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Abstract Visual Reasoning: An Algebraic Approach for Solving Raven's Progressive Matrices

Abstract Visual Reasoning: An Algebraic Approach for Solving...

引用

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

作者： Xu, Jingyi Vaidya, Tushar Wu, Yufei Chandra, Saket Lai, Zhangsheng Chong, Kai Fong Ernest Singapore Univ Technol & Design Singapore Singapore Nanyang Technol Univ Singapore Singapore Singapore Polytech Singapore Singapore

ISBN: (纸本)9798350301298

We introduce algebraic machine reasoning, a new reasoning framework that is well-suited for abstract reasoning. Effectively, algebraic machine reasoning reduces the difficult process of novel problem-solving to routine algebraic computation. The fundamental algebraic objects of interest are the ideals of some suitably initialized polynomial ring. We shall explain how solving Raven's Progressive Matrices (RPMs) can be realized as computational problems in algebra, which combine various well-known algebraic subroutines that include: Computing the Grobner basis of an ideal, checking for ideal containment, etc. Crucially, the additional algebraic structure satisfied by ideals allows for more operations on ideals beyond set-theoretic operations. Our algebraic machine reasoning framework is not only able to select the correct answer from a given answer set, but also able to generate the correct answer with only the question matrix given. Experiments on the I-RAVEN dataset yield an overall 93.2% accuracy, which significantly outperforms the current state-of-the-art accuracy of 77.0% and exceeds human performance at 84.4% accuracy.

关键词： and reasoning language vision

来源：评论

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Compacting Binary Neural Networks by Sparse Kernel Selection

Compacting Binary Neural Networks by Sparse Kernel Selection

引用

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

作者： Wang, Yikai Huang, Wenbing Dong, Yinpeng Sun, Fuchun Yao, Anbang Tsinghua Univ Dept Comp Sci & Technol State Key Lab Intelligent Technol & Syst BNRist Ctr Beijing Peoples R China Renmin Univ China Gaoling Sch Artificial Intelligence Suzhou Peoples R China RealAI Shenzhen Peoples R China Intel Labs China Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Binary Neural Network (BNN) represents convolution weights with 1-bit values, which enhances the efficiency of storage and computation. This paper is motivated by a previously revealed phenomenon that the binary kernels in successful BNNs are nearly power-law distributed: their values are mostly clustered into a small number of codewords. This phenomenon encourages us to compact typical BNNs and obtain further close performance through learning non-repetitive kernels within a binary kernel subspace. Specifically, we regard the binarization process as kernel grouping in terms of a binary codebook, and our task lies in learning to select a smaller subset of codewords from the full codebook. We then leverage the Gumbel-Sinkhorn technique to approximate the codeword selection process, and develop the Permutation Straight-Through Estimator (PSTE) that is able to not only optimize the selection process end-to-end but also maintain the non-repetitive occupancy of selected codewords. Experiments verify that our method reduces both the model size and bit-wise computational costs, and achieves accuracy improvements compared with state-of-the-art BNNs under comparable budgets.

关键词： Efficient and scalable vision

来源：评论

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Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution

Local Implicit Normalizing Flow for Arbitrary-Scale Image Su...

引用

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

作者： Yao, Jie-En Tsao, Li-Yuan Lo, Yi-Chen Tseng, Roy Chang, Chia-Che Lee, Chun-Yi Natl Tsing Hua Univ 1 ElsaLab Hsinchu Taiwan MediaTek Inc Hsinchu Taiwan

ISBN: (纸本)9798350301298

Flow-based methods have demonstrated promising results in addressing the ill-posed nature of super-resolution (SR) by learning the distribution of high-resolution (HR) images with the normalizing flow. However, these methods can only perform a predefined fixed-scale SR, limiting their potential in real-world applications. Meanwhile, arbitrary-scale SR has gained more attention and achieved great progress. Nonetheless, previous arbitrary-scale SR methods ignore the ill-posed problem and train the model with per-pixel L1 loss, leading to blurry SR outputs. In this work, we propose "Local Implicit Normalizing Flow" (LINF) as a unified solution to the above problems. LINF models the distribution of texture details under different scaling factors with normalizing flow. Thus, LINF can generate photo-realistic HR images with rich texture details in arbitrary scale factors. We evaluate LINF with extensive experiments and show that LINF achieves the state-of-the-art perceptual quality compared with prior arbitrary-scale SR methods.

