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Model Barrier: A Compact Un-Transferable Isolation Domain for Model Intellectual Property Protection

Model Barrier: A Compact Un-Transferable Isolation Domain fo...

引用

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

作者： Wang, Lianyu Wang, Meng Zhang, Daoqiang Fu, Huazhu Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China Agcy Sci Technol & Res STAR IHPC Singapore 138632 Singapore

ISBN: (纸本)9798350301298

As scientific and technological advancements result from human intellectual labor and computational costs, protecting model intellectual property (IP) has become increasingly important to encourage model creators and owners. Model IP protection involves preventing the use of well-trained models on unauthorized domains. To address this issue, we propose a novel approach called Compact Un-Transferable Isolation Domain (CUTI-domain), which acts as a barrier to block illegal transfers from authorized to unauthorized domains. Specifically, CUTI-domain blocks cross-domain transfers by highlighting the private style features of the authorized domain, leading to recognition failure on unauthorized domains with irrelevant private style features. Moreover, we provide two solutions for using CUTI-domain depending on whether the unauthorized domain is known or not: target-specified CUTI-domain and target-free CUTI-domain. Our comprehensive experimental results on four digit datasets, CIFAR10 & STL10, and VisDA-2017 dataset demonstrate that CUTI-domain can be easily implemented as a plug-and-play module with different backbones, providing an efficient solution for model IP protection.

关键词： accountability ethics in vision fairness privacy Transparency

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DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients

DeepLSD: Line Segment Detection and Refinement with Deep Ima...

引用

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

作者： Pautrat, Remi Barath, Daniel Larsson, Viktor Oswald, Martin R. Pollefeys, Marc Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Lund Univ Lund Sweden Univ Amsterdam Amsterdam Netherlands Microsoft Mixed Real & AI Zurich Lab Zurich Switzerland

ISBN: (纸本)9798350301298

Line segments are ubiquitous in our human-made world and are increasingly used in vision tasks. They are complementary to feature points thanks to their spatial extent and the structural information they provide. Traditional line detectors based on the image gradient are extremely fast and accurate, but lack robustness in noisy images and challenging conditions. Their learned counterparts are more repeatable and can handle challenging images, but at the cost of a lower accuracy and a bias towards wireframe lines. We propose to combine traditional and learned approaches to get the best of both worlds: an accurate and robust line detector that can be trained in the wild without ground truth lines. Our new line segment detector, DeepLSD, processes images with a deep network to generate a line attraction field, before converting it to a surrogate image gradient magnitude and angle, which is then fed to any existing handcrafted line detector. Additionally, we propose a new optimization tool to refine line segments based on the attraction field and vanishing points. This refinement improves the accuracy of current deep detectors by a large margin. We demonstrate the performance of our method on low-level line detection metrics, as well as on several downstream tasks using multiple challenging datasets. The source code and models are available at https://***/cvg/DeepLSD.

关键词： Low-level vision

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SAM-CLIP: Merging vision Foundation Models towards Semantic and Spatial Understanding

SAM-CLIP: Merging Vision Foundation Models towards Semantic ...

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

作者： Wang, Haoxiang Vasu, Pavan Kumar Anasosalu Faghri, Fartash Vemulapalli, Raviteja Farajtabar, Mehrdad Mehta, Sachin Rastegari, Mohammad Tuzel, Oncel Pouransari, Hadi Apple Cupertino CA 95014 USA Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9798350365474

