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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

共 3320 条记录，以下是2311-2320 订阅

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Behavioral Analysis of vision-and-Language Navigation Agents

Behavioral Analysis of Vision-and-Language Navigation Agents

引用

conference on computer vision and pattern recognition (CVPR)

作者： Zijiao Yang Arjun Majumdar Stefan Lee Oregon State University Georgia Institute of Technology

To be successful, vision-and-Language Navigation (VLN) agents must be able to ground instructions to actions based on their surroundings. In this work, we develop a methodology to study agent behavior on a skill-specific basis - examining how well existing agents ground instructions about stopping, turning, and moving towards specified objects or rooms. Our approach is based on generating skill-specific interventions and measuring changes in agent predictions. We present a detailed case study analyzing the behavior of a recent agent and then compare multiple agents in terms of skill-specific competency scores. This analysis suggests that biases from training have lasting effects on agent behavior and that existing models are able to ground simple referring expressions. Our comparisons between models show that skill-specific scores correlate with improvements in overall VLN task performance.

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TempSAL - Uncovering Temporal Information for Deep Saliency Prediction

TempSAL - Uncovering Temporal Information for Deep Saliency ...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Bahar Aydemir Ludo Hoffstetter Tong Zhang Mathieu Salzmann Sabine Süsstrunk School of Computer and Communication Sciences EPFL Switzerland

Deep saliency prediction algorithms complement the object recognition features, they typically rely on additional information such as scene context, semantic relationships, gaze direction, and object dissimilarity. However, none of these models consider the temporal nature of gaze shifts during image observation. We introduce a novel saliency prediction model that learns to output saliency maps in sequential time intervals by exploiting human temporal attention patterns. Our approach locally modulates the saliency predictions by combining the learned temporal maps. Our experiments show that our method outperforms the state-of-the-art models, including a multi-duration saliency model, on the SALICON benchmark and CodeCharts1k dataset. Our code is publicly available on GitHub 1 1 https://***/Tempsal/.

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Learning to Name Classes for vision and Language Models

Learning to Name Classes for Vision and Language Models

引用

conference on computer vision and pattern recognition (CVPR)

作者： Sarah Parisot Yongxin Yang Steven McDonagh Huawei Noah's Ark Lab

Large scale vision and language models can achieve impressive zero-shot recognition performance by mapping class specific text queries to image content. Two distinct challenges that remain however, are high sensitivity to the choice of handcrafted class names that define queries, and the difficulty of adaptation to new, smaller datasets. Towards addressing these problems, we propose to leverage available data to learn, for each class, an optimal word embedding as a function of the visual content. By learning new word embeddings on an otherwise frozen model, we are able to retain zero-shot capabilities for new classes, easily adapt models to new datasets, and adjust potentially erroneous, non-descriptive or ambiguous class names. We show that our solution can easily be integrated in image classification and object detection pipelines, yields significant performance gains in multiple scenarios and provides insights into model biases and labelling errors.

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Uni-NLX: Unifying Textual Explanations for vision and vision-Language Tasks

Uni-NLX: Unifying Textual Explanations for Vision and Vision...

引用

International conference on computer vision workshops (ICCV workshops)

作者： Fawaz Sammani Nikos Deligiannis ETRO Department Vrije Universiteit Brussel Brussels Belgium Imec Leuven Belgium

Natural Language Explanations (NLE) aim at supplementing the prediction of a model with human-friendly natural text. Existing NLE approaches involve training separate models for each downstream task. In this work, we propose Uni-NLX, a unified framework that consolidates all NLE tasks into a single and compact multi-task model using a unified training objective of text generation. Additionally, we introduce two new NLE datasets: 1) ImageNetX, a dataset of 144K samples for explaining ImageNet categories, and 2) VQA-ParaX, a dataset of 123K samples for explaining the task of Visual Question Answering (VQA). Both datasets are derived leveraging large language models (LLMs). By training on the 1M combined NLE samples, our single unified framework is capable of simultaneously performing seven NLE tasks including VQA, visual recognition and visual reasoning tasks with 7× fewer parameters, demonstrating comparable performance to the independent task-specific models in previous approaches, and in certain tasks even outperforming them. 1

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ViTs for SITS: vision Transformers for Satellite Image Time Series

ViTs for SITS: Vision Transformers for Satellite Image Time ...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Michail Tarasiou Erik Chavez Stefanos Zafeiriou Imperial College London

