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Adversarial Normalization: I Can visualize Everything (ICE)

Adversarial Normalization: I Can visualize Everything (ICE)

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

作者： Choi, Hoyoung Jin, Seungwan Han, Kyungsik Hanyang Univ Seoul South Korea

ISBN: (纸本)9798350301298

vision transformers use [CLS] tokens to predict image classes. Their explainability visualization has been studied using relevant information from [CLS] tokens or focusing on attention scores during self-attention. Such visualization, however, is challenging because of the dependence of the structure of a vision transformer on skip connections and attention operators, the instability of non-linearities in the learning process, and the limited reflection of self-attention scores on relevance. We argue that the output vectors for each input patch token in a vision transformer retain the image information of each patch location, which can facilitate the prediction of an image class. In this paper, we propose ICE (Adversarial Normalization: I Can visualize Everything), a novel method that enables a model to directly predict a class for each patch in an image;thus, advancing the effective visualization of the explainability of a vision transformer. Our method distinguishes background from foreground regions by predicting background classes for patches that do not determine image classes. We used the DeiT-S model, the most representative model employed in studies, on the explainability visualization of vision transformers. On the ImageNet-Segmentation dataset, ICE outperformed all explainability visualization methods for four cases depending on the model size. We also conducted quantitative and qualitative analyses on the tasks of weakly-supervised object localization and unsupervised object discovery. On the CUB200-2011 and PASCALVOC07/12 datasets, ICE achieved comparable performance to the state-of-the-art methods. We incorporated ICE into the encoder of DeiT-S and improved efficiency by 44.01% on the ImageNet dataset over that achieved by the original DeiT-S model. We showed performance on the accuracy and efficiency comparable to EViT, the state-of-the-art pruning model, demonstrating the effectiveness of ICE. The code is available at https://***/Hanyang-HCC-La

关键词： Explainable computer vision

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Fusing Pre-trained Language Models with Multimodal Prompts through Reinforcement Learning

Fusing Pre-trained Language Models with Multimodal Prompts t...

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

作者： Yu, Youngjae Chung, Jiwan Yun, Heeseung Hessel, Jack Park, Jae Sung Lu, Ximing Zellers, Rowan Ammanabrolu, Prithviraj Le Bras, Ronan Kim, Gunhee Choi, Yejin Allen Inst Artificial Intelligence Seattle WA USA OpenAI Seattle WA USA Yonsei Univ Dept Artificial Intelligence Seoul South Korea Seoul Natl Univ Dept Comp Sci & Engn Seoul South Korea Univ Washington Paul G Allen Sch Comp Sci Seattle WA 98195 USA

ISBN: (纸本)9798350301298

Language models are capable of commonsense reasoning: while domain-specific models can learn from explicit knowledge (e.g. commonsense graphs [6], ethical norms [25]), and larger models like GPT-3 [7] manifest broad commonsense reasoning capacity. Can their knowledge be extended to multimodal inputs such as images and audio without paired domain data? In this work, we propose ESPER (Extending Sensory PErception with Reinforcement learning) which enables text-only pretrained models to address multimodal tasks such as visual commonsense reasoning. Our key novelty is to use reinforcement learning to align multimodal inputs to language model generations without direct supervision: for example, our reward optimization relies only on cosine similarity derived from CLIP [52] and requires no additional paired (image, text) data. Experiments demonstrate that ESPER outperforms baselines and prior work on a variety of multimodal text generation tasks ranging from captioning to commonsense reasoning;these include a new benchmark we collect and release, the ESP dataset, which tasks models with generating the text of several different domains for each image. Our code and data are publicly released at https://***/JiwanChung/esper.

关键词： and reasoning language vision

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Training Debiased Subnetworks with Contrastive Weight Pruning

Training Debiased Subnetworks with Contrastive Weight Prunin...

