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Improving Image recognition by Retrieving from Web-Scale Image-Text Data

Improving Image Recognition by Retrieving from Web-Scale Ima...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Ahmet Iscen Alireza Fathi Cordelia Schmid Google Research

Retrieval augmented models are becoming increasingly popular for computer vision tasks after their recent success in NLP problems. The goal is to enhance the recognition capabilities of the model by retrieving similar examples for the visual input from an external memory set. In this work, we introduce an attention-based memory module, which learns the importance of each retrieved example from the memory. Compared to existing approaches, our method removes the influence of the irrelevant retrieved examples, and retains those that are beneficial to the input query. We also thoroughly study various ways of constructing the memory dataset. Our experiments show the benefit of using a massive-scale memory dataset of 1B image-text pairs, and demonstrate the performance of different memory representations. We evaluate our method in three different classification tasks, namely long-tailed recognition, learning with noisy labels, and fine-grained classification, and show that it achieves state-of-the-art accuracies in ImageNet-LT, Places-LT and Webvision datasets.

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HIG: Hierarchical Interlacement Graph Approach to Scene Graph Generation in Video Understanding

HIG: Hierarchical Interlacement Graph Approach to Scene Grap...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Trong-Thuan Nguyen Pha Nguyen Khoa Luu CVIU Lab University of Arkansas

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Visual interactivity understanding within visual scenes presents a significant challenge in computer vision. Existing methods focus on complex interactivities while leveraging a simple relationship model. These methods, however, struggle with a diversity of appearance, situation, position, interaction, and relation in videos. This limitation hinders the ability to fully comprehend the interplay within the complex visual dynamics of subjects. In this paper, we delve into interactivities understanding within visual content by deriving scene graph representations from dense interactivities among humans and objects. To achieve this goal, we first present a new dataset containing Appearance-Situation-Position-Interaction-Relation predicates, named ASPIRe, offering an extensive collection of videos marked by a wide range of interactivities. Then, we propose a new approach named Hierarchical Interlacement Graph (HIG), which leverages a unified layer and graph within a hierarchical structure to provide deep insights into scene changes across five distinct tasks. Our approach demonstrates superior performance to other methods through extensive experiments conducted in various scenarios.

关键词： Visualization computer vision Scalability Computational modeling Benchmark testing pattern recognition Task analysis

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Contrastive Mean-Shift Learning for Generalized Category Discovery

Contrastive Mean-Shift Learning for Generalized Category Dis...

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conference on computer vision and pattern recognition (cvpr)

作者： Sua Choi Dahyun Kang Minsu Cho Pohang University of Science and Technology (POSTECH) South Korea

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We address the problem of generalized category discovery (GCD) that aims to partition a partially labeled collection of images; only a small part of the collection is labeled and the total number of target classes is unknown. To address this generalized image clustering problem, we revisit the mean-shift algorithm, i.e., a classic, powerful technique for mode seeking, and incorporate it into a contrastive learning framework. The proposed method, dubbed Contrastive Mean-Shift (CMS) learning, trains an embedding network to produce representations with better clustering properties by an iterative process of mean shift and contrastive update. Experiments demonstrate that our method, both in settings with and without the total number of clusters being known, achieves state-of-the-art performance on six public GCD benchmarks without bells and whistles.

关键词： Training Representation learning computer vision Clustering algorithms Estimation Contrastive learning Benchmark testing

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Regressive Domain Adaptation for Unsupervised Keypoint Detection

Regressive Domain Adaptation for Unsupervised Keypoint Detec...

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

作者： Jiang, Junguang Ji, Yifei Wang, Ximei Liu, Yufeng Wang, Jianmin Long, Mingsheng Tsinghua Univ Sch Software BNRist Beijing Peoples R China Kuaishou Technol Y Tech Beijing Peoples R China

ISBN: (纸本)9781665445092

Domain adaptation (DA) aims at transferring knowledge from a labeled source domain to an unlabeled target domain. Though many DA theories and algorithms have been proposed, most of them are tailored into classification settings and may fail in regression tasks, especially in the practical keypoint detection task. To tackle this difficult but significant task, we present a method of regressive domain adaptation (RegDA) for unsupervised keypoint detection. Inspired by the latest theoretical work, we first utilize an adversarial regressor to maximize the disparity on the target domain and train a feature generator to minimize this disparity. However, due to the high dimension of the output space, this regressor fails to detect samples that deviate from the support of the source. To overcome this problem, we propose two important ideas. First, based on our observation that the probability density of the output space is sparse, we introduce a spatial probability distribution to describe this sparsity and then use it to guide the learning of the adversarial regressor. Second, to alleviate the optimization difficulty in the high-dimensional space, we innovatively convert the minimax game in the adversarial training to the minimization of two opposite goals. Extensive experiments show that our method brings large improvement by 8% to 11% in terms of PCK on different datasets.

