Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

We propose a technique for producing ‘visual explanations’ for decisions from a large class of Convolutional Neural Network (CNN)-based models, making them more transparent and explainable. Our approach—Gradient-weighted Class Activation Mapping (Grad-CAM), uses the gradients of any target concept (...

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Vydáno v:	International journal of computer vision Ročník 128; číslo 2; s. 336 - 359
Hlavní autoři:	Selvaraju, Ramprasaath R., Cogswell, Michael, Das, Abhishek, Vedantam, Ramakrishna, Parikh, Devi, Batra, Dhruv
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.02.2020 Springer Springer Nature B.V
Témata:	Artificial Intelligence Artificial neural networks Computer Imaging Computer Science Computer vision Decisions Failure modes Image classification Image Processing and Computer Vision Localization Machine learning Machine vision Mapping Mobile computing Neural networks Pattern Recognition Pattern Recognition and Graphics Predictions Questions Vision Transparency Explanations Interpretability Visualizations Grad-CAM Visual explanations
ISSN:	0920-5691, 1573-1405
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Abstract	We propose a technique for producing ‘visual explanations’ for decisions from a large class of Convolutional Neural Network (CNN)-based models, making them more transparent and explainable. Our approach—Gradient-weighted Class Activation Mapping (Grad-CAM), uses the gradients of any target concept (say ‘dog’ in a classification network or a sequence of words in captioning network) flowing into the final convolutional layer to produce a coarse localization map highlighting the important regions in the image for predicting the concept. Unlike previous approaches, Grad-CAM is applicable to a wide variety of CNN model-families: (1) CNNs with fully-connected layers ( e.g. VGG), (2) CNNs used for structured outputs ( e.g. captioning), (3) CNNs used in tasks with multi-modal inputs ( e.g. visual question answering) or reinforcement learning, all without architectural changes or re-training . We combine Grad-CAM with existing fine-grained visualizations to create a high-resolution class-discriminative visualization, Guided Grad-CAM, and apply it to image classification, image captioning, and visual question answering (VQA) models, including ResNet-based architectures. In the context of image classification models, our visualizations (a) lend insights into failure modes of these models (showing that seemingly unreasonable predictions have reasonable explanations), (b) outperform previous methods on the ILSVRC-15 weakly-supervised localization task, (c) are robust to adversarial perturbations, (d) are more faithful to the underlying model, and (e) help achieve model generalization by identifying dataset bias. For image captioning and VQA, our visualizations show that even non-attention based models learn to localize discriminative regions of input image. We devise a way to identify important neurons through Grad-CAM and combine it with neuron names (Bau et al. in Computer vision and pattern recognition, 2017) to provide textual explanations for model decisions. Finally, we design and conduct human studies to measure if Grad-CAM explanations help users establish appropriate trust in predictions from deep networks and show that Grad-CAM helps untrained users successfully discern a ‘stronger’ deep network from a ‘weaker’ one even when both make identical predictions. Our code is available at https://github.com/ramprs/grad-cam/ , along with a demo on CloudCV (Agrawal et al., in: Mobile cloud visual media computing, pp 265–290. Springer, 2015) ( http://gradcam.cloudcv.org ) and a video at http://youtu.be/COjUB9Izk6E .
