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Ante-Hoc Generation of Task-Agnostic Interpretation Maps

Ante-Hoc Generation of Task-Agnostic Interpretation Maps

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Akash Guna, R.T. Benitez, Raul Sikha, O.K. Amrita School of Engineering Department of Computer Science and Engineering Coimbatore India Universitat Politècnica de Catalunya BarcelonaTech Departament of Automatic Control Spain

ISBN: (纸本)9798350302493

Existing explainability approaches for convolutional neural networks (CNNs) are mainly applied after training (post-hoc) which is generally unreliable. Ante-hoc explainers trained simultaneously with the CNN are more reliable. However, current ante-hoc explanation methods mainly generate inexplicit concept-based explanations tailored to specific tasks. To address these limitations, we propose a task-agnostic ante-hoc framework that can generate interpretation maps to visually explain any CNN. Our framework simultaneously trains the CNN and a weighting network - an explanation generation module. The generated maps are self-explanatory, eliminating the need for manual identification of concepts. We demonstrate that our method can interpret classification, facial landmark detection, and image captioning tasks. We show that our framework is explicit, faithful, and stable through experiments. To the best of our knowledge, this is the first ante-hoc CNN explanation strategy that produces visual explanations generic to CNNs for different tasks. © 2023 ieee.

关键词： computer vision

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A Closer Look at Blind Super-Resolution: Degradation Models, Baselines, and Performance Upper Bounds

A Closer Look at Blind Super-Resolution: Degradation Models,...

引用

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

作者： Zhang, Wenlong Shi, Guangyuan Liu, Yihao Dong, Chao Wu, Xiao-Ming HongKong Polytech Univ Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Degradation models play an important role in Blind super-resolution (SR). The classical degradation model, which mainly involves blur degradation, is too simple to simulate real-world scenarios. The recently proposed practical degradation model includes a full spectrum of degradation types, but only considers complex cases that use all degradation types in the degradation process, while ignoring many important corner cases that are common in the real world. To address this problem, we propose a unified gated degradation model to generate a broad set of degradation cases using a random gate controller. Based on the gated degradation model, we propose simple baseline networks that can effectively handle non-blind, classical, practical degradation cases as well as many other corner cases. To fairly evaluate the performance of our baseline networks against state-of-the-art methods and understand their limits, we introduce the performance upper bound of an SR network for every degradation type. Our empirical analysis shows that with the unified gated degradation model, the proposed baselines can achieve much better performance than existing methods in quantitative and qualitative results, which are close to the performance upper bounds.

关键词： Degradation Analytical models computer vision Upper bound conferences Superresolution Logic gates

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Multi-level Domain Adaptation for Lane Detection

Multi-level Domain Adaptation for Lane Detection

引用

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

作者： Li, Chenguang Zhang, Boheng Shi, Jia Cheng, Guangliang SenseTime Res Shanghai Peoples R China Tsinghua Univ Beijing Peoples R China Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA Shanghai AI Lab Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We focus on bridging domain discrepancy in lane detection among different scenarios to greatly reduce extra annotation and re-training costs for autonomous driving. Critical factors hinder the performance improvement of cross-domain lane detection that conventional methods only focus on pixel-wise loss while ignoring shape and position priors of lanes. To address the issue, we propose the Multi-level Domain Adaptation (MLDA) framework, a new perspective to handle cross-domain lane detection at three complementary semantic levels of pixel, instance and category. Specifically, at pixel level, we propose to apply cross-class confidence constraints in self-training to tackle the imbalanced confidence distribution of lane and background. At instance level, we go beyond pixels to treat segmented lanes as instances and facilitate discriminative features in target domain with triplet learning, which effectively rebuilds the semantic context of lanes and contributes to alleviating the feature confusion. At category level, we propose an adaptive inter-domain embedding module to utilize the position prior of lanes during adaptation. In two challenging datasets, i.e. TuSimple and CULane, our approach improves lane detection performance by a large margin with gains of 8.8% on accuracy and 7.4% on F1-score respectively, compared with state-of-the-art domain adaptation algorithms.

