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Exploring Discontinuity for Video Frame Interpolation

Exploring Discontinuity for Video Frame Interpolation

引用

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

作者： Lee, Sangjin Lee, Hyeongmin Shin, Chajin Son, Hanbin Lee, Sangyoun Yonsei Univ Seoul South Korea Korea Inst Sci & Technol KIST Seoul South Korea

ISBN: (纸本)9798350301298

Video frame interpolation (VFI) is the task that synthesizes the intermediate frame given two consecutive frames. Most of the previous studies have focused on appropriate frame warping operations and refinement modules for the warped frames. These studies have been conducted on natural videos containing only continuous motions. However, many practical videos contain various unnatural objects with discontinuous motions such as logos, user interfaces and subtitles. We propose three techniques that can make the existing deep learning-based VFI architectures robust to these elements. First is a novel data augmentation strategy called figure-text mixing (FTM) which can make the models learn discontinuous motions during training stage without any extra dataset. Second, we propose a simple but effective module that predicts a map called discontinuity map (D-map), which densely distinguishes between areas of continuous and discontinuous motions. Lastly, we propose loss functions to give supervisions of the discontinuous motion areas which can be applied along with FTM and D-map. We additionally collect a special test benchmark called Graphical Discontinuous Motion (GDM) dataset consisting of some mobile games and chatting videos. Applied to the various state-of-the-art VFI networks, our method significantly improves the interpolation qualities on the videos from not only GDM dataset, but also the existing benchmarks containing only continuous motions such as Vimeo90K, UCF101, and DAVIS.

关键词： Low-level vision

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Discriminative Co-Saliency and Background Mining Transformer for Co-Salient Object Detection

Discriminative Co-Saliency and Background Mining Transformer...

引用

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

作者： Li, Long Han, Junwei Zhang, Ni Liu, Nian Khan, Salman Cholakkal, Hisham Anwer, Rao Muhammad Khan, Fahad Shahbaz Northwestern Polytech Univ Grande Prairie AB Canada Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates Australian Natl Univ Canberra Australia Linkoping Univ CVL Linkoping Sweden

ISBN: (纸本)9798350301298

Most previous co-salient object detection works mainly focus on extracting co-salient cues via mining the consistency relations across images while ignore explicit exploration of background regions. In this paper, we propose a Discriminative co-saliency and background Mining Transformer framework (DMT) based on several economical multi-grained correlation modules to explicitly mine both co-saliency and background information and effectively model their discrimination. Specifically, we first propose a region-to-region correlation module for introducing inter-image relations to pixel-wise segmentation features while maintaining computational efficiency. Then, we use two types of pre-defined tokens to mine co-saliency and background information via our proposed contrast-induced pixel-to-token correlation and co-saliency token-to-token correlation modules. We also design a token-guided feature refinement module to enhance the discriminability of the segmentation features under the guidance of the learned tokens. We perform iterative mutual promotion for the segmentation feature extraction and token construction. Experimental results on three benchmark datasets demonstrate the effectiveness of our proposed method. The source code is available at: https://***/dragonlee258079/DMT.

关键词： Low-level vision

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Joint Video Multi-Frame Interpolation and Deblurring under Unknown Exposure Time

Joint Video Multi-Frame Interpolation and Deblurring under U...

引用

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

作者： Shang, Wei Ren, Dongwei Yang, Yi Zhang, Hongzhi Ma, Kede Zuo, Wangmeng Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China City Univ Hong Kong Hong Kong Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Natural videos captured by consumer cameras often suffer from low framerate and motion blur due to the combination of dynamic scene complexity, lens and sensor imperfection, and less than ideal exposure setting. As a result, computational methods that jointly perform video frame interpolation and deblurring begin to emerge with the unrealistic assumption that the exposure time is known and fixed. In this work, we aim ambitiously for a more realistic and challenging task - joint video multi-frame interpolation and deblurring under unknown exposure time. Toward this goal, we first adopt a variant of supervised contrastive learning to construct an exposure-aware representation from input blurred frames. We then train two U-Nets for intra-motion and inter-motion analysis, respectively, adapting to the learned exposure representation via gain tuning. We finally build our video reconstruction network upon the exposure and motion representation by progressive exposure-adaptive convolution and motion refinement. Extensive experiments on both simulated and real-world datasets show that our optimized method achieves notable performance gains over the state-of-the-art on the joint video x8 interpolation and deblurring task. Moreover, on the seemingly implausible x16 interpolation task, our method outperforms existing methods by more than 1.5 dB in terms of PSNR.

