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ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based Polishing

ConZIC: Controllable Zero-shot Image Captioning by Sampling-...

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

作者： Zeng, Zequn Zhang, Hao Lu, Ruiying Wang, Dongsheng Chen, Bo Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

ISBN: (纸本)9798350301298

Zero-shot capability has been considered as a new revolution of deep learning, letting machines work on tasks without curated training data. As a good start and the only existing outcome of zero-shot image captioning (IC), ZeroCap abandons supervised training and sequentially searches every word in the caption using the knowledge of large-scale pre-trained models. Though effective, its autoregressive generation and gradient-directed searching mechanism limit the diversity of captions and inference speed, respectively. Moreover, ZeroCap does not consider the controllability issue of zero-shot IC. To move forward, we propose a framework for Controllable Zero-shot IC, named ConZIC. The core of ConZIC is a novel sampling-based non-autoregressive language model named GibbsBERT, which can generate and continuously polish every word. Extensive quantitative and qualitative results demonstrate the superior performance of our proposed ConZIC for both zero-shot IC and controllable zero-shot IC. Especially, ConZIC achieves about 5x faster generation speed than ZeroCap, and about 1.5x higher diversity scores, with accurate generation given different control signals. Our code is available at https://***/joeyz0z/ConZIC.

关键词： language reasoning vision

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Part-based Pseudo Label Refinement for Unsupervised Person Re-identification

Part-based Pseudo Label Refinement for Unsupervised Person R...

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

作者： Cho, Yoonki Kim, Woo Jae Hong, Seunghoon Yoon, Sung-Eui Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Unsupervised person re-identification (re-ID) aims at learning discriminative representations for person retrieval from unlabeled data. Recent techniques accomplish this task by using pseudo-labels, but these labels are inherently noisy and deteriorate the accuracy. To overcome this problem, several pseudo-label refinement methods have been proposed, but they neglect the fine-grained local context essential for person re-ID. In this paper, we propose a novel Part-based Pseudo Label Refinement (PPLR) framework that reduces the label noise by employing the complementary relationship between global and part features. Specifically, we design a cross agreement score as the similarity of k-nearest neighbors between feature spaces to exploit the reliable complementary relationship. Based on the cross agreement, we refine pseudo-labels of global features by ensembling the predictions of part features, which collectively alleviate the noise in global feature clustering. We further refine pseudo-labels of part features by applying label smoothing according to the suitability of given labels for each part. Thanks to the reliable complementary information provided by the cross agreement score, our PPLR effectively reduces the influence of noisy labels and learns discriminative representations with rich local contexts. Extensive experimental results on Market-1501 and MSMT17 demonstrate the effectiveness of the proposed method over the state-of-the-art performance. The code is available at https://***/yoonkicho/PPLR.

关键词： Representation learning computer vision Smoothing methods Codes Machine vision Benchmark testing Reliability engineering

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2005 ieee computer Society conference on computer vision and pattern recognition - Copyright Page

2005 IEEE Computer Society Conference on Computer Vision and...

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conference on computer vision and pattern recognition (CVPR)

Copyright and Reprint Permissions: Abstracting is permitted with credit to the source. Libraries may photocopy beyond the limits of US copyright law, for private use of patrons, those articles in this volume that carry a code at the bottom of the first page, provided that the per-copy fee indicated in the code is paid through the Copyright Clearance Center. The papers in this book comprise the proceedings of the meeting mentioned on the cover and title page. They reflect the authors' opinions and, in the interests of timely dissemination, are published as presented and without change. Their inclusion in this publication does not necessarily constitute endorsement by the editors or the Institute of Electrical and Electronics Engineers, Inc.

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A Lightweight Network for High Dynamic Range Imaging

A Lightweight Network for High Dynamic Range Imaging

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

作者： Yan, Qingsen Zhang, Song Chen, Weiye Liu, Yuhang Zhang, Zhen Zhang, Yanning Shi, Javen Qinfeng Gong, Dong Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia Xidian Univ Sch Artificial Intelligence Xian Peoples R China Xidian Univ Guangzhou Inst Technol Xian Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Univ New South Wales Sch Comp Sci & Engn Sydney NSW Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Multi-frame high dynamic range (HDR) reconstruction methods try to expand the range of illuminance with differently exposed images. They suffer from ghost artifacts when camera jittering or object moving. Several methods can generate high-quality HDR images with high computational complexity, but the inference process is too slow. However, the network with small parameters will produce unsatisfactory results. To balance the quality and computational complexity, we propose a lightweight network for HDR imaging that has small parameters and fast speed. Specifically, following AHDRNet, we employ a spatial attention module to detect the misaligned regions to avoid ghost artifacts. Considering the missing details in over/under- exposure regions, we propose a dual attention module for selectively retaining information to force the fusion network to learn more details for degenerated regions. Furthermore, we employ an encoder-decoder structure with a lightweight block to achieve the fusion process. As a result, the high-quality content and features can be reconstructed after the attention module. Finally, we fuse high-resolution features and the encoder-decoder features into the HDR imaging results. Experimental results demonstrate that the proposed method performs favorably against the state-of-the-art methods, achieving a PSNR of 39.05 and a PSNR-mu of 37.27 with 156.12 GMAcs in NTIRE 2022 HDR Challenge (Track 2 Fidelity).