关键词： computer vision Costs Lane detection Shape Image edge detection conferences Semantics

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DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D vision

DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-bas...

引用

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

作者： Ling, Lu Sheng, Yichen Tu, Zhi Zhao, Wentian Xin, Cheng Wan, Kun Yu, Lantao Guo, Qianyu Yu, Zixun Lu, Yawen Li, Xuanmao Sun, Xingpeng Ashok, Rohan Mukherjee, Aniruddha Kang, Hao Kong, Xiangrui Hua, Gang Zhang, Tianyi Benes, Bedrich Bera, Aniket Purdue Univ W Lafayette IN 47907 USA Adobe Inc San Jose CA USA Rutgers State Univ New Brunswick NJ USA Google Inc Mountain View CA USA Huazhong Univ Sci & Technol Wuhan Peoples R China Wormpex AI Res Bellevue WA USA

ISBN: (纸本)9798350353006

We have witnessed significant progress in deep learning-based 3D vision, ranging from neural radiance field (NeRF) based 3D representation learning to applications in novel view synthesis (NVS). However, existing scene-level datasets for deep learning-based 3D vision, limited to either synthetic environments or a narrow selection of real-world scenes, are quite insufficient. This insufficiency not only hinders a comprehensive benchmark of existing methods but also caps what could be explored in deep learning-based 3D analysis. To address this critical gap, we present DL3DV-10K, a large-scale scene dataset, featuring 51.2 million frames from 10,510 videos captured from 65 types of point-of-interest (POI) locations, covering both bounded and unbounded scenes, with different levels of reflection, transparency, and lighting. We conducted a comprehensive benchmark of recent NVS methods on DL3DV-10K, which revealed valuable insights for future research in NVS. In addition, we have obtained encouraging results in a pilot study to learn generalizable NeRF from DL3DV-10K, which manifests the necessity of a large-scale scene-level dataset to forge a path toward a foundation model for learning 3D representation. Our DL3DV-10K dataset, benchmark results, and models will be publicly accessible.

关键词： Contrastive Learning

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SketchXAI: A First Look at Explainability for Human Sketches

SketchXAI: A First Look at Explainability for Human Sketches

引用

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

作者： Qu, Zhiyu Gryaditskayal, Yulia Li, Ke Pang, Kaiyue Xiang, Tao Song, Yi-Zhe Univ Surrey SketchX CVSSP Guildford Surrey England Beijing Univ Posts & Telecommun Beijing Peoples R China IFlyTek Surrey Joint Res Ctr Artificial Intellige Guildford Surrey England

ISBN: (纸本)9798350301298

This paper, for the very first time, introduces human sketches to the landscape of XAI (Explainable Artificial Intelligence). We argue that sketch as a "human-centred" data form, represents a natural interface to study explainability. We focus on cultivating sketch-specific explainability designs. This starts by identifying strokes as a unique building block that offers a degree of flexibility in object construction and manipulation impossible in photos. Following this, we design a simple explainability-friendly sketch encoder that accommodates the intrinsic properties of strokes: shape, location, and order. We then move on to define the first ever XAI task for sketch, that of stroke location inversion (SLI). Just as we have heat maps for photos, and correlation matrices for text, SLI offers an explainability angle to sketch in terms of asking a network how well it can recover stroke locations of an unseen sketch. We offer qualitative results for readers to interpret as snapshots of the SLI process in the paper, and as GIFs on the project page. A minor but interesting note is that thanks to its sketch-specific design, our sketch encoder also yields the best sketch recognition accuracy to date while having the smallest number of parameters. The code is available at https://***.

