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DeFMO: Deblurring and Shape Recovery of Fast Moving Objects

DeFMO: Deblurring and Shape Recovery of Fast Moving Objects

引用

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

作者： Rozumnyi, Denys Oswald, Martin R. Ferrari, Vittorio Matas, Jiri Pollefeys, Marc Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Google Res Mountain View CA 94043 USA Microsoft Mixed Real & AI Zurich Lab Zurich Switzerland Czech Tech Univ Visual Recognit Grp Prague Czech Republic

ISBN: (纸本)9781665445092

Objects moving at high speed appear significantly blurred when captured with cameras. The blurry appearance is especially ambiguous when the object has complex shape or texture. In such cases, classical methods, or even humans, are unable to recover the object's appearance and motion. We propose a method that, given a single image with its estimated background, outputs the object's appearance and position in a series of sub frames as if captured by a high-speed camera (i.e. temporal super-resolution). The proposed generative model embeds an image of the blurred object into a latent space representation, disentangles the background, and renders the sharp appearance. Inspired by the image formation model, we design novel self-supervised loss function terms that boost performance and show good generalization capabilities. The proposed DeFMO method is trained on a complex synthetic dataset, yet it performs well on real-world data from several datasets. DeFMO outperforms the state of the art and generates high-quality temporal super-resolution frames.

关键词： Training computer vision Shape Computational modeling Superresolution Cameras pattern recognition

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Discrete-continuous Action Space Policy Gradient-based Attention for Image-Text Matching

Discrete-continuous Action Space Policy Gradient-based Atten...

引用

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

作者： Yan, Shiyang Yu, Li Xie, Yuan Nanjing Univ Informat Sci & Technol Nanjing Peoples R China East China Normal Univ Shanghai Peoples R China

ISBN: (纸本)9781665445092

Image-text matching is an important multi-modal task with massive applications. It tries to match the image and the text with similar semantic information. Existing approaches do not explicitly transform the different modalities into a common space. Meanwhile, the attention mechanism which is widely used in image-text matching models does not have supervision. We propose a novel attention scheme which projects the image and text embedding into a common space and optimises the attention weights directly towards the evaluation metrics. The proposed attention scheme can be considered as a kind of supervised attention and requiring no additional annotations. It is trained via a novel Discrete-continuous action space policy gradient algorithm, which is more effective in modelling complex action space than previous continuous action space policy gradient. We evaluate the proposed methods on two widely-used benchmark datasets: Flickr3Ok and MS-COCO, out-performing the previous approaches by a large margin.

关键词： Measurement computer vision Annotations Computational modeling Semantics Transforms Benchmark testing

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SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation

SSTVOS: Sparse Spatiotemporal Transformers for Video Object ...

引用

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

作者： Duke, Brendan Ahmed, Abdalla Wolf, Christian Aarabi, Parham Taylor, Graham W. Univ Toronto Toronto ON Canada Univ Guelph Guelph ON Canada Univ Lyon INSA Lyon LIRIS Lyon France Modiface Inc Toronto ON Canada Vector Inst Toronto ON Canada

ISBN: (纸本)9781665445092

In this paper we introduce a Transformer-based approach to video object segmentation (VOS). To address compounding error and scalability issues of prior work, we propose a scalable, end-to-end method for VOS called Sparse Spatiotemporal Transformers (SST). SST extracts per-pixel representations for each object in a video using sparse attention over spatiotemporal features. Our attention-based formulation for VOS allows a model to learn to attend over a history of multiple frames and provides suitable inductive bias for performing correspondence-like computations necessary for solving motion segmentation. We demonstrate the effectiveness of attention-based over recurrent networks in the spatiotemporal domain. Our method achieves competitive results on YouTube-VOS and DAVIS 2017 with improved scalability and robustness to occlusions compared with the state of the art.

