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Fooling Polarization-based vision using Locally Controllable Polarizing Projection

Fooling Polarization-based Vision using Locally Controllable...

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

作者： Li, Zhuoxiao Zhong, Zhihang Nobuhara, Shohei Nishino, Ko Zheng, Yinqiang Univ Tokyo Tokyo Japan Shanghai Artificial Intelligence Lab Shanghai Peoples R China Kyoto Univ Kyoto Japan

ISBN: (纸本)9798350353006

Polarization is a fundamental property of light that encodes abundant information regarding surface shape, material, illumination and viewing geometry. The computer vision community has witnessed a blossom of polarization-based vision applications, such as reflection removal, shape-from-polarization (SfP), transparent object segmentation and color constancy, partially due to the emergence of single-chip mono/color polarization sensors that make polarization data acquisition easier than ever. However, is polarization-based vision vulnerable to adversarial attacks? If so, is that possible to realize these adversarial attacks in the physical world, without being perceived by human eyes? In this paper, we warn the community of the vulnerability of polarization-based vision, which can be more serious than RGB-based vision. By adapting a commercial LCD projector, we achieve locally controllable polarizing projection, which is successfully utilized to fool state-of-the-art polarization-based vision algorithms for glass segmentation and SfP. Compared with existing physical attacks on RGB-based vision, which always suffer from the trade-off between attack efficacy and eye conceivability, the adversarial attackers based on polarizing projection are contact-free and visually imperceptible, since naked human eyes can rarely perceive the difference of viciously manipulated polarizing light and ordinary illumination. This poses unprecedented risks on polarization-based vision, for which due attentions should be paid and counter measures be considered.

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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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CAD-SIGNet: CAD Language Inference from Point Clouds using Layer-wise Sketch Instance Guided Attention

CAD-SIGNet: CAD Language Inference from Point Clouds using L...

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

作者： Khan, Mohammad Sadil Dupont, Elona Ali, Sk Aziz Cherenkova, Kseniya Kacem, Anis Aouada, Djamila Univ Luxembourg SnT Luxembourg Luxembourg German Res Ctr Artificial Intelligence Berlin Germany Artec3D Luxembourg Luxembourg

ISBN: (纸本)9798350353013;9798350353006

Reverse engineering in the realm of computer-Aided Design (CAD) has been a longstanding aspiration, though not yet entirely realized. Its primary aim is to uncover the CAD process behind a physical object given its 3D scan. We propose CAD- SIGNet, an end-to-end trainable and aeto-regressive architecture to recover the design history of a CAD model represented as a sequence of sketch-and-extrusion from an input point cloud. Our model learns CAD visual-language representations by layer-wise cross-attention between point cloud and CAD language embedding. In particular, a new Sketch instance Guided Attention (SGA) module is proposed in order to reconstruct the fine-grained details of the sketches. Thanks to its auto-regressive nature, CAD-SIGNet not only reconstructs a unique full design history of the corresponding CAD model given an input point cloud but also provides multiple plausible design choices. This allows for an interactive reverse engineering scenario by providing designers with multiple next step choices along with the design process. Extensive experiments on publicly available CAD datasets showcase the effectiveness of our approach against existing baseline models in two settings, namely, full design history recovery and conditional autocompletion from point clouds.

关键词： 3D Point Clouds computer-Aided Design (CAD) Reverse Engineering vision Language Models

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Pre-trained vision and Language Transformers Are Few-Shot Incremental Learners

Pre-trained Vision and Language Transformers Are Few-Shot In...

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

作者： Park, Keon-Hee Song, Kyungwoo Park, Gyeong-Moon Kyung Hee Univ Seoul South Korea Yonsei Univ Seoul South Korea

ISBN: (纸本)9798350353006

Few-Shot Class Incremental Learning (FSCIL) is a task that requires a model to learn new classes incrementally without forgetting when only a few samples for each class are given. FSCIL encounters two significant challenges: catastrophic forgetting and overfitting, and these challenges have driven prior studies to primarily rely on shallow models, such as ResNet-18. Even though their limited capacity can mitigate both forgetting and overfitting issues, it leads to inadequate knowledge transfer during few-shot incremental sessions. In this paper, we argue that large models such as vision and language transformers pre-trained on large datasets can be excellent few-shot incremental learners. To this end, we propose a novel FSCIL framework called PriViLege, Pre-trained vision and Language transformers with prompting functions and knowledge distillation. Our framework effectively addresses the challenges of catastrophic forgetting and overfitting in large models through new pre-trained knowledge tuning (PKT) and two losses: entropy-based divergence loss and semantic knowledge distillation loss. Experimental results show that the proposed PriViLege significantly outperforms the existing state-of-the-art methods with a large margin, e.g., +9.38% in CUB200, +20.58% in CIFAR-100, and +13.36% in miniImageNet. Our implementation code is available at https://***/KHU-AGI/PriViLege.

