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Graph classification and clustering based on vector space embedding
This book is concerned with a fundamentally novel approach to graph-based pattern recognition based on vector space embedding of graphs. It aims at condensing the high representational power of graphs into a computationally efficient and mathematically convenient feature vector.
eBook
Diagrammatic Reasoning in AI
Pioneering work shows how using Diagrams facilitates the design of better AI systems The publication of Diagrammatic Reasoning in AI marks an important milestone for anyone seeking to design graphical user interfaces to support decision-making and problem-solving tasks. The author expertly demonstrates how diagrammatic representations can simplify our interaction with increasingly complex information technologies and computer-based information systems. In particular, the book emphasizes how diagrammatic user interfaces can help us better understand and visualize artificial intelligence (AI) systems. It examines how diagrammatic reasoning enhances various AI programming strategies used to emulate human thinking and problem-solving, including: Expert systems Model-based reasoning Inexact reasoning such as certainty factors and Bayesian networks Logic reasoning A key part of the book is its extensive development of applications and graphical illustrations, drawing on such fields as the physical sciences, macroeconomics, finance, business logistics management, and medicine. Despite such tremendous diversity of usage, in terms of applications and diagramming notations, the book classifies and organizes diagrams around six major themes: system topology; sequence and flow; hierarchy and classification; association; cause and effect; and logic reasoning. Readers will benefit from the author's discussion of how diagrams can be more than just a static picture or representation and how diagrams can be a central part of an intelligent user interface, meant to be manipulated and modified, and in some cases, utilized to infer solutions to difficult problems. This book is ideal for many different types of readers: practitioners and researchers in AI and human-computer interaction; business and computing professionals; graphic designers and designers of graphical user interfaces; and just about anyone interested in understanding the power of diagrams. By discovering the many different types of diagrams and their applications in AI, all readers will gain a deeper appreciation of diagrammatic reasoning.
eBook
The Atlas of AI
The hidden costs of artificial intelligence, from natural
resources and labor to privacy and freedom What happens
when artificial intelligence saturates political life and depletes
the planet? How is AI shaping our understanding of ourselves and
our societies? In this book Kate Crawford reveals how this
planetary network is fueling a shift toward undemocratic governance
and increased inequality. Drawing on more than a decade of
research, award-winning science, and technology, Crawford reveals
how AI is a technology of extraction: from the energy and minerals
needed to build and sustain its infrastructure, to the exploited
workers behind \"automated\" services, to the data AI collects from
us. Rather than taking a narrow focus on code and algorithms,
Crawford offers us a political and a material perspective on what
it takes to make artificial intelligence and where it goes wrong.
While technical systems present a veneer of objectivity, they are
always systems of power. This is an urgent account of what is at
stake as technology companies use artificial intelligence to
reshape the world.
eBook
Exploiting Diffusion Prior for Real-World Image Super-Resolution
We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution. Specifically, by employing our time-aware encoder, we can achieve promising restoration results without altering the pre-trained synthesis model, thereby preserving the generative prior and minimizing training cost. To remedy the loss of fidelity caused by the inherent stochasticity of diffusion models, we employ a controllable feature wrapping module that allows users to balance quality and fidelity by simply adjusting a scalar value during the inference process. Moreover, we develop a progressive aggregation sampling strategy to overcome the fixed-size constraints of pre-trained diffusion models, enabling adaptation to resolutions of any size. A comprehensive evaluation of our method using both synthetic and real-world benchmarks demonstrates its superiority over current state-of-the-art approaches. Code and models are available at https://github.com/IceClear/StableSR.
Journal Article
Springer handbook of robotics
The second edition of this handbook provides a state-of-the-art overview on the various aspects in the rapidly developing field of robotics. Reaching for the human frontier, robotics is vigorously engaged in the growing challenges of new emerging domains.
eBook
Context Autoencoder for Self-supervised Representation Learning
We present a novel masked image modeling (MIM) approach, context autoencoder (CAE), for self-supervised representation pretraining. We pretrain an encoder by making predictions in the encoded representation space. The pretraining tasks include two tasks: masked representation prediction—predict the representations for the masked patches, and masked patch reconstruction—reconstruct the masked patches. The network is an encoder–regressor–decoder architecture: the encoder takes the visible patches as input; the regressor predicts the representations of the masked patches, which are expected to be aligned with the representations computed from the encoder, using the representations of visible patches and the positions of visible and masked patches; the decoder reconstructs the masked patches from the predicted encoded representations. The CAE design encourages the separation of learning the encoder (representation) from completing the pertaining tasks: masked representation prediction and masked patch reconstruction tasks, and making predictions in the encoded representation space empirically shows the benefit to representation learning. We demonstrate the effectiveness of our CAE through superior transfer performance in downstream tasks: semantic segmentation, object detection and instance segmentation, and classification. The code will be available at https://github.com/Atten4Vis/CAE.
Journal Article
Image Matching Across Wide Baselines: From Paper to Practice
We introduce a comprehensive benchmark for local features and robust estimation algorithms, focusing on the downstream task—the accuracy of the reconstructed camera pose—as our primary metric. Our pipeline’s modular structure allows easy integration, configuration, and combination of different methods and heuristics. This is demonstrated by embedding dozens of popular algorithms and evaluating them, from seminal works to the cutting edge of machine learning research. We show that with proper settings, classical solutions may still outperform the perceived state of the art. Besides establishing the actual state of the art, the conducted experiments reveal unexpected properties of structure from motion pipelines that can help improve their performance, for both algorithmic and learned methods. Data and code are online (https://github.com/ubc-vision/image-matching-benchmark), providing an easy-to-use and flexible framework for the benchmarking of local features and robust estimation methods, both alongside and against top-performing methods. This work provides a basis for the Image Matching Challenge (https://image-matching-challenge.github.io).
Journal Article
Unsupervised Scale-Consistent Depth Learning from Video
We propose a monocular depth estimation method SC-Depth, which requires only unlabelled videos for training and enables the scale-consistent prediction at inference time. Our contributions include: (i) we propose a geometry consistency loss, which penalizes the inconsistency of predicted depths between adjacent views; (ii) we propose a self-discovered mask to automatically localize moving objects that violate the underlying static scene assumption and cause noisy signals during training; (iii) we demonstrate the efficacy of each component with a detailed ablation study and show high-quality depth estimation results in both KITTI and NYUv2 datasets. Moreover, thanks to the capability of scale-consistent prediction, we show that our monocular-trained deep networks are readily integrated into ORB-SLAM2 system for more robust and accurate tracking. The proposed hybrid Pseudo-RGBD SLAM shows compelling results in KITTI, and it generalizes well to the KAIST dataset without additional training. Finally, we provide several demos for qualitative evaluation. The source code is released on GitHub.
Journal Article