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Learning Co-segmentation by Segment Swapping for Retrieval and Discovery

Learning Co-segmentation by Segment Swapping for Retrieval a...

引用

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

作者： Shen, Xi Efros, Alexei A. Joulin, Armand Aubry, Mathieu Ecole Ponts ParisTech LIGM UMR 8049 Champs Sur Marne France Univ Calif Berkeley Berkeley CA 94720 USA Facebook AI Res Menlo Pk CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The goal of this work is to efficiently identify visually similar patterns in images, e.g. identifying an artwork detail copied between an engraving and an oil painting, or recognizing parts a night-time photograph visible in its daytime counterpart. Lack of training data is a key challenge for this co-segmentation task. We present a simple yet surprisingly effective approach to overcome this difficulty: we generate synthetic training pairs by selecting segments in an image and copy-pasting them into another image. We then learn to predict the repeated region masks. We find that it is crucial to predict the correspondences as an auxiliary task and to use Poisson blending and style transfer on the training pairs to generalize on real data. We analyse results with two deep architectures relevant to our joint image analysis task: a transformer-based architecture and Sparse Nc-Net, a recent network designed to predict coarse correspondences using 4D convolutions. We show our approach provides clear improvements for artwork details retrieval on the Brueghel dataset and achieves competitive performance on two place recognition benchmarks, Tokyo247 and Pitts30K. We also demonstrate the potential of our approach for unsupervised image collection analysis by introducing a spectral graph clustering approach to object discovery and demonstrating it on the object discovery dataset of [49] and the Brueghel dataset. Our code and data are available at http://***/similar to shenx/SegSwap/.

关键词： Training Image segmentation Oils Training data computer architecture Oil insulation Transformers

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A Lightweight Network for High Dynamic Range Imaging

A Lightweight Network for High Dynamic Range Imaging

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

作者： Yan, Qingsen Zhang, Song Chen, Weiye Liu, Yuhang Zhang, Zhen Zhang, Yanning Shi, Javen Qinfeng Gong, Dong Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia Xidian Univ Sch Artificial Intelligence Xian Peoples R China Xidian Univ Guangzhou Inst Technol Xian Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Univ New South Wales Sch Comp Sci & Engn Sydney NSW Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Multi-frame high dynamic range (HDR) reconstruction methods try to expand the range of illuminance with differently exposed images. They suffer from ghost artifacts when camera jittering or object moving. Several methods can generate high-quality HDR images with high computational complexity, but the inference process is too slow. However, the network with small parameters will produce unsatisfactory results. To balance the quality and computational complexity, we propose a lightweight network for HDR imaging that has small parameters and fast speed. Specifically, following AHDRNet, we employ a spatial attention module to detect the misaligned regions to avoid ghost artifacts. Considering the missing details in over/under- exposure regions, we propose a dual attention module for selectively retaining information to force the fusion network to learn more details for degenerated regions. Furthermore, we employ an encoder-decoder structure with a lightweight block to achieve the fusion process. As a result, the high-quality content and features can be reconstructed after the attention module. Finally, we fuse high-resolution features and the encoder-decoder features into the HDR imaging results. Experimental results demonstrate that the proposed method performs favorably against the state-of-the-art methods, achieving a PSNR of 39.05 and a PSNR-mu of 37.27 with 156.12 GMAcs in NTIRE 2022 HDR Challenge (Track 2 Fidelity).

关键词： computer vision Fuses conferences Force Dynamic range Reconstruction algorithms Cameras

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Collecting Cross-Modal Presence-Absence Evidence for Weakly-Supervised Audio-Visual Event Perception

Collecting Cross-Modal Presence-Absence Evidence for Weakly-...

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

作者： Gao, Junyu Chen, Mengyuan Xu, Changsheng Chinese Acad Sci CASIA Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

With only video-level event labels, this paper targets at the task of weakly-supervised audio-visual event perception (WS-AVEP), which aims to temporally localize and categorize events belonging to each modality. Despite the recent progress, most existing approaches either ignore the unsynchronized property of audio-visual tracks or discount the complementary modality for explicit enhancement. We argue that, for an event residing in one modality, the modality itself should provide ample presence evidence of this event, while the other complementary modality is encouraged to afford the absence evidence as a reference signal. To this end, we propose to collect Cross-Modal Presence-Absence Evidence (CMPAE) in a unified framework. Specifically, by leveraging uni-modal and cross-modal representations, a presence-absence evidence collector (PAEC) is designed under Subjective Logic theory. To learn the evidence in a reliable range, we propose a joint-modal mutual learning (JML) process, which calibrates the evidence of diverse audible, visible, and audi-visible events adaptively and dynamically. Extensive experiments show that our method surpasses state-of-the-arts (e.g., absolute gains of 3.6% and 6.1% in terms of event-level visual and audio metrics). Code is available in ***/MengyuanChen21/CVPR2023-CMPAE.