关键词： Low-level vision

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Probing Sentiment-Oriented Pre-Training Inspired by Human Sentiment Perception Mechanism

Probing Sentiment-Oriented Pre-Training Inspired by Human Se...

引用

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

作者： Feng, Tinglei Liu, Jiaxuan Yan, Jufeng Nankai Univ Coll Comp Sci TMCC Tianjin Peoples R China

ISBN: (纸本)9798350301298

Pre-training of deep convolutional neural networks (DCNNs) plays a crucial role in the field of visual sentiment analysis (VSA). Most proposed methods employ the off-the-shelf backbones pre-trained on large-scale object classification datasets (i.e., ImageNet). While it boosts performance for a big margin against initializing model states from random, we argue that DCNNs simply pre-trained on ImageNet may excessively focus on recognizing objects, but failed to provide high-level concepts in terms of sentiment. To address this long-term overlooked problem, we propose a sentiment-oriented pre-training method that is built upon human visual sentiment perception (VSP) mechanism. Specifically, we factorize the process of VSP into three steps, namely stimuli taking, holistic organizing, and high-level perceiving. From imitating each VSP step, a total of three models are separately pre-trained via our devised sentiment-aware tasks that contribute to excavating sentiment-discriminated representations. Moreover, along with our elaborated multi-model amalgamation strategy, the prior knowledge learned from each perception step can be effectively transferred into a single target model, yielding substantial performance gains. Finally, we verify the superiorities of our proposed method over extensive experiments, covering mainstream VSA tasks from single-label learning (SLL), multi-label learning (MLL), to label distribution learning (LDL). Experiment results demonstrate that our proposed method leads to unanimous improvements in these downstream tasks. Our code is released on https://***/tinglyfeng/sentiment_pretraining.

关键词： vision applications and systems

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Out-of-Distributed Semantic Pruning for Robust Semi-Supervised Learning

Out-of-Distributed Semantic Pruning for Robust Semi-Supervis...

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

作者： Wang, Yu Qiao, Pengchong Liu, Chang Song, Guoli Zheng, Xiawu Chen, Jie Peking Univ Sch Elect & Comp Engn Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China Tsinghua Univ BNRist Beijing Peoples R China Peking Univ AI Sci AI4S Preferred Program Shenzhen Grad Sch Beijing Peoples R China

ISBN: (纸本)9798350301298

Recent advances in robust semi-supervised learning (SSL) typically filter out-of-distribution (OOD) information at the sample level. We argue that an overlooked problem of robust SSL is its corrupted information on semantic level, practically limiting the development of the field. In this paper, we take an initial step to explore and propose a unified framework termed OOD Semantic Pruning (OSP), which aims at pruning OOD semantics out from in-distribution (ID) features. Specifically, (i) we propose an aliasing OOD matching module to pair each ID sample with an OOD sample with semantic overlap. (ii) We design a soft orthogonality regularization, which first transforms each ID feature by suppressing its semantic component that is collinear with paired OOD sample. It then forces the predictions before and after soft orthogonality decomposition to be consistent. Being practically simple, our method shows a strong performance in OOD detection and ID classification on challenging benchmarks. In particular, OSP surpasses the previous state-of-the-art by 13.7% on accuracy for ID classification and 5.9% on AUROC for OOD detection on TinyImageNet dataset. The source codes are publicly available at https://***/rain305f/OSP.

