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TlTScore: Towards Long-Tail Effects in Text-to-Visual Evaluation with Generative Foundation Models

TlTScore: Towards Long-Tail Effects in Text-to-Visual Evalua...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Pengliang Ji Junchen Liu Carnegie Mellon University UC Berkeley

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Evaluation of generative foundation models (GenFMs) for text-to-visual tasks has been enhanced by automatic alignment metrics such as CLIPScore, complementing human feedback. However, existing evaluation methods suffer from a severe long-tail effect, where the balance between token count and semantic validity in the initial step, hinders the accurate evaluation of advanced aspects such as composition. We analyze this drawback and attribute it to a lack of symbolic reasoning attention, while GenFMs demonstrate strong discriminative abilities in handling symbolism. To this end, we propose a pioneering paradigm for evaluating GenFMs’ text-to-visual (T2V) generation using neuro-symbolic thinking to mitigate the long-tail effect. By explicitly embedding Mixture-of-experts (MoE) Large vision Models (LVMs), we introduce symbolic-level understanding while maintaining the strong neuro-level reasoning capability. Through the fusion of semantic and compositional knowledge at the neuro-to-symbolic level, our approach outperforms state-of-the-art T2V evaluation methods, exhibiting stronger compositional reasoning ability on Winoground and better alignment with human judgment. We also demonstrate our impressive effectiveness on diverse tasks, including text-to-3D and text-to-video. To further advance the T2V evaluation of GenFMs, we propose a challenging benchmark that includes richer and more diverse compositional and semantic information compared to Winoground. Overall, our work opens a new direction for neuro-to-symbolic visio-linguistic evaluation of GenFMs and aims to drive further progress in the field.

关键词： Measurement computer vision Accuracy conferences Semantics Benchmark testing Cognition

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Surveillance Face Presentation Attack Detection Challenge

Surveillance Face Presentation Attack Detection Challenge

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Hao Fang Ajian Liu Jun Wan Sergio Escalera Hugo Jair Escalante Zhen Lei School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China MAIS Institute of Automation Chinese Academy of Sciences Beijing China Universitat de Barcelona and Computer Vision Center Barcelona Catalonia Spain JInstituto Nacional de Astrofísica Óptica y Electrónica Puebla Mexico CAIR HKISI CAS

Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, most of the studies lacked consideration of long-distance scenarios. Specifically, compared with FAS in traditional scenes such as phone unlocking, face payment, and self-service security inspection, FAS in long-distance such as station squares, parks, and self-service supermarkets are equally important, but it has not been sufficiently explored yet. In order to fill this gap in the FAS community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask). SuHiFiMask contains 10,195 videos from 101 subjects of different age groups, which are collected by 7 mainstream surveillance cameras. Based on this dataset and protocol-3 for evaluating the robustness of the algorithm under quality changes, we organized a face presentation attack detection challenge in surveillance scenarios. It attracted 180 teams for the development phase with a total of 37 teams qualifying for the final round. The organization team re-verified and re-ran the submitted code and used the results as the final ranking. In this paper, we present an overview of the challenge, including an introduction to the dataset used, the definition of the protocol, the evaluation metrics, and the announcement of the competition results. Finally, we present the top-ranked algorithms and the research ideas provided by the competition for attack detection in long-range surveillance scenarios.

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Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero-shot Medical Image Segmentation

Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP f...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Sidra Aleem Fangyijie Wang Mayug Maniparambil Eric Arazo Julia Dietlmeier Kathleen Curran Noel E. O’Connor Suzanne Little ML-Labs Dublin City University ML-Labs University College Dublin Centre for Applied AI (CeADAR) University College Dublin Ireland Insight SFI Centre for Data Analytics Dublin City University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The Segment Anything Model (SAM) and CLIP are remarkable vision foundation models (VFMs). SAM, a prompt-driven segmentation model, excels in segmentation tasks across diverse domains, while CLIP is renowned for its zero-shot recognition capabilities. However, their unified potential has not yet been explored in medical image segmentation. To adapt SAM, to medical imaging, existing methods primarily rely on tuning strategies that require extensive data or prior prompts tailored to the specific task, making it particularly challenging when only a limited number of data samples are available. This work presents an in-depth exploration of integrating SAM and CLIP into a unified framework for medical image segmentation. Specifically, we propose a simple unified framework, SaLIP, for organ segmentation. Initially, SAM is used for part-based segmentation within the image, followed by CLIP to retrieve the mask corresponding to the region of interest (ROI) from the pool of SAM’s generated masks. Finally, SAM is prompted by the retrieved ROI to segment a specific organ. Thus, SaLIP is training/fine-tuning free and does not rely on domain expertise or labeled data for prompt engineering. Our method shows substantial enhancements in zero-shot segmentation, showcasing notable improvements in DICE scores across diverse segmentation tasks like brain (63.46%), lung (50.11%), and fetal head (30.82%), when compared to un-prompted SAM. Code and text prompts are available at SaLIP.

