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Rethinking Reconstruction Autoencoder-Based Out-of-Distribution Detection

Rethinking Reconstruction Autoencoder-Based Out-of-Distribut...

引用

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

作者： Zhou, Yibo Beihang Univ Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In some scenarios, classifier requires detecting out-of-distribution samples far from its training data. With desirable characteristics, reconstruction autoencoder-based methods deal with this problem by using input reconstruction error as a metric of novelty vs. normality. We formulate the essence of such approach as a quadruplet domain translation with an intrinsic bias to only query for a proxy of conditional data uncertainty. Accordingly, an improvement direction is formalized as maximumly compressing the autoencoder's latent space while ensuring its reconstructive power for acting as a described domain translator. From it, strategies are introduced including semantic reconstruction, data certainty decomposition and normalized L2 distance to substantially improve original methods, which together establish state-of-the-art performance on various benchmarks, e.g., the FPR@95%TPR of CIFAR-100 vs. TinyImagenet-crop on Wide-ResNet is 0.2%. Importantly, our method works without any additional data, hard-to-implement structure, time-consuming pipeline, and even harming the classification accuracy of known classes.

关键词： Measurement computer vision Uncertainty Semantics Pipelines Training data Detectors

来源：评论

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引用

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

作者： Minh Hieu Phan The-Anh Ta Son Lam Phung Long Tran-Thanh Bouzerdoum, Abdesselam Univ Wollongong Wollongong NSW Australia FPT Software AIC Hanoi Vietnam VinAI Res Hanoi Vietnam Univ Warwick Coventry W Midlands England Hamad Bin Khalifa Univ Ar Rayyan Qatar

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Deep learning models are known to suffer from the problem of catastrophic forgetting when they incrementally learn new classes. Continual learning for semantic segmentation (CSS) is an emerging field in computer vision. We identify a problem in CSS: A model tends to be confused between old and new classes that are visually similar, which makes it forget the old ones. To address this gap, we propose REMINDER - a new CSS framework and a novel class similarity knowledge distillation (CSW-KD) method. Our CSW-KD method distills the knowledge of a previous model on old classes that are similar to the new one. This provides two main benefits: (i) selectively revising old classes that are more likely to be forgotten, and (ii) better learning new classes by relating them with the previously seen classes. Extensive experiments on Pascal-VOC 2012 and ADE20k datasets show that our approach outperforms state-of-the-art methods on standard CSS settings by up to 7.07% and 8.49%, respectively.

关键词： Deep learning Representation learning computer vision Shape Computational modeling Machine vision Semantics

来源：评论

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Ensembling Off-the-shelf Models for GAN Training

Ensembling Off-the-shelf Models for GAN Training

引用

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

作者： Kumari, Nupur Zhang, Richard Shechtman, Eli Zhu, Jun-Yan Carnegie Mellon Univ Pittsburgh PA 15213 USA Adobe San Jose CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The advent of large-scale training has produced a cornucopia of powerful visual recognition models. However, generative models, such as GANs, have traditionally been trained from scratch in an unsupervised manner. Can the collective "knowledge" from a large bank of pretrained vision models be leveraged to improve GAN training? If so, with so many models to choose from, which one(s) should be selected, and in what manner are they most effective? We find that pretrained computer vision models can significantly improve performance when used in an ensemble of discriminators. Notably, the particular subset of selected models greatly affects performance. We propose an effective selection mechanism, by probing the linear separability between real and fake samples in pretrained model embeddings, choosing the most accurate model, and progressively adding it to the discriminator ensemble. Interestingly, our method can improve GAN training in both limited data and large-scale settings. Given only 10k training samples, our FID on LSUN CAT matches the StyIeGAN2 trained on 1.6M images. On the full dataset, our method improves FID by 1.5 to 2x on cat, church, and horse categories of LSUN.