关键词： Few-shot learning Incremental learning Parameter Efficient Tuning

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BoQ: A Place is Worth a Bag of Learnable Queries

BoQ: A Place is Worth a Bag of Learnable Queries

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

作者： Ali-Bey, Amar Chaib-Draa, Brahim Giguere, Philippe Univ Laval Dept Comp Sci & Software Engn Quebec City PQ Canada

ISBN: (纸本)9798350353006

In visual place recognition, accurately identifying and matching images of locations under varying environmental conditions and viewpoints remains a significant challenge. In this paper, we introduce a new technique, called Bag-of-Queries (BoQ), which learns a set of global queries, designed to capture universal place-specific attributes. Unlike existing techniques that employ self-attention and generate the queries directly from the input, BoQ employ distinct learnable global queries, which probe the input features via cross-attention, ensuring consistent information aggregation. In addition, this technique provides an interpretable attention mechanism and integrates with both CNN and vision Transformer backbones. The performance of BoQ is demonstrated through extensive experiments on 14 large-scale benchmarks. It consistently outperforms current state-of-the-art techniques including NetVLAD, MixVPR and EigenPlaces. Moreover, despite being a global retrieval technique (one-stage), BoQ surpasses two-stage retrieval methods, such as Patch-NetVLAD, TransVPR and R2Former, all while being orders of magnitude faster and more efficient. The code and model weights are publicly available at https://***/amaralibey/Bag-of-Queries.

关键词： Image Geo-localization Image Retrieval Image-based Localization NetVLAD Place recognition Visual Localization Visual Place recognition VPR

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EmotiEffNet and Temporal Convolutional Networks in Video-based Facial Expression recognition and Action Unit Detection

EmotiEffNet and Temporal Convolutional Networks in Video-bas...

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

作者： Savchenko, Andrey, V Sidorova, Anna P. Sber AI Lab Moscow Russia HSE Univ Lab Algorithms & Technol Network Anal Nizhnii Novgorod Russia HSE Univ Nizhnii Novgorod Russia

ISBN: (纸本)9798350365474

This paper examines the video-based facial expression recognition and action unit detection tasks. We propose to use pre-trained EmotiEffNet models for frame-level facial feature extraction and feed them into the Temporal Convolutional Networks to take into account the dynamics of facial expressions. In addition, we study the possibility of combining facial processing with audio feature extraction to improve the accuracy of audio-visual expression recognition. Experimental results for two tasks from the sixth Affective Behavior Analysis in-the-Wild challenge demonstrate that our approach lets us significantly improve quality metrics on validation sets compared to existing non-ensemble techniques. As a result, our approach took third place in the action unit detection and fourth place in the expression recognition.

关键词： Action unit detection EmotiEffNet models Facial expression recognition HSEmotion library

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Improved Zero-Shot Classification by Adapting VLMs with Text Descriptions

Improved Zero-Shot Classification by Adapting VLMs with Text...

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

作者： Saha, Oindrila Van Horn, Grant Maji, Subhransu Univ Massachusetts Amherst MA 01003 USA

ISBN: (纸本)9798350353006

The zero-shot performance of existing vision-language models (VLMs) such as CLIP [29] is limited by the availability of large-scale, aligned image and text datasets in specific domains. In this work, we leverage two complementary sources of information-descriptions of categories generated by large language models (LLMs) and abundant, fine- grained image classification datasets-to improve the zero-shot classification performance of VLMs across finegrained domains. On the technical side, we develop methods to train VLMs with this "bag-level" image-text supervision. We find that simply using these attributes at test-time does not improve performance, but our training strategy, for example, on the iNaturalist [41] dataset, leads to an average improvement of 4-5% in zero-shot classification accuracy for novel categories of birds [42] and flowers [23]. Similar improvements are observed in domains where a subset of the categories was used to fine-tune the model. By prompting LLMs in various ways, we generate descriptions that capture visual appearance, habitat, and geographic regions and pair them with existing attributes such as the taxonomic structure of the categories. We systematically evaluate their ability to improve zero-shot cat-egorization in natural domains. Our findings suggest that geographic priors can be just as effective and are complementary to visual appearance. Our method also outperforms prior work on prompt-based tuning of VLMs. We release the benchmark, consisting of 14 datasets at https: //***/cvl-umass/AdaptCLIPZS, which will contribute to future research in zero-shot recognition.

