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A Large-scale Comprehensive Dataset and Copy-overlap Aware Evaluation Protocol for Segment-level Video Copy Detection

A Large-scale Comprehensive Dataset and Copy-overlap Aware E...

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

作者： He, Sifeng Yang, Xudong Jiang, Chen Liang, Gang Zhang, Wei Pan, Tan Wang, Qing Xu, Furong Li, Chunguang Liu, Jingxiong Xu, Hui Huang, Kaiming Cheng, Yuan Qian, Feng Zhang, Xiaobo Yang, Lei Ant Grp Hangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we introduce VCSL (Video Copy Segment Localization), a new comprehensive segment-level annotated video copy dataset. Compared with existing copy detection datasets restricted by either video-level annotation or small-scale, VCSL not only has two orders of magnitude more segment-level labelled data, with 160k realistic video copy pairs containing more than 280k localized copied segment pairs, but also covers a variety of video categories and a wide range of video duration. All the copied segments inside each collected video pair are manually extracted and accompanied by precisely annotated starting and ending timestamps. Alongside the dataset, we also propose a novel evaluation protocol that better measures the prediction accuracy of copy overlapping segments between a video pair and shows improved adaptability in different scenarios. By benchmarking several baseline and state-of-the-art segment-level video copy detection methods with the proposed dataset and evaluation metric, we provide a comprehensive analysis that uncovers the strengths and weaknesses of current approaches, hoping to open up promising directions for future works. The VCSL dataset, metric and benchmark codes are all publicly available at https://***/alipay/VCSL.

关键词： Measurement Location awareness computer vision Protocols Video on demand Current measurement Benchmark testing

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N-Gram in Swin Transformers for Efficient Lightweight Image Super-Resolution

N-Gram in Swin Transformers for Efficient Lightweight Image ...

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

作者： Choi, Haram Lee, Jeongmin Yang, Jihoon Sogang Univ Dept Comp Sci & Engn Seoul South Korea LG Innotek Seoul South Korea

ISBN: (纸本)9798350301298

While some studies have proven that Swin Transformer (Swin) with window self-attention (WSA) is suitable for single image super-resolution (SR), the plain WSA ignores the broad regions when reconstructing high-resolution images due to a limited receptive field. In addition, many deep learning SR methods suffer from intensive computations. To address these problems, we introduce the N-Gram context to the low-level vision with Transformers for the first time. We define N-Gram as neighboring local windows in Swin, which differs from text analysis that views N-Gram as consecutive characters or words. N-Grams interact with each other by sliding-WSA, expanding the regions seen to restore degraded pixels. Using the N-Gram context, we propose NGswin, an efficient SR network with SCDP bottleneck taking multi-scale outputs of the hierarchical encoder. Experimental results show that NGswin achieves competitive performance while maintaining an efficient structure when compared with previous leading methods. Moreover, we also improve other Swin-based SR methods with the N-Gram context, thereby building an enhanced model: SwinIR-NG. Our improved SwinIR-NG outperforms the current best lightweight SR approaches and establishes state-of-the-art results. Codes are available at https://***/rami0205/NGramSwin.

关键词： Low-level vision

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Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs

Scaling Up Your Kernels to 31x31: Revisiting Large Kernel De...

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

作者： Ding, Xiaohan Zhang, Xiangyu Han, Jungong Ding, Guiguang Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol BNRis Sch Software Beijing Peoples R China MEGVII Technol Beijing Peoples R China Aberystwyth Univ Comp Sci Dept Aberystwyth SY23 3FL Dyfed Wales

