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Feature-level Frankenstein: Eliminating Variations for Discriminative recognition 32

Feature-level Frankenstein: Eliminating Variations for Discr...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Liu, Xiaofeng Li, Site Kong, Lingsheng Xie, Wanqing Jia, Ping You, Jane Kumar, B. V. K. Carnegie Mellon Univ Pittsburgh PA 15213 USA Chinese Acad Sci CIOMP Changchun Peoples R China Harbin Engn Univ Harbin Peoples R China Harvard Univ Cambridge MA 02138 USA Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

ISBN: (纸本)9781728132938

Recent successes of deep learning-based recognition rely on maintaining the content related to the main-task label. However, how to explicitly dispel the noisy signals for better generalization remains an open issue. We systematically summarize the detrimental factors as task-relevant/irrelevant semantic variations and unspecified latent variation. In this paper, we cast these problems as an adversarial minimax game in the latent space. Specifically, we propose equipping an end-to-end conditional adversarial network with the ability to decompose an input sample into three complementary parts. The discriminative representation inherits the desired invariance property guided by prior knowledge of the task, which is marginally independent to the task-relevant/irrelevant semantic and latent variations. Our proposed framework achieves top performance on a serials of tasks, including digits recognition, lighting, makeup, disguise-tolerant face recognition, and facial attributes recognition.

关键词： Biometrics Categorization recognition: Detection Retrieval

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Practical Evaluation of Adversarial Robustness via Adaptive Auto Attack

Practical Evaluation of Adversarial Robustness via Adaptive ...

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

作者： Liu, Ye Cheng, Yaya Gao, Lianli Liu, Xianglong Zhang, Qilong Song, Jingkuan Univ Elect Sci & Technol China Ctr Future Media Chengdu Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China Beihang Univ Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Defense models against adversarial attacks have grown significantly, but the lack of practical evaluation methods has hindered progress. Evaluation can be defined as looking for defense models' lower bound of robustness given a budget number of iterations and a test dataset. A practical evaluation method should be convenient (i.e., parameterfree), efficient (i.e., fewer iterations) and reliable (i.e., approaching the lower bound of robustness). Towards this target, we propose a parameter-free Adaptive Auto Attack (A3) evaluation method which addresses the efficiency and reliability in a test-time-training fashion. Specifically, by observing that adversarial examples to a specific defense model follow some regularities in their starting points, we design an Adaptive Direction Initialization strategy to speed up the evaluation. Furthermore, to approach the lower bound of robustness under the budget number of iterations, we propose an online statistics-based discarding strategy that automatically identifies and abandons hard-to-attack images. Extensive experiments on nearly 50 widely-used defense models demonstrate the effectiveness of our A3. By consuming much fewer iterations than existing methods, i.e., 1/10 on average (10x speed up), we achieve lower robust accuracy in all cases. Notably, we won first place out of 1681 teams in cvpr 2021 White-box Adversarial Attacks on Defense Models competitions with this method. Code is available at: https://***/liuye6666/adaptive_auto_attack

关键词： Adaptation models computer vision Codes Computational modeling Robustness pattern recognition Iterative methods

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The Fastest Deformable Part Model for Object Detection 27

The Fastest Deformable Part Model for Object Detection

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Yan, Junjie Lei, Zhen Wen, Longyin Li, Stan Z. Chinese Acad Sci Inst Automat Ctr Biometr & Secur Res Beijing 100864 Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100864 Peoples R China

ISBN: (纸本)9781479951178

This paper solves the speed bottleneck of deformable part model (DPM), while maintaining the accuracy in detection on challenging datasets. Three prohibitive steps in cascade version of DPM are accelerated, including 2D correlation between root filter and feature map, cascade part pruning and HOG feature extraction. For 2D correlation, the root filter is constrained to be low rank, so that 2D correlation can be calculated by more efficient linear combination of 1D correlations. A proximal gradient algorithm is adopted to progressively learn the low rank filter in a discriminative manner. For cascade part pruning, neighborhood aware cascade is proposed to capture the dependence in neighborhood regions for aggressive pruning. Instead of explicit computation of part scores, hypotheses can be pruned by scores of neighborhoods under the first order approximation. For HOG feature extraction, look-up tables are constructed to replace expensive calculations of orientation partition and magnitude with simpler matrix index operations. Extensive experiments show that (a) the proposed method is 4 times faster than the current fastest DPM method with similar accuracy on Pascal VOC, (b) the proposed method achieves state-of-the-art accuracy on pedestrian and face detection task with frame-rate speed.

关键词： deformable part model face detection object detection Object detection Facial recognition digital power management Deformable models cascades Neighborhood tree surgery

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Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs

Eyes Wide Shut? Exploring the Visual Shortcomings of Multimo...