关键词： Fine-grained Classification Large Language Model Multimodal Model vision Language Model Zero Shot Learning

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Situational Awareness Matters in 3D vision Language Reasoning

Situational Awareness Matters in 3D Vision Language Reasonin...

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

作者： Man, Yunze Gui, Liang-Yan Wang, Yu-Xiong Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9798350353006

Being able to carry out complicated vision language reasoning tasks in 3D space represents a significant milestone in developing household robots and human-centered embodied AI. In this work, we demonstrate that a critical and distinct challenge in 3D vision language reasoning is the situational awareness, which incorporates two key components: (1) The autonomous agent grounds its self-location based on a language prompt. (2) The agent answers open-ended questions from the perspective of its calculated position. To address this challenge, we introduce SIG3D, an end-to-end Situation-Grounded model for 3D vision language reasoning. We tokenize the 3D scene into sparse voxel representation, and propose a language-grounded situation estimator, followed by a situated question answering module. Experiments on the SQA3D and ScanQA datasets show that SIG3D outperforms state-of-the-art models in situational estimation and question answering by a large margin (e.g., an enhancement of over 30% on situation accuracy). Subsequent analysis corroborates our architectural design choices, explores the distinct functions of visual and textual tokens, and highlights the importance of situational awareness in the domain of 3D question-answering. Project page is available at https://***/situation3d.

关键词： vision-Language Multi-modal 3D Reasoning

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Multi-scale Dynamic and Hierarchical Relationship Modeling for Facial Action Units recognition

Multi-scale Dynamic and Hierarchical Relationship Modeling f...

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

作者： Wang, Zihan Song, Siyang Luo, Cheng Deng, Songhe Xie, Weicheng Shen, Linlin Shenzhen Univ Sch Comp Sci & Software Engn Comp Vis Inst Shenzhen Peoples R China Shenzhen Univ Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen Peoples R China Univ Leicester Leicester Leics England Monash Univ Clayton Vic Australia

ISBN: (纸本)9798350353013;9798350353006

Human facial action units (AUs) are mutually related in a hierarchical manner, as not only they are associated with each other in both spatial and temporal domains but also AUs located in the same/close facial regions show stronger relationships than those of different facial regions. While none of existing approach thoroughly model such hierarchical inter-dependencies among AUs, this paper proposes to comprehensively model multi-scale AU-related dynamic and hierarchical spatio-temporal relationship among AUs for their occurrences recognition. Specifically, we first propose a novel multi-scale temporal differencing network with an adaptive weighting block to explicitly capture facial dynamics across frames at different spatial scales, which specifically considers the heterogeneity of range and magnitude in different AUs' activation. Then, a two-stage strategy is introduced to hierarchically model the relationship among AUs based on their spatial distribution (i.e., local and cross-region AU relationship modelling). Experimental results achieved on BP4D and DISFA show that our approach is the new state-of-the-art in the field of AU occurrence recognition. Our code is publicly available at https://***/CVI-SZU/MDHR.

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DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D Reassembly

DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D ...

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

作者： Scarpellini, Gianluca Fiorini, Stefano Giuliari, Francesco Morerio, Pietro Del Bue, Alessio Ist Italiano Tecnol IIT Pattern Anal & Comp Vis PAVIS Genoa Italy

ISBN: (纸本)9798350353006

Reassembly tasks play a fundamental role in many fields and multiple approaches exist to solve specific reassembly problems. In this context, we posit that a general unified model can effectively address them all, irrespective of the input data type (images, 3D, etc.). We introduce DiffAssemble, a Graph Neural Network (GNN)-based architecture that learns to solve reassembly tasks using a diffusion model formulation. Our method treats the elements of a set, whether pieces of 2D patch or 3D object fragments, as nodes of a spatial graph. Training is performed by introducing noise into the position and rotation of the elements and iteratively denoising them to reconstruct the coherent initial pose. DiffAssemble achieves state-of-the-art (SOTA) results in most 2D and 3D reassembly tasks and is the first learning-based approach that solves 2D puzzles for both rotation and translation. Furthermore, we highlight its remarkable reduction in run-time, performing 11 times faster than the quickest optimization-based method for puzzle solving. Code available at https:// ***/IITPAVIS/DiffAssemble

关键词： diffusion model graph neural network puzzle reassembly

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