关键词： 2023 ieee/CVF conference on computer vision and pattern recognition (CVPR)

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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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Holistic Approach to Measure Sample-level Adversarial Vulnerability and its Utility in Building Trustworthy Systems

Holistic Approach to Measure Sample-level Adversarial Vulner...

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

作者： Nayak, Gaurav Kumar Rawal, Ruchit Lal, Rohit Patil, Himanshu Chakraborty, Anirban Indian Inst Sci Bangalore Karnataka India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Adversarial attack perturbs an image with an imperceptible noise, leading to incorrect model prediction. Recently, a few works showed inherent bias associated with such attack (robustness bias), where certain subgroups in a dataset (e.g. based on class, gender, etc.) are less robust than others. This bias not only persists even after adversarial training, but often results in severe performance discrepancies across these subgroups. Existing works characterize the subgroup's robustness bias by only checking individual sample's proximity to the decision boundary. In this work, we argue that this measure alone is not sufficient and validate our argument via extensive experimental analysis. It has been observed that adversarial attacks often corrupt the high-frequency components of the input image. We, therefore, propose a holistic approach for quantifying adversarial vulnerability of a sample by combining these different perspectives, i.e., degree of model's reliance on high-frequency features and the (conventional) sample-distance to the decision boundary. We demonstrate that by reliably estimating adversarial vulnerability at the sample level using the proposed holistic metric, it is possible to develop a trustworthy system where humans can be alerted about the incoming samples that are highly likely to be misclassified at test time. This is achieved with better precision when our holistic metric is used over individual measures. To further corroborate the utility of the proposed holistic approach, we perform knowledge distillation in a limited-sample setting. We observe that the student network trained with the subset of samples selected using our combined metric performs better than both the competing baselines, viz., where samples are selected randomly or based on their distances to the decision boundary.

关键词： Measurement Training Knowledge engineering computer vision conferences Predictive models Reliability engineering

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Enhancing Deformable Local Features by Jointly Learning to Detect and Describe Keypoints

Enhancing Deformable Local Features by Jointly Learning to D...

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

作者： Potje, Guilherme Cadar, Felipe Araujo, Andre Martins, Renato Nascimento, Erickson R. Univ Fed Minas Gerais Belo Horizonte Brazil Google Res New York NY USA Univ Bourgogne Dijon France Univ Lorraine LORIA Inria Thionville France Microsoft Redmond WA USA

ISBN: (纸本)9798350301298

Local feature extraction is a standard approach in computer vision for tackling important tasks such as image matching and retrieval. The core assumption of most methods is that images undergo affine transformations, disregarding more complicated effects such as non-rigid deformations. Furthermore, incipient works tailored for non-rigid correspondence still rely on keypoint detectors designed for rigid transformations, hindering performance due to the limitations of the detector. We propose DALF (Deformation-Aware Local Features), a novel deformation-aware network for jointly detecting and describing keypoints, to handle the challenging problem of matching deformable surfaces. All network components work cooperatively through a feature fusion approach that enforces the descriptors' distinctiveness and invariance. Experiments using real deforming objects showcase the superiority of our method, where it delivers 8% improvement in matching scores compared to the previous best results. Our approach also enhances the performance of two real-world applications: deformable object retrieval and non-rigid 3D surface registration. Code for training, inference, and applications are publicly available at verlab. ***/descriptors/dalf_cvpr23.

关键词： Low-level vision

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Spatial-Temporal Graph-Based AU Relationship Learning for Facial Action Unit Detection

Spatial-Temporal Graph-Based AU Relationship Learning for Fa...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Wang, Zihan Song, Siyang Luo, Cheng Zhou, Yuzhi Wu, Shiling Xie, Weicheng Shen, Linlin Shenzhen University Computer Vision Institute School of Computer Science & Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China University of Leicester School of Computing and Mathematical Sciences United Kingdom

ISBN: (纸本)9798350302493

This paper presents our Facial Action Units (AUs) detection submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approach consists of three main modules: (i) a pre-trained facial representation encoder which produce a strong facial representation from each input face image in the input sequence;(ii) an AU-specific feature generator that specifically learns a set of AU features from each facial representation;and (iii) a spatio-temporal graph learning module that constructs a spatio-temporal graph representation. This graph representation describes AUs contained in all frames and predicts the occurrence of each AU based on both the modeled spatial information within the corresponding face and the learned temporal dynamics among frames. The experimental results show that our approach outperformed the baseline and the spatio-temporal graph representation learning allows our model to generate the best results among all ablated systems. Our model ranks at the 4th place in the AU recognition track at the 5th ABAW Competition. Our code is publicly available at https://***/wzh125/ABAW-5. © 2023 ieee.