关键词： computer vision Fuses conferences Force Dynamic range Reconstruction algorithms Cameras

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Siamese Cascaded Region Proposal Networks for Real-Time Visual Tracking 32

Siamese Cascaded Region Proposal Networks for Real-Time Visu...

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32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Fan, Heng Ling, Haibin Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA

ISBN: (纸本)9781728132938

Recently, the regionproposal networks (RPN) have been combinedwith the Siamese networkfor tracking,and shown excellent accuracy with high efficiency. Nevertheless, previously proposedone-stage Siamese-RPNtrackersdegenerate in presence of similar distractorsand large scale variation. Addressing these issues, we propose a multi-stage tracking framework, Siamese Cascaded RPN (C-RPN), which consists of a sequence of RPNs cascadedfrom deep high-level to shallow low-level layers in a Siamese network. Comparedto previous solutions, C-RPN has severaladvantages: (1) Each RPN is trained using the outputs of RPN in the previous stage. Such process stimulates hardnegative sampling, resulting in more balanced training samples. Consequently, the RPNs are sequentially more discriminative in distinguishingdifficult background (i.e., similar distractors). (2) Multi-level features arefully leveragedthrough a novelfeature transferblock (FTB)for each RPNfurther improving the discriminabilityof C-RPN using both high-level semantic and low-level spatial information. (3) With multiple steps of regressions, C-RPN progressively refines the location and shape of the target in each RPN with adjusted anchor boxes in the previous stage, which makes localization more accurate. C-RPN is trained end-to-end with the multi-task lossfunction. In inference, C-RPN is deployed as it is, without any temporaladaption,for real-time tracking. In extensive experiments on OTB-2013, OTB-2015, VOT2016, VOT-2017, LaSOT and TrackingNet, C-RPN consistently achieves state-of-the-artresultsand runs in real-time.

关键词： Deep Learning Motion and Tracking vision Applications and Systems

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Temporal Alignment Networks for Long-term Video

Temporal Alignment Networks for Long-term Video

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

作者： Han, Tengda Xie, Weidi Zisserman, Andrew Univ Oxford Visual Geometry Grp Oxford England Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The objective of this paper is a temporal alignment network that ingests long term video sequences, and associated text sentences, in order to: (1) determine if a sentence is alignable with the video;and (2) if it is alignable, then determine its alignment. The challenge is to train such networks from large-scale datasets, such as HowTo100M, where the associated text sentences have significant noise, and are only weakly aligned when relevant. Apart from proposing the alignment network, we also make four contributions: (i) we describe a novel co-training method that enables to denoise and train on raw instructional videos without using manual annotation, despite the considerable noise;(ii) to benchmark the alignment performance, we manually curate a 10-hour subset of HowTo100M, totalling 80 videos, with sparse temporal descriptions. Our proposed model, trained on HowTo100M, outperforms strong baselines (CLIP, MIL-NCE) on this alignment dataset by a significant margin;(iii) we apply the trained model in the zero-shot settings to multiple downstream video understanding tasks and achieve state-of-the-art results, including text-video retrieval on YouCook2, and weakly supervised video action segmentation on Breakfast-Action. (iv) we use the automatically-aligned HowTo100M annotations for end-to-end finetuning of the backbone model, and obtain improved performance on downstream action recognition tasks.

关键词： computer vision Annotations Computational modeling Video sequences Noise reduction Manuals Benchmark testing

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OpenBias: Open-set Bias Detection in Text-to-Image Generative Models

OpenBias: Open-set Bias Detection in Text-to-Image Generativ...

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

作者： D'Inca, Moreno Peruzzo, Elia Mancini, Massimiliano Xu, Dejia Goel, Vidit Xu, Xinggian Wang, Zhangyang Shi, Humphrey Sebe, Nicu Univ Trento Trento Italy UT Austin Austin TX USA SHI Labs Georgia Tech & UIUC Atlanta GA USA Picsart AI Res PAIR Miami FL USA

ISBN: (纸本)9798350353006

Text-to-image generative models are becoming increasingly popular and accessible to the general public. As these models see large-scale deployments, it is necessary to deeply investigate their safety and fairness to not disseminate and perpetuate any kind of biases. However, existing works focus on detecting closed sets of biases defined a priori, limiting the studies to well-known concepts. In this paper, we tackle the challenge of open-set bias detection in text-to-image generative models presenting OpenBias, a new pipeline that identifies and quantifies the severity of biases agnostically, without access to any precompiled set. OpenBias has three stages. In the first phase, we leverage a Large Language Model (LLM) to propose biases given a set of captions. Secondly, the target generative model produces images using the same set of captions. Lastly, a vision Question Answering model recognizes the presence and extent of the previously proposed biases. We study the behavior of Stable Diffusion 1.5, 2, and XL emphasizing new biases, never investigated before. Via quantitative experiments, we demonstrate that OpenBias agrees with current closed-set bias detection methods and human judgement.