关键词： Explainable computer vision

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Trust Your IMU: Consequences of Ignoring the IMU Drift

Trust Your IMU: Consequences of Ignoring the IMU Drift

引用

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

作者： Ornhag, Marcus Valtonen Persson, Patrik Wadenback, Marten Astrom, Kalle Heyden, Anders Lund Univ Ctr Math Sci Lund Sweden Linkoping Univ Dept Elect Engn Linkoping Sweden

ISBN: (纸本)9781665487399

In this paper, we argue that modern pre-integration methods for inertial measurement units (IMUs) are accurate enough to ignore the drift for short time intervals. This allows us to consider a simplified camera model, which in turn admits further intrinsic calibration. We develop the first-ever solver to jointly solve the relative pose problem with unknown and equal focal length and radial distortion profile while utilizing the IMU data. Furthermore, we show significant speed-up compared to state-of-the-art algorithms, with small or negligible loss in accuracy for partially calibrated setups. The proposed algorithms are tested on both synthetic and real data, where the latter is focused on navigation using unmanned aerial vehicles (UAVs). We evaluate the proposed solvers on different commercially available low-cost UAVs, and demonstrate that the novel assumption on IMU drift is feasible in real-life applications. The extended intrinsic auto-calibration enables us to use distorted input images, making tedious calibration processes obsolete, compared to current state-of-the-art methods.

关键词： computer vision Measurement units Codes conferences Inertial navigation Distortion Real-time systems

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GAF-NAU: Gramian Angular Field encoded Neighborhood Attention U-Net for Pixel-Wise Hyperspectral Image Classification

GAF-NAU: Gramian Angular Field encoded Neighborhood Attentio...

引用

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

作者： Paheding, Sidike Reyes, Abel A. Kasaragod, Anush Oommen, Thomas Michigan Technol Univ Houghton MI 49931 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Hyperspectral image (HSI) classification is the most vibrant area of research in the hyperspectral community due to the rich spectral information contained in HSI can greatly aid in identifying objects of interest. However, inherent non-linearity between materials and the corresponding spectral profiles brings two major challenges in HSI classification: interclass similarity and intraclass variability. Many advanced deep learning methods have attempted to address these issues from the perspective of a region/patch-based approach, instead of a pixel-based alternate. However, the patch-based approaches hypothesize that neighborhood pixels of a target pixel in a fixed spatial window belong to the same class. And this assumption is not always true. To address this problem, we herein propose a new deep learning architecture, namely Gramian Angular Field encoded Neighborhood Attention U-Net (GAF-NAU), for pixel-based HSI classification. The proposed method does not require regions or patches centered around a raw target pixel to perform 2D-CNN based classification, instead, our approach transforms 1D pixel vector in HSI into 2D angular feature space using Gramian Angular Field (GAF) and then embed it to a new neighborhood attention network to suppress irrelevant angular feature while emphasizing on pertinent features useful for HSI classification task. Evaluation results on three publicly available HSI datasets demonstrate the superior performance of the proposed model. The source code available at https://***/MAIN-Lab/GAF-NAU/

关键词： Training Deep learning Transforms Logic gates pattern recognition Object recognition Task analysis

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HSI-Guided Intrinsic Image Decomposition for Outdoor Scenes

HSI-Guided Intrinsic Image Decomposition for Outdoor Scenes

引用

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

作者： Zhang, Fan You, Shaodi Li, Yu Fu, Ying Beijing Inst Technol Beijing Peoples R China Univ Amsterdam Amsterdam Netherlands Int Digital Econ Acad Shenzhen Peoples R China Jiangsu Univ Sci & Technol Zhenjiang Jiangsu Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Intrinisic image decomposition (IID) aims to recover the reflectance and shading components from images and is the prerequisite to many downstream computer vision applications, such as image editing and image relighting. Due to the inherent difficulty in acquiring ground truth reflectance and shading, existing datasets are either synthetic indoor scenes or objects using graphics rendering (e.g., CGIntrinsics and ShapeNet etc.) or real photos with very sparse manual annotation (e.g., IIW and SAW etc.). Accompanied with the complex nature of outdoor scenes, most IID methods focus on the decomposition of indoor environment. There is still a long way to go before we can handle IID of outdoor scenes. In this paper, we take the attempt to perform intrinsic image decomposition for outdoor scenes when RGB image is not the only thing we can get from the enviroment. With the observation of prior work where nir-infrared (NIR) images are transparent to a range of colourants/dyes, we propose to extend it to more spectra by collecting hyperspectral imaging (HSI) data which are well aligned with RGB images and to perform IID with both of them. We also apply existing mainstream IID methods for comparison to examine current progress and challenges at the road towards IID outdoors. We still make some improvements and find problems when performing IID for outdoor scenes, even though we do not handle it perfectly. The data we collect will be made publicly available for further potential investigation.