关键词： Training computer vision Games Superluminescent diodes Minimization Probability distribution Generators

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Adversarial Score Distillation: When Score Distillation Meets GAN

Adversarial Score Distillation: When Score Distillation Meet...

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conference on computer vision and pattern recognition (cvpr)

作者： Min Wei Jingkai Zhou Junyao Sun Xuesong Zhang Beijing University of Posts and Telecommunications South China University of Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Existing score distillation methods are sensitive to classifier-free guidance (CFG) scale, manifested as over-smoothness or instability at small CFG scales, while over-saturation at large ones. To explain and analyze these issues, we revisit the derivation of Score Distillation Sampling (SDS) and decipher existing score distillation with the Wasserstein Generative Adversarial Network (WGAN) paradigm. With the WGAN paradigm, we find that existing score distillation either employs a fixed sub-optimal discriminator or conducts incomplete discriminator optimization, resulting in the scale-sensitive issue. We propose the Adversarial Score Distillation (ASD), which maintains an optimizable discriminator and updates it using the complete optimization objective. Experiments show that the proposed ASD performs favorably in 2D distillation and text-to-3D tasks against existing methods. Furthermore, to explore the generalization ability of our paradigm, we extend ASD to the image editing task, which achieves competitive results. The project page and code are at this link.

关键词： computer vision Codes Computational modeling Generative adversarial networks pattern recognition Optimization

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One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking

One-Shot Neural Ensemble Architecture Search by Diversity-Gu...

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

作者： Chen, Minghao Fu, Jianlong Ling, Haibin SUNY Stony Brook Stony Brook NY 11794 USA Microsoft Res Asia Beijing Peoples R China Microsoft Beijing Peoples R China

ISBN: (纸本)9781665445092

Despite remarkable progress achieved, most neural architecture search (NAS) methods focus on searching for one single accurate and robust architecture. To further build models with better generalization capability and performance, model ensemble is usually adopted and performs better than stand-alone models. Inspired by the merits of model ensemble, we propose to search for multiple diverse models simultaneously as an alternative way to find powerful models. Searching for ensembles is non-trivial and has two key challenges: enlarged search space and potentially more complexity for the searched model. In this paper, we propose a one-shot neural ensemble architecture search (NEAS) solution that addresses the two challenges. For the first challenge, we introduce a novel diversity-based metric to guide search space shrinking, considering both the potentiality and diversity of candidate operators. For the second challenge, we enable a new search dimension to learn layer sharing among different models for efficiency purposes. The experiments on ImageNet clearly demonstrate that our solution can improve the supernet's capacity of ranking ensemble architectures, and further lead to better search results. The discovered architectures achieve superior performance compared with state-of-the-arts such as MobileNetV3 and EfficientNet families under aligned settings. Moreover, we evaluate the generalization ability and robustness of our searched architecture on the COCO detection benchmark and achieve a 3.1% improvement on AP compared with MobileNetV3. Codes and models are available here.

关键词： computer vision Codes computer architecture Benchmark testing Extraterrestrial measurements Robustness Complexity theory

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Towards Accurate 3D Human Motion Prediction from Incomplete Observations

Towards Accurate 3D Human Motion Prediction from Incomplete ...

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

作者： Cui, Qiongjie Sun, Huaijiang Nanjing Univ Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781665445092

Predicting accurate and realistic future human poses from historically observed sequences is a fundamental task in the intersection of computer vision, graphics, and artificial intelligence. Recently, continuous efforts have been devoted to addressing this issue, which has achieved remarkable progress. However, the existing work is seriously limited by complete observation, that is, once the historical motion sequence is incomplete (with missing values), it can only produce unexpected predictions or even deformities. Furthermore, due to inevitable reasons such as occlusion and the lack of equipment precision, the incompleteness of motion data occurs frequently, which hinders the practical application of current algorithms. In this work, we first notice this challenging problem, i.e., how to generate high-fidelity human motion predictions from incomplete observations. To solve it, we propose a novel multi-task graph convolutional network (MT-GCN). Specifically, the model involves two branches, in which the primary task is to focus on forecasting future 3D human actions accurately, while the auxiliary one is to repair the missing value of the incomplete observation. Both of them are integrated into a unified framework to share the spatio-temporal representation, which improves the final performance of each collaboratively. On three large-scale datasets, for various data missing scenarios in the real world, extensive experiments demonstrate that our approach is consistently superior to the state-of-the-art methods in which the missing values from incomplete observations are not explicitly analyzed.

