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vision-Language Pre-Training with Triple Contrastive Learning

Vision-Language Pre-Training with Triple Contrastive Learnin...

引用

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

作者： Yang, Jinyu Duan, Jiali Tran, Son Xu, Yi Chanda, Sampath Chen, Liqun Zeng, Belinda Chilimbi, Trishul Huang, Junzhou Univ Texas Arlington Arlington TX 76019 USA Amazon Seattle WA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

vision-language representation learning largely benefits from image-text alignment through contrastive losses (e.g., InfoNCE loss). The success of this alignment strategy is attributed to its capability in maximizing the mutual information (MI) between an image and its matched text. However;simply performing cross-modal alignment (CMA) ignores data potential within each modality, which may result in degraded representations. For instance, although CMA-based models are able to map image-text pairs close together in the embedding space, they fail to ensure that similar inputs from the same modality stay close by. This problem can get even worse when the pre-training data is noisy. In this paper, we propose triple contrastive learning (TCL) for vision-language pre-training by leveraging both cross-modal and intra-modal self-supervision. Besides CMA, TCL introduces an intra-modal contrastive objective to provide complementary benefits in representation learning. To take advantage of localized and structural information from image and text input, TCL further maximizes the average MI between local regions of image/text and their global summary. To the best of our knowledge, ours is the first work that takes into account local structure information for multi-modality representation learning. Experimental evaluations show that our approach is competitive and achieves the new state of the art on various common down-stream vision-language tasks such as image-text retrieval and visual question answering.

关键词： Representation learning Visualization computer vision Question answering (information retrieval) pattern recognition Noise measurement Task analysis

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Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation

Practical Stereo Matching via Cascaded Recurrent Network wit...

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

作者： Li, Jiankun Wang, Peisen Xiong, Pengfei Cai, Tao Yan, Ziwei Yang, Lei Liu, Jiangyu Fan, Haoqiang Liu, Shuaicheng Megvii Res Beijing Peoples R China Tencent Shenzhen Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

With the advent of convolutional neural networks, stereo matching algorithms have recently gained tremendous progress. However, it remains a great challenge to accurately extract disparities from real-world image pairs taken by consumer-level devices like smartphones, due to practical complicating factors such as thin structures, non-ideal rectification, camera module inconsistencies and various hard-case scenes. In this paper, we propose a set of innovative designs to tackle the problem of practical stereo matching: 1) to better recover fine depth details, we design a hierarchical network with recurrent refinement to update disparities in a coarse-to-fine manner, as well as a stacked cascaded architecture for inference;2) we propose an adaptive group correlation layer to mitigate the impact of erroneous rectification;3) we introduce a new synthetic dataset with special attention to difficult cases for better generalizing to real-world scenes. Our results not only rank 1st on both Middlebury and ETH3D benchmarks, outperforming existing state-of-the-art methods by a notable margin, but also exhibit high-quality details for real-life photos, which clearly demonstrates the efficacy of our contributions.

关键词： Correlation Adaptive systems Training data Benchmark testing Network architecture Real-time systems pattern recognition

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Layer-wise Searching for 1-bit Detectors

Layer-wise Searching for 1-bit Detectors

引用

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

作者： Xu, Sheng Zhao, Junhe Lu, Jinhu Zhang, Baochang Han, Shumin Doermann, David Beihang Univ Sch Automat Sci & Elect Engn Beijing Peoples R China Beihang Univ State Key Lab Software Dev Environm Beijing Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing Peoples R China Shenzhen Acad Aerosp Technol Shenzhen Peoples R China Baidu Inc Dept Comp Vis Technol Beijing Peoples R China SUNY Buffalo Buffalo NY USA

ISBN: (纸本)9781665445092

1-bit detectors show great promise for resource-constrained embedded devices but often suffer from a significant performance gap compared with their real-valued counterparts. The primary reason lies in the error during binarization. This paper presents a layer-wise searching (LWS) strategy to generate 1-bit detectors that maintain a performance very close to the original real-valued model. The approach introduces angular and amplitude loss functions to increase detector capacity. At 1-bit layers, it exploits a differentiable binarization search (DBS) to minimize the angular error in a student-teacher framework. We also learn the scale factor by minimizing the amplitude loss in the same student-teacher framework. Extensive experiments show that LWS-Det outperforms state-of-the-art 1-bit detectors by a considerable margin on the PASCAL VOC and COCO datasets. For example, the LWS-Det achieves 1-bit Faster-RCNN with ResNet-34 backbone within 2.0% mAP of its real-valued counterpart on the PASCAL VOC dataset.

