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EPI-Guided Cost Construction Network for Light Field Disparity Estimation

EPI-Guided Cost Construction Network for Light Field Dispari...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Wang, Tun Chen, Rongshan Cong, Ruixuan Yang, Da Cui, Zhenglong Li, Fangping Sheng, Hao Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang Hangzhou Innovation Institute Yuhang Xixi Octagon City Hangzhou310023 China Macao Polytechnic University Faculty of Applied Sciences 999078 China

ISBN: (纸本)9798350302493

Recent learning-based light field (LF) disparity estimation methods construct cost volume by sequentially shifting each sub-aperture image (SAI) with a series of predefined offsets. They only use the visual information of SAIs and lose the geometry of LF. In this paper, we design a simple network that can cleverly integrate EPI features with cost volume to estimate the disparity. Firstly, we propose an efficient EPI extraction module to use abundant line characteristics. Secondly, we offer an EPI-Cost volume construction module that can create volume guided by the EPI line and the color consistency of images. Finally, after completing it, we adopt an inter-volume fusion module to considerably correlate the validity of EPI lines in both directions. Experimental results show the proposed method achieves state-of-the-art performance in the quantitative and qualitative evaluation of the UrbanLF-Syn dataset. © 2023 ieee.

关键词： Cost estimating

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VideoSAGE: Video Summarization with Graph Representation Learning

VideoSAGE: Video Summarization with Graph Representation Lea...

引用

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

作者： Jose M. Rojas Chaves Subarna Tripathi Intel Corporation Intel Labs

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We propose a graph-based representation learning framework for video summarization. First, we convert an input video to a graph where nodes correspond to each of the video frames. Then, we impose sparsity on the graph by connecting only those pairs of nodes that are within a specified temporal distance. We then formulate the video summarization task as a binary node classification problem, precisely classifying video frames whether they should belong to the output summary video. A graph constructed this way aims to capture long-range interactions among video frames, and the sparsity ensures the model trains without hitting the memory and compute bottleneck. Experiments on two datasets(SumMe and TVSum) demonstrate the effectiveness of the proposed nimble model compared to existing state-of-the-art summarization approaches while being one order of magnitude more efficient in compute time and memory.

关键词： Representation learning computer vision Computational modeling conferences Memory management pattern recognition

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Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection

Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Ob...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Yu, Kaicheng Tao, Tang Xie, Hongwei Lin, Zhiwei Liang, Tingting Wang, Bing Chen, Peng Hao, Dayang Wang, Yongtao Liang, Xiaodan Sun Yat-sen University Shenzhen Campus China Alibaba Group Autonomous Driving Lab China Peking University Wangxuan Institute of Computer Technology China

ISBN: (纸本)9798350302493

To achieve autonomous driving, developing 3D detection fusion methods, which aim to fuse the camera and LiDAR information, has draw great research interest in recent years. As a common practice, people rely on large-scale datasets to fairly compare the performance of different methods. While these datasets have been carefully cleaned to ideally minimize any potential noise, we observe that they cannot truly reflect the data seen on a real autonomous vehicle, whose data tends to be noisy due to various reasons. This hinders the ability to simply estimate the robust performance under realistic noisy settings. To this end, we collect a series of real-world cases with noisy data distribution, and systematically formulate a robustness benchmark toolkit. It can simulate these cases on any clean dataset, which has the camera and LiDAR input modality. We showcase the effectiveness of our toolkit by establishing two novel robustness benchmarks on widely-adopted datasets, nuScenes and Waymo, then holistically evaluate the state-of-the-art fusion methods. We discover that: i) most fusion methods, when solely developed on these data, tend to fail inevitably when there is a disruption to the LiDAR input;ii) the improvement of the camera input is significantly inferior to the LiDAR one. We publish the robust fusion dataset, benchmark, detailed documents and instructions on https://***/robust-benchmark-website. © 2023 ieee.

关键词： Cameras

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Dynamic Distinction Learning: Adaptive Pseudo Anomalies for Video Anomaly Detection

Dynamic Distinction Learning: Adaptive Pseudo Anomalies for ...