关键词： detection recognition: Categorization retrieval

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Fine-Grained Classification with Noisy Labels

Fine-Grained Classification with Noisy Labels

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

作者： Wei, Qi Feng, Lei Sun, Haoliang Wang, Ren Guo, Chenhui Yin, Yilong Shandong Univ Sch Software Jinan Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

ISBN: (纸本)9798350301298

Learning with noisy labels (LNL) aims to ensure model generalization given a label-corrupted training set. In this work, we investigate a rarely studied scenario of LNL on fine-grained datasets (LNL-FG), which is more practical and challenging as large inter-class ambiguities among fine-grained classes cause more noisy labels. We empirically show that existing methods that work well for LNL fail to achieve satisfying performance for LNL-FG, arising the practical need of effective solutions for LNL-FG. To this end, we propose a novel framework called stochastic noise-tolerated supervised contrastive learning (SNSCL) that confronts label noise by encouraging distinguishable representation. Specifically, we design a noise-tolerated supervised contrastive learning loss that incorporates a weight-aware mechanism for noisy label correction and selectively updating momentum queue lists. By this mechanism, we mitigate the effects of noisy anchors and avoid inserting noisy labels into the momentum-updated queue. Besides, to avoid manually-defined augmentation strategies in contrastive learning, we propose an efficient stochastic module that samples feature embeddings from a generated distribution, which can also enhance the representation ability of deep models. SNSCL is general and compatible with prevailing robust LNL strategies to improve their performance for LNL-FG. Extensive experiments demonstrate the effectiveness of SNSCL.

关键词： detection recognition: Categorization retrieval

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Adaptive Sparse Convolutional Networks with Global Context Enhancement for Faster Object Detection on Drone Images

Adaptive Sparse Convolutional Networks with Global Context E...

引用

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

作者： Du, Bowei Huang, Yecheng Chen, Jiaxin Huang, Di Beihang Univ State Key Lab Software Dev Environm Beijing Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China Beihang Univ Hangzhou Innovat Inst Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Object detection on drone images with low-latency is an important but challenging task on the resource-constrained unmanned aerial vehicle (UAV) platform. This paper investigates optimizing the detection head based on the sparse convolution, which proves effective in balancing the accuracy and efficiency. Nevertheless, it suffers from inadequate integration of contextual information of tiny objects as well as clumsy control of the mask ratio in the presence of foreground with varying scales. To address the issues above, we propose a novel global context-enhanced adaptive sparse convolutional network (CEASC). It first develops a context-enhanced group normalization (CE-GN) layer, by replacing the statistics based on sparsely sampled features with the global contextual ones, and then designs an adaptive multi-layer masking strategy to generate optimal mask ratios at distinct scales for compact foreground coverage, promoting both the accuracy and efficiency. Extensive experimental results on two major benchmarks, i.e. VisDrone and UAVDT, demonstrate that CEASC remarkably reduces the GFLOPs and accelerates the inference procedure when plugging into the typical state-of-the-art detection frameworks (e.g. RetinaNet and GFL V1) with competitive performance. Code is available at https://***/Cuogeihong/CEASC.

关键词： detection recognition: Categorization retrieval

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Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive Learning

Multiple Instance Learning via Iterative Self-Paced Supervis...

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

作者： Liu, Kangning Zhu, Weicheng Shen, Yiqiu Liu, Sheng Razavian, Narges Geras, Krzysztof J. Fernandez-Granda, Carlos NYU Ctr Data Sci New York NY 10003 USA NYU Grossman Sch Med New York NY 10003 USA Courant Inst Math Sci New York NY USA

ISBN: (纸本)9798350301298

Learning representations for individual instances when only bag-level labels are available is a fundamental challenge in multiple instance learning (MIL). Recent works have shown promising results using contrastive self-supervised learning (CSSL), which learns to push apart representations corresponding to two different randomly-selected instances. Unfortunately, in real-world applications such as medical image classification, there is often class imbalance, so randomly-selected instances mostly belong to the same majority class, which precludes CSSL from learning inter-class differences. To address this issue, we propose a novel framework, Iterative Self-paced Supervised Contrastive Learning for MIL Representations (ItS2CLR), which improves the learned representation by exploiting instance-level pseudo labels derived from the bag-level labels. The framework employs a novel self-paced sampling strategy to ensure the accuracy of pseudo labels. We evaluate ItS2CLR on three medical datasets, showing that it improves the quality of instance-level pseudo labels and representations, and outperforms existing MIL methods in terms of both bag and instance level accuracy. (1)