AbstractList	We propose a technique for producing ‘visual explanations’ for decisions from a large class of Convolutional Neural Network (CNN)-based models, making them more transparent and explainable. Our approach—Gradient-weighted Class Activation Mapping (Grad-CAM), uses the gradients of any target concept (say ‘dog’ in a classification network or a sequence of words in captioning network) flowing into the final convolutional layer to produce a coarse localization map highlighting the important regions in the image for predicting the concept. Unlike previous approaches, Grad-CAM is applicable to a wide variety of CNN model-families: (1) CNNs with fully-connected layers (e.g.VGG), (2) CNNs used for structured outputs (e.g.captioning), (3) CNNs used in tasks with multi-modal inputs (e.g.visual question answering) or reinforcement learning, all without architectural changes or re-training. We combine Grad-CAM with existing fine-grained visualizations to create a high-resolution class-discriminative visualization, Guided Grad-CAM, and apply it to image classification, image captioning, and visual question answering (VQA) models, including ResNet-based architectures. In the context of image classification models, our visualizations (a) lend insights into failure modes of these models (showing that seemingly unreasonable predictions have reasonable explanations), (b) outperform previous methods on the ILSVRC-15 weakly-supervised localization task, (c) are robust to adversarial perturbations, (d) are more faithful to the underlying model, and (e) help achieve model generalization by identifying dataset bias. For image captioning and VQA, our visualizations show that even non-attention based models learn to localize discriminative regions of input image. We devise a way to identify important neurons through Grad-CAM and combine it with neuron names (Bau et al. in Computer vision and pattern recognition, 2017) to provide textual explanations for model decisions. Finally, we design and conduct human studies to measure if Grad-CAM explanations help users establish appropriate trust in predictions from deep networks and show that Grad-CAM helps untrained users successfully discern a ‘stronger’ deep network from a ‘weaker’ one even when both make identical predictions. Our code is available at https://github.com/ramprs/grad-cam/, along with a demo on CloudCV (Agrawal et al., in: Mobile cloud visual media computing, pp 265–290. Springer, 2015) (http://gradcam.cloudcv.org) and a video at http://youtu.be/COjUB9Izk6E. We propose a technique for producing 'visual explanations' for decisions from a large class of Convolutional Neural Network (CNN)-based models, making them more transparent and explainable. Our approach-Gradient-weighted Class Activation Mapping (Grad-CAM), uses the gradients of any target concept (say 'dog' in a classification network or a sequence of words in captioning network) flowing into the final convolutional layer to produce a coarse localization map highlighting the important regions in the image for predicting the concept. Unlike previous approaches, Grad-CAM is applicable to a wide variety of CNN model-families: (1) CNNs with fully-connected layers (e.g.VGG), (2) CNNs used for structured outputs (e.g.captioning), (3) CNNs used in tasks with multi-modal inputs (e.g.visual question answering) or reinforcement learning, all without architectural changes or re-training. We combine Grad-CAM with existing fine-grained visualizations to create a high-resolution class-discriminative visualization, Guided Grad-CAM, and apply it to image classification, image captioning, and visual question answering (VQA) models, including ResNet-based architectures. In the context of image classification models, our visualizations (a) lend insights into failure modes of these models (showing that seemingly unreasonable predictions have reasonable explanations), (b) outperform previous methods on the ILSVRC-15 weakly-supervised localization task, (c) are robust to adversarial perturbations, (d) are more faithful to the underlying model, and (e) help achieve model generalization by identifying dataset bias. For image captioning and VQA, our visualizations show that even non-attention based models learn to