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

作者： Demetris Lappas Vasileios Argyriou Dimitrios Makris School of Computer Science and Mathematics Kingston University London UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We introduce Dynamic Distinction Learning (DDL) for Video Anomaly Detection, a novel video anomaly detection methodology that combines pseudo-anomalies, dynamic anomaly weighting, and a distinction loss function to improve detection accuracy. By training on pseudo-anomalies, our approach adapts to the variability of normal and anomalous behaviors without fixed anomaly thresholds. Our model showcases superior performance on the Ped2, Avenue and ShanghaiTech datasets, where individual models are tailored for each scene. These achievements highlight DDL’s effectiveness in advancing anomaly detection, offering a scalable and adaptable solution for video surveillance challenges. Our work can be found on: https://***/demetrislappas/***

关键词： Training Adaptation models Technological innovation computer vision Accuracy conferences Refining

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NTIRE 2024 Challenge on Stereo Image Super-Resolution: Methods and Results

NTIRE 2024 Challenge on Stereo Image Super-Resolution: Metho...

引用

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

作者： Longguang Wang Yulan Guo Juncheng Li Hongda Liu Yang Zhao Yingqian Wang Zhi Jin Shuhang Gu Radu Timofte

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper summarizes the 3rd NTIRE challenge on stereo image super-resolution (SR) with a focus on new solutions and results. The task of this challenge is to super-resolve a low-resolution stereo image pair to a high-resolution one with a magnification factor of × 4 under a limited computational budget. Compared with single image SR, the major challenge of this challenge lies in how to exploit additional information in another viewpoint and how to maintain stereo consistency in the results. This challenge has 2 tracks, including one track on bicubic degradation and one track on real degradations. In total, 108 and 70 participants were successfully registered for each track, respectively. In the test phase, 14 and 13 teams successfully submitted valid results with PSNR (RGB) scores better than the baseline. This challenge establishes a new benchmark for stereo image SR.

关键词： Degradation computer vision conferences Superresolution Benchmark testing pattern recognition

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SuperLoRA: Parameter-Efficient Unified Adaptation for Large vision Models

SuperLoRA: Parameter-Efficient Unified Adaptation for Large ...

引用

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

作者： Xiangyu Chen Jing Liu Ye Wang Pu Perry Wang Matthew Brand Guanghui Wang Toshiaki Koike-Akino University of Kansas Lawrence KS USA Mitsubishi Electric Research Laboratories (MERL) Cambridge MA USA Toronto Metropolitan University Toronto Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Low-rank adaptation (LoRA) and its variants are widely employed in fine-tuning large models, including large language models for natural language processing and diffusion models for computer vision. This paper proposes a generalized framework called SuperLoRA that unifies and extends different LoRA variants, which can be realized under different hyper-parameter settings. Introducing new options with grouping, folding, shuffling, projection, and tensor decomposition, SuperLoRA offers high flexibility and demonstrates superior performance, with up to 10-fold gain in parameter efficiency for transfer learning tasks.

关键词： computer vision Adaptation models Tensors Computational modeling Large language models conferences Transfer learning

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What is Point Supervision Worth in Video Instance Segmentation?

What is Point Supervision Worth in Video Instance Segmentati...

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

作者： Shuaiyi Huang De-An Huang Zhiding Yu Shiyi Lan Subhashree Radhakrishnan Jose M. Alvarez Abhinav Shrivastava Anima Anandkumar University of Maryland College Park NVIDIA Caltech

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Video instance segmentation (VIS) is a challenging vision task that aims to detect, segment, and track objects in videos. Conventional VIS methods rely on densely-annotated object masks which are expensive. We reduce the human annotations to only one point for each object in a video frame during training, and obtain high-quality mask predictions close to fully supervised models. Our proposed training method consists of a class-agnostic proposal generation module to provide rich negative samples and a spatio-temporal point-based matcher to match the object queries with the provided point annotations. Comprehensive experiments on three VIS benchmarks demonstrate competitive performance of the proposed framework, nearly matching fully supervised methods.

关键词： Instance segmentation Training computer vision Annotations Shape Object segmentation Object detection

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An End-to-End vision Transformer Approach for Image Copy Detection

An End-to-End Vision Transformer Approach for Image Copy Det...

引用

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

作者： Jiahe Steven Lee Wynne Hsu Mong Li Lee Institute of Data Science National University of Singapore Centre for Trusted Internet and Community National University of Singapore

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Image copy detection is one of the pivotal tools to safeguard online information integrity. The challenge lies in determining whether a query image is an edited copy, which necessitates the identification of candidate source images through a retrieval process. The process requires discriminative features comprising of both global descriptors that are designed to be augmentation-invariant and local descriptors that can capture salient foreground objects to assess whether a query image is an edited copy of some source reference image. This work describes an end-to-end solution that leverage a vision Transformer model to learn such discriminative features and perform implicit matching between the query image and the reference image. Experimental results on two benchmark datasets demonstrate that the proposed solution outperforms state-of-the-art methods. Case studies illustrate the effectiveness of our approach in matching reference images from which the query images have been copy-edited.