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We revisit large kernel design in modern convolutional neural networks (CNNs). Inspired by recent advances in vision transformers (ViTs), in this paper, we demonstrate that using a few large convolutional kernels instead of a stack of small kernels could be a more powerful paradigm. We suggested five guidelines, e.g., applying re-parameterized large depth-wise convolutions, to design efficient high-performance large-kernel CNNs. Following the guidelines, we propose RepLKNet, a pure CNN architecture whose kernel size is as large as 31x31, in contrast to commonly used 3x3. RepLKNet greatly closes the performance gap between CNNs and ViTs, e.g., achieving comparable or superior results than Swin Transformer on ImageNet and a few typical downstream tasks, with lower latency. RepLKNet also shows nice scalability to big data and large models, obtaining 87.8% top-1 accuracy on ImageNet and 56.0% mIoU on ADE20K, which is very competitive among the state-of-the-arts with similar model sizes. Our study further reveals that, in contrast to small-kernel CNNs, large-kernel CNNs have much larger effective receptive fields and higher shape bias rather than texture bias. Code & models at https://***/megvii-research/RepLKNet.

关键词： Convolutional codes Shape Scalability Transformers Data models pattern recognition Convolutional neural networks

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Visual Program Distillation: Distilling Tools and Programmatic Reasoning into vision-Language Models

Visual Program Distillation: Distilling Tools and Programmat...

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

作者： Hu, Yushi Stretcu, Otilia Lu, Chun-Ta Viswanathan, Krishnamurthy Hata, Kenji Luo, Enming Krishna, Ranjay Fuxman, Ariel Google Res Mountain View CA 94043 USA Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9798350353006

Solving complex visual tasks such as "Who invented the musical instrument on the right?" involves a composition of skills: understanding space, recognizing instruments, and also retrieving prior knowledge. Recent work shows promise by decomposing such tasks using a large language model (LLM) into an executable program that invokes specialized vision models. However, generated programs are error-prone: they omit necessary steps, include spurious ones, and are unable to recover when the specialized models give incorrect outputs. Moreover, they require loading multiple models, incurring high latency and computation costs. We propose Visual Program Distillation (VPD), an instruction tuning framework that produces a vision-language model (VLM) capable of solving complex visual tasks with a single forward pass. VPD distills the reasoning ability of LLMs by using them to sample multiple candidate programs, which are then executed and verified to identify a correct one. It translates each correct program into a language description of the reasoning steps, which are then distilled into a VLM. Extensive experiments show that VPD improves the VLM's ability to count, understand spatial relations, and reason compositionally. Our VPD-trained PaLI-X outperforms all prior VLMs, achieving state-of-the-art performance across complex vision tasks, including MMBench, OK-VQA, A-OKVQA, TallyQA, POPE, and Hateful Memes. An evaluation with human annotators also confirms that VPD improves model response factuality and consistency. Finally, experiments on content moderation demonstrate that VPD is also helpful for adaptation to real-world applications with limited data.

关键词： cross-modal reasoning distillation instruction tuning large language model multimodal synthetic data tools vision-language model visual programming

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ARCS: Accurate Rotation and Correspondence Search

ARCS: Accurate Rotation and Correspondence Search

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

作者： Peng, Liangzu Tsakiris, Manolis C. Vidal, Rene Johns Hopkins Univ Baltimore MD 21218 USA ShanghaiTech Univ Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper is about the old Wahba problem in its more general form, which we call "simultaneous rotation and correspondence search". In this generalization we need to find a rotation that best aligns two partially overlapping 3D point sets, of sizes m and n respectively with m >= n. We first propose a solver, ARCS, that i) assumes noiseless point sets in general position, ii) requires only 2 inliers, iii) uses O(m log m) time and O(m) space, and iv) can successfully solve the problem even with, e.g., m, n approximate to 10(6) in about 0.1 seconds. We next robustify ARCS to noise, for which we approximately solve consensus maximization problems using ideas from robust subspace learning and interval stabbing. Thirdly, we refine the approximately found consensus set by a Riemannian subgradient descent approach over the space of unit quaternions, which we show converges globally to an E-stationary point in O(epsilon(-4)) iterations, or locally to the ground-truth at a linear rate in the absence of noise. We combine these algorithms into ARCS+, to simultaneously search for rotations and correspondences. Experiments show that ARCS+ achieves state-of-the-art performance on large-scale datasets with more than 10(6) points with a 10(4) time-speedup over alternative methods. https://***/liangzu/ARCS

