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共 11890 条记录，以下是721-730 订阅

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JAWS: Just A Wild Shot for Cinematic Transfer in Neural Radiance Fields

JAWS: Just A Wild Shot for Cinematic Transfer in Neural Radi...

引用

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

作者： Wang, Xi Courant, Robin Shi, Jinglei Marchand, Eric Christie, Marc Univ Rennes CNRS INRIA IRISA Rennes France Ecole Polytech IP Paris LIX Paris France Nankai Univ VCIP CS Tianjin Peoples R China

ISBN: (纸本)9798350301298

This paper presents JAWS, an optimization-driven approach that achieves the robust transfer of visual cinematic features from a reference in-the-wild video clip to a newly generated clip. To this end, we rely on an implicit-neural-representation (INR) in a way to compute a clip that shares the same cinematic features as the reference clip. We propose a general formulation of a camera optimization problem in an INR that computes extrinsic and intrinsic camera parameters as well as timing. By leveraging the differentiability of neural representations, we can back-propagate our designed cinematic losses measured on proxy estimators through a NeRF network to the proposed cinematic parameters directly. We also introduce specific enhancements such as guidance maps to improve the overall quality and efficiency. Results display the capacity of our system to replicate well known camera sequences from movies, adapting the framing, camera parameters and timing of the generated video clip to maximize the similarity with the reference clip.

关键词： vision + graphics

来源：评论

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Optimal Proposal Learning for Deployable End-to-End Pedestrian Detection

Optimal Proposal Learning for Deployable End-to-End Pedestri...

引用

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

作者： Song, Xiaolin Chen, Binghui Li, Pengyu He, Jun-Yan Wang, Biao Geng, Yifeng Xie, Xuansong Zhang, Honggang Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9798350301298

End-to-end pedestrian detection focuses on training a pedestrian detection model via discarding the Non-Maximum Suppression (NMS) post-processing. Though a few methods have been explored, most of them still suffer from longer training time and more complex deployment, which cannot be deployed in the actual industrial applications. In this paper, we intend to bridge this gap and propose an Optimal Proposal Learning (OPL) framework for deployable end-to-end pedestrian detection. Specifically, we achieve this goal by using CNN-based light detector and introducing two novel modules, including a Coarse-to-Fine (C2F) learning strategy for proposing precise positive proposals for the Ground-Truth (GT) instances by reducing the ambiguity of sample assignment/output in training/testing respectively, and a Completed Proposal Network (CPN) for producing extra information compensation to further recall the hard pedestrian samples. Extensive experiments are conducted on CrowdHuman, TJU-Ped and Caltech, and the results show that our proposed OPL method significantly outperforms the competing methods.

关键词： detection recognition: Categorization retrieval

来源：评论

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Benchmarking Self-Supervised Learning on Diverse Pathology Datasets

Benchmarking Self-Supervised Learning on Diverse Pathology D...

引用

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

作者： Kang, Mingu Song, Heon Park, Seonwook Yoo, Donggeun Pereira, Sergio Lunit Inc Seoul South Korea

ISBN: (纸本)9798350301298

Computational pathology can lead to saving human lives, but models are annotation hungry and pathology images are notoriously expensive to annotate. Self-supervised learning (SSL) has shown to be an effective method for utilizing unlabeled data, and its application to pathology could greatly benefit its downstream tasks. Yet, there are no principled studies that compare SSL methods and discuss how to adapt them for pathology. To address this need, we execute the largest-scale study of SSL pre-training on pathology image data, to date. Our study is conducted using 4 representative SSL methods on diverse downstream tasks. We establish that large-scale domain-aligned pre-training in pathology consistently out-performs ImageNet pre-training in standard SSL settings such as linear and fine-tuning evaluations, as well as in low-label regimes. Moreover, we propose a set of domain-specific techniques that we experimentally show leads to a performance boost. Lastly, for the first time, we apply SSL to the challenging task of nuclei instance segmentation and show large and consistent performance improvements. We release the pre-trained model weights(1).

关键词： cell microscopy Medical and biological vision

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LightPainter: Interactive Portrait Relighting with Freehand Scribble

LightPainter: Interactive Portrait Relighting with Freehand ...