关键词： Graphics computer vision Solid modeling Three-dimensional displays Predictive models Maintenance engineering Prediction algorithms

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OmniSDF: Scene Reconstruction Using Omnidirectional Signed Distance Functions and Adaptive Binoctrees

OmniSDF: Scene Reconstruction Using Omnidirectional Signed D...

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conference on computer vision and pattern recognition (cvpr)

作者： Hakyeong Kim Andreas Meuleman Hyeonjoong Jang James Tompkin Min H. Kim KAIST INRIA Brown University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We present a method to reconstruct indoor and outdoor static scene geometry and appearance from an omnidirectional video moving in a small circular sweep. This setting is challenging because of the small baseline and large depth ranges, making it difficult to find ray crossings. To better constrain the optimization, we estimate geometry as a signed distance field within a spherical binoctree data structure and use a complementary efficient tree traversal strategy based on a breadth-first search for sampling. Unlike regular grids or trees, the shape of this structure well-matches the camera setting, creating a better memory-quality trade-off. From an initial depth estimate, the binoc-tree is adaptively subdivided throughout the optimization, previous methods use a fixed depth that leaves the scene undersampled. In comparison with three neural optimization methods and two non-neural methods, ours shows de-creased geometry error on average, especially in a detailed scene, while significantly reducing the required number of voxels to represent such details

关键词： Geometry computer vision Shape Optimization methods Data structures Cameras pattern recognition

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Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and Baseline

Towards Fast and Accurate Real-World Depth Super-Resolution:...

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

作者： He, Lingzhi Zhu, Hongguang Li, Feng Bai, Huihui Cong, Runmin Zhang, Chunjie Lin, Chunyu Liu, Meiqin Zhao, Yao Beijing Jiaotong Univ Inst Informat Sci Beijing Key Lab Adv Informat Sci & Network Beijing 100044 Peoples R China

ISBN: (纸本)9781665445092

Depth maps obtained by commercial depth sensors are always in low-resolution, making it difficult to be used in various computer vision tasks. Thus, depth map super-resolution (SR) is a practical and valuable task, which upscales the depth map into high-resolution (HR) space. However, limited by the lack of real-world paired low-resolution (LR) and HR depth maps, most existing methods use downsampling to obtain paired training samples. To this end, we first construct a large-scale dataset named "RGB-D-D", which can greatly promote the study of depth map SR and even more depth-related real-world tasks. The "D-D" in our dataset represents the paired LR and HR depth maps captured from mobile phone and Lucid Helios respectively ranging from indoor scenes to challenging outdoor scenes. Besides, we provide a fast depth map super-resolution (FDSR) baseline, in which the high-frequency component adaptively decomposed from RGB image to guide the depth map SR. Extensive experiments on existing public datasets demonstrate the effectiveness and efficiency of our network compared with the state-of-the-art methods. Moreover, for the real-world LR depth maps, our algorithm can produce more accurate HR depth maps with clearer boundaries and to some extent correct the depth value errors.

关键词： Training computer vision Superresolution Focusing Estimation Benchmark testing Mobile handsets

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Fusing Personal and Environmental Cues for Identification and Segmentation of First-Person Camera Wearers in Third-Person Views

Fusing Personal and Environmental Cues for Identification an...

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conference on computer vision and pattern recognition (cvpr)

作者： Ziwei Zhao Yuchen Wang Chuhua Wang Indiana University Bloomington

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

As wearable cameras become more popular, an important question emerges: how to identify camera wearers within the perspective of conventional static cameras. The drastic difference between first-person (egocentric) and third-person (exocentric) camera views makes this a challenging task. We present PersonEnvironmentNet (PEN), a framework designed to integrate information from both the individuals in the two views and geometric cues inferred from the background environment. To facilitate research in this direction, we also present TF2023, a novel dataset comprising synchronized first-person and third-person views, along with masks of camera wearers and labels associating these masks with the respective first-person views. In addition, we propose a novel quantitative metric designed to measure a model's ability to comprehend the relationship between the two views. Our experiments re- veal that PEN outperforms existing methods. The code and dataset are available at https://***/ziweizhao1993/PEN.

关键词： Measurement computer vision Codes Focusing Cameras pattern recognition Synchronization

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