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Where is my Wallet? Modeling Object Proposal Sets for Egocentric Visual Query Localization

Where is my Wallet? Modeling Object Proposal Sets for Egocen...

引用

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

作者： Xu, Mengmeng Li, Yanghao Fu, Cheng-Yang Ghanem, Bernard Xiang, Tao Perez-Rua, Juan-Manuel Meta AI Menlo Pk CA 94025 USA KAUST Thuwal Saudi Arabia

ISBN: (纸本)9798350301298

This paper deals with the problem of localizing objects in image and video datasets from visual exemplars. In particular, we focus on the challenging problem of egocentric visual query localization. We first identify grave implicit biases in current query-conditioned model design and visual query datasets. Then, we directly tackle such biases at both frame and object set levels. Concretely, our method solves these issues by expanding limited annotations and dynamically dropping object proposals during training. Additionally, we propose a novel transformer-based module that allows for object-proposal set context to be considered while incorporating query information. We name our module Conditioned Contextual Transformer or CocoFormer. Our experiments show the proposed adaptations improve egocentric query detection, leading to a better visual query localization system in both 2D and 3D configurations. Thus, we can improve frame-level detection performance from 26.28% to 31.26% in AP, which correspondingly improves the VQ2D and VQ3D localization scores by significant margins. Our improved context-aware query object detector ranked first and second respectively in the VQ2D and VQ3D tasks in the 2nd Ego4D challenge. In addition to this, we showcase the relevance of our proposed model in the Few-Shot Detection (FSD) task, where we also achieve SOTA results. Our code is available at https: //***/facebookresearch/vq2d_cvpr.

关键词： detection recognition: Categorization retrieval

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Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution

Local Implicit Normalizing Flow for Arbitrary-Scale Image Su...

引用

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

作者： Yao, Jie-En Tsao, Li-Yuan Lo, Yi-Chen Tseng, Roy Chang, Chia-Che Lee, Chun-Yi Natl Tsing Hua Univ 1 ElsaLab Hsinchu Taiwan MediaTek Inc Hsinchu Taiwan

ISBN: (纸本)9798350301298

Flow-based methods have demonstrated promising results in addressing the ill-posed nature of super-resolution (SR) by learning the distribution of high-resolution (HR) images with the normalizing flow. However, these methods can only perform a predefined fixed-scale SR, limiting their potential in real-world applications. Meanwhile, arbitrary-scale SR has gained more attention and achieved great progress. Nonetheless, previous arbitrary-scale SR methods ignore the ill-posed problem and train the model with per-pixel L1 loss, leading to blurry SR outputs. In this work, we propose "Local Implicit Normalizing Flow" (LINF) as a unified solution to the above problems. LINF models the distribution of texture details under different scaling factors with normalizing flow. Thus, LINF can generate photo-realistic HR images with rich texture details in arbitrary scale factors. We evaluate LINF with extensive experiments and show that LINF achieves the state-of-the-art perceptual quality compared with prior arbitrary-scale SR methods.

关键词： Low-level vision

来源：评论

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Exploring Structured Semantic Prior for Multi Label recognition with Incomplete Labels

Exploring Structured Semantic Prior for Multi Label Recognit...

引用

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

作者： Ding, Zixuan Wang, Ao Chen, Hui Zhang, Qiang Liu, Pengzhang Bao, Yongjun Yan, Weipeng Han, Jungong Xidian Univ Xian Peoples R China Tsinghua Univ Beijing Peoples R China BNRist Beijing Peoples R China Hangzhou Zhuoxi Inst Brain & Intelligence Hangzhou Peoples R China JD Com Beijing Peoples R China Univ Sheffield Dept Comp Sci Sheffield S Yorkshire England Univ Sheffield Ctr Machine Intelligence Sheffield S Yorkshire England

ISBN: (纸本)9798350301298

Multi-label recognition (MLR) with incomplete labels is very challenging. Recent works strive to explore the image-to-label correspondence in the vision-language model, i.e., CLIP [22], to compensate for insufficient annotations. In spite of promising performance, they generally overlook the valuable prior about the label-to-label correspondence. In this paper, we advocate remedying the deficiency of label supervision for the MLR with incomplete labels by deriving a structured semantic prior about the label-to-label correspondence via a semantic prior prompter. We then present a novel Semantic Correspondence Prompt Network (SCPNet), which can thoroughly explore the structured semantic prior. A Prior-Enhanced Self-Supervised Learning method is further introduced to enhance the use of the prior. Comprehensive experiments and analyses on several widely used benchmark datasets show that our method significantly outperforms existing methods on all datasets, well demonstrating the effectiveness and the superiority of our method. Our code will be available at https://***/jameslahm/SCPNet.