localize discriminative regions of input image. We devise a way to identify important neurons through Grad-CAM and combine it with neuron names (Bau et al. in Computer vision and pattern recognition, 2017) to provide textual explanations for model decisions. Finally, we design and conduct human studies to measure if Grad-CAM explanations help users establish appropriate trust in predictions from deep networks and show that Grad-CAM helps untrained users successfully discern a 'stronger' deep network from a 'weaker' one even when both make identical predictions. Our code is available at We propose a technique for producing ‘visual explanations’ for decisions from a large class of Convolutional Neural Network (CNN)-based models, making them more transparent and explainable. Our approach—Gradient-weighted Class Activation Mapping (Grad-CAM), uses the gradients of any target concept (say ‘dog’ in a classification network or a sequence of words in captioning network) flowing into the final convolutional layer to produce a coarse localization map highlighting the important regions in the image for predicting the concept. Unlike previous approaches, Grad-CAM is applicable to a wide variety of CNN model-families: (1) CNNs with fully-connected layers ( e.g. VGG), (2) CNNs used for structured outputs ( e.g. captioning), (3) CNNs used in tasks with multi-modal inputs ( e.g. visual question answering) or reinforcement learning, all without architectural changes or re-training . We combine Grad-CAM with existing fine-grained visualizations to create a high-resolution class-discriminative visualization, Guided Grad-CAM, and apply it to image classification, image captioning, and visual question answering (VQA) models, including ResNet-based architectures. In the context of image classification models, our visualizations (a) lend insights into failure modes of these models (showing that seemingly unreasonable predictions have reasonable explanations), (b) outperform previous methods on the ILSVRC-15 weakly-supervised localization task, (c) are robust to adversarial perturbations, (d) are more faithful to the underlying model, and (e) help achieve model generalization by identifying dataset bias. For image captioning and VQA, our visualizations show that even non-attention based models learn to localize discriminative regions of input image. We devise a way to identify important neurons through Grad-CAM and combine it with neuron names (Bau et al. in Computer vision and pattern recognition, 2017) to provide textual explanations for model decisions. Finally, we design and conduct human studies to measure if Grad-CAM explanations help users establish appropriate trust in predictions from deep networks and show that Grad-CAM helps untrained users successfully discern a ‘stronger’ deep network from a ‘weaker’ one even when both make identical predictions. Our code is available at https://github.com/ramprs/grad-cam/ , along with a demo on CloudCV (Agrawal et al., in: Mobile cloud visual media computing, pp 265–290. Springer, 2015) ( http://gradcam.cloudcv.org ) and a video at http://youtu.be/COjUB9Izk6E .
Audience	Academic
Author	Vedantam, Ramakrishna Cogswell, Michael Parikh, Devi Batra, Dhruv Das, Abhishek Selvaraju, Ramprasaath R.
Author_xml	– sequence: 1 givenname: Ramprasaath R. surname: Selvaraju fullname: Selvaraju, Ramprasaath R. email: ramprs@gatech.edu organization: Georgia Institute of Technology – sequence: 2 givenname: Michael surname: Cogswell fullname: Cogswell, Michael organization: Georgia Institute of Technology – sequence: 3 givenname: Abhishek surname: Das fullname: Das, Abhishek organization: Georgia Institute of Technology – sequence: 4 givenname: Ramakrishna surname: Vedantam fullname: Vedantam, Ramakrishna organization: Georgia Institute of Technology – sequence: 5 givenname: Devi surname: Parikh fullname: Parikh, Devi organization: Georgia Institute of Technology, Facebook AI Research – sequence: 6 givenname: Dhruv surname: Batra fullname: Batra, Dhruv organization: Georgia Institute of Technology, Facebook AI Research
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PublicationDate	2020-02-01
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PublicationTitle	International journal of computer vision