The landscape of publicly available vision foundation models (VFMs), such as CLIP and Segment Anything Model (SAM), is expanding rapidly. VFMs are endowed with distinct capabilities stemming from their pre-training objectives. For instance, CLIP excels in semantic understanding, while SAM specializes in spatial understanding for segmentation. In this work, we introduce a simple recipe to efficiently merge VFMs into a unified model that absorbs their expertise. Our method integrates techniques of multi-task learning, continual learning, and distillation. Further, it demands significantly less computational cost compared to traditional multi-task training from scratch, and it only needs a small fraction of the pre-training datasets that were initially used to train individual models. By applying our method to SAM and CLIP, we obtain SAM-CLIP : a unified model that combines the capabilities of SAM and CLIP into a single vision transformer. Compared with deploying SAM and CLIP independently, our merged model, SAM-CLIP, reduces storage and compute costs for inference, making it well-suited for edge device applications. We show that SAM-CLIP not only retains the foundational strengths of SAM and CLIP, but also introduces synergistic functionalities, notably in zero-shot semantic segmentation, where SAM-CLIP establishes new state-of-the-art results on 5 benchmarks. It outperforms previous models that are specifically designed for this task by a large margin, including +6.8% and +5.9% mean IoU improvement on Pascal-VOC and COCO-Stuff datasets, respectively.

关键词： CLIP Foundation Model Model Merging Segmentation

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Beyond the Screen: Evaluating Deepfake Detectors under Moire pattern Effects

Beyond the Screen: Evaluating Deepfake Detectors under Moire...

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

作者： Tariq, Razaib Heo, Minji Woo, Simon S. Tariq, Shahroz Sungkyunkwan Univ Seoul South Korea CSIROs Data61 Eveleigh Australia

ISBN: (纸本)9798350365474

The detection of deepfakes is crucial for mitigating the societal impact of falsified video content. Despite the development of various algorithms for this purpose, challenges arise for detectors in real-world scenarios, especially when users capture deepfake content from screens and upload it online or when detectors operate on external devices like smartphones, requiring the capture of potential deepfakes through the camera for evaluation. A significant challenge in these scenarios is the presence of Moire patterns, which degrade image quality and complicate conventional classification methods, notably deep neural networks (DNNs). However, the impact of Moire patterns on the effectiveness of deepfake detection systems has not been adequately explored. This study aims to investigate how capturing deepfake videos via digital screen cameras affects the accuracy of detection mechanisms. We introduced the Moire patterns by capturing the display of a monitor using a smartphone camera and conducted empirical evaluations using four widely recognized datasets: CelebDF, DFD, DFDC, and FF++. We compare the performance of twelve SOTA detectors on deepfake videos captured under the influence of Moire patterns. Our findings reveal a performance decrease of up to 33.1 and 31.3 percentage points for image- and video-based detectors. Therefore, highlighting the challenges posed by Moire patterns and other naturally induced artifacts is critical for improving the effectiveness of real-world deepfake detection efforts. To facilitate further research, we will release the Moire pattern impact version of CelebDF, DFD, DFDC, and FF++ datasets with this paper. Our code is available here: https: //***/Razaib-Tariq/deepmoire

关键词： Deepfake Fake Media Media Forensics Moire pattern

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Tri-Perspective View for vision-Based 3D Semantic Occupancy Prediction

Tri-Perspective View for Vision-Based 3D Semantic Occupancy ...

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

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

ISBN: (纸本)9798350301298

Modern methods for vision-centric autonomous driving perception widely adopt the bird's-eye-view (BEV) representation to describe a 3D scene. Despite its better efficiency than voxel representation, it has difficulty describing the fine-grained 3D structure of a scene with a single plane. To address this, we propose a tri-perspective view (TPV) representation which accompanies BEV with two additional perpendicular planes. We model each point in the 3D space by summing its projected features on the three planes. To lift image features to the 3D TPV space, we further propose a transformer-based TPV encoder (TPVFormer) to obtain the TPV features effectively. We employ the attention mechanism to aggregate the image features corresponding to each query in each TPV plane. Experiments show that our model trained with sparse supervision effectively predicts the semantic occupancy for all voxels. We demonstrate for the first time that using only camera inputs can achieve comparable performance with LiDAR-based methods on the LiDAR segmentation task on nuScenes. Code: https://***/wzzheng/TPVFormer.

关键词： Autonomous driving

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Mask3D: Pre-training 2D vision Transformers by Learning Masked 3D Priors

Mask3D: Pre-training 2D Vision Transformers by Learning Mask...