关键词： Graphic methods

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PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image Classification

PIP-Net: Patch-Based Intuitive Prototypes for Interpretable ...

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

作者： Nauta, Meike Schloetterer, Joerg van Keulen, Maurice Seifert, Christin Univ Twente Enschede Netherlands Univ Duisburg Essen Essen Germany

ISBN: (纸本)9798350301298

Interpretable methods based on prototypical patches recognize various components in an image in order to explain their reasoning to humans. However, existing prototype-based methods can learn prototypes that are not in line with human visual perception, i.e., the same prototype can refer to different concepts in the real world, making interpretation not intuitive. Driven by the principle of explainability-bydesign, we introduce PIP-Net (Patch-based Intuitive Prototypes Network): an interpretable image classification model that learns prototypical parts in a self-supervised fashion which correlate better with human vision. PIP-Net can be interpreted as a sparse scoring sheet where the presence of a prototypical part in an image adds evidence for a class. The model can also abstain from a decision for out-of-distribution data by saying "I haven't seen this before". We only use image-level labels and do not rely on any part annotations. PIP-Net is globally interpretable since the set of learned prototypes shows the entire reasoning of the model. A smaller local explanation locates the relevant prototypes in one image. We show that our prototypes correlate with ground-truth object parts, indicating that PIP-Net closes the "semantic gap" between latent space and pixel space. Hence, our PIP-Net with interpretable prototypes enables users to interpret the decision making process in an intuitive, faithful and semantically meaningful way. Code is available at https://***/M- Nauta/PIPNet.

关键词： Explainable computer vision

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Fair Scratch Tickets: Finding Fair Sparse Networks without Weight Training

Fair Scratch Tickets: Finding Fair Sparse Networks without W...

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

作者： Tang, Pengwei Yao, Wei Li, Zhicong Liu, Yong Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China

ISBN: (纸本)9798350301298

Recent studies suggest that computer vision models come at the risk of compromising fairness. There are extensive works to alleviate unfairness in computer vision using pre-processing, in-processing, and post-processing methods. In this paper, we lead a novel fairness-aware learning paradigm for in-processing methods through the lens of the lottery ticket hypothesis (LTH) in the context of computer vision fairness. We randomly initialize a dense neural network and find appropriate binary masks for the weights to obtain fair sparse subnetworks without any weight training. Interestingly, to the best of our knowledge, we are the first to discover that such sparse subnetworks with inborn fairness exist in randomly initialized networks, achieving an accuracy-fairness trade-off comparable to that of dense neural networks trained with existing fairness-aware in-processing approaches. We term these fair subnetworks as Fair Scratch Tickets (FSTs). We also theoretically provide fairness and accuracy guarantees for them. In our experiments, we investigate the existence of FSTs on various datasets, target attributes, random initialization methods, sparsity patterns, and fairness surrogates. We also find that FSTs can transfer across datasets and investigate other properties of FSTs.

关键词： accountability ethics in vision fairness privacy Transparency

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Is BERT Blind? Exploring the Effect of vision-and-Language Pretraining on Visual Language Understanding

Is BERT Blind? Exploring the Effect of Vision-and-Language P...

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

作者： Alper, Morris Fiman, Michael Averbuch-Elor, Hadar Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9798350301298

Most humans use visual imagination to understand and reason about language, but models such as BERT reason about language using knowledge acquired during text-only pretraining. In this work, we investigate whether vision-and-language pretraining can improve performance on text-only tasks that involve implicit visual reasoning, focusing primarily on zero-shot probing methods. We propose a suite of visual language understanding (VLU) tasks for probing the visual reasoning abilities of text encoder models, as well as various non-visual natural language understanding (NLU) tasks for comparison. We also contribute a novel zero-shot knowledge probing method, Stroop probing, for applying models such as CLIP to text-only tasks without needing a prediction head such as the masked language modelling head of models like BERT. We show that SOTA multimodally trained text encoders outperform unimodally trained text encoders on the VLU tasks while being under-performed by them on the NLU tasks, lending new context to previously mixed results regarding the NLU capabilities of multimodal models. We conclude that exposure to images during pretraining affords inherent visual reasoning knowledge that is reflected in language-only tasks that require implicit visual reasoning. Our findings bear importance in the broader context of multimodal learning, providing principled guidelines for the choice of text encoders used in such contexts.

关键词： and reasoning language vision

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