关键词： Bias computer vision Fairness Generative AI Text-to-Image

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In Defense of Pre-trained ImageNet Architectures for Real-time Semantic Segmentation of Road-driving Images 32

In Defense of Pre-trained ImageNet Architectures for Real-ti...

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32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Orsic, Marin Kreso, Ivan Bevandic, Petra Segvic, Sinisa Univ Zagreb Fac Elect Engn & Comp Zagreb Croatia

ISBN: (纸本)9781728132938

Recent success of semantic segmentation approaches on demanding road driving datasets has spurred interest in many related application fields. Many of these applications involve real-time prediction on mobile platforms such as cars, drones and various kinds of robots. Real-time setup is challenging due to extraordinary computational complexity involved. Many previous works address the challenge with custom lightweight architectures which decrease computational complexity by reducing depth, width and layer capacity with respect to general purpose architectures. We propose an alternative approach which achieves a significantly better performance across a wide range of computing budgets. First,we rely on a light-weight general purpose architecture as the main recognition engine. Then, we leverage light-weight up sampling with lateral connections as the most cost-effective solution to restore the prediction resolution. Finally, we propose to enlarge the receptive field by fusing shared features at multiple resolutions in a novel fashion. Experiments on several road driving datasets show a substantial advantage of the proposed approach, either with ImageNet pre-trained parameters or when we learn from scratch. Our Cityscapes test submission entitled SwiftNetRN-18 delivers 75.5% MIoU and achieves 39.9 Hz on 1024x2048 images on GTX1080Ti.

关键词： Categorization Deep Learning recognition: Detection Retrieval Robotics + Driving Scene Analysi vision Applications and Systems

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Instance Credibility Inference for Few-Shot Learning

Instance Credibility Inference for Few-Shot Learning

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

作者： Wang, Yikai Xu, Chengming Liu, Chen Zhang, Li Fu, Yanwei Fudan Univ Sch Data Sci Shanghai Peoples R China Univ Oxford Dept Engn Sci Oxford England Fudan Univ MOE Frontiers Ctr Brain Sci Shanghai Peoples R China Fudan Univ Shanghai Key Lab Intelligent Informat Proc Shanghai Peoples R China

ISBN: (纸本)9781728171685

Few-shot learning (FSL) aims to recognize new objects with extremely limited training data for each category. Previous efforts are made by either leveraging meta-learning paradigm or novel principles in data augmentation to alleviate this extremely data-scarce problem. In contrast, this paper presents a simple statistical approach, dubbed Instance Credibility Inference (ICI) to exploit the distribution support of unlabeled instances for few-shot learning. Specifically, we first train a linear classifier with the labeled few-shot examples and use it to infer the pseudolabels for the unlabeled data. To measure the credibility of each pseudo-labeled instance, we then propose to solve another linear regression hypothesis by increasing the sparsity of the incidental parameters and rank the pseudo-labeled instances with their sparsity degree. We select the most trustworthy pseudo-labeled instances alongside the labeled examples to re-train the linear classifier. This process is iterated until all the unlabeled samples are included in the expanded training set, i.e. the pseudo-label is converged for unlabeled data pool. Extensive experiments under two fewshot settings show that our simple approach can establish new state-of-the-arts on four widely used few-shot learning benchmark datasets including miniImageNet, tieredImageNet, CIFAR-FS, and CUB. Our code is available at: https://***/Yikai-Wang/ICI-FSL

关键词： computer vision

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Neural Transformation Fields for Arbitrary-Styled Font Generation

Neural Transformation Fields for Arbitrary-Styled Font Gener...

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

作者： Fu, Bin He, Junjun Wang, Jianjun Qiao, Yu Chinese Acad Sci Shenzhen Inst Adv Technol ShenZhen Key Lab Comp Vis & Pattern Recognit Beijing Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

Few-shot font generation (FFG), aiming at generating font images with a few samples, is an emerging topic in recent years due to the academic and commercial values. Typically, the FFG approaches follow the style-content disentanglement paradigm, which transfers the target font styles to characters by combining the content representations of source characters and the style codes of reference samples. Most existing methods attempt to increase font generation ability via exploring powerful style representations, which may be a sub-optimal solution for the FFG task due to the lack of modeling spatial transformation in transferring font styles. In this paper, we model font generation as a continuous transformation process from the source character image to the target font image via the creation and dissipation of font pixels, and embed the corresponding transformations into a neural transformation field. With the estimated transformation path, the neural transformation field generates a set of intermediate transformation results via the sampling process, and a font rendering formula is developed to accumulate them into the target font image. Extensive experiments show that our method achieves state-of-the-art performance on few-shot font generation task, which demonstrates the effectiveness of our proposed model. Our implementation is available at: https://***/fubinfb/NTF.

关键词： Image and video synthesis and generation

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