these pitfalls, we introduce ODG-CLIP, harnessing the semantic prowess of the vision-language model, CLIP. Our framework brings forth three primary innovations: Firstly, distinct from prevailing paradigms, we conceptualize ODG as a multi-class classification challenge encompassing both known and novel categories. Central to our approach is modeling a unique prompt tailored for detecting unknown class samples, and to train this, we employ a readily accessible stable diffusion model, elegantly generating proxy images for the open class. Secondly, aiming for domain-tailored classification (prompt) weights while ensuring a balance of precision and simplicity, we devise a novel visual style-centric prompt learning mechanism. Finally, we infuse images with class-discriminative knowledge derived from the prompt space to augment the fidelity of CLIP's visual embeddings. We introduce a novel objective to safeguard the continuity of this infused semantic intel across domains, especially for the shared classes. Through rigorous testing on diverse datasets, covering closed and open-set DG contexts, ODG-CLIP demonstrates clear supremacy, consistently outpacing peers with performance boosts between 8%-16%. Code will be available at https://***/mainaksingha01/ODG-CLIP.

关键词： open domain generalization vision-language models

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From a Bird's Eye View to See: Joint Camera and Subject Registration without the Camera Calibration

From a Bird's Eye View to See: Joint Camera and Subject Regi...

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

作者： Qian, Zekun Han, Ruize Feng, Wei Wang, Song Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China City Univ Hong Kong Hong Kong Peoples R China Univ South Carolina Columbia SC 29208 USA

ISBN: (纸本)9798350353013;9798350353006

We tackle a new problem of multi-view camera and subject registration in the bird's eye view (BEV) without pre-given camera calibration, which promotes the multi-view subject registration problem to a new calibration-free stage. This greatly alleviates the limitation in many practical applications. However, this is a very challenging problem since its only input is several RGB images from different first-person views (FPVs), without the BEV image and the calibration of the FPVs, while the output is a unified plane aggregated from all views with the positions and orientations of both the subjects and cameras in a BEV. For this purpose, we propose an end-to-end framework solving camera and subject registration together by taking advantage of their mutual dependence, whose main idea is as below: i) creating a subject view-transform module (VTM) to project each pedestrian from FPV to a virtual BEV, ii) deriving a multi-view geometry-based spatial alignment module (SAM) to estimate the relative camera pose in a unified BEV, iii) selecting and refining the subject and camera registration results within the unified BEV. We collect a new large-scale synthetic dataset with rich annotations for training and evaluation. Additionally, we also collect a real dataset for cross-domain evaluation. The experimental results show the remarkable effectiveness of our method. The code and proposed datasets are available at BEVSee.

关键词： Camera Registration computer vision Multiview Detection Subject Registration

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Text-image Alignment for Diffusion-based Perception

Text-image Alignment for Diffusion-based Perception

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

作者： Kondapanenil, Neehar Marksl, Markus Knott, Manuel Guimaraes, Rogerio Perona, Pietro CALTECH Pasadena CA 91125 USA Swiss Data Sci Ctr Empa ETH Zurich Zurich Switzerland

ISBN: (纸本)9798350353006

Diffusion models are generative models with impressive text-to-image synthesis capabilities and have spurred a new wave of creative methods for classical machine learning tasks. However, the best way to harness the perceptual knowledge of these generative models for visual tasks is still an open question. Specifically, it is unclear how to use the prompting interface when applying diffusion backbones to vision tasks. We find that automatically generated captions can improve text-image alignment and significantly enhance a model's cross-attention maps, leading to better perceptual performance. Our approach improves upon the current state-of-the-art (SOTA) in diffusion-based semantic segmentation on ADE20K and the current overall SOTA for depth estimation on NYUv2. Furthermore, our method generalizes to the cross-domain setting. We use model personalization and caption modifications to align our model to the target domain and find improvements over unaligned baselines. Our cross-domain object detection model, trained on Pascal VOC, achieves SOTA results on Watercolor2K. Our cross-domain segmentation method, trained on Cityscapes, achieves SOTA results on Dark Zurich-val and Nighttime Driving. Project page: ***/TADP/Code page: ***/damaggu/TADP

