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Long-term Video Frame Interpolation via Feature Propagation

Long-term Video Frame Interpolation via Feature Propagation

引用

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

作者： Argaw, Dawit Mureja Kweon, In So Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Video frame interpolation (VFI) works generally predict intermediate frame(s) by first estimating the motion between inputs and then warping the inputs to the target time with the estimated motion. This approach, however, is not optimal when the temporal distance between the input sequence increases as existing motion estimation modules cannot effectively handle large motions. Hence, VFI works perform well for small frame gaps and perform poorly as the frame gap increases. In this work, we propose a novel framework to address this problem. We argue that when there is a large gap between inputs, instead of estimating imprecise motion that will eventually lead to inaccurate interpolation, we can safely propagate from one side of the input up to a reliable time frame using the other input as a reference. Then, the rest of the intermediate frames can be interpolated using standard approaches as the temporal gap is now narrowed. To this end, we propose a propagation network (PNet) by extending the classic feature-level forecasting with a novel motion-to-feature approach. To be thorough, we adopt a simple interpolation model along with PNet as our MI model and design a simple procedure to train the MI model in an end-to-end manner. Experimental results on several benchmark datasets confirm the effectiveness of our method for long-term VFI compared to state-of-the-art approaches.

关键词： Interpolation computer vision Tracking Motion estimation computer network reliability pattern recognition Reliability

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Grounded Question-Answering in Long Egocentric Videos

Grounded Question-Answering in Long Egocentric Videos

引用

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

作者： Di, Shangzhe Xie, Weidi Shanghai Jiao Tong Univ CMIC Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350353006

Existing approaches to video understanding, mainly designed for short videos from a third-person perspective, are limited in their applicability in certain fields, such as robotics. In this paper, we delve into open-ended question-answering (QA) in long, egocentric videos, which allows individuals or robots to inquire about their own past visual experiences. This task presents unique challenges, including where did I put lettuce? Choices: (A) pantry (B) refrigerator (C) cupboard (D) draw 20-50s Answer: in the fridge / (B) refrigerator the complexity of temporally grounding queries within extensive video content, the high resource demands for precise data annotation, and the inherent difficulty of evaluating open-ended answers due to their ambiguous nature. Our proposed approach tackles these challenges by (i) integrating query grounding and answering within a unified model to reduce error propagation;(ii) employing large language models for efficient and scalable data synthesis;and (iii) introducing a close-ended QA task for evaluation, to manage answer ambiguity. Extensive experiments demonstrate the effectiveness of our method, which also achieves state-of-the-art performance on the QAEgo4D and Ego4D-NLQ benchmarks. Code, data, and models are open-sourced (1).

关键词： egocentric vision video grounding video question answering

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Crowd Counting in the Frequency Domain

Crowd Counting in the Frequency Domain

引用

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

作者： Shu, Weibo Wan, Jia Tan, Kay Chen Kwong, Sam Chan, Antoni B. City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper investigates crowd counting in the frequency domain, which is a novel direction compared to the traditional view in the spatial domain. By transforming the density map into the frequency domain and using the properties of the characteristic function, we propose a novel method that is simple, effective, and efficient. The solid theoretical analysis ends up as an implementation-friendly loss function, which requires only standard tensor operations in the training process. We prove that our loss function is an upper bound of the pseudo sup norm metric between the ground truth and the prediction density map (over all of their sub-regions), and demonstrate its efficacy and efficiency versus other loss functions. The experimental results also show its competitiveness to the state-of-the-art on five benchmark data sets: ShanghaiTech A & B, UCF-QNRF, JHU++, and NWPU. Our codes will be available at: wb-shu/Crowd_Couniing_in_the_Frequency_Domain

关键词： Training Upper bound Tensors Image analysis Frequency-domain analysis Machine vision Design methodology

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PlenVDB: Memory Efficient VDB-Based Radiance Fields for Fast Training and Rendering

PlenVDB: Memory Efficient VDB-Based Radiance Fields for Fast...