关键词： Performance evaluation Training computer vision Satellite broadcasting Detectors Object detection pattern recognition

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Using stereo matching for 2-D face recognition across pose

Using stereo matching for 2-D face recognition across pose

引用

ieee conference on computer vision and pattern recognition

作者： Castillo, Carlos D. Jacobs, David W. Univ Maryland Dept Comp Sci College Pk MD 20742 USA

ISBN: (纸本)9781424411795

We propose using stereo matching for 2-D face recognition across pose. We match one 2-D query image to one 2-D gallery image without performing 3-D reconstruction. Then the cost of this matching is used to evaluate the similarity of the two images. We show that this cost is robust to pose variations. To illustrate this idea we built a face recognition system on top of a dynamic programming stereo matching algorithm. The method works well even when the epipolar lines we use do not exactly fit the viewpoints. We have tested our approach on the PIE dataset. In all the experiments, our method demonstrates effective performance compared with other algorithms.

关键词： pattern matching

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L2G: A Simple Local-to-Global Knowledge Transfer Framework for Weakly Supervised Semantic Segmentation

L2G: A Simple Local-to-Global Knowledge Transfer Framework f...

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

作者： Jiang, Peng-Tao Yang, Yuqi Hou, Qibin Wei, Yunchao Nankai Univ CS TMCC Tianjin Peoples R China Beijing Jiaotong Univ Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Mining precise class-aware attention maps, a.k.a, class activation maps, is essential for weakly supervised semantic segmentation. In this paper, we present L2G, a simple online local-to-global knowledge transfer framework for high-quality object attention mining. We observe that classification models can discover object regions with more details when replacing the input image with its local patches. Taking this into account, we first leverage a local classification network to extract attentions from multiple local patches randomly cropped from the input image. Then, we utilize a global network to learn complementary attention knowledge across multiple local attention maps online. Our framework conducts the global network to learn the captured rich object detail knowledge from a global view and thereby produces high-quality attention maps that can be directly used as pseudo annotations for semantic segmentation networks. Experiments show that our method attains 72.1% and 44.2% mIoU scores on the validation set of PASCAL VOC 2012 and MS COCO 2014, respectively, setting new state-of-the-art records. Code is available at https://***/PengtaoJiang/L2G.

关键词： Knowledge engineering Image segmentation computer vision Codes Shape Annotations Semantics

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Driver state monitor from DELPHI

Driver state monitor from DELPHI

引用

conference on computer vision and pattern recognition

作者： Edenborough, N Hammoud, R Harbach, A Ingold, A Kisacanin, B Malawey, P Newman, T Scharenbroch, G Skiver, S Smith, M Wilhelm, A Witt, G Yoder, E Zhang, H Delphi Electronics and Safety United States

ISBN: (纸本)0769523722

We present an automotive-grade, real-time, vision-based Driver State Monitor. Upon detecting and tracking the driver's facial features, the system analyzes eye-closures and head pose to infer his/her fatigue or distraction. This information is used to warn the driver and to modulate the actions of other safety systems. The purpose of this monitor is to increase road safety by preventing drivers from falling asleep or from being overly distracted, and to improve the effectiveness of other safety systems.

关键词： computer vision

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Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon

Shadows can be Dangerous: Stealthy and Effective Physical-wo...

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

作者： Zhong, Yiqi Liu, Xianming Zhai, Deming Jiang, Junjun Ji, Xiangyang Harbin Inst Technol Harbin Peoples R China Peng Cheng Lab Shenzhen Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Estimating the risk level of adversarial examples is essential for safely deploying machine learning models in the real world. One popular approach for physical-world attacks is to adopt the "sticker-pasting" strategy, which however suffers from some limitations, including difficulties in access to the target or printing by valid colors. A new type of non-invasive attacks emerged recently, which attempt to cast perturbation onto the target by optics based tools, such as laser beam and projector. However, the added optical patterns are artificial but not natural. Thus, they are still conspicuous and attention-grabbed, and can be easily noticed by humans. In this paper, we study a new type of optical adversarial examples, in which the perturbations are generated by a very common natural phenomenon, shadow, to achieve naturalistic and stealthy physical-world adversarial attack under the black-box setting. We extensively evaluate the effectiveness of this new attack on both simulated and real-world environments. Experimental results on traffic sign recognition demonstrate that our algorithm can generate adversarial examples effectively, reaching 98.23% and 90.47% success rates on LISA and GTSRB test sets respectively, while continuously misleading a moving camera over 95% of the time in real-world scenarios. We also offer discussions about the limitations and the defense mechanism of this attack.

