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Transformation Driven Visual Reasoning

Transformation Driven Visual Reasoning

引用

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

作者： Hong, Xin Lan, Yanyan Pang, Liang Guo, Jiafeng Cheng, Xueqi Chinese Acad Sci Inst Comp Technol CAS Key Lab Network Data Sci & Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Tsinghua Univ Inst AI Ind Res Beijing Peoples R China

ISBN: (纸本)9781665445092

This paper defines a new visual reasoning paradigm by introducing an important factor, i.e. transformation. The motivation comes from the fact that most existing visual reasoning tasks, such as CLEVR in VQA, are solely defined to test how well the machine understands the concepts and relations within static settings, like one image. We argue that this kind of slate driven visual reasoning approach has limitations in reflecting whether the machine has the ability to infer the dynamics between different states, which has been shown as important as state-level reasoning for human cognition in Piaget's theory. To tackle this problem, we propose a novel transformation driven visual reasoning task. Given both the initial and final states, the target is to infer the corresponding single-step or multi-step transformation, represented as a triplet (object, attribute, value) or a sequence of triplets, respectively. Following this definition, a new dataset namely TRANCE is constructed on the basis of CLEVR, including three levels of settings, i.e. Basic (single-step transformation), Event (multi-step transformation), and View (multi-step transformation with variant views). Experimental results show that the state-of-the-art visual reasoning models perform well on Basic, but are still far from human-level intelligence on Event and View. We believe the proposed new paradigm will boost the development of machine visual reasoning. More advanced methods and real data need to be investigated in this direction.

关键词： Visualization computer vision Computational modeling Cognition Data models pattern recognition Task analysis

来源：评论

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ALGM: Adaptive Local-then-Global Token Merging for Efficient Semantic Segmentation with Plain vision Transformers

ALGM: Adaptive Local-then-Global Token Merging for Efficient...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Narges Norouzi Svetlana Orlova Daan De Geus Gijs Dubbelman Eindhoven University of Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

This work presents Adaptive Local-then-Global Merging (ALGM), a token reduction method for semantic segmentation networks that use plain vision Transformers. ALGM merges tokens in two stages: (1) In the first network layer, it merges similar tokens within a small local window and (2) halfway through the network, it merges similar tokens across the entire image. This is motivated by an analysis in which we found that, in those situations, tokens with a high cosine similarity can likely be merged without a drop in segmentation quality. With extensive experiments across multiple datasets and network configurations, we show that ALGM not only significantly improves the throughput by up to 100%, but can also enhance the mean IoU by up to + 1.1, thereby achieving a better trade-off between segmentation quality and efficiency than existing methods. Moreover, our approach is adaptive during inference, meaning that the same model can be used for optimal efficiency or accuracy, depending on the application. Code is available at https://***/ALGM.

关键词： computer vision Adaptation models Codes Adaptive systems Semantic segmentation Computational modeling Merging

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Learning Better Visual Dialog Agents with Pretrained Visual-Linguistic Representation

Learning Better Visual Dialog Agents with Pretrained Visual-...

引用

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

作者： Tu, Tao Ping, Qing Thattai, Govindarajan Tur, Gokhan Natarajan, Prem Natl Taiwan Univ Taipei Taiwan Amazon Alexa AI Sunnyvale CA 15212 USA

ISBN: (纸本)9781665445092

GuessWhat?! is a visual dialog guessing game which incorporates a Questioner agent that generates a sequence of questions, while an Oracle agent answers the respective questions about a target object in an image. Based on this dialog history between the Questioner and the Oracle, a Guesser agent makes a final guess of the target object. While previous work has focused on dialogue policy optimization and visual-linguistic information fusion, most work learns the vision-linguistic encoding for the three agents solely on the GuessWhat?! dataset without shared and prior knowledge of vision-linguistic representation. To bridge these gaps, this paper proposes new Oracle, Guesser and Questioner models that take advantage of a pretrained vision-linguistic model, VilBERT. For Oracle model, we introduce a two-way background/target fusion mechanism to understand both intra and inter-object questions. For Guesser model, we introduce a state-estimator that best utilizes VilBERT's strength in single-turn referring expression comprehension. For the Questioner, we share the state-estimator from pretrained Guesser with Questioner to guide the question generator. Experimental results show that our proposed models outperform state-of-the-art models significantly by 7%, 10%, 12% for Oracle, Guesser and End-to-End Questioner respectively.