关键词： Image segmentation Adaptation models Filtering Inference mechanisms Lung Robustness pattern recognition

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Rethinking Implicit Neural Representations For vision Learners 48

Rethinking Implicit Neural Representations For Vision Learne...

引用

48th ieee International conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Song, Yiran Zhou, Qianyu Ma, Lizhuang Shanghai Jiao Tong University China

ISBN: (纸本)9781728163277

Implicit Neural Representations (INRs) are powerful to parameterize continous signals in computer vision. However, almost all INRs methods are limited to low-level tasks, e.g., image/video compression, super-resolution, and image generation. The questions on how to explore INRs to high-level tasks and deep networks are still under-explored. Existing INRs methods suffer from two problems: 1) narrow theoretical definitions of INRs are inapplicable to high-level tasks;2) lack of representation capabilities to deep networks. Motivated by above facts, we reformulate the definitions of INRs from a novel perspective, and propose an innovative Implicit Neural Representation Network (INRN), which is the first study of INRs to tackle both low-level and high-level tasks. Specifically, we present three key designs for basic blocks in INRN along with two different stacking ways and corresponding loss functions. Extensive experiments with analysis on both low-level task (image fitting) and high-level vision tasks (image classification, object detection, instance segmentation) demonstrate the effectiveness of the proposed method. © 2023 ieee.

关键词： computer vision Implicit Neural Representations pattern recognition Representation Learning

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PV-Cap: 3D Dynamic Scene Understanding Through Open Physics-based Vocabulary

PV-Cap: 3D Dynamic Scene Understanding Through Open Physics-...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Hidetomo Sakaino Thao Nguyen Phuong Vinh Nguyen Duy Data Solution Dept. AI-Image Group (FCJ.ABC) FPT Consulting Japan FPT Software Department of Computer Science and Communications Engineering Waseda University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recently, large vision Language (VL) models, i.e., CLIP, have demonstrated impressive capabilities in training solely on internet-scale image-language pairs. Moreover, almost all VL models have tackled indoor objects under controlled illumination and camera views. However, outdoor 3D environments are time-varying uncontrolled scenes under natural phenomena. Therefore, captions from such unseen scenes and objects are hard to obtain in a state-of-the-art (SOTA) one-shot algorithm, resulting in insufficient captions. This paper proposes PV-Cap (Physics-based Vocabulary for Caption) for enhancing 3D scene understanding through enriched captions. Since many tasks in understanding 3D dynamic scenes are hard to deal with, PVCap aims to disentangle such complexities through multiple grouped Deep Learning and vision Language models step-wisely. Proposed i-VQA (iterative VQA) and 3D-CPP (3D Contrastive Physical-Scale Pretraining) extended from SOTA 2D-CLIP also contribute to generating physical and 3D-based captions. Using many images with 3D dynamic events, i.e., road scenes with traffic flow and accidents, experiments have demonstrated the usability and effectiveness of proposed PV-Cap over SOTA models in terms of segmentation and captions.

关键词： Deep learning Training Solid modeling Vocabulary Three-dimensional displays Roads Cameras

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E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance Segmentation

E2EC: An End-to-End Contour-based Method for High-Quality Hi...

引用

2022 ieee/CVF conference on computer vision and pattern recognition, CVPR 2022

作者： Zhang, Tao Wei, Shiqing Ji, Shunping Wuhan University Wuhan China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Contour-based instance segmentation methods have developed rapidly recently but feature rough and hand-crafted front-end contour initialization, which restricts the model performance, and an empirical and fixed backend predicted-label vertex pairing, which contributes to the learning difficulty. In this paper, we introduce a novel contour-based method, named E2EC, for high-quality instance segmentation. Firstly, E2EC applies a novel learnable contour initialization architecture instead of hand-crafted contour initialization. This consists of a contour initialization module for constructing more explicit learning goals and a global contour deformation module for taking advantage of all of the vertices' features better. Secondly, we propose a novel label sampling scheme, named multi-direction alignment, to reduce the learning difficulty. Thirdly, to improve the quality of the boundary details, we dynamically match the most appropriate predicted-ground truth vertex pairs and propose the corresponding loss function named dynamic matching loss. The experiments showed that E2EC can achieve a state-of-the-art performance on the KITTI INStance (KINS) dataset, the Semantic Boundaries Dataset (SBD), the Cityscapes and the COCO dataset. E2EC is also efficient for use in real-time applications, with an inference speed of 36 fps for $512\times 512$ images on an NVIDIA A6000 GPU. Code will be released at https://***/zhang-tao-whu/e2ec. © 2022 ieee.

关键词： Image segmentation Shape Semantics Graphics processing units computer architecture Detectors Predictive models

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BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment

BasicVSR++: Improving Video Super-Resolution with Enhanced P...