关键词： Printing Laser theory computer vision Codes Perturbation methods Machine vision Machine learning

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Joint Physical-Digital Facial Attack Detection Via Simulating Spoofing Clues

Joint Physical-Digital Facial Attack Detection Via Simulatin...

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

作者： He, Xianhua Liang, Dashuang Yang, Song Hao, Zhanlong Ma, Hui Mao, Binjie Li, Xi Wang, Yao Yan, Pengfei Liu, Ajian Meituan Vis AI Dept Beijing Peoples R China MUST Taipa Macao Peoples R China CASIA MAIS Beijing Peoples R China

ISBN: (纸本)9798350365474

Face recognition systems are frequently subjected to a variety of physical and digital attacks of different types. Previous methods have achieved satisfactory performance in scenarios that address physical attacks and digital attacks, respectively. However, few methods are considered to integrate a model that simultaneously addresses both physical and digital attacks, implying the necessity to develop and maintain multiple models. To jointly detect physical and digital attacks within a single model, we propose an innovative approach that can adapt to any network architecture. Our approach mainly contains two types of data augmentation, which we call Simulated Physical Spoofing Clues augmentation (SPSC) and Simulated Digital Spoofing Clues augmentation (SDSC). SPSC and SDSC augment live samples into simulated attack samples by simulating spoofing clues of physical and digital attacks, respectively, which significantly improve the capability of the model to detect "unseen" attack types. Extensive experiments show that SPSC and SDSC can achieve state-of-the-art generalization in Protocols 2.1 and 2.2 of the UniAttackData dataset, respectively. Our method won first place in "Unified Physical-Digital Face Attack Detection" of the 5th Face Anti-spoofing Challenge@cvpr2024. Our final submission obtains 3.75% APCER, 0.93% BPCER, and 2.34% ACER, respectively. Our code is available at https: // ***/ Xianhua- He/ cvpr2024- faceanti-spoofing- challenge.

关键词： Face recognition

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Tracking multiple mouse contours (without too many samples)

Tracking multiple mouse contours (without too many samples)

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conference on computer vision and pattern recognition

作者： Branson, K Belongie, S Univ Calif San Diego Dept Comp Sci & Engn La Jolla CA 92093 USA

ISBN: (纸本)0769523722

We present a particle filtering algorithm for robustly tracking the contours of multiple deformable objects through severe occlusions. Our algorithm combines a multiple blob tracker with a contour tracker in a manner that keeps the required number of samples small. This is a natural combination because both algorithms have complementary strengths. The multiple blob tracker uses a natural multitarget model and searches a smaller and simpler space. On the other hand, contour tracking gives more fine-tuned results and relies on cues that are available during severe occlusions. Our choice of combination of these two algorithms accentuates the advantages of each. We demonstrate good performance on challenging video of three identical mice that contains multiple instances of severe occlusion.

关键词： Mice (computer peripherals)

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SC-Tune: Unleashing Self-Consistent Referential Comprehension in Large vision Language Models

SC-Tune: Unleashing Self-Consistent Referential Comprehensio...

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

作者： Yue, Tongtian Cheng, Jie Guo, Longteng Dai, Xingyuan Zhao, Zijia He, Xingjian Xiong, Gang Lv, Yisheng Liu, Jing CASIA Lab Cognit & Decis Intelligence Complex Syst Beijing Peoples R China CASIA State Key Lab Multimodal Artificial Intelligence Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9798350353006

Recent trends in Large vision Language Models (LVLMs) research have been increasingly focusing on advancing beyond general image understanding towards more nuanced, object-level referential comprehension. In this paper, we present and delve into the self-consistency capability of LVLMs, a crucial aspect that reflects the models' ability to both generate informative captions for specific objects and subsequently utilize these captions to accurately re-identify the objects in a closed-loop process. This capability significantly mirrors the precision and reliability of fine-grained visual-language understanding. Our findings reveal that the self-consistency level of existing LVLMs falls short of expectations, posing limitations on their practical applicability and potential. To address this gap, we introduce a novel fine-tuning paradigm named Self-Consistency Tuning (SC-Tune). It features the synergistic learning of a cyclic describer-locator system. This paradigm is not only data-efficient but also exhibits generalizability across multiple LVLMs. Through extensive experiments, we demonstrate that SC-Tune significantly elevates performance across a spectrum of object-level vision-language benchmarks and maintains competitive or improved performance on image-level vision-language benchmarks. Both our model and code will be publicly available at https://***/ivattyue/SC-Tune.

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