关键词： Visualization computer vision Games Reinforcement learning Generators Encoding pattern recognition

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ManiFPT: Defining and Analyzing Fingerprints of Generative Models

ManiFPT: Defining and Analyzing Fingerprints of Generative M...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Hae Jin Song Mahyar Khayatkhoei Wael AbdAlmageed University of Southern California USC Information Sciences Institute Clemson University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Recent works have shown that generative models leave traces of their underlying generative process on the generated samples, broadly referred to as fingerprints of a generative model, and have studied their utility in detecting synthetic images from real ones. However, the extend to which these fingerprints can distinguish between various types of synthetic image and help identify the underlying generative process remain under-explored. In particular, the very definition of a fingerprint remains unclear, to our knowledge. To that end, in this work, we formalize the definition of artifact and fingerprint in generative models, propose an algorithm for computing them in practice, and finally study its effectiveness in distinguishing a large array of different generative models. We find that using our proposed definition can significantly improve the performance on the task of identifying the underlying generative process from samples (model attribution) compared to existing methods. Additionally, we study the structure of the fingerprints, and observe that it is very predictive of the effect of different design choices on the generative process.

关键词： Measurement Analytical models computer vision Computational modeling Fingerprint recognition

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Finsler-Laplace-Beltrami Operators with Application to Shape Analysis

Finsler-Laplace-Beltrami Operators with Application to Shape...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Simon Weber Thomas Dages Maolin Gao Daniel Cremers Technical University of Munich Munich Center for Machine Learning Technion - Israel Institute of Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The Laplace-Beltrami operator (LBO) emerges from studying manifolds equipped with a Riemannian metric. It is often called the swiss army knife of geometry processing as it allows to capture intrinsic shape information and gives rise to heat diffusion, geodesic distances, and a mul-titude of shape descriptors. It also plays a central role in geometric deep learning. In this work, we explore Finsler manifolds as a generalization of Riemannian manifolds. We revisit the Finsler heat equation and derive a Finsler heat kernel and a Finsler-Laplace-Beltrami Operator (FLBO): a novel theoretically justified anisotropic Laplace-Beltrami operator (ALBO). In experimental evaluations we demon-strate that the proposed FLBO is a valuable alternative to the traditional Riemannian-based LBO and ALBOs for spa-tialfiltering and shape correspondence estimation. We hope that the proposed Finsler heat kernel and the FLBO will inspire further exploration of Finsler geometry in the computer vision community.

关键词： Heating systems Manifolds Geometry Deep learning computer vision Shape Reviews

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Revisiting Sampson Approximations for Geometric Estimation Problems

Revisiting Sampson Approximations for Geometric Estimation P...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Felix Rydell Angélica Torres Viktor Larsson KTH Royal Institute of Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Many problems in computer vision can be formulated as geometric estimation problems, i.e. given a collection of measurements (e.g. point correspondences) we wish to fit a model (e.g. an essential matrix) that agrees with our observations. This necessitates some measure of how much an observation “agrees” with a given model. A natural choice is to consider the smallest perturbation that makes the observation exactly satisfy the constraints. However, for many problems, this metric is expensive or otherwise intractable to compute. The so-called Sampson error approximates this geometric error through a linearization scheme. For epipolar geometry, the Sampson error is a popular choice and in practice known to yield very tight approximations of the corresponding geometric residual (the reprojection error). In this paper we revisit the Sampson approximation and provide new theoretical insights as to why and when this approximation works, as well as provide explicit bounds on the tightness under some mild assumptions. Our theoretical results are validated in several experiments on real data and in the context of different geometric estimation tasks.