PublicationTitleAbbrev	Int J Comput Vis
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Publisher	Springer US Springer Springer Nature B.V
Publisher_xml	– name: Springer US – name: Springer – name: Springer Nature B.V
References	Das, A., Agrawal, H., Zitnick, C. L., Parikh, D., & Batra, D. (2016). Human attention in visual question answering: Do humans and deep networks look at the same regions? In EMNLP. SilverDHuangAMaddisonCJGuezASifreLVan Den DriesscheGSchrittwieserJAntonoglouIPanneershelvamVLanctotMMastering the game of go with deep neural networks and tree searchNature2016529758748448910.1038/nature16961 Selvaraju, R. R., Lee, S., Shen, Y., Jin, H., Ghosh, S., Heck, L., Batra, D., & Parikh, D. (2019) Taking a hint: Leveraging explanations to make vision and language models more grounded. In Proceedings of the international conference on computer vision (ICCV). Goodfellow, I. J., Shlens, J., & Szegedy, C. (2015). Explaining and harnessing adversarial examples. stat. BengioYCourvilleAVincentPRepresentation learning: A review and new perspectivesIEEE Transactions on Pattern Analysis and Machine Intelligence20133581798182810.1109/TPAMI.2013.50 Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., & Torralba, A. (2017). Places: A 10 million image database for scene recognition. IEEE transactions on pattern analysis and machine intelligence. Gan, C., Wang, N., Yang, Y., Yeung, D.-Y., & Hauptmann, A. G. (2015). Devnet: A deep event network for multimedia event detection and evidence recounting. In CVPR. Johnson, J., Karpathy, A., & Fei-Fei, L. (2016). DenseCap: Fully convolutional localization networks for dense captioning. In CVPR. Lu, J., Lin, X., Batra, D., & Parikh, D. (2015). Deeper LSTM and normalized CNN visual question answering model. https://github.com/VT-vision-lab/VQA_LSTM_CNN. Chen, X., Fang, H., Lin, T.-Y., Vedantam, R., Gupta, S., Dollár, P., & Zitnick, C. L. (2015). Microsoft COCO captions: Data collection and evaluation server. arXiv preprint arXiv:1504.00325. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should i trust you?” Explaining the predictions of any classifier. In SIGKDD. Dosovitskiy, A., & Brox, T. (2015). Inverting convolutional networks with convolutional networks. In CVPR. Gordon, D., Kembhavi, A., Rastegari, M., Redmon, J., Fox, D., & Farhadi, A. (2017). Iqa: Visual question answering in interactive environments. arXiv preprint arXiv:1712.03316. Johns, E., Mac Aodha, O., & Brostow, G. J. (2015). Becoming the expert—interactive multi-class machine teaching. In CVPR. Agrawal, H., Mathialagan, C. S., Goyal, Y., Chavali, N., Banik, P., Mohapatra, A., Osman, A., & Batra, D. (2015). CloudCV: Large scale distributed computer vision as a cloud service. In Mobile cloud visual media computing (pp. 265–290). Springer. Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2015). Is object localization for free?—weakly-supervised learning with convolutional neural networks. In CVPR. Cinbis, R. G., Verbeek, J., & Schmid, C. (2016). Weakly supervised object localization with multi-fold multiple instance learning. IEEE transactions on pattern analysis and machine intelligence. Lu, J., Yang, J., Batra, D., & Parikh, D. (2016). Hierarchical question-image co-attention for visual question answering. In NIPS. Agrawal, A., Batra, D., & Parikh, D. (2016). Analyzing the behavior of visual question answering models. In EMNLP. Lin, M., Chen, Q., & Yan, S. (2014a). Network in network. In ICLR. Das, A., Kottur, S., Moura, J. M., Lee, S., & Batra, D. (2017b). Learning cooperative visual dialog agents with deep reinforcement learning. In Proceedings of the IEEE international conference on computer vision (ICCV). Zhang, J., Lin, Z., Brandt, J., Shen, X., & Sclaroff, S. (2016). Top-down neural attention by excitation backprop. In ECCV. Lipton, Z. C. (2016). The mythos of model interpretability. arXiv preprint arXiv:1606.03490v3. Das, A., Datta, S., Gkioxari, G., Lee, S., Parikh, D., & Batra, D. (2018). Embodied question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In ICLR. Mahendran, A., & Vedaldi, A. (2016a). Salient deconvolutional networks. In European conference on computer vision. Vinyals, O., Toshev, A., Bengio, S., & Erhan, D. (2015). Show and tell: A neural image caption generator. In CVPR. Bazzani, L., Bergamo, A., Anguelov, D., Torresani, L. (2016). Self-taught object localization with deep networks. In WACV. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In CVPR. Malinowski, M., Rohrbach, M., & Fritz, M. (2015). Ask your neurons: A neural-based approach to answering questions about images. In ICCV. 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Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818–2826). Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). ImageNet: A large-scale hierarchical image database. In CVPR. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014). Caffe: Convolutional architecture for fast feature embedding. In ACM MM. Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014b). Microsoft coco: Common objects in context. In ECCV. Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2014). Learning and transferring mid-level image representations using convolutional neural networks. In CVPR. Selvaraju, R. R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., & Batra, D. (2016). Grad-CAM: Why did you say that? Visual explanations from deep networks via gradient-based localization. CoRR. arXiv:1610.02391 Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning deep features for discriminative localization. In CVPR. Mahendran, A., & Vedaldi, A. (2016b). Visualizing deep convolutional neural networks using natural pre-images. International Journal of Computer Vision, 1–23. JacksonPIntroduction to expert systems19983Boston, MAAddison-Wesley Longman Publishing Co., Inc,0634.68086 Zhou, B., Khosla, A., Lapedriza, À., Oliva, A., & Torralba, A. (2014). Object detectors emerge in deep scene cnns. CoRR. arXiv:1412.6856 Kolesnikov, A., & Lampert, C. H. (2016). Seed, expand and constrain: Three principles for weakly-supervised image segmentation. In ECCV. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In NIPS. de Vries, H., Strub, F., Chandar, S., Pietquin, O., Larochelle, H., & Courville, A. C. (2017). Guesswhat?! visual object discovery through multi-modal dialogue. 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Striving for simplicity: The all convolutional net. CoRR. arXiv:1412.6806 Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In CVPR. D Silver (1228_CR51) 2016; 529 Y Bengio (1228_CR6) 2013; 35 1228_CR53 1228_CR52 1228_CR11 1228_CR55 1228_CR10 1228_CR54 1228_CR50 1228_CR17 1228_CR16 1228_CR19 1228_CR18 1228_CR13 1228_CR57 1228_CR12 1228_CR56 1228_CR15 1228_CR59 1228_CR14 1228_CR58 1228_CR3 1228_CR4 1228_CR5 1228_CR1 1228_CR2 1228_CR42 1228_CR41 1228_CR44 1228_CR43 1228_CR7 1228_CR8 1228_CR9 1228_CR40 1228_CR49 1228_CR46 1228_CR45 1228_CR48 1228_CR47 1228_CR31 1228_CR30 1228_CR33 1228_CR32 1228_CR39 1228_CR38 1228_CR35 P Jackson (1228_CR26) 1998 1228_CR34 1228_CR37 1228_CR36 1228_CR20 1228_CR22 1228_CR21 1228_CR60 1228_CR62 1228_CR61 1228_CR28 1228_CR27 1228_CR29 1228_CR24 1228_CR23 1228_CR25
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In NIPS: Dataset and methods for multilingual image question answering. – reference: Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In ICLR. – reference: Agrawal, H., Mathialagan, C. S., Goyal, Y., Chavali, N., Banik, P., Mohapatra, A., Osman, A., & Batra, D. (2015). CloudCV: Large scale distributed computer vision as a cloud service. In Mobile cloud visual media computing (pp. 265–290). Springer. – reference: Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., & Torralba, A. (2017). Places: A 10 million image database for scene recognition. IEEE transactions on pattern analysis and machine intelligence. – reference: Zhang, J., Lin, Z., Brandt, J., Shen, X., & Sclaroff, S. (2016). Top-down neural attention by excitation backprop. In ECCV. – reference: Cinbis, R. G., Verbeek, J., & Schmid, C. (2016). Weakly supervised object localization with multi-fold multiple instance learning. IEEE transactions on pattern analysis and machine intelligence. – reference: Simonyan, K., Vedaldi, A., & Zisserman, A. (2013). Deep inside convolutional networks: Visualising image classification models and saliency maps. CoRR. arXiv:1312.6034 – reference: Zhou, B., Khosla, A., Lapedriza, À., Oliva, A., & Torralba, A. (2014). Object detectors emerge in deep scene