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SG-Net: Spatial Granularity Network for One-Stage Video Instance Segmentation

SG-Net: Spatial Granularity Network for One-Stage Video Inst...

引用

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

作者： Liu, Dongfang Cui, Yiming Tan, Wenbo Chen, Yingjie Purdue Univ W Lafayette IN 47907 USA Univ Florida Gainesville FL 32611 USA Hangzhou Dian Zi Univ Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781665445092

Video instance segmentation (VIS) is a new and critical task in computer vision. To date, top-performing VIS methods extend the two-stage Mask R-CNN by adding a tracking branch, leaving plenty of room for improvement. In contrast, we approach the VIS task from a new perspective and propose a one-stage spatial granularity network (SG-Net). Compared to the conventional two-stage methods, SG-Net demonstrates four advantages: 1) Our method has a one-stage compact architecture and each task head (detection, segmentation, and tracking) is crafted interdependently so they can effectively share features and enjoy the joint optimization;2) Our mask prediction is dynamically performed on the sub-regions of each detected instance, leading to high-quality masks of fine granularity;3) Each of our task predictions avoids using expensive proposal-based RoI features, resulting in much reduced runtime complexity per instance;4) Our tracking head models objects' centerness movements for tracking, which effectively enhances the tracking robustness to different object appearances. In evaluation, we present state-of-the-art comparisons on the YouTube-VIS dataset. Extensive experiments demonstrate that our compact one-stage method can achieve improved performance in both accuracy and inference speed. We hope our SG-Net could serve as a strong and flexible baseline for the VIS task. Our code will be available here(1).

关键词： computer vision Runtime Tracking computer architecture Predictive models Feature extraction Robustness

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Alpha Invariance: On Inverse Scaling Between Distance and Volume Density in Neural Radiance Fields

Alpha Invariance: On Inverse Scaling Between Distance and Vo...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Joshua Ahn Haochen Wang Raymond A. Yeh Greg Shakhnarovich University of Chicago TTI-Chicago Purdue University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Scale-ambiguity in 3D scene dimensions leads to magnitude-ambiguity of volumetric densities in neural radiance fields, i.e., the densities double when scene size is halved, and vice versa. We call this property alpha invariance. For NeRFs to better maintain alpha invariance, we recommend 1) parameterizing both distance and volume densities in log space, and 2) a discretization-agnostic initialization strategy to guarantee high ray transmittance. We revisit a few popular radiance field models and find that these systems use various heuristics to deal with issues arising from scene scaling. We test their behaviors and show our recipe to be more robust. Visit our project page at https://***/p/alpha-invariance.

关键词： computer vision Three-dimensional displays computer architecture Neural radiance field pattern recognition

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CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models

CausalVAE: Disentangled Representation Learning via Neural S...

引用

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

作者： Yang, Mengyue Liu, Furui Chen, Zhitang Shen, Xinwei Hao, Jianye Wang, Jun Huawei Noahs Ark Lab Shenzhen Peoples R China UCL London England Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Learning disentanglement aims at finding a low dimensional representation which consists of multiple explanatory and generative factors of the observational data. The framework of variational autoencoder (VAE) is commonly used to disentangle independent factors from observations. However, in real scenarios, factors with semantics are not necessarily independent. Instead, there might be an underlying causal structure which renders these factors dependent. We thus propose a new VAE based framework named CausalVAE, which includes a Causal Layer to transform independent exogenous factors into causal endogenous ones that correspond to causally related concepts in data. We further analyze the model identifiabitily, showing that the proposed model learned from observations recovers the true one up to a certain degree. Experiments are conducted on various datasets, including synthetic and real word benchmark CelebA. Results show that the causal representations learned by CausalVAE are semantically interpretable, and their causal relationship as a Directed Acyclic Graph (DAG) is identified with good accuracy. Furthermore, we demonstrate that the proposed CausalVAE model is able to generate counterfactual data through "do-operation" to the causal factors.

关键词： Analytical models Directed acyclic graph computer vision Semantics Transforms Benchmark testing Data models

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引用

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

作者： Hu, Zijian Yang, Zhengyu Hu, Xuefeng Nevatia, Ram Univ Southern Calif Los Angeles CA 90007 USA

ISBN: (纸本)9781665445092

A common classification task situation is where one has a large amount of data available for training, but only a small portion is annotated with class labels. The goal of semi-supervised training, in this context, is to improve classification accuracy by leverage information not only from labeled data but also from a large amount of unlabeled data. Recent works [2, 1, 26] have developed significant improvements by exploring the consistency constrain between differently augmented labeled and unlabeled data. Following this path, we propose a novel unsupervised objective that focuses on the less studied relationship between the high confidence unlabeled data that are similar to each other. The new proposed Pair Loss minimizes the statistical distance between high confidence pseudo labels with similarity above a certain threshold. Combining the Pair Loss with the techniques developed by the MixMatch family [2, 1, 26], our proposed SimPLE algorithm shows significant performance gains over previous algorithms on CIFAR-100 and Mini-ImageNet [31], and is on par with the state-of-the-art methods on CIFAR-10 and SVHN. Furthermore, SimPLE also outperforms the state-of-the-art methods in the transfer learning setting, where models are initialized by the weights pre-trained on ImageNet[15] or DomainNet-Real[23].