关键词： computer vision Codes Runtime Scalability Computational modeling Object segmentation Transformers

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Swift Parameter-free Attention Network for Efficient Super-Resolution

Swift Parameter-free Attention Network for Efficient Super-R...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Cheng Wan Hongyuan Yu Zhiqi Li Yihang Chen Yajun Zou Yuqing Liu Xuanwu Yin Kunlong Zuo Georgia Institute of Technology Xiaomi Inc

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Single Image Super-Resolution (SISR) is a crucial task in low-level computer vision, aiming to reconstruct high-resolution images from low-resolution counterparts. Conventional attention mechanisms have significantly improved SISR performance but often result in complex network structures and large number of parameters, leading to slow inference speed and large model size. To address this issue, we propose the Swift Parameter-free Attention Network (SPAN), a highly efficient SISR model that balances parameter count, inference speed, and image quality. SPAN employs a novel parameter-free attention mechanism, which leverages symmetric activation functions and residual connections to enhance high-contribution information and suppress redundant information. Our theoretical analysis demonstrates the effectiveness of this design in achieving the attention mechanism’s purpose. We evaluate SPAN on multiple benchmarks, showing that it outperforms existing efficient super-resolution models in terms of both image quality and inference speed, achieving a significant quality-speed trade-off. This makes SPAN highly suitable for real-world applications, particularly in resource-constrained scenarios. Notably, we won the first place both in the overall performance track and runtime track of the NTIRE 2024 efficient super-resolution challenge. Our code and models are made publicly available at https://***/hongyuanyu/span.

关键词： Image quality Performance evaluation computer vision Attention mechanisms Runtime Computational modeling Superresolution

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SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Events

SUTD-TrafficQA: A Question Answering Benchmark and an Effici...

引用

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

作者： Xu, Li Huang, He Liu, Jun Singapore Univ Technol & Design Informat Syst Technol & Design Singapore Singapore

ISBN: (纸本)9781665445092

Traffic event cognition and reasoning in videos is an important task that has a wide range of applications in intelligent transportation, assisted driving, and autonomous vehicles. In this paper, we create a novel dataset, SUTD-TrafficQA (Traffic Question Answering), which takes the form of video QA based on the collected 10,080 in-the-wild videos and annotated 62,535 QA pairs, for benchmarking the cognitive capability of causal inference and event understanding models in complex traffic scenarios. Specifically, we propose 6 challenging reasoning tasks corresponding to various traffic scenarios, so as to evaluate the reasoning capability over different kinds of complex yet practical traffic events. Moreover, we propose Eclipse, a novel Efficient glimpse network via dynamic inference, in order to achieve computation-efficient and reliable video reasoning. The experiments show that our method achieves superior performance while reducing the computation cost significantly. The project page: https://github. com/SUTDCV/ SUTD- TrafficQA.

关键词： Transportation Benchmark testing Knowledge discovery Cognition Computational efficiency pattern recognition Reliability

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Uncalibrated Neural Inverse Rendering for Photometric Stereo of General Surfaces

Uncalibrated Neural Inverse Rendering for Photometric Stereo...

引用

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

作者： Kaya, Berk Kumar, Suryansh Oliveira, Carlos Ferrari, Vittorio Van Gool, Luc Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Google Res Mountain View CA USA Katholieke Univ Leuven Leuven Belgium

ISBN: (纸本)9781665445092

This paper presents an uncalibrated deep neural network framework for the photometric stereo problem. For training models to solve the problem, existing neural network-based methods either require exact light directions or ground-truth surface normals of the object or both. However, in practice, it is challenging to procure both of this information precisely, which restricts the broader adoption of photometric stereo algorithms for vision application. To bypass this difficulty, we propose an uncalibrated neural inverse rendering approach to this problem. Our method first estimates the light directions from the input images and then optimizes an image reconstruction loss to calculate the surface normals, bidirectional reflectance distribution function value, and depth. Additionally, our formulation explicitly models the concave and convex parts of a complex surface to consider the effects of interreflections in the image formation process. Extensive evaluation of the proposed method on the challenging subjects generally shows comparable or better results than the supervised and classical approaches.

关键词： Geometry Training Reflectivity Surface reconstruction Three-dimensional displays Optimized production technology Rendering (computer graphics)

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Open World Compositional Zero-Shot Learning

Open World Compositional Zero-Shot Learning

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

作者： Mancini, Massimiliano Naeem, Muhammad Ferjad Xian, Yongqin Akata, Zeynep Univ Tubingen Tubingen Germany Tech Univ Munich Munich Germany MPI Informat Saarbrucken Germany MPI Intelligent Syst Saarbrucken Germany

ISBN: (纸本)9781665445092

Compositional Zero-Shot learning (CZSL) requires to recognize state-object compositions unseen during training. In this work, instead of assuming prior knowledge about the unseen compositions, we operate in the open world setting, where the search space includes a large number of unseen compositions some of which might be unfeasible. In this setting, we start from the cosine similarity between visual features and compositional embeddings. After estimating the feasibility score of each composition, we use these scores to either directly mask the output space or as a margin for the cosine similarity between visual features and compositional embeddings during training. Our experiments on two standard CZSL benchmarks show that all the methods suffer severe performance degradation when applied in the open world setting. While our simple CZSL model achieves state-of-the-art performances in the closed world scenario, our feasibility scores boost the performance of our approach in the open world setting, clearly outperforming the previous state of the art. Code is available at:https://***/ExplainableML/czsl.