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

作者： Park, Geon Yeong Lee, Sangmin Lee, Sang Wan Ye, Jong Chul Korea Adv Inst Sci & Technol KAIST Bio & Brain Engn Daejeon South Korea Korea Adv Inst Sci & Technol KAIST Math Sci Daejeon South Korea Korea Adv Inst Sci & Technol KAIST Kim Jaechul Grad Sch AI Daejeon South Korea

ISBN: (纸本)9798350301298

Neural networks are often biased to spuriously correlated features that provide misleading statistical evidence that does not generalize. This raises an interesting question: "Does an optimal unbiased functional subnetwork exist in a severely biased network? If so, how to extract such subnetwork?" While empirical evidence has been accumulated about the existence of such unbiased subnetworks, these observations are mainly based on the guidance of ground-truth unbiased samples. Thus, it is unexplored how to discover the optimal subnetworks with biased training datasets in practice. To address this, here we first present our theoretical insight that alerts potential limitations of existing algorithms in exploring unbiased subnetworks in the presence of strong spurious correlations. We then further elucidate the importance of bias-conflicting samples on structure learning. Motivated by these observations, we propose a Debiased Contrastive Weight Pruning (DCWP) algorithm, which probes unbiased subnetworks without expensive group annotations. Experimental results demonstrate that our approach significantly outperforms state-of-the-art debiasing methods despite its considerable reduction in the number of parameters.

关键词： accountability ethics in vision fairness privacy Transparency

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Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generation

Dynamic Graph Enhanced Contrastive Learning for Chest X-ray ...

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

作者： Li, Mingjie Lin, Bingqian Chen, Zicong Lin, Haokun Liang, Xiaodan Chang, Xiaojun Univ Technol Sydney ReLER AAII Sydney Australia Sun Yat Sen Univ Sch ISE Guangzhou Peoples R China Mohamed bin Zayed Univ Artificial Intelligence Dept Comp Vis Abu Dhabi U Arab Emirates Peng Cheng Natl Lab Shenzhen Peoples R China Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Automatic radiology reporting has great clinical potential to relieve radiologists from heavy workloads and improve diagnosis interpretation. Recently, researchers have enhanced data-driven neural networks with medical knowledge graphs to eliminate the severe visual and textual bias in this task. The structures of such graphs are exploited by using the clinical dependencies formed by the disease topic tags via general knowledge and usually do not update during the training process. Consequently, the fixed graphs can not guarantee the most appropriate scope of knowledge and limit the effectiveness. To address the limitation, we propose a knowledge graph with Dynamic structure and nodes to facilitate chest X-ray report generation with Contrastive Learning, named DCL. In detail, the fundamental structure of our graph is pre-constructed from general knowledge. Then we explore specific knowledge extracted from the retrieved reports to add additional nodes or redefine their relations in a bottom-up manner. Each image feature is integrated with its very own updated graph before being fed into the decoder module for report generation. Finally, this paper introduces Image-Report Contrastive and Image-Report Matching losses to better represent visual features and textual information. Evaluated on IU-Xray and MIMIC-CXR datasets, our DCL outperforms previous state-of-the-art models on these two benchmarks.

关键词： cell microscopy Medical and biological vision

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Leverage Interactive Affinity for Affordance Learning

Leverage Interactive Affinity for Affordance Learning

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

作者： Luo, Hongchen Zhai, Wei Zhang, Jing Cao, Yang Tao, Dacheng Univ Sci & Technol China Hefei Peoples R China Univ Sydney Camperdown Australia JD Explore Acad Beijing Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China

ISBN: (纸本)9798350301298

Perceiving potential "action possibilities" (i.e., affordance) regions of images and learning interactive functionalities of objects from human demonstration is a challenging task due to the diversity of human-object interactions. Prevailing affordance learning algorithms often adopt the label assignment paradigm and presume that there is a unique relationship between functional region and affordance label, yielding poor performance when adapting to unseen environments with large appearance variations. In this paper, we propose to leverage interactive affinity for affordance learning, *** interactive affinity from human-object interaction and transferring it to non-interactive objects. Interactive affinity, which represents the contacts between different parts of the human body and local regions of the target object, can provide inherent cues of interconnectivity between humans and objects, thereby reducing the ambiguity of the perceived action possibilities. Specifically, we propose a pose-aided interactive affinity learning framework that exploits human pose to guide the network to learn the interactive affinity from human-object interactions. Particularly, a keypoint heuristic perception (KHP) scheme is devised to exploit the keypoint association of human pose to alleviate the uncertainties due to interaction diversities and contact occlusions. Besides, a contact-driven affordance learning (CAL) dataset is constructed by collecting and labeling over 5, 000 images. Experimental results demonstrate that our method outperforms the representative models regarding objective metrics and visual quality. Code and dataset: ***/lhc1224/PIAL-Net.