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

作者： Hou, Ji Dai, Xiaoliang He, Zijian Dai, Angela Niessner, Matthias Meta Real Labs Menlo Pk CA 94025 USA Tech Univ Munich Munich Germany

ISBN: (纸本)9798350301298

Current popular backbones in computer vision, such as vision Transformers (ViT) and ResNets are trained to perceive the world from 2D images. However, to more effectively understand 3D structural priors in 2D backbones, we propose Mask3D to leverage existing large-scale RGB-D data in a self-supervised pre-training to embed these 3D priors into 2D learned feature representations. In contrast to traditional 3D contrastive learning paradigms requiring 3D reconstructions or multi-view correspondences, our approach is simple: we formulate a pre-text reconstruction task by masking RGB and depth patches in individual RGB-D frames. We demonstrate the Mask3D is particularly effective in embedding 3D priors into the powerful 2D ViT backbone, enabling improved representation learning for various scene understanding tasks, such as semantic segmentation, instance segmentation and object detection. Experiments show that Mask3D notably outperforms existing self-supervised 3D pre-training approaches on ScanNet, NYUv2, and Cityscapes image understanding tasks, with an improvement of +6.5% mIoU against the state-of-the-art Pri3D on ScanNet image semantic segmentation.

关键词： 3D from single images

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Event-Based Eye Tracking. AIS 2024 Challenge Survey

Event-Based Eye Tracking. AIS 2024 Challenge Survey

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

作者： Wang, Zuowen Gao, Chang Wu, Zongwei Conde, Marcos, V Timofte, Radu Liu, Shih-Chii Chen, Qinyu Zha, Zheng-jun Zhai, Wei Han, Han Liao, Bohao Wu, Yuliang Wan, Zengyu Wang, Zhong Cao, Yang Tan, Ganchao Chen, Jinze Pei, Yan Ru Bruers, Sasskia Crouzet, Sebastien McLelland, Douglas Coenen, Oliver Zhang, Baoheng Gao, Yizhao Li, Jingyuan So, Hayden Kwok-Hay Bich, Philippe Boretti, Chiara Prono, Luciano Lica, Mircea Dinucu-Jianu, David Griu, Catalin Lin, Xiaopeng Ren, Hongwei Cheng, Bojun Zhang, Xinan Vial, Valentin Yezzi, Anthony Tsai, James Univ Zurich Inst Neuroinformat Zurich Switzerland Swiss Fed Inst Technol Zurich Switzerland Delft Univ Technol Delft Netherlands Univ Wurzburg Wurzburg Germany Leiden Univ Leiden Netherlands Univ Sci & Technol China Hefei Anhui Peoples R China Brainchip Inc Laguna Hills CA USA Univ Hong Kong Hong Kong Peoples R China Politecn Torino Turin Italy Hong Kong Univ Sci & Technol Guangzhou Guangzhou Guangdong Peoples R China Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9798350365474

This survey reviews the AIS 2024 Event-Based Eye Tracking (EET) Challenge. The task of the challenge focuses on processing eye movement recorded with event cameras and predicting the pupil center of the eye. The challenge emphasizes efficient eye tracking with event cameras to achieve good task accuracy and efficiency trade-off. During the challenge period, 38 participants registered for the Kaggle competition, and 8 teams submitted a challenge factsheet. The novel and diverse methods from the submitted factsheets are reviewed and analyzed in this survey to advance future event-based eye tracking research.