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

作者： Yan, Han Liu, Celong Ma, Chao Mei, Xing Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Shanghai Peoples R China ByteDance Inc Beijing Peoples R China

ISBN: (纸本)9798350301298

In this paper, we present a new representation for neural radiance fields that accelerates both the training and the inference processes with VDB, a hierarchical data structure for sparse volumes. VDB takes both the advantages of sparse and dense volumes for compact data representation and efficient data access, being a promising data structure for NeRF data interpolation and ray marching. Our method, Plenoptic VDB (PlenVDB), directly learns the VDB data structure from a set of posed images by means of a novel training strategy and then uses it for real-time rendering. Experimental results demonstrate the effectiveness and the efficiency of our method over previous arts: First, it converges faster in the training process. Second, it delivers a more compact data format for NeRF data presentation. Finally, it renders more efficiently on commodity graphics hardware. Our mobile PlenVDB demo achieves 30+ FPS, 1280x720 resolution on an iPhone12 mobile phone. Check *** for details.

关键词： vision + graphics

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Adversarial Feature Augmentation for Unsupervised Domain Adaptation 31

Adversarial Feature Augmentation for Unsupervised Domain Ada...

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31st ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Volpi, Riccardo Morerio, Pietro Savarese, Silvio Murino, Vittorio Ist Italiano Tecnol Pattern Anal & Comp Vis Genoa Italy Stanford Univ Stanford Vis & Learning Lab Stanford CA 94305 USA Univ Verona Comp Sci Dept Verona Italy

ISBN: (纸本)9781538664209

Recent works showed that Generative Adversarial Networks (GANs) can be successfully applied in unsupervised domain adaptation, where, given a labeled source dataset and an unlabeled target dataset, the goal is to train powerful classifiers for the target samples. In particular, it was shown that a GAN objective function can be used to learn target features indistinguishable from the source ones. In this work, we extend this framework by (i) forcing the learned feature extractor to be domain-invariant, and (ii) training it through data augmentation in the feature space, namely performing feature augmentation. While data augmentation in the image space is a well established technique in deep learning, feature augmentation has not yet received the same level of attention. We accomplish it by means of a feature generator trained by playing the GAN minimax game against source features. Results show that both enforcing domain-invariance and performing feature augmentation lead to superior or comparable performance to state-of-the-art results in several unsupervised domain adaptation benchmarks.

关键词： Feature extraction Gallium nitride Training Games Generative adversarial networks Generators Neural networks

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Weakly Supervised Video Moment Retrieval From Text Queries 32

Weakly Supervised Video Moment Retrieval From Text Queries

引用

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

作者： Mithun, Niluthpol Chowdhury Paul, Sujoy Roy-Chowdhury, Amit K. Univ Calif Riverside Elect & Comp Engn Riverside CA 92521 USA

ISBN: (纸本)9781728132938

There have been a few recent methods proposed in text to video moment retrieval using natural language queries, but requiring full supervision during training. However, acquiring a large number of training videos with temporal boundary annotations for each text description is extremely time-consuming and often not scalable. In order to cope with this issue, in this work, we introduce the problem of learning from weak labels for the task of text to video moment retrieval. The weak nature of the supervision is because, during training, we only have access to the video-text pairs rather than the temporal extent of the video to which different text descriptions relate. We propose a joint visual-semantic embedding based framework that learns the notion of relevant segments from video using only video-level sentence descriptions. Specifically, our main idea is to utilize latent alignment between video frames and sentence descriptions using Text-Guided Attention (TGA). TGA is then used during the test phase to retrieve relevant moments. Experiments on two benchmark datasets demonstrate that our method achieves comparable performance to state-of-the-art fully supervised approaches.

关键词： Categorization recognition: Detection Retrieval vision + Language

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MEDIC: Remove Model Backdoors via Importance Driven Cloning

MEDIC: Remove Model Backdoors via Importance Driven Cloning

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

作者： Xu, Qiuling Tao, Guanhong Honorio, Jean Liu, Yingqi An, Shengwei Shen, Guangyu Cheng, Siyuan Zhang, Xiangyu Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9798350301298

We develop a novel method to remove injected backdoors in deep learning models. It works by cloning the benign behaviors of a trojaned model to a new model of the same structure. It trains the clone model from scratch on a very small subset of samples and aims to minimize a cloning loss that denotes the differences between the activations of important neurons across the two models. The set of important neurons varies for each input, depending on their magnitude of activations and their impact on the classification result. We theoretically show our method can better recover benign functions of the backdoor model. Meanwhile, we prove our method can be more effective in removing backdoors compared with fine-tuning. Our experiments show that our technique can effectively remove nine different types of backdoors with minor benign accuracy degradation, outperforming the state-of-the-art backdoor removal techniques that are based on fine-tuning, knowledge distillation, and neuron pruning.(1)