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

作者： Tong, Shengbang Liu, Zhuang Zhai, Yuexiang Ma, Yi Lecun, Yann Xie, Saining NYU New York NY 10003 USA Meta FAIR Menlo Pk CA 94025 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (纸本)9798350353006

Is vision good enough for language? Recent advancements in multimodal models primarily stem from the powerful reasoning abilities of large language models (LLMs). However, the visual component typically depends only on the instance-level contrastive language-image pre-training ( CLIP). Our research reveals that the visual capabilities in recent MultiModal LLMs (MLLMs) still exhibit systematic shortcomings. To understand the roots of these errors, we explore the gap between the visual embedding space of CLIP and vision-only self-supervised learning. We identify "CLIP-blind pairs" - images that CLIP perceives as similar despite their clear visual differences. With these pairs, we construct the Multimodal Visual patterns (MMVP) benchmark. MMVP exposes areas where state-of-the-art systems, including GPT-4V, struggle with straightforward questions across nine basic visual patterns, often providing incorrect answers and hallucinated explanations. We further evaluate various CLIP-based vision-and-language models and found a notable correlation between visual patterns that challenge CLIP models and those problematic for multimodal LLMs. As an initial effort to address these issues, we propose a Mixture of Features (MoF) approach, demonstrating that integrating vision self-supervised learning features with MLLMs can significantly enhance their visual grounding capabilities. Together, our research suggests visual representation learning remains an open challenge, and accurate visual grounding is crucial for future successful multimodal systems.

关键词： Multimodal LLMs vision Language Model

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Divide-and-Conquer for Lane-Aware Diverse Trajectory Prediction

Divide-and-Conquer for Lane-Aware Diverse Trajectory Predict...

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

作者： Narayanan, Sriram Moslemi, Ramin Pittaluga, Francesco Liu, Buyu Chandraker, Manmohan NEC Labs Amer Princeton NJ 08540 USA Univ Calif San Diego San Diego CA USA

ISBN: (纸本)9781665445092

Trajectory prediction is a safety-critical tool for autonomous vehicles to plan and execute actions. Our work addresses two key challenges in trajectory prediction, learning multimodal outputs, and better predictions by imposing constraints using driving knowledge. Recent methods have achieved strong performances using Multi-Choice Learning objectives like winner-takes-all (WTA) or best-of-many. But the impact of those methods in learning diverse hypotheses is under-studied as such objectives highly depend on their initialization for diversity. As our first contribution, we propose a novel Divide-And-Conquer (DAC) approach that acts as a better initialization technique to WTA objective, resulting in diverse outputs without any spurious modes. Our second contribution is a novel trajectory prediction framework called ALAN that uses existing lane centerlines as anchors to provide trajectories constrained to the input lanes. Our framework provides multi-agent trajectory outputs in a forward pass by capturing interactions through hypercolumn descriptors and incorporating scene information in the form of rasterized images and per-agent lane anchors. Experiments on synthetic and real data show that the proposed DAC captures the data distribution better compare to other WTA family of objectives. Further, we show that our ALAN approach provides on par or better performance with SOTA methods evaluated on Nuscenes urban driving benchmark.

关键词： Couplings Context-aware services computer vision Semantics Tools Benchmark testing Trajectory

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PATS: Patch Area Transportation with Subdivision for Local Feature Matching

PATS: Patch Area Transportation with Subdivision for Local F...

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

作者： Ni, Junjie Li, Yijin Huang, Zhaoyang Li, Hongsheng Bao, Hujun Cui, Zhaopeng Zhang, Guofeng Zhejiang Univ State Key Lab CAD&CG Hangzhou Peoples R China ZJU SenseTime Joint Lab 3D Vision Hangzhou Peoples R China Chinese Univ Hong Kong Multimedia Lab Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Local feature matching aims at establishing sparse correspondences between a pair of images. Recently, detector-free methods present generally better performance but are not satisfactory in image pairs with large scale differences. In this paper, we propose Patch Area Transportation with Subdivision (PATS) to tackle this issue. Instead of building an expensive image pyramid, we start by splitting the original image pair into equal-sized patches and gradually resizing and subdividing them into smaller patches with the same scale. However, estimating scale differences between these patches is non-trivial since the scale differences are determined by both relative camera poses and scene structures, and thus spatially varying over image pairs. Moreover, it is hard to obtain the ground truth for real scenes. To this end, we propose patch area transportation, which enables learning scale differences in a self-supervised manner. In contrast to bipartite graph matching, which only handles one-to-one matching, our patch area transportation can deal with many-to-many relationships. PATS improves both matching accuracy and coverage, and shows superior performance in downstream tasks, such as relative pose estimation, visual localization, and optical flow estimation. The source code is available at https://***/pats/.

关键词： 3D from multi-view and sensors

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LAFEAT: Piercing Through Adversarial Defenses with Latent Features

LAFEAT: Piercing Through Adversarial Defenses with Latent Fe...