关键词： Low-level vision

来源：评论

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Compression-Aware Video Super-Resolution

Compression-Aware Video Super-Resolution

引用

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

作者： Wang, Yingwei Isobe, Takashi Jia, Xu Tao, Xin Lu, Huchuan Tai, Yu-Wing Dalian Univ Technol Dalian Peoples R China Xiaohongshu Inc Shanghai Peoples R China Kuaishou Technol Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Videos stored on mobile devices or delivered on the Internet are usually in compressed format and are of various unknown compression parameters, but most video super-resolution (VSR) methods often assume ideal inputs resulting in large performance gap between experimental settings and real-world applications. In spite of a few pioneering works being proposed recently to super-resolve the compressed videos, they are not specially designed to deal with videos of various levels of compression. In this paper, we propose a novel and practical compression-aware video super-resolution model, which could adapt its video enhancement process to the estimated compression level. A compression encoder is designed to model compression levels of input frames, and a base VSR model is then conditioned on the implicitly computed representation by inserting compression-aware modules. In addition, we propose to further strengthen the VSR model by taking full advantage of meta data that is embedded naturally in compressed video streams in the procedure of information fusion. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of the proposed method on compressed VSR benchmarks. The codes will be available at https://***/aprBlue/CAVSR

关键词： Low-level vision

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Tell Me What Happened: Unifying Text-guided Video Completion via Multimodal Masked Video Generation

Tell Me What Happened: Unifying Text-guided Video Completion...

引用

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

作者： Fu, Tsu-Jui Yu, Licheng Zhang, Ning Fu, Cheng-Yang Su, Jong-Chyi Wang, William Yang Bell, Sean UC Santa Barbara Santa Barbara CA 93106 USA Meta Menlo Pk CA USA NEC Labs Amer Princeton NJ USA

ISBN: (纸本)9798350301298

Generating a video given the first several static frames is challenging as it anticipates reasonable future frames with temporal coherence. Besides video prediction, the ability to rewind from the last frame or infilling between the head and tail is also crucial, but they have rarely been explored for video completion. Since there could be different outcomes from the hints of just a few frames, a system that can follow natural language to perform video completion may significantly improve controllability. Inspired by this, we introduce a novel task, text-guided video completion (TVC), which requests the model to generate a video from partial frames guided by an instruction. We then propose Multimodal Masked Video Generation (MMVG) to address this TVC task. During training, MMVG discretizes the video frames into visual tokens and masks most of them to perform video completion from any time point. At inference time, a single MMVG model can address all 3 cases of TVC, including video prediction, rewind, and infilling, by applying corresponding masking conditions. We evaluate MMVG in various video scenarios, including egocentric, animation, and gaming. Extensive experimental results indicate that MMVG is effective in generating high-quality visual appearances with text guidance for TVC.

关键词： and reasoning language vision

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Label-Free Liver Tumor Segmentation

Label-Free Liver Tumor Segmentation

引用

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

作者： Hu, Qixin Chen, Yixiong Xiao, Junfei Sun, Shuwen Chen, Jieneng Yuille, Alan Zhou, Zongwei Huazhong Univ Sci & Technol Wuhan Peoples R China Chinese Univ Hong Kong Shenzhen Shenzhen Peoples R China Johns Hopkins Univ Baltimore MD USA Nanjing Med Univ Affiliated Hosp 1 Nanjing Peoples R China

ISBN: (纸本)9798350301298

We demonstrate that AI models can accurately segment liver tumors without the need for manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two intriguing advantages: (I) realistic in shape and texture, which even medical professionals can confuse with real tumors;(II) effective for training AI models, which can perform liver tumor segmentation similarly to the model trained on real tumors-this result is exciting because no existing work, using synthetic tumors only, has thus far reached a similar or even close performance to real tumors. This result also implies that manual efforts for annotating tumors voxel by voxel (which took years to create) can be significantly reduced in the future. Moreover, our synthetic tumors can automatically generate many examples of small (or even tiny) synthetic tumors and have the potential to improve the success rate of detecting small liver tumors, which is critical for detecting the early stages of cancer. In addition to enriching the training data, our synthesizing strategy also enables us to rigorously assess the AI robustness.

关键词： cell microscopy Medical and biological vision

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Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation

Mask DINO: Towards A Unified Transformer-based Framework for...

引用

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

作者： Li, Feng Zhang, Hao Xu, Huaizhe Liu, Shilong Zhang, Lei Ni, Lionel M. Shum, Heimg-Yeung Hong Kong Univ Sci & Technol Hong Kong Peoples R China Tsinghua Univ Inst AI Dept CST BNIZist Ctr Beijing Peoples R China Int Digital Econ Acad IDEA Shenzhen Peoples R China Hong Kong Univ Sci & Technol Guangzhou Peoples R China

ISBN: (纸本)9798350301298

In this paper we present Mask DINO, a unified object detection and segmentation framework. Mask DINO extends DINO (DETR with Improved Denoising Anchor Boxes) by adding a mask prediction branch which supports all image segmentation tasks (instance, panoptic, and semantic). It makes use of the query embeddings from DINO to dot-product a high-resolution pixel embedding map to predict a set of binary masks. Some key components in DINO are extended for segmentation through a shared architecture and training process. Mask DINO is simple, efficient, and scalable, and it can benefit from joint large-scale detection and segmentation datasets. Our experiments show that Mask DINO significantly outperforms all existing specialized segmentation methods, both on a ResNet-50 backbone and a pre-trained model with SwinL backbone. Notably, Mask DINO establishes the best results to date on instance segmentation (54.5 AP on COCO), panoptic segmentation (59.4 PQ on COCO), and semantic segmentation (60.8 mIoU on ADE20K) among models under one billion parameters. Code is available at https://github. com/ IDEAResearch/MaskDINO.