关键词： computer vision dynamic vision sensor event camera eye tracking

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LOFI: LOng-tailed FIne-Grained Network for Food recognition

LOFI: LOng-tailed FIne-Grained Network for Food Recognition

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

作者： Rodriguez-de-Vera, Jesus M. Estepa, Imanol G. Bolanos, Marc Nagarajan, Bhalaji Radeva, Petia Univ Barcelona Barcelona Spain AIGecko Technol SL Barcelona Spain

ISBN: (纸本)9798350365474

Food recognition plays a crucial role in several healthcare applications. Nevertheless, it presents significant computer vision challenges such as long-tailed and fine-grained distributions that hinder its progress. In this work, we propose LOFI, a Long-tailed Fine-grained Network aimed specifically at tackling these food recognition challenges by improving the feature learning capabilities of food recognition models. Specifically, we improve vanilla R-CNN architecture by tailoring it for food recognition. We design an efficient multi-task framework for fine-grained food recognition, which exploits the lexical similarity of dishes during training to improve the discriminative ability of the network. Secondly, we include a Graph Confidence Propagation module based on graph neural networks to aggregate the information of overlapping detections and refine the final prediction of the network. Extensive analysis and ablations of different components of LOFI highlight that it successfully addresses the targeted problems and leads to noticeable gains in performance. Remarkably, the proposed method achieves competitive results and outperforms the current state-of-the-art methods in three public food benchmarks: UECFood-256, AiCrowd Food Challenge 2022, and UECFood-100 segmented.

关键词： Fine-grained Food visual recognition Graph Neural Networks Instance segmentation Long-tailed Object detection

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AAPL: Adding Attributes to Prompt Learning for vision-Language Models

AAPL: Adding Attributes to Prompt Learning for Vision-Langua...

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

作者： Kim, Gahyeon Kim, Sohee Lee, Seokju Korea Inst Energy Technol KENTECH Naju South Korea

ISBN: (纸本)9798350365474

Recent advances in large pre-trained vision-language models have demonstrated remarkable performance on zero-shot downstream tasks. Building upon this, recent studies, such as CoOp and CoCoOp, have proposed the use of prompt learning, where context within a prompt is replaced with learnable vectors, leading to significant improvements over manually crafted prompts. However, the performance improvement for unseen classes is still marginal, and to tackle this problem, data augmentation has been frequently used in traditional zero-shot learning techniques. Through our experiments, we have identified important issues in CoOp and CoCoOp: the context learned through traditional image augmentation is biased toward seen classes, negatively impacting generalization to unseen classes. To address this problem, we propose adversarial token embedding to disentangle low-level visual augmentation features from highlevel class information when inducing bias in learnable prompts. Through our novel mechanism called "Adding Attributes to Prompt Learning", AAPL, we guide the learnable context to effectively extract text features by focusing on high-level features for unseen classes. We have conducted experiments across 11 datasets, and overall, AAPL shows favorable performances compared to the existing methods in few-shot learning, zero-shot learning, cross-dataset, and domain generalization tasks.

关键词： prompt learning vision language model VLMs

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Angelic Patches for Improving Third-Party Object Detector Performance

Angelic Patches for Improving Third-Party Object Detector Pe...

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

作者： Si, Wenwen Li, Shuo Park, Sangdon Lee, Insup Bastani, Osbert Univ Penn Dept Comp & Info Sci Philadelphia PA 19104 USA Georgia Inst Technol Sch Cybersecur & Privacy Atlanta GA 30332 USA

ISBN: (纸本)9798350301298

Deep learning models have shown extreme vulnerability to distribution shifts such as synthetic perturbations and spatial transformations. In this work, we explore whether we can adopt the characteristics of adversarial attack methods to help improve robustness of object detection to distribution shifts such as synthetic perturbations and spatial transformations. We study a class of realistic object detection settings wherein the target objects have control over their appearance. To this end, we propose a reversed Fast Gradient Sign Method (FGSM) to obtain these angelic patches that significantly increase the detection probability, even without pre-knowledge of the perturbations. In detail, we apply the patch to each object instance simultaneously, strengthening not only classification, but also bounding box accuracy. Experiments demonstrate the efficacy of the partial-covering patch in solving the complex bounding box problem. More importantly, the performance is also transferable to different detection models even under severe affine transformations and deformable shapes. To the best of our knowledge, we are the first object detection patch that achieves both cross-model efficacy and multiple patches. We observed average accuracy improvements of 30% in the real-world experiments. Our code is available at: https: //***/averysi224/angelic_patches.

关键词： computer vision for social good

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