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Guided Integrated Gradients: an Adaptive Path Method for Removing Noise

Guided Integrated Gradients: an Adaptive Path Method for Rem...

引用

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

作者： Kapishnikov, Andrei Venugopalan, Subhashini Avci, Besim Wedin, Ben Terry, Michael Bolukbasi, Tolga Google Res Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Integrated Gradients (IG) [29] is a commonly used feature attribution method for deep neural networks. While IG has many desirable properties, the method often produces spurious/noisy pixel attributions in regions that are not related to the predicted class when applied to visual models. While this has been previously noted [27], most existing solutions [25, 17] are aimed at addressing the symptoms by explicitly reducing the noise in the resulting attributions. In this work, we show that one of the causes of the problem is the accumulation of noise along the IG path. To minimize the effect of this source of noise, we propose adapting the attribution path itself - conditioning the path not just on the image but also on the model being explained. We introduce Adaptive Path Methods (APMs) as a generalization of path methods, and Guided IG as a specific instance of an APM. Empirically, Guided IG creates saliency maps better aligned with the model's prediction and the input image that is being explained. We show through qualitative and quantitative experiments that Guided IG outperforms other, related methods in nearly every experiment.

关键词： Measurement Deep learning Adaptation models Visualization computer vision Computational modeling Predictive models

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Learning Imbalanced Data with vision Transformers

Learning Imbalanced Data with Vision Transformers

引用

conference on computer vision and pattern recognition (cvpr)

作者： Zhengzhuo Xu Ruikang Liu Shuo Yang Zenghao Chai Chun Yuan Shenzhen International Graduate School Tsinghua University China

The real-world data tends to be heavily imbalanced and severely skew the data-driven deep neural networks, which makes Long-Tailed recognition (LTR) a massive challenging task. Existing LTR methods seldom train vision Transformers (ViTs) with Long-Tailed (LT) data, while the off-the-shelf pretrain weight of ViTs always leads to unfair comparisons. In this paper, we systematically investigate the ViTs' performance in LTR and propose LiVT to train ViTs from scratch only with LT data. With the observation that ViTs suffer more severe LTR problems, we conduct Masked Generative Pretraining (MGP) to learn generalized features. With ample and solid evidence, we show that MGP is more robust than supervised manners. Although Binary Cross Entropy (BCE) loss performs well with ViTs, it struggles on the LTR tasks. We further propose the balanced BCE to ameliorate it with strong theoretical groundings. Specially, we derive the unbiased extension of Sigmoid and compensate extra logit margins for deploying it. Our Bal-BCE contributes to the quick convergence of ViTs in just a few epochs. Extensive experiments demonstrate that with MGP and Bal-BCE, LiVT successfully trains ViTs well without any additional data and outperforms comparable state-of-the-art methods significantly, e.g., our ViT-B achieves 81.0% Top-1 accuracy in iNaturalist 2018 without bells and whistles. Code is available at https://***/XuZhengzhuo/LiVT.

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clDice - a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation

clDice - a Novel Topology-Preserving Loss Function for Tubul...

引用

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

作者： Shit, Suprosanna Paetzold, Johannes C. Sekuboyina, Anjany Ezhov, Ivan Unger, Alexander Zhylka, Andrey Pluim, Josien P. W. Bauer, Ulrich Menze, Bjoern H. Tech Univ Munich Munich Germany Eindhoven Univ Technol Eindhoven Netherlands

ISBN: (纸本)9781665445092

Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research. For such structures, the topology is their most important characteristic;particularly preserving connectedness: in the case of vascular networks, missing a connected vessel entirely alters the blood-flow dynamics. We introduce a novel similarity measure termed centerline-Dice (short clDice), which is calculated on the intersection of the segmentation masks and their (morphological) skeleta. We theoretically prove that clDice guarantees topology preservation up to homotopy equivalence for binary 2D and 3D segmentation. Extending this, we propose a computationally efficient, differentiable loss function (soft-clDice) for training arbitrary neural segmentation networks. We benchmark the soft-clDice loss on five public datasets, including vessels, roads and neurons (2D and 3D). Training on soft-clDice leads to segmentation with more accurate connectivity information, higher graph similarity, and better volumetric scores.

关键词： Training computer vision Three-dimensional displays Network topology Roads Neurons Neural networks

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Research on Identifying Differential Behavior in Complex Scenarios 39

Research on Identifying Differential Behavior in Complex Sce...

