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Unsupervised Salient Object Detection with Spectral Cluster Voting

Unsupervised Salient Object Detection with Spectral Cluster ...

引用

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

作者： Shin, Gyungin Albanie, Samuel Xie, Weidi Univ Oxford Visual Geometry Grp Oxford England Univ Cambridge Dept Engn Cambridge England Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we tackle the challenging task of unsupervised salient object detection (SOD) by leveraging spectral clustering on self-supervised features. We make the following contributions: (i) We revisit spectral clustering and demonstrate its potential to group the pixels of salient objects across various self-supervised features, e.g., MoCov2, SwAV, and DINO;(ii) Given mask proposals from multiple applications of spectral clustering on image features computed from different self-supervised models, we propose a simple but effective winner-takes-all voting mechanism for selecting the salient masks, leveraging object priors based on framing and distinctiveness;(iii) Using the selected object segmentation as pseudo groundtruth masks, we train a salient object detector, termed SELF-MASK, which outperforms prior approaches on three unsupervised SOD benchmarks.

关键词： conferences Object detection Object segmentation Detectors Benchmark testing Feature extraction pattern recognition

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An Attention-based Method for Multi-label Facial Action Unit Detection

An Attention-based Method for Multi-label Facial Action Unit...

引用

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

作者： Le Hoai, Duy Lim, Eunchae Choi, Eunbin Kim, Sieun Pant, Sudarshan Lee, Guee-Sang Kim, Soo-Huyng Yang, Hyung-Jeong Chonnam Natl Univ Gwangju South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Facial Action Coding System is an approach for modeling the complexity of human emotional expression. Automatic action unit (AU) detection is a crucial research area in human-computer interaction. This paper describes our submission to the third Affective Behavior Analysis in-the-wild (ABAW) competition 2022. We proposed a method for detecting facial action units in the video. In the first stage, a lightweight CNN-based feature extractor is employed to extract the feature map from each video frame. Then, an attention module is applied to refine the attention map. The attention encoded vector is derived using a weighted sum of the feature map and the attention scores later. Finally, the sigmoid function is used at the output layer to make the prediction suitable for multi-label AUs detection. We achieved a macro F1 score of 0.48 on the validation set and 0.4206 on the test set compared to 0.39 and 0.3650 from the ABAW challenge baseline model.

关键词： Human computer interaction Gold computer vision conferences Computational modeling Feature extraction Encoding

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NL-FFC: Non-Local Fast Fourier Convolution for Image Super Resolution

NL-FFC: Non-Local Fast Fourier Convolution for Image Super R...

引用

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

作者： Sinha, Abhishek Kumar Moorthi, S. Manthira Dhar, Debajyoti Space Applicat Ctr Signal & Image Proc Grp Ahmadabad India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Deep neural networks have shown promising results in image super-resolution by learning a complex mapping from low resolution to high resolution image. However, most of the approaches learns to upsample by using convolution in spatial domain and are confined to local features. This results into restricting the receptive field of the network and therefore deteriorates the overall quality of the high-resolution image. To alleviate this issue, we propose an architecture that learns both local and global features, and fuses them together to generate high quality images. The network uses a non-local attention aided Fast Fourier Convolutions (NL-FFC) to widen the receptive field and learn long-range dependencies. The analyses further show that these Fourier features implicitly provide faster convergence on low frequency components only to learn prior for unobserved high frequency components. The model generalizes well to different datasets. We further investigate the role of non-local attention, and the ratio of local and global features to maximize the performance gain in the ablation study.

关键词： Deep learning Convolution Fuses Superresolution Neural networks Performance gain pattern recognition

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ViTOL: vision Transformer for Weakly Supervised Object Localization

ViTOL: Vision Transformer for Weakly Supervised Object Local...

引用

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

作者： Gupta, Saurav Lakhotia, Sourav Rawat, Abhay Tallamraju, Rahul Mercedes Benz Res & Dev India Bengaluru India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Weakly supervised object localization (WSOL) aims at predicting object locations in an image using only image-level category labels. Common challenges that image classification models encounter when localizing objects are, (a) they tend to look at the most discriminative features in an image that confines the localization map to a very small region, (b) the localization maps are class agnostic, and the models highlight objects of multiple classes in the same image and, (c) the localization performance is affected by background noise. To alleviate the above challenges we introduce the following simple changes through our proposed method ViTOL. We leverage the vision-based transformer for self-attention and introduce a patch-based attention dropout layer (p-ADL) to increase the coverage of the localization map and a gradient attention rollout mechanism to generate class-dependent attention maps. We conduct extensive quantitative, qualitative and ablation experiments on the ImageNet-1K and CUB datasets. We achieve state-of-the-art MaxBoxAcc-V2 localization scores of 70.47% and 73.17% on the two datasets respectively.