In this paper we introduce the Temporo-Spatial vision Transformer (TSViT), a fully-attentional model for general Satellite Image Time Series (SITS) processing based on the vision Transformer (ViT). TSViT splits a SITS record into non-overlapping patches in space and time which are tokenized and subsequently processed by a factorized temporo-spatial encoder. We argue, that in contrast to natural images, a temporal-then-spatial factorization is more intuitive for SITS processing and present experimental evidence for this claim. Additionally, we enhance the model's discriminative power by introducing two novel mechanisms for acquisition-time-specific temporal positional encodings and multiple learnable class tokens. The effect of all novel design choices is evaluated through an extensive ablation study. Our proposed architecture achieves state-of-the-art performance, surpassing previous approaches by a significant margin in three publicly available SITS semantic segmentation and classification datasets. All model, training and evaluation codes can be found at https://***/michaeltrs/DeepSatModels.

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Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge Survey

Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS...

引用

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

作者： Marcos V. Conde Zhijun Lei Wen Li Ioannis Katsavounidis Radu Timofte Min Yan Xin Liu Qian Wang Xiaoqian Ye Zhan Du Tiansen Zhang Zhiyuan Li Hao Wei Chenyang Ge Jiangtao Lv Long Sun Jinshan Pan Jiangxin Dong Jinhui Tang Menghan Zhou Yiqiang Yan Kihwan Yoon Ganzorig Gankhuyag Jae-Hyeon Lee Ui-Jin Choi Hyeon-Cheol Moon Tae-Hyun Jeong Yoonmo Yang Jae-Gon Kim Jinwoo Jeong Sunjei Kim Xintao Qiu Yuanbo Zhou Kongxian Wu Xinwei Dai Hui Tang Wei Deng Qingquan Gao Tong Tong Long Peng Jiaming Guo Xin Di Bohao Liao Zhibo Du Peize Xia Renjing Pei Yang Wang Yang Cao Zhengjun Zha Bingnan Han Hongyuan Yu Zhuoyuan Wu Cheng Wan Yuqing Liu Haodong Yu Jizhe Li Zhijuan Huang Yuan Huang Yajun Zou Xianyu Guan Qi Jia Heng Zhang Xuanwu Yin Kunlong Zuo Dongyang Zhang Tianle Liu Huaian Chen Yi Jin CAIDAS & IFI Computer Vision Lab. University of Wurzbürg Meta

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper introduces a novel benchmark for efficient image upscaling as part of the AIS 2024 Real-Time Image Super-Resolution (RTSR) Challenge, which aims to up scale compressed images from 540p to native 4K resolution (4x factor) in real-time on commercial GPUs. For this, we use a diverse test set containing diverse 4K images ranging from digital art to gaming and photography. The images are compressed using the modern AVIF codec, instead of JPEG. All the proposed methods improve PSNR fidelity over Lanczos interpolation, and process images under 30ms. Out of the 160 participants, 25 teams submitted their code. This survey considers only the most novel solutions models, making it the most comprehensive benchmark on real-time SR of compressed images using modern codecs.

关键词： Surveys Photography Image coding Codecs Superresolution Transform coding Benchmark testing

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Generalizable Implicit Neural Representations via Instance pattern Composers

Generalizable Implicit Neural Representations via Instance P...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Chiheon Kim Doyup Lee Saehoon Kim Minsu Cho Wook-Shin Han Kakao Brain POSTECH

Despite recent advances in implicit neural representations (INRs), it remains challenging for a coordinate-based multi-layer perceptron (MLP) of INRs to learn a common representation across data instances and generalize it for unseen instances. In this work, we introduce a simple yet effective framework for generalizable INRs that enables a coordinate-based MLP to represent complex data instances by modulating only a small set of weights in an early MLP layer as an instance pattern composer; the remaining MLP weights learn pattern composition rules for common representations across instances. Our generalizable INR frame-work is fully compatible with existing meta-learning and hypernetworks in learning to predict the modulated weight for unseen instances. Extensive experiments demonstrate that our method achieves high performance on a wide range of domains such as an audio, image, and 3D object, while the ablation study validates our weight modulation.

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NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results

NTIRE 2024 Challenge on Low Light Image Enhancement: Methods...