关键词： Reflectivity computer vision Roads Surface acoustic waves Manuals Rendering (computer graphics) Image decomposition

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Fast and Memory-Efficient Network Towards Efficient Image Super-Resolution

Fast and Memory-Efficient Network Towards Efficient Image Su...

引用

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

作者： Du, Zongcai Liu, Ding Liu, Jie Tang, Jie Wu, Gangshan Fu, Lean Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China ByteDance Inc Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Runtime and memory consumption are two important aspects for efficient image super-resolution (EISR) models to be deployed on resource-constrained devices. Recent advances in EISR [16, 32] exploit distillation and aggregation strategies with plenty of channel split and concatenation operations to fully use limited hierarchical features. In contrast, sequential network operations avoid frequently accessing preceding states and extra nodes, and thus are beneficial to reducing the memory consumption and runtime overhead. Following this idea, we design our lightweight network backbone by mainly stacking multiple highly optimized convolution and activation layers and decreasing the usage of feature fusion. We propose a novel sequential attention branch, where every pixel is assigned an important factor according to local and global contexts, to enhance high-frequency details. In addition, we tailor the residual block for EISR and propose an enhanced residual block (ERB) to further accelerate the network inference. Finally, combining all the above techniques, we construct a fast and memory-efficient network (FMEN) and its small version FMEN-S, which runs 33% faster and reduces 74% memory consumption compared with the state-of-the-art EISR model: E-RFDN, the champion in [49]. Besides, FMEN-S achieves the lowest memory consumption and the second shortest runtime in NTIRE 2022 challenge on efficient super-resolution [28]. Code is available at https://***/NJU-Jet/FMEN.

关键词： computer vision Runtime Codes Convolution conferences Memory management Superresolution

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A Hybrid Network of CNN and Transformer for Lightweight Image Super-Resolution

A Hybrid Network of CNN and Transformer for Lightweight Imag...

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

作者： Fang, Jinsheng Lin, Hanjiang Chen, Xinyu Zeng, Kun Minnan Normal Univ Zhangzhou Peoples R China Minjiang Univ Fuzhou Fujian Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recently, a number of CNN based methods have made great progress in single image super-resolution. However, these existing architectures commonly build massive number of network layers, bringing high computational complexity and heavy memory consumption, which is inappropriate to be applied on embedded terminals such as mobile platforms. In order to solve this problem, we propose a hybrid network of CNN and Transformer (HNCT) for lightweight image super-resolution. In general, HNCT consists of four parts, which are shallow feature extraction module, Hybrid Blocks of CNN and Transformer (HBCTs), dense feature fusion module and up-sampling module, respectively. By combining CNN and Transformer, HBCT extracts deep features beneficial for super-resolution reconstruction in consideration of both local and non-local priors, while being lightweight and flexible enough. Enhanced spatial attention is introduced in HBCT to further improve performance. Extensive experimental results show our HNCT is superior to the state-of-the-art methods in terms of super-resolution performance and model complexity. Moreover, we won the second best PSNR and the least activation operations in NTIRE 2022 Efficient SR Challenge. Code is available at https://***/lhjthp/HNCT.

关键词： computer vision Codes conferences Superresolution Memory management Transformers Feature extraction

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