引用

39th Youth Academic Annual conference of Chinese-Association-of-Automation (YAC)

作者： Zhao, Shitao Fang, Kai Feng, Zihao China Acad Railway Sci Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9798350390780;9798350379228

Group behavior recognition is a significant research area in computer vision and pattern recognition. However, recognizing an individual who behaves differently from the majority in complex scenes, such as malls and transport stations, is even more crucial. This paper proposes a novel approach for group behavior recognition based on deep learning and graph theory. The approach comprises two main stages: feature extraction and graph-based classification. In the feature extraction stage, visual features are extracted from each individual in the group. In the graph-based classification stage, a graph representation of the group is constructed, and graph convolutional networks (GCNs) are utilized to classify the group behavior and differentiate the anomalous behavior.

关键词： Group behavior recognition complex scenarios

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Stay Positive: Non-Negative Image Synthesis for Augmented Reality

Stay Positive: Non-Negative Image Synthesis for Augmented Re...

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

作者： Luo, Katie Yang, Guandao Xian, Wenqi Haraldsson, Harald Hariharan, Bharath Belongie, Serge Cornell Univ Ithaca NY 14853 USA

ISBN: (纸本)9781665445092

In applications such as optical see-through and projector augmented reality, producing images amounts to solving non-negative image generation, where one can only add light to an existing image. Most image generation methods, however, are ill-suited to this problem setting, as they make the assumption that one can assign arbitrary color to each pixel. In fact, naive application of existing methods fails even in simple domains such as MNIST digits, since one cannot create darker pixels by adding light. We know, however, that the human visual system can be fooled by optical illusions involving certain spatial configurations of brightness and contrast. Our key insight is that one can leverage this behavior to produce high quality images with negligible artifacts. For example, we can create the illusion of darker patches by brightening surrounding pixels. We propose a novel optimization procedure to produce images that satisfy both semantic and non-negativity constraints. Our approach can incorporate existing state-of-the-art methods, and exhibits strong performance in a variety of tasks including image-to-image translation and style transfer.

关键词： Image synthesis Image color analysis Semantics Visual systems Optical imaging pattern recognition Task analysis

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PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds

PAConv: Position Adaptive Convolution with Dynamic Kernel As...

引用

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

作者： Xu, Mutian Ding, Runyu Zhao, Hengshuang Qi, Xiaojuan Univ Hong Kong Hong Kong Peoples R China Univ Oxford Oxford England

ISBN: (纸本)9781665445092

We introduce Position Adaptive Convolution (PAConv), a generic convolution operation for 3D point cloud processing. The key of PAConv is to construct the convolution kernel by dynamically assembling basic weight matrices stored in Weight Bank, where the coefficients of these weight matrices are self-adaptively learned from point positions through ScoreNet. In this way, the kernel is built in a data-driven manner, endowing PAConv with more flexibility than 2D convolutions to better handle the irregular and unordered point cloud data. Besides, the complexity of the learning process is reduced by combining weight matrices instead of brutally predicting kernels from point positions. Furthermore, different from the existing point convolution operators whose network architectures are often heavily engineered, we integrate our PAConv into classical MLP-based point cloud pipelines without changing network configurations. Even built on simple networks, our method still approaches or even surpasses the state-of-the-art models, and significantly improves baseline performance on both classification and segmentation tasks, yet with decent efficiency. Thorough ablation studies and visualizations are provided to understand PAConv. Code is released on hups://***/CVMI-Lab/PAConv.

关键词： computer vision Three-dimensional displays Convolution Computational modeling Pipelines Network architecture pattern recognition

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TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors

TrafficSim: Learning to Simulate Realistic Multi-Agent Behav...

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

作者： Suo, Simon Regalado, Sebastian Casas, Sergio Urtasun, Raquel Uber ATG Pittsburgh PA 15201 USA Univ Toronto Toronto ON Canada Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9781665445092

Simulation has the potential to massively scale evaluation of self-driving systems, enabling rapid development as well as safe deployment. Bridging the gap between simulation and the real world requires realistic multi-agent behaviors. Existing simulation environments rely on heuristic-based models that directly encode traffic rules, which cannot capture irregular maneuvers (e.g., nudging, U-turns) and complex interactions (e.g., yielding, merging). In contrast, we leverage real-world data to learn directly from human demonstration, and thus capture more naturalistic driving behaviors. To this end, we propose TRAFFICSIM, a multi-agent behavior model for realistic traffic simulation. In particular, we parameterize the policy with an implicit latent variable model that generates socially-consistent plans for all actors in the scene jointly. To learn a robust policy amenable for long horizon simulation, we unroll the policy in training and optimize through the fully differentiable simulation across time. Our learning objective incorporates both human demonstrations as well as common sense. We show TRAFFICSIM generates significantly more realistic traffic scenarios as compared to a diverse set of baselines. Notably, we can exploit trajectories generated by TRAFFICSIM as effective data augmentation for training better motion planner.