引用

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

作者： Xiaoning Liu Zongwei Wu Ao Li Florin-Alexandru Vasluianu Yulun Zhang Shuhang Gu Le Zhang Ce Zhu Radu Timofte Zhi Jin Hongjun Wu Chenxi Wang Haitao Ling Yuanhao Cai Hao Bian Yuxin Zheng Jing Lin Alan Yuille Ben Shao Jin Guo Tianli Liu Mohao Wu Yixu Feng Shuo Hou Haotian Lin Yu Zhu Peng Wu Wei Dong Jinqiu Sun Yanning Zhang Qingsen Yan Wenbin Zou Weipeng Yang Yunxiang Li Qiaomu Wei Tian Ye Sixiang Chen Zhao Zhang Suiyi Zhao Bo Wang Yan Luo Zhichao Zuo Mingshen Wang Junhu Wang Yanyan Wei Xiaopeng Sun Yu Gao Jiancheng Huang Hongming Chen Xiang Chen Hui Tang Yuanbin Chen Yuanbo Zhou Xinwei Dai Xintao Qiu Wei Deng Qinquan Gao Tong Tong Mingjia Li Jin Hu Xinyu He Xiaojie Guo Sabarinathan Sabarinathan K Uma A Sasithradevi B Sathya Bama S. Mohamed Mansoor Roomi V. Srivatsav Jinjuan Wang Long Sun Qiuying Chen Jiahong Shao Yizhi Zhang Marcos V. Conde Daniel Feijoo Juan C. Benito Alvaro García Jaeho Lee Seongwan Kim Sharif S M A Nodirkhuja Khujaev Roman Tsoy Ali Murtaza Uswah Khairuddin Ahmad ’Athif Mohd Faudzi Sampada Malagi Amogh Joshi Nikhil Akalwadi Chaitra Desai Ramesh Ashok Tabib Uma Mudenagudi Wenyi Lian Wenjing Lian Jagadeesh Kalyanshetti Vijayalaxmi Ashok Aralikatti Palani Yashaswini Nitish Upasi Dikshit Hegde Ujwala Patil Sujata C Xingzhuo Yan Wei Hao Minghan Fu Pooja Choksy Anjali Sarvaiya Kishor Upla Kiran Raja Hailong Yan Yunkai Zhang Baiang Li Jingyi Zhang Huan Zheng

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective network design or solution capable of generating brighter, clearer, and visually appealing results when dealing with a variety of conditions, including ultra-high resolution (4K and beyond), non-uniform illumination, backlighting, extreme darkness, and night scenes. A notable total of 428 participants registered for the challenge, with 22 teams ultimately making valid submissions. This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field.

关键词： computer vision Image resolution Reviews conferences Lighting pattern recognition Image enhancement

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Connecting vision and Language with Video Localized Narratives

Connecting Vision and Language with Video Localized Narrativ...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Paul Voigtlaender Soravit Changpinyo Jordi Pont-Tuset Radu Soricut Vittorio Ferrari Google Research

We propose Video Localized Narratives, a new form of multimodal video annotations connecting vision and language. In the original Localized Narratives [36], annotators speak and move their mouse simultaneously on an image, thus grounding each word with a mouse trace segment. However, this is challenging on a video. Our new protocol empowers annotators to tell the story of a video with Localized Narratives, capturing even complex events involving multiple actors interacting with each other and with several passive objects. We annotated 20k videos of the OVIS, UVO, and Oops datasets, totalling 1.7M words. Based on this data, we also construct new benchmarks for the video narrative grounding and video question answering tasks, and provide reference results from strong baseline models. Our annotations are available at https://***/video-localized-narratives/

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Visual recognition by Request

Visual Recognition by Request

引用

conference on computer vision and pattern recognition (CVPR)

作者： Chufeng Tang Lingxi Xie Xiaopeng Zhang Xiaolin Hu Qi Tian Department of Computer Science and Technology Tsinghua University Huawei Inc. Chinese Institute for Brain Research (CIBR) IDG/McGovern Institute for Brain Research THBI Tsinghua University

Humans have the ability of recognizing visual semantics in an unlimited granularity, but existing visual recognition algorithms cannot achieve this goal. In this paper, we establish a new paradigm named visual recognition by request (ViRReq 1 1 We recommend the readers to pronounce ViRReqas/virik/.) to bridge the gap. The key lies in decomposing visual recognition into atomic tasks named requests and leveraging a knowledge base, a hierarchical and text-based dictionary, to assist task definition. ViRReq allows for (i) learning complicated whole-part hierarchies from highly incomplete annotations and (ii) inserting new concepts with minimal efforts. We also establish a solid baseline by integrating language-driven recognition into recent semantic and instance segmentation methods, and demonstrate its flexible recognition ability on CPP and ADE20K, two datasets with hierarchical whole-part annotations.

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