关键词： Embodied vision: Active agents simulation

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Certified Adversarial Robustness Within Multiple Perturbation Bounds

Certified Adversarial Robustness Within Multiple Perturbatio...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Nandi, Soumalya Addepalli, Sravanti Rangwani, Harsh Babu, R. Venkatesh Indian Institute of Science Vision and AI Lab Bengaluru India

ISBN: (纸本)9798350302493

Randomized smoothing (RS) is a well known certified defense against adversarial attacks, which creates a smoothed classifier by predicting the most likely class under random noise perturbations of inputs during inference. While initial work focused on robustness to 2 norm perturbations using noise sampled from a Gaussian distribution, subsequent works have shown that different noise distributions can result in robustness to other p norm bounds as well. In general, a specific noise distribution is optimal for defending against a given p norm based attack. In this work, we aim to improve the certified adversarial robustness against multiple perturbation bounds simultaneously. Towards this, we firstly present a novel certification scheme, that effectively combines the certificates obtained using different noise distributions to obtain optimal results against multiple perturbation bounds. We further propose a novel training noise distribution along with a regularized training scheme to improve the certification within both 1 and 2 perturbation norms simultaneously. Contrary to prior works, we compare the certified robustness of different training algorithms across the same natural (clean) accuracy, rather than across fixed noise levels used for training and certification. We also empirically invalidate the argument that training and certifying the classifier with the same amount of noise gives the best results. The proposed approach achieves improvements on the ACR (Average Certified Radius) metric across both 1 and 2 perturbation bounds. Code available at https://***/valiisc/NU-Certified-Robustness © 2023 ieee.

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S³C: Semi-Supervised VQA Natural Language Explanation via Self-Critical Learning

S<SUP>3</SUP>C: Semi-Supervised VQA Natural Language Explana...

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

作者： Suo, Wei Sun, Mengyang Liu, Weisong Gao, Yiqi Wang, Peng Zhang, Yanning Wu, Qi Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Northwestern Polytech Univ Ningbo Inst Xian Peoples R China Northwestern Polytech Univ Sch Cybersecur Xian Peoples R China Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9798350301298

VQA Natural Language Explanation (VQA-NLE) task aims to explain the decision-making process of VQA models in natural language. Unlike traditional attention or gradient analysis, free-text rationales can be easier to understand and gain users' trust. Existing methods mostly use post-hoc or self-rationalization models to obtain a plausible explanation. However, these frameworks are bottlenecked by the following challenges: 1) the reasoning process cannot be faithfully responded to and suffer from the problem of logical inconsistency. 2) Human-annotated explanations are expensive and time-consuming to collect. In this paper, we propose a new Semi-Supervised VQA-NLE via Self-Critical Learning ((SC)-C-3), which evaluates the candidate explanations by answering rewards to improve the logical consistency between answers and rationales. With a semi-supervised learning framework, the (SC)-C-3 can benefit from a tremendous amount of samples without human-annotated explanations. A large number of automatic measures and human evaluations all show the effectiveness of our method. Meanwhile, the framework achieves a new state-of-the-art performance on the two VQA-NLE datasets.

关键词： and reasoning language vision

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VQACL: A Novel Visual Question Answering Continual Learning Setting

VQACL: A Novel Visual Question Answering Continual Learning ...

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

作者： Zhang, Xi Zhang, Feifei Xu, Changsheng Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Tianjin Univ Technol Sch Comp Sci & Engn Tianjin Peoples R China

ISBN: (纸本)9798350301298

Research on continual learning has recently led to a variety of work in unimodal community, however little attention has been paid to multimodal tasks like visual question answering (VQA). In this paper, we establish a novel VQA Continual Learning setting named VQACL, which contains two key components: a dual-level task sequence where visual and linguistic data are nested, and a novel composition testing containing new skill-concept combinations. The former devotes to simulating the ever-changing multimodal datastream in real world and the latter aims at measuring models' generalizability for cognitive reasoning. Based on our VQACL, we perform in-depth evaluations of five well-established continual learning methods, and observe that they suffer from catastrophic forgetting and have weak generalizability. To address above issues, we propose a novel representation learning method, which leverages a sample-specific and a sample-invariant feature to learn representations that are both discriminative and generalizable for VQA. Furthermore, by respectively extracting such representation for visual and textual input, our method can explicitly disentangle the skill and concept. Extensive experimental results illustrate that our method significantly outperforms existing models, demonstrating the effectiveness and compositionality of the proposed approach. The code is available at https://***/zhangxi1997/VQACL.