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

作者： Yu, Yunrui Gao, Xitong Xu, Cheng-Zhong Univ Macau Macau Sar Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Deep convolutional neural networks are susceptible to adversarial attacks. They can be easily deceived to give an incorrect output by adding a tiny perturbation to the input. This presents a great challenge in making CNNs robust against such attacks. An influx of new defense techniques have been proposed to this end. In this paper, we show that latent features in certain "robust" models are surprisingly susceptible to adversarial attacks. On top of this, we introduce a unified l(infinity)-norm white-box attack algorithm which harnesses latent features in its gradient descent steps, namely LAFEAT. We show that not only is it computationally much more efficient for successful attacks, but it is also a stronger adversary than the current state-of-the-art across a wide range of defense mechanisms. This suggests that model robustness could be contingent on the effective use of the defender's hidden components, and it should no longer be viewed from a holistic perspective.

关键词： Degradation Schedules computer vision Computational modeling Perturbation methods Lattices Robustness

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SPLATNet: Sparse Lattice Networks for Point Cloud Processing 31

SPLATNet: Sparse Lattice Networks for Point Cloud Processing

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31st ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Su, Hang Jampani, Varun Sun, Deqing Maji, Subhransu Kalogerakis, Evangelos Yang, Ming-Hsuan Kautz, Jan UMass Amherst Amherst MA 01003 USA NVIDIA Santa Clara CA USA UC Merced Merced CA USA

ISBN: (纸本)9781538664209

We present a network architecture for processing point clouds that directly operates on a collection of points represented as a sparse set of samples in a high-dimensional lattice. Naively applying convolutions on this lattice scales poorly, both in terms of memory and computational cost, as the size of the lattice increases. Instead, our network uses sparse bilateral convolutional layers as building blocks. These layers maintain efficiency by using indexing structures to apply convolutions only on occupied parts of the lattice, and allow flexible specifications of the lattice structure enabling hierarchical and spatially-aware feature learning, as well as joint 2D-3D reasoning. Both point-based and image-based representations can be easily incorporated in a network with such layers and the resulting model can be trained in an end-to-end manner. We present results on 3D segmentation tasks where our approach outperforms existing state-of-the-art techniques.

关键词： Three-dimensional displays Lattices Convolution Two dimensional displays Shape computer architecture Standards

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DEPTH FROM FOCUS WITH ONE IMAGE

DEPTH FROM FOCUS WITH ONE IMAGE

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1994 ieee computer-Society conference on computer vision and pattern recognition

作者： JAHNE, B GEISSLER, P UNIV CALIF SAN DIEGO SCRIPPS INST OCEANOGLA JOLLACA 92093

ISBN: (纸本)0818658258

A novel depth-from-focus technique is introduced that needs only a single image. It is based on a precise knowledge of the 3-D point spread function and requires objects of uniform brightness and simple shapes. Using adequate low-level image processing techniques, the true area of the object and the distance from the focal plane is obtained from parameters such as the apparent (blurred) area of the object and the mean brightness in this area. The technique has been applied to measure the size distribution of bubbles submerged by breaking waves. A depth criterion is used to define a virtual measuring volume that is roughly proportional to the size of the bubbles.

关键词： Optical character recognition

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Sound and Visual Representation Learning with Multiple Pretraining Tasks

Sound and Visual Representation Learning with Multiple Pretr...

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

作者： Vasudevan, Arun Balajee Dai, Dengxin Van Gool, Luc Swiss Fed Inst Technol Zurich Switzerland MPI Informat Saarbrucken Germany Katholieke Univ Leuven Leuven Belgium

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Different self-supervised tasks (SSL) reveal different features from the data. The learned feature representations can exhibit different performance for each downstream task. In this light, this work aims to combine Multiple SSL tasks (Multi-SSL) that generalizes well for all downstream tasks. For this study, we investigate binaural sounds and image data. For binaural sounds, we propose three SSL tasks namely, spatial alignment, temporal synchronization of foreground objects and binaural sounds and temporal gap prediction. We investigate several approaches of Multi-SSL and give insights into the downstream task performance on video retrieval, spatial sound super resolution, and semantic prediction using OmniAudio dataset. Our experiments on binaural sound representations demonstrate that Multi-SSL via incremental learning (IL) of SSL tasks outperforms single SSL task models and fully supervised models in the downstream task performance. As a check of applicability on other modalities, we also formulate our Multi-SSL models for image representation learning and we use the recently proposed SSL tasks, MoCov2 and DenseCL. Here, Multi-SSL surpasses recent methods such as MoCov2, DenseCL and DetCo by 2.06%, 3.27% and 1.19% on VOC07 classification and +2.83, +1.56 and +1.61 AP on COCO detection.

关键词： Representation learning Visualization Semantics Image representation Predictive models Market research pattern recognition

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