关键词： vision applications and systems

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Raw Image Reconstruction with Learned Compact Metadata

Raw Image Reconstruction with Learned Compact Metadata

引用

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

作者： Wang, Yufei Yu, Yi Yang, Wenhan Guo, Lanqing Chau, Lap-Pui Kot, Alex C. Wen, Bihan Nanyang Technol Univ Singapore Singapore Peng Cheng Lab Shenzhen Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China

ISBN: (纸本)9798350301298

While raw images exhibit advantages over sRGB images (e.g., linearity and fine-grained quantization level), they are not widely used by common users due to the large storage requirements. Very recent works propose to compress raw images by designing the sampling masks in the raw image pixel space, leading to suboptimal image representations and redundant metadata. In this paper, we propose a novel framework to learn a compact representation in the latent space serving as the metadata in an end-to-end manner. Furthermore, we propose a novel sRGB-guided context model with the improved entropy estimation strategies, which leads to better reconstruction quality, smaller size of metadata, and faster speed. We illustrate how the proposed raw image compression scheme can adaptively allocate more bits to image regions that are important from a global perspective. The experimental results show that the proposed method can achieve superior raw image reconstruction results using a smaller size of the metadata on both uncompressed sRGB images and JPEG images. The code will be released at https://***/wyf0912/R2LCM.

关键词： Low-level vision

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Pointersect: Neural Rendering with Cloud-Ray Intersection

Pointersect: Neural Rendering with Cloud-Ray Intersection

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

作者： Chang, Jen-Hao Rick Chen, Wei -Yu Raujan, Anurag Yi, Kwang Moo Tuzel, Oncel Apple Cupertino CA 95014 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ British Columbia Vancouver BC V5Z 1M9 Canada

ISBN: (纸本)9798350301298

We propose a novel method that renders point clouds as if they are surfaces. The proposed method is differentiable and requires no scene-specific optimization. This unique capability enables, out-of-the-box, surface normal estimation, rendering room-scale point clouds, inverse rendering, and ray tracing with global illumination. Unlike existing work that focuses on converting point clouds to other representations-e.g., surfaces or implicit functions-our key idea is to directly infer the intersection of a light ray with the underlying surface represented by the given point cloud. Specifically, we train a set transformer that, given a small number of local neighbor points along a light ray, provides the intersection point, the surface normal, and the material blending weights, which are used to render the outcome of this light ray. Localizing the problem into small neighborhoods enables us to train a model with only 48 meshes and apply it to unseen point clouds. Our model achieves higher estimation accuracy than state-of-the-art surface reconstruction and point-cloud rendering methods on three test sets. When applied to room-scale point clouds, without any scene-specific optimization, the model achieves competitive quality with the state-of-the-art novel-view rendering methods. Moreover, we demonstrate ability to render and manipulate Lidar-scanned point clouds such as lighting control and object insertion.

关键词： vision + graphics

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Event-based Video Frame Interpolation with Cross-Modal Asymmetric Bidirectional Motion Fields

Event-based Video Frame Interpolation with Cross-Modal Asymm...

引用

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

作者： Kim, Taewoo Chae, Yujeong Jang, Hyun-Kurl Yoon, Kuk-Jin Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9798350301298

Video Frame Interpolation (VFI) aims to generate intermediate video frames between consecutive input frames. Since the event cameras are bio-inspired sensors that only encode brightness changes with a micro-second temporal resolution, several works utilized the event camera to enhance the performance of VFI. However, existing methods estimate bidirectional inter-frame motion fields with only events or approximations, which can not consider the complex motion in real-world scenarios. In this paper, we propose a novel event-based VFI framework with cross-modal asymmetric bidirectional motion field estimation. In detail, our EIF-BiOFNet utilizes each valuable characteristic of the events and images for direct estimation of inter-frame motion fields without any approximation methods. Moreover, we develop an interactive attention-based frame synthesis network to efficiently leverage the complementary warping-based and synthesis-based features. Finally, we build a large-scale event-based VFI dataset, ERF-X170FPS, with a high frame rate, extreme motion, and dynamic textures to overcome the limitations of previous event-based VFI datasets. Extensive experimental results validate that our method shows significant performance improvement over the state-of-the-art VFI methods on various datasets. Our project pages are available at: https://***/intelpro/CBMNet

关键词： Low-level vision

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