关键词： detection recognition: Categorization retrieval

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Vector Quantization with Self-Attention for Quality-Independent Representation Learning

Vector Quantization with Self-Attention for Quality-Independ...

引用

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

作者： Yang, Zhou Dong, Weisheng Li, Xin Huang, Mengluan Sun, Yulin Shi, Guangming Xidian Univ Xian Peoples R China West Virginia Univ Morgantown WV USA

ISBN: (纸本)9798350301298

Recently, the robustness of deep neural networks has drawn extensive attention due to the potential distribution shift between training and testing data (e.g., deep models trained on high-quality images are sensitive to corruption during testing). Many researchers attempt to make the model learn invariant representations from multiple corrupted data through data augmentation or image-pair-based feature distillation to improve the robustness. Inspired by sparse representation in image restoration, we opt to address this issue by learning image-quality-independent feature representation in a simple plug-and-play manner, that is, to introduce discrete vector quantization (VQ) to remove redundancy in recognition models. Specifically, we first add a codebook module to the network to quantize deep features. Then we concatenate them and design a self-attention module to enhance the representation. During training, we enforce the quantization of features from clean and corrupted images in the same discrete embedding space so that an invariant quality-independent feature representation can be learned to improve the recognition robustness of low-quality images. Qualitative and quantitative experimental results show that our method achieved this goal effectively, leading to a new state-of-the-art result of 43.1 % mCE on ImageNet-C with ResNet50 as the backbone. On other robustness benchmark datasets, such as ImageNet-R, our method also has an accuracy improvement of almost 2%. The source code is available at https://***/faculty/wsdong/Projects/***

关键词： detection recognition: Categorization retrieval

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Neural Dependencies Emerging from Learning Massive Categories

Neural Dependencies Emerging from Learning Massive Categorie...

引用

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

作者： Feng, Ruili Zheng, Kecheng Zhu, Kai Shen, Yujun Zhao, Jian Huang, Yukun Zhao, Deli Zhou, Jingren Jordan, Michael Zha, Zheng-Jun Univ Sci & Technol China Hefei Peoples R China Ant Grp Hangzhou Peoples R China Alibaba Grp Hangzhou Peoples R China Univ Calif Berkeley Berkeley CA USA

ISBN: (纸本)9798350301298

This work presents two astonishing findings on neural networks learned for large-scale image classification. 1) Given a well-trained model, the logits predicted for some category can be directly obtained by linearly combining the predictions of a few other categories, which we call neural dependency. 2) Neural dependencies exist not only within a single model, but even between two independently learned models, regardless of their architectures. Towards a theoretical analysis of such phenomena, we demonstrate that identifying neural dependencies is equivalent to solving the Covariance Lasso (CovLasso) regression problem proposed in this paper. Through investigating the properties of the problem solution, we confirm that neural dependency is guaranteed by a redundant logit covariance matrix, which condition is easily met given massive categories, and that neural dependency is highly sparse, implying that one category correlates to only a few others. We further empirically show the potential of neural dependencies in understanding internal data correlations, generalizing models to unseen categories, and improving model robustness with a dependency-derived regularizer. Code to reproduce the results in this paper is available at https://***/RuiLiFeng/Neural-Dependencies.