关键词： cell microscopy Medical and biological vision

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EC² : Emergent Communication for Embodied Control

EC<SUP>2</SUP> : Emergent Communication for Embodied Control

引用

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

作者： Mu, Yao Yao, Shunyu Ding, Mingyu Luo, Ping Gan, Chuang Univ Hong Kong Hong Kong Peoples R China Princeton Univ Princeton NJ USA UMass Amherst Amherst MA USA MIT IBM Watson AI Lab Cambridge MA USA

ISBN: (纸本)9798350301298

Embodied control requires agents to leverage multimodal pre-training to quickly learn how to act in new environments, where video demonstrations contain visual and motion details needed for low-level perception and control, and language instructions support generalization with abstract, symbolic structures. While recent approaches apply contrastive learning to force alignment between the two modalities, we hypothesize better modeling their complementary differences can lead to more holistic representations for downstream adaption. To this end, we propose Emergent Communication for Embodied Control (EC2), a novel scheme to pre-train video-language representations for few-shot embodied control. The key idea is to learn an unsupervised "language" of videos via emergent communication, which bridges the semantics of video details and structures of natural language. We learn embodied representations of video trajectories, emergent language, and natural language using a language model, which is then used to finetune a lightweight policy network for downstream control. Through extensive experiments in Metaworld and Franka Kitchen embodied benchmarks, EC2 is shown to consistently outperform previous contrastive learning methods for both videos and texts as task inputs. Further ablations confirm the importance of the emergent language, which is beneficial for both video and language learning, and significantly superior to using pre-trained video captions. We also present a quantitative and qualitative analysis of the emergent language and discuss future directions toward better understanding and leveraging emergent communication in embodied tasks.

关键词： Embodied vision: Active agents simulation

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BioNet: A Biologically-inspired Network for Face recognition

BioNet: A Biologically-inspired Network for Face Recognition

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

作者： Li, Pengyu Terminus Grp Terminus Labs Beijing Peoples R China

ISBN: (纸本)9798350301298

Recently, whether and how cutting-edge Neuroscience findings can inspire Artificial Intelligence (AI) confuse both communities and draw much discussion. As one of the most critical fields in AI, computer vision (CV) also pays much attention to the discussion. To show our ideas and experimental evidence to the discussion, we focus on one of the most broadly researched topics both in Neuroscience and CV fields, i.e., Face recognition (FR). Neuroscience studies show that face attributes are essential to the human face-recognizing system. How the attributes contribute also be explained by the Neuroscience community. Even though a few CV works improved the FR performance with attribute enhancement, none of them are inspired by the human face-recognizing mechanism nor boosted performance significantly. To show our idea experimentally, we model the biological characteristics of the human face-recognizing system with classical Convolutional Neural Network Operators (CNN Ops) purposely. We name the proposed Biologically-inspired Network as BioNet. Our BioNet consists of two cascade sub-networks, i.e., the Visual Cortex Network (VCN) and the Inferotemporal Cortex Network (ICN). The VCN is modeled with a classical CNN backbone. The proposed ICN comprises three biologically-inspired modules, i.e., the Cortex Functional Compartmentalization, the Compartment Response Transform, and the Response Intensity Modulation. The experiments prove that: 1) The cutting-edge findings about the human face-recognizing system can further boost the CNN-based FR network. 2) With the biological mechanism, both identity-related attributes (e.g., gender) and identity-unrelated attributes (e.g., expression) can benefit the deep FR models. Surprisingly, the identity-unrelated ones contribute even more than the identity-related ones. 3) The proposed BioNet significantly boosts state-of-the-art on standard FR benchmark datasets. For example, BioNet boosts IJB-B@1e-6 from 52.12% to 68.28% and MegaFace fr

关键词： body gesture Humans: Face movement pose

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