关键词： Training computer vision Codes Computational modeling Transfer learning Performance gain Semisupervised learning

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Deep Video Matting via Spatio-Temporal Alignment and Aggregation

Deep Video Matting via Spatio-Temporal Alignment and Aggrega...

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

作者： Sun, Yanan Wang, Guanzhi Gu, Qiao Tang, Chi-Keung Tai, Yu-Wing HKUST Daejeon South Korea Stanford Univ Stanford CA 94305 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Kuaishou Technol Seoul South Korea

ISBN: (纸本)9781665445092

Despite the significant progress made by deep learning in natural image matting, there has been so far no representative work on deep learning for video matting due to the inherent technical challenges in reasoning temporal domain and lack of large-scale video matting datasets. In this paper, we propose a deep learning-based video matting framework which employs a novel and effective spatio-temporal feature aggregation module (ST-FAM). As optical flow estimation can be very unreliable within matting regions, ST-FAM is designed to effectively align and aggregate information across different spatial scales and temporal frames within the network decoder. To eliminate frame-by-frame trimap annotations, a lightweight interactive trimap propagation network is also introduced. The other contribution consists of a large-scale video matting dataset with groundtruth alpha mattes for quantitative evaluation and real-world high-resolution videos with trimaps for qualitative evaluation. Quantitative and qualitative experimental results show that our framework significantly outperforms conventional video matting and deep image matting methods applied to video in presence of multi frame temporal information.

关键词： Deep learning computer vision Annotations Estimation Coherence Predictive models Cognition

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Remote Sensing Image Object Detection Method with Feature Denoising Fusion Module 7

Remote Sensing Image Object Detection Method with Feature De...

引用

7th ieee Advanced Information Technology, Electronic and Automation Control conference, IAEAC 2024

作者： Chen, Penghui Li, Qishen Li, Qiufeng Wu, Zhongyu Nanchang Hangkong University School of Information Engineering Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Jiangxi Nanchang China Nanchang Hangkong University School of Software Jiangxi Nanchang China Jiangxi Nanchang China

ISBN: (纸本)9798350339161

Remote sensing object detection is an important research area in computer vision, widely applied in both military and civilian domains. However, challenges in remote sensing image object detection such as large image sizes, complex backgrounds, and significant variations in target scales are prevalent. To address these issues, this paper proposes a new Feature Denoising and Fusion Module (FDFM) aimed at enhancing the accuracy and robustness of object detection. This module comprises a Multi-Scale Denoising Submodule(MDS) and an Attention Optimization Submodule(AOS). The Multi-Scale Denoising Module aims to suppress lower-level texture noise by utilizing higher-level semantic features before the fusion process, reducing the impact of lower-level noise on subsequent multi-scale feature fusion. Meanwhile, the Attention Optimization Module seeks to enhance the precision of self-attention computations within the Multi-Scale Denoising Module without increasing the parameter count. The efficacy of this method was evaluated on public datasets DOTA, VisDrone, VOC and COCO, showing improvements in comparison to baseline models. © 2024 ieee.

关键词： Object detection

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Lifelong Person Re-Identification via Adaptive Knowledge Accumulation

Lifelong Person Re-Identification via Adaptive Knowledge Acc...

引用

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

作者： Pu, Nan Chen, Wei Liu, Yu Bakker, Erwin M. Lew, Michael S. Leiden Univ LIACS Media Lab Leiden Netherlands Dalian Univ Technol Int Sch Informat Sci & Engn Dalian Peoples R China

ISBN: (纸本)9781665445092

Person re-identification (ReID) methods always learn through a stationary domain that is fixed by the choice of a given dataset. In many contexts (e.g., lifelong learning), those methods are ineffective because the domain is continually changing in which case incremental learning over multiple domains is required potentially. In this work we explore a new and challenging ReID task, namely lifelong person re-identification (LReID), which enables to learn continuously across multiple domains and even generalise on new and unseen domains. Following the cognitive processes in the human brain, we design an Adaptive Knowledge Accumulation (AKA) framework that is endowed with two crucial abilities: knowledge representation and knowledge operation. Our method alleviates catastrophic forgetting on seen domains and demonstrates the ability to generalize to unseen domains. Correspondingly, we also provide a new and large-scale benchmark for LReID. Extensive experiments demonstrate our method outperforms other competitors by a margin of 5.8% mAP in generalising evaluation.