关键词： Training Degradation Visualization computer vision Computational modeling Knowledge based systems Benchmark testing

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SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences

SceneGraphFusion: Incremental 3D Scene Graph Prediction from...

引用

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

作者： Wu, Shun-Cheng Wald, Johanna Tateno, Keisuke Navab, Nassir Tombari, Federico Tech Univ Munich Munich Germany Google Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Scene graphs are a compact and explicit representation successfully used in a variety of 2D scene understanding tasks. This work proposes a method to incrementally build up semantic scene graphs from a 3D environment given a sequence of RGB-D frames. To this end, we aggregate PointNet features from primitive scene components by means of a graph neural network. We also propose a novel attention mechanism well suited for partial and missing graph data present in such an incremental reconstruction scenario. Although our proposed method is designed to run on submaps of the scene, we show it also transfers to entire 3D scenes. Experiments show that our approach outperforms 3D scene graph prediction methods by a large margin and its accuracy is on par with other 3D semantic and panoptic segmentation methods while running at 35Hz.

关键词： computer vision Three-dimensional displays Simultaneous localization and mapping Fuses Semantics Prediction methods Graph neural networks

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Learning Tensor Low-Rank Prior for Hyperspectral Image Reconstruction

Learning Tensor Low-Rank Prior for Hyperspectral Image Recon...

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

作者： Zhang, Shipeng Wang, Lizhi Zhang, Lei Huang, Hua Xi An Jiao Tong Univ Xian Peoples R China Beijing Inst Technol Beijing Peoples R China Beijing Normal Univ Beijing Peoples R China

ISBN: (纸本)9781665445092

Snapshot hyperspectral imaging has been developed to capture the spectral information of dynamic scenes. In this paper, we propose a deep neural network by learning the tensor low-rank prior of hyperspectral images (HSI) in the feature domain to promote the reconstruction quality. Our method is inspired by the canonical-polyadic (CP) decomposition theory, where a low-rank tensor can be expressed as a weight summation of several rank-1 component tensors. Specifically, we first learn the tensor low-rank prior of the image features with two steps: (a) we generate rank-1 tensors with discriminative components to collect the contextual information from both spatial and channel dimensions of the image features;(b) we aggregate those rank-1 tensors into a low-rank tensor as a 3D attention map to exploit the global correlation and refine the image features. Then, we integrate the learned tensor low-rank prior into an iterative optimization algorithm to obtain an end-to-end HSI reconstruction. Experiments on both synthetic and real data demonstrate the superiority of our method.

关键词： Deep learning computer vision Tensors Three-dimensional displays Correlation Iterative algorithms pattern recognition

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NeuralFusion: Online Depth Fusion in Latent Space

NeuralFusion: Online Depth Fusion in Latent Space

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

作者： Weder, Silvan Schonberger, Johannes L. Pollefeys, Marc Oswald, Martin R. Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Microsoft Mixed Real & AI Zurich Lab Zurich Switzerland

ISBN: (纸本)9781665445092

We present a novel online depth map fusion approach that learns depth map aggregation in a latent feature space. While previous fusion methods use an explicit scene representation like signed distance functions (SDFs), we propose a learned feature representation for the fusion. The key idea is a separation between the scene representation used for the fusion and the output scene representation, via an additional translator network. Our neural network architecture consists of two main parts: a depth and feature fusion sub-network, which is followed by a translator sub-network to produce the final surface representation (e.g. TSDF) for visualization or other tasks. Our approach is an online process, handles high noise levels, and is particularly able to deal with gross outliers common for photometric stereo-based depth maps. Experiments on real and synthetic data demonstrate improved results compared to the state of the art, especially in challenging scenarios with large amounts of noise and outliers. The source code will be made available at https://***/weders/NeuralFusion.

关键词： Shape Superresolution Neural networks Real-time systems Encoding pattern recognition Task analysis

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