关键词： computer vision theory

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Deep Deterministic Uncertainty: A New Simple Baseline

Deep Deterministic Uncertainty: A New Simple Baseline

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

作者： Mukhoti, Jishnu Kirsch, Andreas van Amersfoort, Joost Torr, Philip H. S. Gal, Yarin Univ Oxford Dept Comp Sci OATML Oxford England Univ Oxford Dept Engn Sci Torr Vis Grp Oxford England

ISBN: (纸本)9798350301298

Reliable uncertainty from deterministic single-forward pass models is sought after because conventional methods of uncertainty quantification are computationally expensive. We take two complex single-forward-pass uncertainty approaches, DUQ and SNGP, and examine whether they mainly rely on a well-regularized feature space. Crucially, without using their more complex methods for estimating uncertainty, we find that a single softmax neural net with such a regularized feature-space, achieved via residual connections and spectral normalization, outperforms DUQ and SNGP's epistemic uncertainty predictions using simple Gaussian Discriminant Analysis post-training as a separate feature-space density estimator-without fine-tuning on OoD data, feature ensembling, or input pre-procressing. Our conceptually simple Deep Deterministic Uncertainty (DDU) baseline can also be used to disentangle aleatoric and epistemic uncertainty and performs as well as Deep Ensembles, the stateof-the art for uncertainty prediction, on several OoD benchmarks (CIFAR-10/100 vs SVHN/Tiny-ImageNet, ImageNet vs ImageNet-O), active learning settings across different model architectures, as well as in large scale vision tasks like semantic segmentation, while being computationally cheaper.

关键词： accountability ethics in vision fairness privacy Transparency

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Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution

Learning Spatial-Temporal Implicit Neural Representations fo...

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

作者： Lu, Yunfan Wang, Zipeng Liu, Minjie Wang, Hongjian Wang, Lin HKUST GZ AI Thrust Guangzhou Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Beijing Peoples R China HKUST Dept Comp Sci & Engn Guangzhou Peoples R China

ISBN: (纸本)9798350301298

Event cameras sense the intensity changes asynchronously and produce event streams with high dynamic range and low latency. This has inspired research endeavors utilizing events to guide the challenging video super-resolution (VSR) task. In this paper, we make the first attempt to address a novel problem of achieving VSR at random scales by taking advantages of the high temporal resolution property of events. This is hampered by the difficulties of representing the spatial-temporal information of events when guiding VSR. To this end, we propose a novel framework that incorporates the spatial-temporal interpolation of events to VSR in a unified framework. Our key idea is to learn implicit neural representations from queried spatial-temporal coordinates and features from both RGB frames and events. Our method contains three parts. Specifically, the Spatial-Temporal Fusion (STF) module first learns the 3D features from events and RGB frames. Then, the Temporal Filter (TF) module unlocks more explicit motion information from the events near the queried timestamp and generates the 2D features. Lastly, the Spatial-Temporal Implicit Representation (STIR) module recovers the SR frame in arbitrary resolutions from the outputs of these two modules. In addition, we collect a real-world dataset with spatially aligned events and RGB frames. Extensive experiments show that our method significantly surpasses the prior-arts and achieves VSR with random scales, e.g., 6.5. Code and dataset are available at https: //***/cvpr23/egvsr.

关键词： Low-level vision

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HOICLIP: Efficient Knowledge Transfer for HOI Detection with vision-Language Models

HOICLIP: Efficient Knowledge Transfer for HOI Detection with...

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

作者： Ning, Shan Qiu, Longtian Liu, Yongfei He, Xuming ShanghaiTech Univ Shanghai Peoples R China ByteDance Inc Beijing Peoples R China Shanghai Engn Res Ctr Intelligent Vis & 1maging Shanghai Peoples R China

ISBN: (纸本)9798350301298

Human-Object Interaction (HOI) detection aims to localize human-object pairs and recognize their interactions. Recently, Contrastive Language-Image Pre-training (CLIP) has shown great potential in providing interaction prior for HOI detectors via knowledge distillation. However, such approaches often rely on large-scale training data and suffer from inferior performance under few/zero-shot scenarios. In this paper, we propose a novel HOI detection framework that efficiently extracts prior knowledge from CLIP and achieves better generalization. In detail, we first introduce a novel interaction decoder to extract informative regions in the visual feature map of CLIP via a cross-attention mechanism, which is then fused with the detection backbone by a knowledge integration block for more accurate human-object pair detection. In addition, prior knowledge in CLIP text encoder is leveraged to generate a classifier by embedding HOI descriptions. To distinguish fine-grained interactions, we build a verb classifier from training data via visual semantic arithmetic and a lightweight verb representation adapter. Furthermore, we propose a training-free enhancement to exploit global HOI predictions from CLIP. Extensive experiments demonstrate that our method outperforms the state of the art by a large margin on various settings, e.g. +4.04 mAP on HICO-Det. The source code is available in https://***/Artanic30/HOICLIP.