关键词： Training Visualization computer vision Adaptation models Codes Cognitive processes Knowledge representation

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Photo-Realistic Image Restoration in the Wild with Controlled vision-Language Models

Photo-Realistic Image Restoration in the Wild with Controlle...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Ziwei Luo Fredrik K. Gustafsson Zheng Zhao Jens Sjölund Thomas B. Schön Uppsala University Karolinska Institutet

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Though diffusion models have been successfully applied to various image restoration (IR) tasks, their performance is sensitive to the choice of training datasets. Typically, diffusion models trained in specific datasets fail to recover images that have out-of-distribution degradations. To address this problem, this work leverages a capable vision-language model and a synthetic degradation pipeline to learn image restoration in the wild (wild IR). More specifically, all low-quality images are simulated with a synthetic degradation pipeline that contains multiple common degradations such as blur, resize, noise, and JPEG compression. Then we introduce robust training for a degradation-aware CLIP model to extract enriched image content features to assist high-quality image restoration. Our base diffusion model is the image restoration SDE (IR-SDE). Built upon it, we further present a posterior sampling strategy for fast noise-free image generation. We evaluate our model on both synthetic and real-world degradation datasets. Moreover, experiments on the unified image restoration task illustrate that the proposed posterior sampling improves image generation quality for various degradations.

关键词： Degradation Training Image synthesis Pipelines Transform coding Diffusion models Feature extraction

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Open-Vocabulary Object Detection Using Captions

Open-Vocabulary Object Detection Using Captions

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

作者： Zareian, Alireza Dela Rosa, Kevin Hu, Derek Hao Chang, Shih-Fu Snap Inc Seattle WA 98121 USA Columbia Univ New York NY 10027 USA

ISBN: (纸本)9781665445092

Despite the remarkable accuracy of deep neural networks in object detection, they are costly to train and scale due to supervision requirements. Particularly, learning more object categories typically requires proportionally more bounding box annotations. Weakly supervised and zero-shot learning techniques have been explored to scale object detectors to more categories with less supervision, but they have not been as successful and widely adopted as supervised models. In this paper, we put forth a novel formulation of the object detection problem, namely open-vocabulary object detection, which is more general, more practical, and more effective than weakly supervised and zero-shot approaches. We propose a new method to train object detectors using bounding box annotations for a limited set of object categories, as well as image-caption pairs that cover a larger variety of objects at a significantly lower cost. We show that the proposed method can detect and localize objects for which no bounding box annotation is provided during training, at a significantly higher accuracy than zero-shot approaches. Meanwhile, objects with bounding box annotation can be detected almost as accurately as supervised methods, which is significantly better than weakly supervised baselines. Accordingly, we establish a new state of the art for scalable object detection.

关键词： Location awareness Training Deep learning computer vision Costs Annotations Object detection

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Pixel-aligned Volumetric Avatars

Pixel-aligned Volumetric Avatars

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

作者： Raj, Amit Zollhofer, Michael Simon, Tomas Saragih, Jason Saito, Shunsuke Hays, James Lombardi, Stephen Georgia Inst Technol Atlanta GA 30332 USA Facebook Real Labs Res Menlo Pk CA USA

ISBN: (纸本)9781665445092

Acquisition and rendering of photo-realistic human heads is a highly challenging research problem of particular importance for virtual telepresence. Currently, the highest quality is achieved by volumetric approaches trained in a person-specific manner on multi-view data. These models better represent fine structure, such as hair, compared to simpler mesh-based models. Volumetric models typically employ a global code to represent facial expressions, such that they can be driven by a small set of animation parameters. While such architectures achieve impressive rendering quality, they can not easily be extended to the multi-identity setting. In this paper, we devise a novel approach for predicting volumetric avatars of the human head given just a small number of inputs. We enable generalization across identities by a novel parameterization that combines neural radiance fields with local, pixel-aligned features extracted directly from the inputs, thus side-stepping the need for very deep or complex networks. Our approach is trained in an end-to-end manner solely based on a photometric re-rendering loss without requiring explicit 3D supervision. We demonstrate that our approach outperforms the existing state of the art in terms of quality and is able to generate faithful facial expressions in a multi-identity setting.

关键词： Hair computer vision Head Three-dimensional displays Telepresence Avatars Neural networks

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