关键词： Location awareness Measurement Visualization Shape Semantics computer architecture Transformers

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Deep-FlexISP: A Three-Stage Framework for Night Photography Rendering

Deep-FlexISP: A Three-Stage Framework for Night Photography ...

引用

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

作者： Liu, Shuai Feng, Chaoyu Wang, Xiaotao Wang, Hao Zhu, Ran Li, Yongqiang Lei, Lei Xiaomi Inc Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Night photography rendering is challenging due to images' high noise level, less vivid color, and low dynamic range. In this work, we propose a three-stage cascade framework named Deep-FlexISP, which decomposes the ISP into three weakly correlated sub-tasks: raw image denoising, white balance, and Bayer to sRGB mapping, for the following considerations. First, task decomposition can enhance the learning ability of the framework and make it easier to converge. Second, weak correlation sub-tasks do not influence each other too much, so the framework has a high degree of freedom. Finally, noise, color, and brightness are essential for night photographs. Our framework can flexibly adjust different styles according to personal preferences with the vital learning ability and the degree of freedom. Compared with the other Deep-ISP methods, our proposed Deep-FlexISP shows state-of-the-art performance and achieves first place in people's choice and photographer's choice in NTIRE 2022 Night Photography Render Challenge.

关键词： Photography Image color analysis Brightness Noise reduction Rendering (computer graphics) pattern recognition Task analysis

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Poisons that are learned faster are more effective

Poisons that are learned faster are more effective

引用

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

作者： Sandoval-Segura, Pedro Singla, Vasu Fowl, Liam Geiping, Jonas Goldblum, Micah Jacobs, David Goldstein, Tom Univ Maryland College Pk MD 20742 USA New York Univ New York NY USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Imperceptible poisoning attacks on entire datasets have recently been touted as methods for protecting data privacy. However, among a number of defenses preventing the practical use of these techniques, early-stopping stands out as a simple, yet effective defense. To gauge poisons' vulnerability to early-stopping, we benchmark error-minimizing, error-maximizing, and synthetic poisons in terms of peak test accuracy over 100 epochs and make a number of surprising observations. First, we find that poisons that reach a low training loss faster have lower peak test accuracy. Second, we find that a current state-of-the-art error-maximizing poison is 7x less effective when poison training is stopped at epoch 8. Third, we find that stronger, more transferable adversarial attacks do not make stronger poisons. We advocate for evaluating poisons in terms of peak test accuracy.

关键词： Training Data privacy Privacy computer vision Toxicology Correlation Perturbation methods

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Once for Both: Single Stage of Importance and Sparsity Search for vision Transformer Compression

Once for Both: Single Stage of Importance and Sparsity Searc...

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

作者： Ye, Hancheng Yu, Chong Ye, Peng Xia, Renqiu Tang, Yansong Lu, Jiwen Chen, Tao Zhang, Bo Fudan Univ Sch Informat Sci & Technol Shanghai Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Fudan Univ Acad Engn & Technol Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Recent vision Transformer Compression (VTC) works mainly follow a two-stage scheme, where the importance score of each model unit is first evaluated or preset in each submodule, followed by the sparsity score evaluation according to the target sparsity constraint. Such a separate evaluation process induces the gap between importance and sparsity score distributions, thus causing high search costs for VTC. In this work, for the first time, we investigate how to integrate the evaluations of importance and sparsity scores into a single stage, searching the optimal subnets in an efficient manner. Specifically, we present OFB, a cost-efficient approach that simultaneously evaluates both importance and sparsity scores, termed Once for Both (OFB), for VTC. First, a bi-mask scheme is developed by entangling the importance score and the differentiable sparsity score to jointly determine the pruning potential (prunability) of each unit. Such a bi-mask search strategy is further used together with a proposed adaptive one-hot loss to realize the progressive-andefficient search for the most important subnet. Finally, Progressive Masked Image Modeling (PMIM) is proposed to regularize the feature space to be more representative during the search process, which may be degraded by the dimension reduction. Extensive experiments demonstrate that OFB can achieve superior compression performance over state-of-the-art searching-based and pruning-based methods under various vision Transformer architectures, meanwhile promoting search efficiency significantly, e.g., costing one GPU search day for the compression of DeiT-S on ImageNet-1K.