关键词： ethics in vision fairness*accountability privacy Transparency

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Ambiguous Medical Image Segmentation using Diffusion Models

Ambiguous Medical Image Segmentation using Diffusion Models

引用

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

作者： Rahman, Aimon Valanarasu, Jeya Maria Jose Hacihaliloglu, Ilker Patel, Vishal M. Johns Hopkins Univ Baltimore MD 21218 USA Univ British Columbia Vancouver BC Canada

ISBN: (纸本)9798350301298

Collective insights from a group of experts have always proven to outperform an individual's best diagnostic for clinical tasks. For the task of medical image segmentation, existing research on AI-based alternatives focuses more on developing models that can imitate the best individual rather than harnessing the power of expert groups. In this paper, we introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent stochastic sampling process of diffusion using only minimal additional learning. We demonstrate on three different medical image modalities- CT, ultrasound, and MRI that our model is capable of producing several possible variants while capturing the frequencies of their occurrences. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks in terms of accuracy while preserving naturally occurring variation. We also propose a new metric to evaluate the diversity as well as the accuracy of segmentation predictions that aligns with the interest of clinical practice of collective insights. Implementation code: https://***/aimansnigdha/Ambiguous-Medical-Image-Segmentation-using-Diffusion-Models.

关键词： cell microscopy Medical and biological vision

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GRAM: Global Reasoning for Multi-Page VQA

GRAM: Global Reasoning for Multi-Page VQA

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

作者： Blau, Tsachi Fogel, Sharon Ronen, Roi Goltst, Alona Per, Shahar Tsi Ben Avraham, Elad Aberdam, Aviad Ganz, Roy Litman, Ron Technion Haifa Israel AWS AI Labs Shanghai Peoples R China

ISBN: (纸本)9798350353006

The increasing use of transformer-based large language models brings forward the challenge of processing long sequences. In document visual question answering (DocVQA), leading methods focus on the single-page setting, while documents can span hundreds of pages. We present GRAM, a method that seamlessly extends pre-trained single-page models to the multi-page setting, without requiring computationally-heavy pretraining. To do so, we leverage a single-page encoder for local page-level understanding, and enhance it with document-level designated layers and learnable tokens, facilitating the flow of information across pages for global reasoning. To enforce our model to utilize the newly introduced document tokens, we propose a tailored bias adaptation method. For additional computational savings during decoding, we introduce an optional compression stage using our compression-transformer(C-Former), reducing the encoded sequence length, thereby allowing a tradeoff between quality and latency. Extensive experiments showcase GRAM's state-of-the-art performance on the benchmarks for multi-page DocVQA, demonstrating the effectiveness of our approach.

关键词： Document Understanding Long Sequence Processing vision Language Models

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Automatic single view building reconstruction by integrating segmentation

Automatic single view building reconstruction by integrating...

引用

2004 ieee computer Society conference on computer vision and pattern recognition workshops, CVPRW 2004

作者： Han, Feng Zhu, Song-Chun Departments of Computer Science and Statistics University of California Los Angeles Los AngelesCA90095 United States

In this paper, we propose a stochastic algorithm using Markov chain Monte Carlo (MCMC) to automatically reconstruct buildings from a single image of architectural scenes by integrating segmentation and reconstruction. Buildings are modelled by two families of generative models: One is parameterized geometric primitives (e.g. boxes, prisms) for 3D structures of buildings. The other is image models for appearances of building surfaces. All the other objects except buildings in the scene are modelled as a 3D background plane with some appearance. Regarding one image of architectural scenes as the 2D projection of the appearances of all the component primitives in the buildings and the background plane to the image plane, we reconstruct buildings under the Bayesian statistical framework by inferring the 3D structure of its component primitives and image models of visible surfaces, which follow some spatial relation prior and reproduce the given image under some projection matrix. The aspect hierarchy is used to generate proposals in primitive space, which can greatly speed up the Markov chain search. © 2004 ieee.

关键词： Buildings

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