cnns. CoRR. arXiv:1412.6856 – reference: Hoiem, D., Chodpathumwan, Y., & Dai, Q. (2012). Diagnosing error in object detectors. In ECCV. – reference: Goodfellow, I. J., Shlens, J., & Szegedy, C. (2015). Explaining and harnessing adversarial examples. stat. – reference: Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). ImageNet: A large-scale hierarchical image database. In CVPR. – reference: Lu, J., Lin, X., Batra, D., & Parikh, D. (2015). Deeper LSTM and normalized CNN visual question answering model. https://github.com/VT-vision-lab/VQA_LSTM_CNN. – reference: Johns, E., Mac Aodha, O., & Brostow, G. J. (2015). Becoming the expert—interactive multi-class machine teaching. In CVPR. – reference: Lu, J., Yang, J., Batra, D., & Parikh, D. (2016). Hierarchical question-image co-attention for visual question answering. In NIPS. – reference: Bau, D., Zhou, B., Khosla, A., Oliva, A., & Torralba, A. (2017). Network dissection: Quantifying interpretability of deep visual representations. In computer vision and pattern recognition. – reference: Kolesnikov, A., & Lampert, C. H. (2016). Seed, expand and constrain: Three principles for weakly-supervised image segmentation. In ECCV. – reference: BengioYCourvilleAVincentPRepresentation learning: A review and new perspectivesIEEE Transactions on Pattern Analysis and Machine Intelligence20133581798182810.1109/TPAMI.2013.50 – reference: SilverDHuangAMaddisonCJGuezASifreLVan Den DriesscheGSchrittwieserJAntonoglouIPanneershelvamVLanctotMMastering the game of go with deep neural networks and tree searchNature2016529758748448910.1038/nature16961 – reference: Vondrick, C., Khosla, A., Malisiewicz, T., & Torralba, A. (2013). HOGgles: Visualizing object detection features. ICCV. – reference: Lipton, Z. C. (2016). The mythos of model interpretability. arXiv preprint arXiv:1606.03490v3. – reference: Bazzani, L., Bergamo, A., Anguelov, D., Torresani, L. (2016). Self-taught object localization with deep networks. In WACV. – reference: Lin, M., Chen, Q., & Yan, S. (2014a). Network in network. In ICLR. – reference: Vinyals, O., Toshev, A., Bengio, S., & Erhan, D. (2015). Show and tell: A neural image caption generator. In CVPR. – reference: Gordon, D., Kembhavi, A., Rastegari, M., Redmon, J., Fox, D., & Farhadi, A. (2017). Iqa: Visual question answering in interactive environments. arXiv preprint arXiv:1712.03316. – reference: Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In CVPR. – reference: Dosovitskiy, A., & Brox, T. (2015). Inverting convolutional networks with convolutional networks. In CVPR. – reference: Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2015). Is object localization for free?—weakly-supervised learning with convolutional neural networks. In CVPR. – reference: Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR. – reference: Das, A., Agrawal, H., Zitnick, C. L., Parikh, D., & Batra, D. (2016). Human attention in visual question answering: Do humans and deep networks look at the same regions? In EMNLP. – reference: Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014). Caffe: Convolutional architecture for fast feature embedding. In ACM MM. – reference: Karpathy, A. (2014). What I learned from competing against a ConvNet on ImageNet. http://karpathy.github.io/2014/09/02/what-i-learned-from-competing-against-a-convnet-on-imagenet/. – reference: Das, A., Kottur, S., Gupta, K., Singh, A., Yadav, D., Moura, J. M., Parikh, D., & Batra, D. (2017a). Visual dialog. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). – reference: Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014b). Microsoft coco: Common objects in context. In ECCV. – reference: Agrawal, A., Batra, D., & Parikh, D. (2016). Analyzing the behavior of visual question answering models. In EMNLP. – reference: Malinowski, M., Rohrbach, M., & Fritz, M. (2015). Ask your neurons: A neural-based approach to answering questions about images. In ICCV. – reference: Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818–2826). – reference: Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Lawrence Zitnick, C., & Parikh, D. (2015). VQA: Visual question answering. In ICCV. – reference: Karpathy, A., & Fei-Fei, L. (2015). Deep visual-semantic alignments for generating image descriptions. In CVPR. – reference: Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In NIPS. – reference: JacksonPIntroduction to expert systems19983Boston, MAAddison-Wesley Longman Publishing Co., Inc,0634.68086 – reference: Gan, C., Wang, N., Yang, Y., Yeung, D.