关键词： Image compression

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SeeTheSeams: Localized Detection of Seam Carving based Image Forgery in Satellite Imagery

SeeTheSeams: Localized Detection of Seam Carving based Image...

引用

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

作者： Gudavalli, Chandrakanth Rosten, Erik Nataraj, Lakshmanan Chandrasekaran, Shivkumar Manjunath, B. S. Mayachitra Inc Santa Barbara CA 93111 USA UC Santa Barbara Elect & Comp Engn Dept Santa Barbara CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Seam carving is a popular technique for content aware image retargeting. It can be used to deliberately manipulate images, for example, change the GPS locations of a building or displace/remove roads in a satellite image. This paper proposes a novel approach for detecting and localizing seams in such images. While there are methods to detect seam carving based manipulations, this is the first time that robust localization and detection of seam carving forgery is made possible. We also propose a seam localization score (SLS) metric to evaluate the effectiveness of localization. The proposed method is evaluated extensively on a large collection of images from different sources, demonstrating a high level of detection and localization performance across these datasets. The code and datasets curated during this work will be released to the public.

关键词： Location awareness Measurement computer vision Satellites Image coding Roads conferences

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BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training

BigDetection: A Large-scale Benchmark for Improved Object De...

引用

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

作者： Cai, Likun Zhang, Zhi Zhu, Yi Zhang, Li Li, Mu Xue, Xiangyang Fudan Univ Shanghai Peoples R China Amazon Inc Seattle WA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Multiple datasets and open challenges for object detection have been introduced in recent years. To build more general and powerful object detection systems, in this paper, we construct a new large-scale benchmark termed BigDetection. Our goal is to simply leverage the training data from existing datasets (LVIS, OpenImages and Object365) with carefully designed principles, and curate a larger dataset for improved detector pre-training. Specifically, we generate a new taxonomy which unifies the heterogeneous label spaces from different sources. Our BigDetection dataset has 600 object categories and contains over 3.4M training images with 36M bounding boxes. It is much larger in multiple dimensions than previous benchmarks, which offers both opportunities and challenges. Extensive experiments demonstrate its validity as a new benchmark for evaluating different object detection methods and its effectiveness as a pre-training dataset. The code and models are available at https://***/amazonresearch/bigdetection.

关键词： Training computer vision conferences Taxonomy Training data Object detection Detectors

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RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback

RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment fro...

引用

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

作者： Yu, Tianyu Yao, Yuan Zhang, Haoye He, Taiwen Han, Yifeng Cui, Ganqu Hu, Jinyi Liu, Zhiyuan Zheng, Hai-Tao Sun, Maosong Tsinghua Univ Beijing Peoples R China Natl Univ Singapore Singapore Singapore Tsinghua Univ Shenzhen Int Grad Sch Beijing Peoples R China Pengcheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350353006

Multimodal Large Language Models (MLLMs) have recently demonstrated impressive capabilities in multimodal understanding, reasoning, and interaction. However, existing MLLMs prevalently suffer from serious hallucination problems, generating text that is not factually grounded in associated images. The problem makes existing MLLMs untrustworthy and thus impractical in real-world (especially high-stakes) applications. To address the challenge, we present RLHF-V, which enhances MLLM trustworthiness via behavior alignment from fine-grained correctional human feedback. Specifically, RLHF-V collects human preference in the form of segment-level corrections on hallucinations, and performs dense direct preference optimization over the human feedback. Comprehensive experiments on five benchmarks in both automatic and human evaluation show that, RLHF-V can enable substantially more trustworthy MLLM behaviors with promising data and computation efficiency. Remarkably, using 1.4k annotated data samples, RLHF-V significantly reduces the hallucination rate of the base MLLM by 34.8%, outperforming the concurrent LLaVA-RLHF trained on 10k annotated data. The final model achieves state-of-the-art performance in trustworthiness among open-source MLLMs, and shows better ro-bustness than GPT-4V in preventing hallucinations aroused from over-generalization.

关键词： hallucination language reasoning vision

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