关键词： Training computer vision Visualization Computational modeling pattern recognition

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Cross-modal Map Learning for vision and Language Navigation

Cross-modal Map Learning for Vision and Language Navigation

引用

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

作者： Georgakis, Georgios Schmeckpeper, Karl Wanchoo, Karan Dan, Soham Miltsakaki, Eleni Roth, Dan Daniilidis, Kostas Univ Penn Philadelphia PA 19104 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We consider the problem of vision-and-Language Navigation (VLN). The majority of current methods for VLN are trained end-to-end using either unstructured memory such as LSTM, or using cross-modal attention over the egocentric observations of the agent. In contrast to other works, our key insight is that the association between language and vision is stronger when it occurs in explicit spatial representations. In this work, we propose a cross-modal map learning model for vision-and-language navigation that first learns to predict the top-down semantics on an egocentric map for both observed and unobserved regions, and then predicts a path towards the goal as a set of waypoints. In both cases, the prediction is informed by the language through cross-modal attention mechanisms. We experimentally test the basic hypothesis that language-driven navigation can be solved given a map, and then show competitive results on the full VLN-CE benchmark.

关键词： computer vision Navigation Grounding Semantics Natural languages Predictive models Benchmark testing

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HyperInverter: Improving StyleGAN Inversion via Hypernetwork

HyperInverter: Improving StyleGAN Inversion via Hypernetwork

引用

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

作者： Dinh, Tan M. Anh Tuan Tran Rang Nguyen Binh-Son Hua VinAI Res Hanoi Vietnam

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Real-world image manipulation has achieved fantastic progress in recent years as a result of the exploration and utilization of GAN latent spaces. GAN inversion is the first step in this pipeline, which aims to map the real image to the latent code faithfully. Unfortunately, the majority of existing GAN inversion methods fail to meet at least one of the three requirements listed below: high reconstruction quality, editability, and fast inference. We present a novel two-phase strategy in this research that fits all requirements at the same time. In the first phase, we train an encoder to map the input image to StyleGAN2 W-space, which was proven to have excellent editability but lower reconstruction quality. In the second phase, we supplement the reconstruction ability in the initial phase by leveraging a series of hypernetworks to recover the missing information during inversion. These two steps complement each other to yield high reconstruction quality thanks to the hypernetwork branch and excellent editability due to the inversion done in the W-space. Our method is entirely encoder-based, resulting in extremely fast inference. Extensive experiments on two challenging datasets demonstrate the superiority of our method.(1)

关键词： Measurement computer vision Codes Pipelines Nonhomogeneous media pattern recognition Iterative methods

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Cascade Transformers for End-to-End Person Search

Cascade Transformers for End-to-End Person Search

引用

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

作者： Yu, Rui Du, Dawei LaLonde, Rodney Davila, Daniel Funk, Christopher Hoogs, Anthony Clipp, Brian Kitware Inc New York NY 12065 USA Penn State Univ University Pk PA 16802 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The goal of person search is to localize a target person from a gallery set of scene images, which is extremely challenging due to large scale variations, pose/viewpoint changes, and occlusions. In this paper, we propose the Cascade Occluded Attention Transformer (COAT) for end-to-end person search. Our three-stage cascade design focuses on detecting people in the first stage, while later stages simultaneously and progressively refine the representation for person detection and re-identification. At each stage the occluded attention transformer applies tighter intersection over union thresholds, forcing the network to learn coarse-to-fine pose/scale invariant features. Meanwhile, we calculate each detection's occluded attention to differentiate a person's tokens from other people or the background. In this way, we simulate the effect of other objects occluding a person of interest at the token-level. Through comprehensive experiments, we demonstrate the benefits of our method by achieving state-of-the-art performance on two benchmark datasets.

关键词： Representation learning computer vision Search methods MIMICs Benchmark testing Transformers pattern recognition

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Compressing Models with Few Samples: Mimicking then Replacing

Compressing Models with Few Samples: Mimicking then Replacin...

引用

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

作者： Wang, Huanyu Liu, Junjie Ma, Xin Yong, Yang Chai, Zhenhua Wu, Jianxin Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Meituan Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Few-sample compression aims to compress a big redundant model into a small compact one with only few samples. If we fine-tune models with these limited few samples directly, models will be vulnerable to overfit and learn almost nothing. Hence, previous methods optimize the compressed model layer-by-layer and try to make every layer have the same outputs as the corresponding layer in the teacher model, which is cumbersome. In this paper, we propose a new framework named Mimicking then Replacing (MiR) for few-sample compression, which firstly urges the pruned model to output the same features as the teacher's in the penultimate layer, and then replaces teacher's layers before penultimate with a well-tuned compact one. Unlike previous layer-wise reconstruction methods, our MiR optimizes the entire network holistically, which is not only simple and effective, but also unsupervised and general. MiR outperforms previous methods with large margins.