关键词： computer vision conferences Benchmark testing Transformers Feature extraction pattern recognition Fake news

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Exposing and Mitigating Spurious Correlations for Cross-Modal Retrieval

Exposing and Mitigating Spurious Correlations for Cross-Moda...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Kim, Jae Myung Sophia Koepke, A. Schmid, Cordelia Akata, Zeynep University of Tübingen Germany PSL Research University Inria Ecole Normale Supérieure CNRS France MPI for Intelligent Systems Germany

ISBN: (纸本)9798350302493

Cross-modal retrieval methods are the preferred tool to search databases for the text that best matches a query image and vice versa. However, image-text retrieval models commonly learn to memorize spurious correlations in the training data, such as frequent object co-occurrence, instead of looking at the actual underlying reasons for the prediction in the image. For image-text retrieval, this manifests in retrieved sentences that mention objects that are not present in the query image. In this work, we introduce ODmAP@k, an object decorrelation metric that measures a model's robustness to spurious correlations in the training data. We use automatic image and text manipulations to control the presence of such object correlations in designated test data. Additionally, our data synthesis technique is used to tackle model biases due to spurious correlations of semantically unrelated objects in the training data. We apply our proposed pipeline, which involves the finetuning of image-text retrieval frameworks on carefully designed synthetic data, to three state-of-the-art models for image-text retrieval. This results in significant improvements for all three models, both in terms of the standard retrieval performance and in terms of our object decorrelation metric. The code is available at https://***/ExplainableML/Spurious-CM-Retrieval. © 2023 ieee.

关键词： computer vision

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NTIRE 2022 Spectral Demosaicing Challenge and Data Set

NTIRE 2022 Spectral Demosaicing Challenge and Data Set

引用

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

作者： Arad, Boaz Timofte, Radu Yahel, Rony Morag, Nimrod Bernat, Amir Wu, Yaqi Wu, Xun Fan, Zhihao Xia, Chenjie Zhang, Feng Liu, Shuai Li, Yongqiang Feng, Chaoyu Lei, Lei Zhang, Mingwei Feng, Kai Zhang, Xun Yao, Jiaxin Zhao, Yongqiang Ma, Suina He, Fan Dong, Yangyang Yu, Shufang Qiu, Difa Liu, Jinhui Bi, Mengzhao Song, Beibei Sun, WenFang Zheng, Jiesi Zhao, Bowen Cao, Yanpeng Yang, Jiangxin Cao, Yanlong Kong, Xiangyu Yu, Jingbo Xue, Yuanyang Xie, Zheng Oddity Tech Ltd New York NY USA Voyage81 Ltd New York NY USA Univ Wurzburg Ctr Artificial Intelligence & Data Sci Wurzburg Germany Swiss Fed Inst Technol Computat Vision Lab Zurich Switzerland Acad Coll Tel Aviv Yaffo Tel Aviv Israel Tel Aviv Univ Tel Aviv Israel Harbin Inst Technol Harbin 150001 Peoples R China Tsinghua Univ Beijing 100084 Peoples R China Univ Shanghai Sci & Technol Shanghai 200093 Peoples R China Zhejiang Univ Hangzhou 310027 Peoples R China Xiaomi Inc Beijing Peoples R China Northwestern Polytech Univ Xian Peoples R China Changan Univ Xian Peoples R China Xidian Univ Xian Peoples R China Zhejiang Univ Sch Mech Engn State Key Lab Fluid Power & Mech Syst Hangzhou 310027 Peoples R China Zhejiang Univ Sch Mech Engn Key Lab Adv Mfg Technol Zhejiang Prov Hangzhou 310027 Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper presents the first challenge on demosaicing of natural spectral images for snapshot hyperspectral imaging systems (HIS) which utilize a multi-spectral filer array (MSFA), i.e., the recovery of whole-scene hyperspectral information from spatially sub-sampled hyperspectral information. This challenge expands the "ARAD_1K" data set to a first-of-its-kind large-scale data set for multi-spectral filter array demosaicing of natural scenes containing 1,000 images. Challenge participants were required to recover hyperspectral information from synthetically generated MSFA images simulating capture by a known calibrated snapshot mosaic hyperspectral camera. The challenge was attended by 157 teams, with 29 teams competing in the final testing phase, 7 of which provided detailed descriptions of their methodology which are included in this report. The performance of these submissions is reviewed and provided here as a gauge for the current state-of-the-art in multi-spectral filter array demosaicing of natural images.

关键词： Performance evaluation Training Runtime Neural networks Cameras Real-time systems pattern recognition

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