关键词： language reasoning vision

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Breaking Through the Haze: An Advanced Non-Homogeneous Dehazing Method based on Fast Fourier Convolution and ConvNeXt

Breaking Through the Haze: An Advanced Non-Homogeneous Dehaz...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Zhou, Han Dong, Wei Liu, Yangyi Chen, Jun Mcmaster University Department of Electrical and Computer Engineering Hamilton Canada University of Alberta Department of Electrical and Computer Engineering Edmonton Canada

ISBN: (纸本)9798350302493

Haze usually leads to deteriorated images with low contrast, color shift and structural distortion. We observe that many deep learning based models exhibit exceptional performance on removing homogeneous haze, but they usually fail to address the challenge of non-homogeneous dehazing. Two main factors account for this situation. Firstly, due to the intricate and. non uniform distribution of dense haze, the recovery of structural and chromatic features with high fidelity is challenging, particularly in regions with heavy haze. Secondly, the existing small scale datasets for non-homogeneous dehazing are inadequate to support reliable learning of feature mappings between hazy images and. their corresponding haze-free counterparts by convolutional neural network (CNN)-based models. To tackle these two challenges, we propose a novel two branch network that leverages 2D discrete wavelete transform (DWT), fast Fourier convolution (FFC) residual block and a pretrained. ConvNeXt model. Specifically, in the DWT-FFC frequency branch, our model exploits DWT to capture more high-frequency features. Moreover, by taking advantage of the large receptive field, provided, by FFC residual blocks, our model is able to effectively explore global contextual information and. produce images with better perceptual quality In the prior knowledge branch, an ImageNet pretrained ConvNeXt as opposed, to Res2Net is adopted. This enables our model to learn more supplementary information and. acquire a stronger generalization ability. The feasibility and effectiveness of the proposed, method, is demonstrated, via extensive experiments and. ablation studies. The code is available at https://***/zhouhll5/DWT-FFC. © 2023 ieee.

关键词： Convolution

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Generating Aligned Pseudo-Supervision from Non-Aligned Data for Image Restoration in Under-Display Camera

Generating Aligned Pseudo-Supervision from Non-Aligned Data ...

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

作者： Feng, Ruicheng Li, Chongyi Chen, Huaijin Li, Shuai Gu, Jinwei Loy, Chen Change Nanyang Technol Univ S Lab Singapore Singapore SenseBrain Technol San Jose CA USA Chinese Univ Hong Kong Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

Due to the difficulty in collecting large-scale and perfectly aligned paired training data for Under-Display Camera (UDC) image restoration, previous methods resort to monitor-based image systems or simulation-based methods, sacrificing the realness of the data and introducing domain gaps. In this work, we revisit the classic stereo setup for training data collection - capturing two images of the same scene with one UDC and one standard camera. The key idea is to "copy" details from a high-quality reference image and "paste" them on the UDC image. While being able to generate real training pairs, this setting is susceptible to spatial misalignment due to perspective and depth of field changes. The problem is further compounded by the large domain discrepancy between the UDC and normal images, which is unique to UDC restoration. In this paper, we mitigate the non-trivial domain discrepancy and spatial misalignment through a novel Transformer-based framework that generates well-aligned yet high-quality target data for the corresponding UDC input. This is made possible through two carefully designed components, namely, the Domain Alignment Module (DAM) and Geometric Alignment Module (GAM), which encourage robust and accurate discovery of correspondence between the UDC and normal views. Extensive experiments show that high-quality and well-aligned pseudo UDC training pairs are beneficial for training a robust restoration network. Code and the dataset are available at https://github. com/ jnjaby/AlignFormer.

关键词： Low-level vision

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