关键词： computer vision Three-dimensional displays Statistical analysis Navigation Quaternions Robot vision systems Pose estimation

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SPIN: Simultaneous Perception, Interaction and Navigation

SPIN: Simultaneous Perception, Interaction and Navigation

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

作者： Uppal, Shagun Agarwal, Ananye Xiong, Haoyu Shaw, Kenneth Pathak, Deepak Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798350353006

While there has been remarkable progress recently in the fields of manipulation and locomotion, mobile manipulation remains a long-standing challenge. Compared to locomotion or static manipulation, a mobile system must make a diverse range of long-horizon tasks feasible in unstructured and dynamic environments. While the applications are broad and interesting, there are a plethora of challenges in developing these systems such as coordination between the base and arm, reliance on onboard perception for perceiving and interacting with the environment, and most importantly, simultaneously integrating all these parts together. Prior works approach the problem using disentangled modular skills for mobility and manipulation that are trivially tied together. This causes several limitations such as compounding errors, delays in decision-making, and no whole-body coordination. In this work, we present a reactive mobile manipulation framework that uses an active visual system to consciously perceive and react to its environment. Similar to how humans leverage whole-body and hand-eye coordination, we develop a mobile manipulator that exploits its ability to move and see, more specifically - to move in order to see and to see in order to move. This allows it to not only move around and interact with its environment but also, choose "when" to perceive "what" using an active visual system. We observe that such an agent learns to navigate around complex cluttered scenarios while displaying agile whole-body coordination using only ego-vision without needing to create environment maps. Videos are available at https://***

关键词： Active vision End-to-end learning Mobile Manipulation Whole-body coordination

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LSVC: A Learning-based Stereo Video Compression Framework

LSVC: A Learning-based Stereo Video Compression Framework

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

作者： Chen, Zhenghao Lu, Guo Hu, Zhihao Liu, Shan Jiang, Wei Xu, Dong Univ Sydney Sydney NSW Australia Beijing Inst Technol Beijing Peoples R China Beihang Univ Beijing Peoples R China Tencent Amer Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this work, we propose the first end-to-end optimized framework for compressing automotive stereo videos (i.e., stereo videos from autonomous driving applications) from both left and right views. Specifically, when compressing the current frame from each view, our framework reduces temporal redundancy by performing motion compensation using the reconstructed intra-view adjacent frame and at the same time exploits binocular redundancy by conducting disparity compensation using the latest reconstructed cross-view frame. Moreover, to effectively compress the introduced motion and disparity offsets for better compensation, we further propose two novel schemes called motion residual compression and disparity residual compression to respectively generate the predicted motion offset and disparity offset from the previously compressed motion offset and disparity offset, such that we can more effectively compress residual offset information for better bit-rate saving. Overall, the entire framework is implemented by the fully-differentiable modules and can be optimized in an end-to-end manner. Our comprehensive experiments on three automotive stereo video benchmarks Cityscapes, KIM 2012 and KIM 2015 demonstrate that our proposed framework outperforms the learning-based single-view video codec and the traditional hand-crafted multi-view video codec.

关键词： Three-dimensional displays Redundancy Video compression Sensors pattern recognition Video codecs Task analysis

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DVC: An End-to-end Deep Video Compression Framework 32

DVC: An End-to-end Deep Video Compression Framework

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

作者： Lu, Guo Ouyang, Wanli Xu, Dong Zhang, Xiaoyun Cai, Chunlei Gao, Zhiyong Shanghai Jiao Tong Univ Shanghai Peoples R China Univ Sydney SenseTime Comp Vis Res Grp Sydney NSW Australia Univ Sydney Sydney NSW Australia