引用

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

作者： Mei, Yiqun Zhang, He Zhang, Xuaner Zhang, Jianming Shu, Zhixin Wang, Yilin Wei, Zijun Yan, Shi Jung, HyunJoon Patel, Vishal M. Johns Hopkins Univ Baltimore MD 21218 USA Adobe Inc San Jose CA USA

ISBN: (纸本)9798350301298

Recent portrait relighting methods have achieved realistic results of portrait lighting effects given a desired lighting representation such as an environment map. However, these methods are not intuitive for user interaction and lack precise lighting control. We introduce LightPainter, a scribble-based relighting system that allows users to interactively manipulate portrait lighting effect with ease. This is achieved by two conditional neural networks, a delighting module that recovers geometry and albedo optionally conditioned on skin tone, and a scribble-based module for relighting. To train the relighting module, we propose a novel scribble simulation procedure to mimic real user scribbles, which allows our pipeline to be trained without any human annotations. We demonstrate high-quality and flexible portrait lighting editing capability with both quantitative and qualitative experiments. User study comparisons with commercial lighting editing tools also demonstrate consistent user preference for our method.

关键词： vision + graphics

来源：评论

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ABCD : Arbitrary Bitwise Coefficient for De-quantization

ABCD : Arbitrary Bitwise Coefficient for De-quantization

引用

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

作者： Han, Woo Kyoung Lee, Byeonghun Park, Sang Hyun Jin, Kyong Hwan Daegu Gyeongbuk Inst Sci & Technol DGIST Daegu South Korea

ISBN: (纸本)9798350301298

Modern displays and contents support more than 8bits image and video. However, bit-starving situations such as compression codecs make low bit-depth (LBD) images (<8bits), occurring banding and blurry artifacts. Previous bit depth expansion (BDE) methods still produce unsatisfactory high bit-depth (HBD) images. To this end, we propose an implicit neural function with a bit query to recover de-quantized images from arbitrarily quantized inputs. We develop a phasor estimator to exploit the information of the nearest pixels. Our method shows superior performance against prior BDE methods on natural and animation images. We also demonstrate our model on YouTube UGC datasets for de-banding. Our source code is available at https://***/WooKyoungHan/ABCD

关键词： Low-level vision

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AVFormer: Injecting vision into Frozen Speech Models for Zero-Shot AV-ASR

AVFormer: Injecting Vision into Frozen Speech Models for Zer...

引用

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

作者： Seo, Paul Hongsuck Nagrani, Arsha Schmid, Cordelia Google Res Mountain View CA 94043 USA

ISBN: (纸本)9798350301298

Audiovisual automatic speech recognition (AV-ASR) aims to improve the robustness of a speech recognition system by incorporating visual information. Training fully supervised multimodal models for this task from scratch, however is limited by the need for large labelled audiovisual datasets (in each downstream domain of interest). We present AVFormer, a simple method for augmenting audio-only models with visual information, at the same time performing lightweight domain adaptation. We do this by (i) injecting visual embeddings into a frozen ASR model using lightweight trainable adaptors. We show that these can be trained on a small amount of weakly labelled video data with minimum additional training time and parameters. (ii) We also introduce a simple curriculum scheme during training which we show is crucial to enable the model to jointly process audio and visual information effectively;and finally (iii) we show that our model achieves state of the art zero-shot results on three different AV-ASR benchmarks (How2, VisSpeech and Ego4D), while also crucially preserving decent performance on traditional audio-only speech recognition benchmarks (LibriSpeech). Qualitative results show that our model effectively leverages visual information for robust speech recognition.

关键词： Multi-modal learning

来源：评论

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Adaptive Sparse Pairwise Loss for Object Re-Identification

Adaptive Sparse Pairwise Loss for Object Re-Identification

引用

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

作者： Zhou, Xiao Zhong, Yujie Cheng, Zhen Liang, Fan Ma, Lin Tsinghua Univ BNRist Dept Automat Beijing 100084 Peoples R China Meituan Inc Beijing Peoples R China

ISBN: (纸本)9798350301298

Object re-identification (ReID) aims to find instances with the same identity as the given probe from a large gallery. Pairwise losses play an important role in training a strong ReID network. Existing pairwise losses densely exploit each instance as an anchor and sample its triplets in a mini-batch. This dense sampling mechanism inevitably introduces positive pairs that share few visual similarities, which can be harmful to the training. To address this problem, we propose a novel loss paradigm termed Sparse Pairwise (SP) loss that only leverages few appropriate pairs for each class in a mini-batch, and empirically demonstrate that it is sufficient for the ReID tasks. Based on the proposed loss framework, we propose an adaptive positive mining strategy that can dynamically adapt to diverse intra-class variations. Extensive experiments show that SP loss and its adaptive variant AdaSP loss outperform other pairwise losses, and achieve state-of-the-art performance across several ReID benchmarks. Code is available at https://***/Astaxanthin/AdaSP.