关键词： Measurement Geometry computer vision Computational modeling Perturbation methods Fitting Estimation

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Coherent Temporal Synthesis for Incremental Action Segmentation

Coherent Temporal Synthesis for Incremental Action Segmentat...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Guodong Ding Hans Golong Angela Yao National University of Singapore

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Data replay is a successful incremental learning technique for images. It prevents catastrophic forgetting by keeping a reservoir of previous data, original or synthe-sized, to ensure the model retains past knowledge while adapting to novel concepts. However, its application in the video domain is rudimentary, as it simply stores frame ex-emplars for action recognition. This paper presents the first exploration of video data replay techniques for incremen-tal action segmentation, focusing on action temporal modeling. We propose a Temporally Coherent Action (TCA) model, which represents actions using a generative model instead of storing individual frames. The integration of a conditioning variable that captures temporal coherence al-lows our model to understand the evolution of action features over time. Therefore, action segments generated by TCA for replay are diverse and temporally coherent. In a 10-task incremental setup on the Breakfast dataset, our approach achieves significant increases in accuracy for up to 22% compared to the baselines.

关键词： Adaptation models computer vision Incremental learning Computational modeling Focusing Coherence Benchmark testing

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CLIP-BEVFormer: Enhancing Multi-View Image-Based BEV Detector with Ground Truth Flow

CLIP-BEVFormer: Enhancing Multi-View Image-Based BEV Detecto...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Chenbin Pan Burhaneddin Yaman Senem Velipasalar Liu Ren Syracuse University Bosch Research North America & Bosch Center for Artificial Intelligence (BCAI)

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Autonomous driving stands as a pivotal domain in computer vision, shaping the future of transportation. Within this paradigm, the backbone of the system plays a crucial role in interpreting the complex environment. However, a notable challenge has been the loss of clear supervision when it comes to Bird's Eye View elements. To address this limitation, we introduce CLIP-BEVFormer, a novel approach that leverages the power of contrastive learning techniques to enhance the multi-view image-derived BEV backbones with ground truth information flow. We conduct extensive experiments on the challenging nuScenes dataset and showcase significant and consistent improvements over the SOTA. Specifically, CLIP-BEVFormer achieves an impressive 8.5% and 9.2% enhancement in terms of NDS and mAP, respectively, over the previous best BEV model on the 3D object detection task.

关键词： computer vision Solid modeling Three-dimensional displays Transportation Detectors Object detection Contrastive learning

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Generative Zero-shot Network Quantization

Generative Zero-shot Network Quantization

引用

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

作者： He, Xiangyu Lu, Jiahao Xu, Weixiang Hu, Qinghao Wang, Peisong Cheng, Jian Chinese Acad Sci Inst Automat Beijing Peoples R China

ISBN: (纸本)9781665448994

Convolutional neural networks are able to learn realistic image priors from numerous training samples in low-level image generation and restoration [66]. We show that, for high-level image recognition tasks, we can further reconstruct "realistic" images of each category by leveraging intrinsic Batch Normalization (BN) statistics without any training data. Inspired by the popular VAE/GAN methods, we regard the zero-shot optimization process of synthetic images as generative modeling to match the distribution of BN statistics. The generated images serve as a calibration set for the following zero-shot network quantizations. Our method meets the needs for quantizing models based on sensitive information, e.g., due to privacy concerns, no data is available. Extensive experiments on benchmark datasets show that, with the help of generated data, our approach consistently outperforms existing data-free quantization methods.

关键词： Training Quantization (signal) Image resolution Image synthesis Training data pattern recognition Image restoration

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Deep Image Compression with Latent Optimization and Piece-wise Quantization Approximation

Deep Image Compression with Latent Optimization and Piece-wi...

引用

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

作者： Wu, Yuyang Qi, Zhiyang Zheng, Huiming Tao, Lvfang Gao, Wei Peking Univ Sch Elect & Comp Engn Shenzhen Grad Sch Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665448994

Benefit from its capability of learning high-dimensional compact representation from raw data, the auto-encoders are widely used in various tasks of data compression. In particular, for deep image compression, auto-encoders generally take the responsibility of mapping original images to the latent representation to be coded. In this paper, we propose a new framework for deep image compression by devising a loss function for latent optimization, and adopting the differentiable approximation of quantization. In our experiments, both subjective and objective results can confirm the effectiveness of our contributions.

关键词： computer vision Image coding Quantization (signal) conferences Data compression pattern recognition Task analysis

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