ISBN: (纸本)9781728132938

Conventional video compression approaches use the predictive coding architecture and encode the corresponding motion information and residual information. In this paper, taking advantage of both classical architecture in the conventional video compression method and the powerful nonlinear representation ability of neural networks, we propose the first end-to-end video compression deep model that jointly optimizes all the components for video compression. Specifically, learning based optical flow estimation is utilized to obtain the motion information and reconstruct the current frames. Then we employ two auto-encoder style neural networks to compress the corresponding motion and residual information. All the modules are jointly learned through a single loss function, in which they collaborate with each other by considering the trade-off between reducing the number of compression bits and improving quality of the decoded video. Experimental results show that the proposed approach can outperform the widely used video coding standard H.264 in terms of PSNR and be even on par with the latest standard H.265 in terms of MS-SSIM.

关键词： Low-level vision vision Applications and Systems

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Virtual Correspondence: Humans as a Cue for Extreme-View Geometry

Virtual Correspondence: Humans as a Cue for Extreme-View Geo...

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

作者： Ma, Wei-Chiu Yang, Anqi Joyce Wang, Shenlong Urtasun, Raquel Torralba, Antonio MIT 77 Massachusetts Ave Cambridge MA 02139 USA Univ Toronto Toronto ON Canada Univ Illinois Urbana IL USA Waabi Toronto ON Canada Vector Inst Toronto ON Canada

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recovering the spatial layout of the cameras and the geometry of the scene from extreme-view images is a long-standing challenge in computer vision. Prevailing 3D reconstruction algorithms often adopt the image matching paradigm and presume that a portion of the scene is co-visible across images, yielding poor performance when there is little overlap among inputs. In contrast, humans can associate visible parts in one image to the corresponding invisible components in another image via prior knowledge of the shapes. Inspired by this fact, we present a novel concept called virtual correspondences (VCs). VCs are a pair of pixels from two images whose camera rays intersect in 3D. Similar to classic correspondences, VCs conform with epipolar geometry;unlike classic correspondences, VCs do not need to be co-visible across views. Therefore VCs can be established and exploited even if images do not overlap. We introduce a method to find virtual correspondences based on humans in the scene. We showcase how VCs can be seamlessly integrated with classic bundle adjustment to recover camera poses across extreme views. Experiments show that our method significantly outperforms state-of-the-art camera pose estimation methods in challenging scenarios and is comparable in the traditional densely captured setup. Our approach also unleashes the potential of multiple downstream tasks such as scene reconstruction from multi-view stereo and novel view synthesis in extreme-view scenarios(1).

关键词： Geometry computer vision Visualization Shape Pose estimation Semantics Layout

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Distilling Self-Supervised vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation

Distilling Self-Supervised Vision Transformers for Weakly-Su...

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

作者： Kang, Dahyun Koniusz, Piotr Cho, Minsu Murray, Naila Meta AI Menlo Pk CA 94025 USA POSTECH Pohang South Korea CSIRO Data61 Canberra Australia Australian Natl Univ Canberra Australia

ISBN: (纸本)9798350301298

We address the task of weakly-supervised few-shot image classification and segmentation, by leveraging a vision Transformer (ViT) pretrained with self-supervision. Our proposed method takes token representations from the self-supervised ViT and leverages their correlations, via self-attention, to produce classification and segmentation predictions through separate task heads. Our model is able to effectively learn to perform classification and segmentation in the absence of pixel-level labels during training, using only image-level labels. To do this it uses attention maps, created from tokens generated by the self-supervised ViT backbone, as pixel-level pseudo-labels. We also explore a practical setup with "mixed" supervision, where a small number of training images contains ground-truth pixel-level labels and the remaining images have only image-level labels. For this mixed setup, we propose to improve the pseudo-labels using a pseudo-label enhancer that was trained using the available ground-truth pixel-level labels. Experiments on Pascal-5(i) and COCO-20(i) demonstrate significant performance gains in a variety of supervision settings, and in particular when little-to-no pixel-level labels are available.

关键词： continual low-shot meta or long-tail learning Transfer

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