关键词： detection recognition: Categorization retrieval

来源：评论

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Ranking Regularization for Critical Rare Classes: Minimizing False Positives at a High True Positive Rate

Ranking Regularization for Critical Rare Classes: Minimizing...

引用

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

作者： Mohammadi, Kiarash Zhao, He Zhai, Mengyao Tung, Frederick Borealis AI Toronto ON Canada Univ Montreal Mila Montreal PQ Canada

ISBN: (纸本)9798350301298

In many real-world settings, the critical class is rare and a missed detection carries a disproportionately high cost. For example, tumors are rare and a false negative diagnosis could have severe consequences on treatment outcomes;fraudulent banking transactions are rare and an undetected occurrence could result in significant losses or legal penalties. In such contexts, systems are often operated at a high true positive rate, which may require tolerating high false positives. In this paper, we present a novel approach to address the challenge of minimizing false positives for systems that need to operate at a high true positive rate. We propose a ranking-based regularization (RankReg) approach that is easy to implement, and show empirically that it not only effectively reduces false positives, but also complements conventional imbalanced learning losses. With this novel technique in hand, we conduct a series of experiments on three broadly explored datasets (CIFAR-10&100 and Melanoma) and show that our approach lifts the previous state-of-the-art performance by notable margins.

关键词： detection recognition: Categorization retrieval

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FFCV: Accelerating Training by Removing Data Bottlenecks

FFCV: Accelerating Training by Removing Data Bottlenecks

引用

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

作者： Leclerc, Guillaume Ilyas, Andrew Engstrom, Logan Park, Sung Min Salman, Hadi Madry, Aleksander MIT Cambridge MA 02139 USA

ISBN: (纸本)9798350301298

We present FFCV, a library for easy and fast machine learning model training. FFCV speeds up model training by eliminating (often subtle) data bottlenecks from the training process. In particular, we combine techniques such as an efficient file storage format, caching, data pre-loading, asynchronous data transfer, and just-in-time compilation to (a) make data loading and transfer significantly more efficient, ensuring that GPUs can reach full utilization;and (b) offload as much data processing as possible to the CPU asynchronously, freeing GPU cycles for training. Using FFCV, we train ResNet-18 and ResNet-50 on the ImageNet dataset with a state-of-the-art tradeoff between accuracy and training time. For example, across the range of ResNet-50 models we test, we obtain the same accuracy as the best baselines in half the time. We demonstrate FFCV's performance, ease-of-use, extensibility, and ability to adapt to resource constraints through several case studies. Detailed installation instructions, documentation, and Slack support channel are available at https://***/.

关键词： Efficient and scalable vision

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Towards Professional Level Crowd Annotation of Expert Domain Data

Towards Professional Level Crowd Annotation of Expert Domain...

引用

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

作者： Wang, Pei Vasconcelos, Nuno Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (纸本)9798350301298

Image recognition on expert domains is usually fine-grained and requires expert labeling, which is costly. This limits dataset sizes and the accuracy of learning systems. To address this challenge, we consider annotating expert data with crowdsourcing. This is denoted as PrOfeSsional lEvel cRowd (POSER) annotation. A new approach, based on semi-supervised learning (SSL) and denoted as SSL with human filtering (SSL-HF) is proposed. It is a human-in-the-loop SSL method, where crowd-source workers act as filters of pseudo-labels, replacing the unreliable confidence thresholding used by state-of-the-art SSL methods. To enable annotation by non-experts, classes are specified implicitly, via positive and negative sets of examples and augmented with deliberative explanations, which highlight regions of class ambiguity. In this way, SSL-HF leverages the strong low-shot learning and confidence estimation ability of humans to create an intuitive but effective labeling experience. Experiments show that SSL-HF significantly outperforms various alternative approaches in several benchmarks.

关键词： Efficient and scalable vision

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