关键词： Training computer vision Merging Traffic control Trajectory Safety pattern recognition

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Intelligent Carpet: Inferring 3D Human Pose from Tactile Signals

Intelligent Carpet: Inferring 3D Human Pose from Tactile Sig...

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

作者： Luo, Yiyue Li, Yunzhu Foshey, Michael Shou, Wan Sharma, Pratyusha Palacios, Tomas Torralba, Antonio Matusik, Wojciech MIT Cambridge MA 02139 USA

ISBN: (纸本)9781665445092

Daily human activities, e.g., locomotion, exercises, and resting, are heavily guided by the tactile interactions between the human and the ground. In this work, leveraging such tactile interactions, we propose a 3D human pose estimation approach using the pressure maps recorded by a tactile carpet as input. We build a low-cost, high-density, large-scale intelligent carpet, which enables the real-time recordings of human-floor tactile interactions in a seamless manner. We collect a synchronized tactile and visual dataset on various human activities. Employing a state-ofthe-art camera-based pose estimation model as supervision, we design and implement a deep neural network model to infer 3D human poses using only the tactile information. Our pipeline can be further scaled up to multi-person pose estimation. We evaluate our system and demonstrate its potential applications in diverse fields.

关键词： Visualization Solid modeling Three-dimensional displays Machine vision Pose estimation Smart homes Skeleton

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Action Shuffle Alternating Learning for Unsupervised Action Segmentation

Action Shuffle Alternating Learning for Unsupervised Action ...

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

作者： Li, Jun Todorovic, Sinisa Oregon State Univ Corvallis OR 97331 USA

ISBN: (纸本)9781665445092

This paper addresses unsupervised action segmentation. Prior work captures the frame-level temporal structure of videos by a feature embedding that encodes time locations of frames in the video. We advance prior work with a new self-supervised learning (SSL) of a feature embedding that accounts for both frame- and action-level structure of videos. Our SSL trains an RNN to recognize positive and negative action sequences, and the RNN's hidden layer is taken as our new action-level feature embedding. The positive and negative sequences consist of action segments sampled from videos, where in the former the sampled action segments respect their time ordering in the video, and in the latter they are shuffled. As supervision of actions is not available and our SSL requires access to action segments, we specify an HMM that explicitly models action lengths, and infer a MAP action segmentation with the Viterbi algorithm. The resulting action segmentation is used as pseudo-ground truth for estimating our action-level feature embedding and updating the HMM. We alternate the above steps within the Generalized EM framework, which ensures convergence. Our evaluation on the Breakfast, YouTube Instructions, and 50Salads datasets gives superior results to those of the state of the art.

关键词： Training computer vision Viterbi algorithm Computational modeling Hidden Markov models pattern recognition Videos

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Learning Continuous Image Representation with Local Implicit Image Function

Learning Continuous Image Representation with Local Implicit...

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

作者： Chen, Yinbo Liu, Sifei Wang, Xiaolong Univ Calif San Diego La Jolla CA 92093 USA NVIDIA Santa Clara CA USA

ISBN: (纸本)9781665445092

How to represent an image? While the visual world is presented in a continuous manner, machines store and see the images in a discrete way with 2D arrays of pixels. In this paper, we seek to learn a continuous representation for images. Inspired by the recent progress in 3D reconstruction with implicit neural representation, we propose Local Implicit Image Function (LIIF), which takes an image coordinate and the 2D deep features around the coordinate as inputs, predicts the RGB value at a given coordinate as an output. Since the coordinates are continuous, LIIF can be presented in arbitrary resolution. To generate the continuous representation for images, we train an encoder with LIIF representation via a self-supervised task with super-resolution. The learned continuous representation can be presented in arbitrary resolution even extrapolate to x30 higher resolution, where the training tasks are not provided. We further show that LIIF representation builds a bridge between discrete and continuous representation in 2D, it naturally supports the learning tasks with size-varied image ground-truths and significantly outperforms the method with resizing the ground-truths.

关键词： Training Bridges Visualization computer vision Three-dimensional displays Superresolution Image representation

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