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2022 ieee/CVF conference on computer vision and pattern recognition, CVPR 2022

作者： Chan, Kelvin C.K. Zhou, Shangchen Xu, Xiangyu Loy, Chen Change Nanyang Technological University S-Lab Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

A recurrent structure is a popular framework choice for the task of video super-resolution. The state-of-the-art method BasicVSR adopts bidirectional propagation with feature alignment to effectively exploit information from the entire input video. In this study, we redesign BasicVsr by proposing second-order grid propagation and flow-guided deformable alignment. We show that by empowering the re-current framework with enhanced propagation and align-ment, one can exploit spatiotemporal information across misaligned video frames more effectively. The new components lead to an improved performance under a simi-lar computational constraint. In particular, our model Ba-sicVSR++ surpasses BasicVSR by a significant 0.82 dB in PSNR with similar number of parameters. BasicVSR++ is generalizable to other video restoration tasks, and obtains three champions and one first runner-up in NTIRE 2021 video restoration challenge. © 2022 ieee.

关键词： computer vision Computational modeling Superresolution Spatiotemporal phenomena pattern recognition Task analysis

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AdvDenoise: Fast Generation Framework of Universal and Robust Adversarial Patches Using Denoise

AdvDenoise: Fast Generation Framework of Universal and Robus...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Jing Li Zigan Wang Jinliang Li School of Economics and Management Tsinghua University Beijing China Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Adversarial patch attacks, which can mislead deep learning models and the human eye in both the digital and physical domains, have led to a trust crisis. Traditional approaches to generating powerful attack patches require extensive, multi-scenario data, but suffer from slow search speeds in adversarial gradient space, resulting in low global attack success rates and high costs. Especially high resource-consuming attack methods are not sufficient to pose sufficient threats, which leads to the vulnerability of defense. To address these challenges, we present a novel framework AdvDenoise to generate universal adversarial patches fast and robustly using denoise. Concretely, we leverage the power of denoising diffusion probabilistic models to craft or optimize these patches, deviating from traditional pure gradient-based methods. We conduct comprehensive experiments on both pre-trained convolutional neural networks and vision transformer detectors, evaluating our method on standard benchmarks as well as in simulated real-world physical settings. The results demonstrate that our framework outperforms strong baselines, achieving higher attack success rates, better transferability across models, and improved robustness to transformations while maintaining visual realism and computational efficiency. When our method’s performance approaches the state-of-the-art, the total time required to generate 100-shots adversarial patches is substantially lower than the state-of-the-art methods, with a remarkable 48.15% reduction in time complexity. The code and examples are publicly available at https://***/advdenoise/advdenoise.

关键词： computer vision Visualization Computational modeling Noise reduction Diffusion models Transformers Robustness

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Multi-view Semantic Information Guidance for Light Field Image Segmentation

Multi-view Semantic Information Guidance for Light Field Ima...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Yiming Li Ruixuan Cong Sizhe Wang Mingyuan Zhao Yang Zhang Fangping Li Hao Sheng State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing P.R.China Beihang Hangzhou Innovation Institute Yuhang Xixi Octagon City Hangzhou P.R.China College of Information Science and Technology Beijing University of Chemical Technology Beijing P.R.China Faculty of Applied Sciences Macao Polytechnic University Macao SAR P.R.China

One of the great important fields of computer vision is semantic segmentation. As for single image semantic segmentation, due to limited available information, it appears poor performance when the occlusion and similar color interference occur, and has difficulty exploiting the rich scene information. In comparison, the special micro-len array structure of light field camera can record multi-view information of the scene, which provides us with a new solution to solve this issue. In this paper, we propose a multi-view semantic information guidance network (MSIGNet) for light field semantic segmentation. It can effectively utilize semantic information from multi-view images to guide pixel feature of center view image. First, we extract feature of each view image and further obtain semantic probability. Then all probabilities are aggregated through a self-adaptive multi-view probability fusion module. Last, the resulting coarse fusion representation interacts with center view feature to obtain the refined segmentation result. The proposed method shows excellent performance on both real-world and synthetic light field datasets.

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IndoFashion : Apparel Classification for Indian Ethnic Clothes

IndoFashion : Apparel Classification for Indian Ethnic Cloth...

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

作者： Rajput, Pranjal Singh Aneja, Shivangi Delft Univ Technol Delft Netherlands Tech Univ Munich Munich Germany

ISBN: (纸本)9781665448994

Cloth categorization is an important research problem that is used by e-commerce websites for displaying correct products to the end-users. Indian clothes have a large number of clothing categories both for men and women. The traditional Indian clothes like "Saree" and "Dhoti" are worn very differently from western clothes like t-shirts and jeans. Moreover, the style and patterns of ethnic clothes have a very different distribution from western outfits. Thus the models trained on standard cloth datasets fail on ethnic outfits. We introduce the first large-scale ethnic dataset of over 106K images with 15 different categories for fine-grained classification of Indian ethnic clothes. We gathered a diverse dataset from a large number of Indian e-commerce websites. We then evaluate several baselines for the cloth classification task on our dataset. We obtain 88.43% classification accuracy. We hope that our dataset would foster research in the development of several algorithms such as cloth classification, landmark detection, especially for ethnic clothes. The dataset is publicly available here (1).

关键词： computer vision conferences Clothing pattern recognition Classification algorithms Task analysis Standards

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