-Y., & Hauptmann, A. G. (2015). Devnet: A deep event network for multimedia event detection and evidence recounting. In CVPR. – reference: Chen, X., Fang, H., Lin, T.-Y., Vedantam, R., Gupta, S., Dollár, P., & Zitnick, C. L. (2015). Microsoft COCO captions: Data collection and evaluation server. arXiv preprint arXiv:1504.00325. – reference: Pinheiro, P. O., & Collobert, R. (2015). From image-level to pixel-level labeling with convolutional networks. In CVPR. – reference: Selvaraju, R. R., Chattopadhyay, P., Elhoseiny, M., Sharma, T., Batra, D., Parikh, D., & Lee, S. (2018). Choose your neuron: Incorporating domain knowledge through neuron-importance. In Proceedings of the European conference on computer vision (ECCV) (pp. 526–541). – reference: Mahendran, A., & Vedaldi, A. (2016a). Salient deconvolutional networks. In European conference on computer vision. – reference: Fang, H., Gupta, S., Iandola, F., Srivastava, R. K., Deng, L., Dollár, P., Gao, J., He, X., Mitchell, M., Platt, J. C., et al. (2015). From captions to visual concepts and back. In CVPR. – reference: Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning deep features for discriminative localization. In CVPR. – reference: Mahendran, A., & Vedaldi, A. (2016b). Visualizing deep convolutional neural networks using natural pre-images. International Journal of Computer Vision, 1–23. – reference: He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In CVPR. – reference: de Vries, H., Strub, F., Chandar, S., Pietquin, O., Larochelle, H., & Courville, A. C. (2017). Guesswhat?! visual object discovery through multi-modal dialogue. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). – reference: Selvaraju, R. R., Lee, S., Shen, Y., Jin, H., Ghosh, S., Heck, L., Batra, D., & Parikh, D. (2019) Taking a hint: Leveraging explanations to make vision and language models more grounded. In Proceedings of the international conference on computer vision (ICCV). – reference: Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2009). The PASCAL visual object classes challenge 2007 (VOC2007) results. http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html. – reference: Johnson, J., Karpathy, A., & Fei-Fei, L. (2016). DenseCap: Fully convolutional localization networks for dense captioning. In CVPR. – reference: Selvaraju, R. R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., & Batra, D. (2016). Grad-CAM: Why did you say that? Visual explanations from deep networks via gradient-based localization. CoRR. arXiv:1610.02391 – reference: Zeiler, M. D., & Fergus, R. (2014). Visualizing and understanding convolutional networks. In ECCV. – reference: Ren, M., Kiros, R., & Zemel, R. (2015). Exploring models and data for image question answering. In NIPS. – reference: Erhan, D., Bengio, Y., Courville, A., & Vincent, P. (2009). Visualizing higher-layer features of a deep network. University of Montreal, 1341. – reference: Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should i trust you?” Explaining the predictions of any classifier. 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Snippet	We propose a technique for producing ‘visual explanations’ for decisions from a large class of Convolutional Neural Network (CNN)-based models, making them... We propose a technique for producing 'visual explanations' for decisions from a large class of Convolutional Neural Network (CNN)-based models, making them...
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StartPage	336
SubjectTerms	Artificial Intelligence Artificial neural networks Computer Imaging Computer Science Computer vision Decisions Failure modes Image classification Image Processing and Computer Vision Localization Machine learning Machine vision Mapping Mobile computing Neural networks Pattern Recognition Pattern Recognition and Graphics Predictions Questions Vision
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Title	Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
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