关键词： ADE20K Alignment Cityscapes Cross-Attention Cross-Domain Depth Estimation Diffusion Domain Shift Generative Models Model Personalization NYUv2 Object Detection Segmentation vision-Language Models

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Analyzing the Impact of Gender Misclassification on Face recognition Accuracy

Analyzing the Impact of Gender Misclassification on Face Rec...

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23rd ieee/CVF Winter conference on Applications of computer vision (WACV)

作者： Gbekevi, Afi Edem Edi Achu, Paloma Vela Pangelinan, Gabriella King, Michael C. Bowyer, Kevin W. Florida Inst Technol Melbourne FL 32901 USA Univ Notre Dame Notre Dame IN 46556 USA

ISBN: (纸本)9798350320565

Automated face recognition technologies have been under scrutiny in recent years due to noted variations in accuracy relative to race and gender. Much of this concern was driven by media coverage of high error rates for women and persons of color reported in an evaluation of commercial gender classification ("gender from face") tools. Many decried the conflation of errors observed in the task of gender classification with the task of face recognition. This motivated the question of whether images that are misclassified by a gender classification algorithm have increased error rate with face recognition algorithms. In the first experiment, we analyze the False Match Rate (FMR) of face recognition for comparisons in which one or both of the images are gender-misclassified. In the second experiment, we examine match scores of gender-misclassified images when compared to images from their labeled versus classified gender. We find that, in general, gender misclassified images are not associated with an increased FMR. For females, non-mated comparisons involving one misclassified image actually shift the resultant impostor distribution to lower similarity scores, representing improved accuracy. To our knowledge, this is the first work to analyze (1) the FMR of one- and two-misclassification error pairs and (2) non-mated match scores for misclassified images against labeled- and classified-gender categories.

关键词： computer vision Error analysis Image color analysis Face recognition conferences Media Classification algorithms

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Real-World Mobile Image Denoising Dataset with Efficient Baselines

Real-World Mobile Image Denoising Dataset with Efficient Bas...

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

作者： Flepp, Roman Ignatov, Andrey Timofte, Radu Van Gool, Luc Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Univ Wurzburg Comp Vis Lab Wurzburg Germany AI Witchlabs Ltd Zollikerberg Switzerland

ISBN: (纸本)9798350353006

The recently increased role of mobile photography has raised the standards of on-device photo processing tremendously. Despite the latest advancements in camera hardware, the mobile camera sensor area cannot be increased significantly due to physical constraints, leading to a pixel size of 0.6-2.0 mu m, which results in strong image noise even in moderate lighting conditions. In the era of deep learning, one can train a CNN model to perform robust image denoising. However, there is still a lack of a substantially diverse dataset for this task. To address this problem, we introduce a novel Mobile Image Denoising Dataset (MIDD) comprising over 400,000 noisy / noise-free image pairs captured under various conditions by 20 different mobile camera sensors. Additionally, we propose a new DPreview test set consisting of data from 294 different cameras for precise model evaluation. Furthermore, we present the efficient baseline model SplitterNet for the considered mobile image denoising task that achieves high numerical and visual results, while being able to process 8MP photos directly on smartphone GPUs in under one second. Thereby outperforming models with similar runtimes. This model is also compatible with recent mobile NPUs, demonstrating an even higher speed when deployed on them. The conducted experiments demonstrate high robustness of the proposed solution when applied to images from previously unseen sensors, showing its high generalizability. The datasets, code and models can be found on the official project website (1,2).

关键词： Computational Photography computer vision Dataset Deep Learning Denoising Denoising Dataset Efficient Denoising Model Image Denoising Image Enhancement Image Restoration Mobile Image Denoising

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