关键词： Representation learning Deep learning computer vision Codes computer architecture Reconstruction algorithms pattern recognition

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Unsupervised Image-to-Image Translation with Generative Prior

Unsupervised Image-to-Image Translation with Generative Prio...

引用

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

作者： Yang, Shuai Jiang, Liming Liu, Ziwei Loy, Chen Change Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Unsupervised image-to-image translation aims to learn the translation between two visual domains without paired data. Despite the recent progress in image translation models, it remains challenging to build mappings between complex domains with drastic visual discrepancies. In this work, we present a novel framework, Generative Prior-guided UNsupervised Image-to-image Translation (GP-UNIT), to improve the overall quality and applicability of the translation algorithm. Our key insight is to leverage the generative prior from pre-trained class-conditional GANs (e.g., BigGAN) to learn rich content correspondences across various domains. We propose a novel coarse-to-fine scheme: we first distill the generative prior to capture a robust coarse-level content representation that can link objects at an abstract semantic level, based on which fine-level content features are adaptively learned for more accurate multi-level content correspondences. Extensive experiments demonstrate the superiority of our versatile framework over state-of-the-art methods in robust, high-quality and diversified translations, even for challenging and distant domains.

关键词： Visualization computer vision Codes Semantics pattern recognition

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Learning Multiple Dense Prediction Tasks from Partially Annotated Data

Learning Multiple Dense Prediction Tasks from Partially Anno...

引用

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

作者： Li, Wei-Hong Liu, Xialei Bilen, Hakan Univ Edinburgh VICO Grp Edinburgh Midlothian Scotland

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Despite the recent advances in multi-task learning of dense prediction problems, most methods rely on expensive labelled datasets. In this paper, we present a label efficient approach and look at jointly learning of multiple dense prediction tasks on partially annotated data (i.e. not all the task labels are available for each image), which we call multi-task partially-supervised learning. We propose a multi-task training procedure that successfully leverages task relations to supervise its multi-task learning when data is partially annotated. In particular, we learn to map each task pair to a joint pairwise task-space which enables sharing information between them in a computationally efficient way through another network conditioned on task pairs, and avoids learning trivial cross-task relations by retaining high-level information about the input image. We rigorously demonstrate that our proposed method effectively exploits the images with unlabelled tasks and outperforms existing semi-supervised learning approaches and related methods on three standard benchmarks.

关键词： Training computer vision Semisupervised learning Benchmark testing Multitasking pattern recognition Computational efficiency

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Disentangling Visual Embeddings for Attributes and Objects

Disentangling Visual Embeddings for Attributes and Objects

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

作者： Saini, Nirat Pham, Khoi Shrivastava, Abhinav Univ Maryland College Pk MD 20742 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We study the problem of compositional zero-shot learning for object-attribute recognition. Prior works use visual features extracted with a backbone network, pre-trained for object classification and thus do not capture the subtly distinct features associated with attributes. To overcome this challenge, these studies employ supervision from the linguistic space, and use pre-trained word embeddings to better separate and compose attribute-object pairs for recognition. Analogous to linguistic embedding space, which already has unique and agnostic embeddings for object and attribute, we shift the focus back to the visual space and propose a novel architecture that can disentangle attribute and object features in the visual space. We use visual decomposed features to hallucinate embeddings that are representative for the seen and novel compositions to better regularize the learning of our model. Extensive experiments show that our method outperforms existing work with significant margin on three datasets: MIT-States, UT-Zappos, and a new benchmark created based on VAW. The code, models, and dataset splits are publicly available at https: //***/nirat1606/OADis.

关键词： Visualization computer vision Codes computer architecture Benchmark testing Linguistics Feature extraction

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