关键词： detection recognition: Categorization retrieval

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Trade-off between Robustness and Accuracy of vision Transformers

Trade-off between Robustness and Accuracy of Vision Transfor...

引用

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

作者： Li, Yanxi Xu, Chang Univ Sydney Fac Engn Sch Comp Sci Sydney NSW Australia

ISBN: (纸本)9798350301298

Although deep neural networks (DNNs) have shown great successes in computer vision tasks, they are vulnerable to perturbations on inputs, and there exists a trade-off between the natural accuracy and robustness to such perturbations, which is mainly caused by the existence of robust non-predictive features and non-robust predictive features. Recent empirical analyses find vision Transformers (ViTs) are inherently robust to various kinds of perturbations, but the aforementioned trade-off still exists for them. In this work, we propose Trade-off between Robustness and Accuracy of vision Transformers (TORA-ViTs), which aims to efficiently transfer ViT models pretrained on natural tasks for both accuracy and robustness. TORA-ViTs consist of two major components, including a pair of accuracy and robustness adapters to extract predictive and robust features, respectively, and a gated fusion module to adjust the trade-off. The gated fusion module takes outputs of a pretrained ViT block as queries and outputs of our adapters as keys and values, and tokens from different adapters at different spatial locations are compared with each other to generate attention scores for a balanced mixing of predictive and robust features. Experiments on ImageNet with various robust benchmarks show that our TORA-ViTs can efficiently improve the robustness of naturally pretrained ViTs while maintaining competitive natural accuracy. Our most balanced setting (TORA-ViTs with lambda = 0.5) can maintain 83.7% accuracy on clean ImageNet and reach 54.7% and 38.0% accuracy under FGSM and PGD white-box attacks, respectively. In terms of various ImageNet variants, it can reach 39.2% and 56.3% accuracy on ImageNet-A and ImageNet-R and reach 34.4% mCE on ImageNet-C.

关键词： Adversarial attack and defense

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PosterLayout: A New Benchmark and Approach for Content-aware Visual-Textual Presentation Layout

PosterLayout: A New Benchmark and Approach for Content-aware...

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

作者： Hsu, Hsiao Yuan He, Xiangteng Peng, Yuxin Kong, Hao Zhang, Qing Peking Univ Wangxuan Inst Comp Technol Beijing Peoples R China Peking Univ Sch Comp Sci Natl Key Lab Multimedia Informat Proc Beijing Peoples R China Meituan Beijing Peoples R China

ISBN: (纸本)9798350301298

Content-aware visual-textual presentation layout aims at arranging spatial space on the given canvas for pre-defined elements, including text, logo, and underlay, which is a key to automatic template-free creative graphic design. In practical applications, e.g., poster designs, the canvas is originally non-empty, and both inter-element relationships as well as inter-layer relationships should be concerned when generating a proper layout. A few recent works deal with them simultaneously, but they still suffer from poor graphic performance, such as a lack of layout variety or spatial non-alignment. Since content-aware visual-textual presentation layout is a novel task, we first construct a new dataset named PKU PosterLayout, which consists of 9,974 poster-layout pairs and 905 images, i.e., non-empty canvases. It is more challenging and useful for greater layout variety, domain diversity, and content diversity. Then, we propose design sequence formation (DSF) that reorganizes elements in layouts to imitate the design processes of human designers, and a novel CNN-LSTM-based conditional generative adversarial network (GAN) is presented to generate proper layouts. Specifically, the discriminator is design-sequence-aware and will supervise the "design" process of the generator. Experimental results verify the usefulness of the new benchmark and the effectiveness of the proposed approach, which achieves the best performance by generating suitable layouts for diverse canvases. The dataset and the source code are available at https://***/PKU-ICSTMIPL/PosterLayout-cvpr2023.

关键词： vision applications and systems

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