Abstract
Large-scale, multimodal models trained on web data such as OpenAI’s CLIP are becoming the foundation of many applications. Yet, they are also more complex to understand, test, and therefore align with human values. In this paper, we propose gScoreCAM—a state-of-the-art method for visualizing the main objects that CLIP is looking at in an image. On zero-shot object detection, gScoreCAM performs similarly to ScoreCAM, the best prior art on CLIP, yet 8 to 10 times faster. Our method outperforms other existing, well-known methods (HilaCAM, RISE, and the entire CAM family) by a large margin, especially in multi-object scenes. gScoreCAM sub-samples \(k = 300\) channels (from 3,072 channels—i.e. reducing complexity by almost 10 times) of the highest gradients and linearly combines them into a final “attention” visualization. We demonstrate the utility and superiority of our method on three datasets: ImageNet, COCO, and PartImageNet. Our work opens up interesting future directions in understanding and de-biasing CLIP.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Code and an interactive demo are at https://github.com/anguyen8/gScoreCAM.
- 2.
We provide a detailed description for Otsu-based bounding box inferencing in Sec. A1.
- 3.
Method with -abs means operate over absolute value of the gradients.
References
Bommasani, R., et al.: On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 (2021)
Radford, A., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748–8763. PMLR (2021)
yzhuoning: yzhuoning/awesome-clip: Awesome list for research on clip (contrastive language-image pre-training) (2022). https://github.com/yzhuoning/Awesome-CLIP. Accessed 18 May 2022
Patashnik, O., Wu, Z., Shechtman, E., Cohen-Or, D., Lischinski, D.: Styleclip: text-driven manipulation of stylegan imagery. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2085–2094 (2021)
nerdyrodent: nerdyrodent/vqgan-clip: Just playing with getting vqgan+clip running locally, rather than having to use colab (2022). https://github.com/nerdyrodent/VQGAN-CLIP. Accessed 18 May 2022
Kim, G., Ye, J.C.: Diffusionclip: text-guided image manipulation using diffusion models. arXiv preprint arXiv:2110.02711 (2021)
Luo, H., et al.: Clip4clip: an empirical study of clip for end to end video clip retrieval. arXiv preprint arXiv:2104.08860 (2021)
Lei, J., et al.: Less is more: clipbert for video-and-language learning via sparse sampling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7331–7341 (2021)
Song, H., Dong, L., Zhang, W.N., Liu, T., Wei, F.: Clip models are few-shot learners: empirical studies on VQA and visual entailment. arXiv preprint arXiv:2203.07190 (2022)
Kwon, G., Ye, J.C.: Clipstyler: image style transfer with a single text condition. arXiv preprint arXiv:2112.00374 (2021)
Vinker, Y., et al.: Clipasso: semantically-aware object sketching. arXiv preprint arXiv:2202.05822 (2022)
Sheng, E., Chang, K.W., Natarajan, P., Peng, N.: The woman worked as a babysitter: on biases in language generation. arXiv preprint arXiv:1909.01326 (2019)
Verge, T.: What a machine learning tool that turns obama white can (and can’t) tell us about ai bias - the verge (2022). www.theverge.com/21298762/face-depixelizer-ai-machine-learning-tool-pulse-stylegan-obama-bias. Accessed 19 May 2022
Li, Q., Mai, L., Alcorn, M.A., Nguyen, A.: A cost-effective method for improving and re-purposing large, pre-trained GANs by fine-tuning their class-embeddings. In: Proceedings of the Asian Conference on Computer Vision (2020)
Phillips, P.J., Hahn, C.A., Fontana, P.C., Broniatowski, D.A., Przybocki, M.A.: Four principles of explainable artificial intelligence. Gaithersburg, Maryland (2020)
Goh, G., et al.: Multimodal neurons in artificial neural networks. Distill 6, e30 (2021)
Chefer, H., Gur, S., Wolf, L.: Generic attention-model explainability for interpreting bi-modal and encoder-decoder transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 397–406 (2021)
Subramanian, S., Merrill, W., Darrell, T., Gardner, M., Singh, S., Rohrbach, A.: Reclip: a strong zero-shot baseline for referring expression comprehension. arXiv preprint arXiv:2204.05991 (2022)
Aflalo, E., et al.: VL-interpret: an interactive visualization tool for interpreting vision-language transformers. arXiv preprint arXiv:2203.17247 (2022)
vijishmadhavan: vijishmadhavan/crop-clip: Crop using clip (2022). https://github.com/vijishmadhavan/Crop-CLIP. Accessed 23 May 2022
Kim, W., Son, B., Kim, I.: ViLT: vision-and-language transformer without convolution or region supervision. In: International Conference on Machine Learning, pp. 5583–5594. PMLR (2021)
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017)
Chattopadhay, A., Sarkar, A., Howlader, P., Balasubramanian, V.N.: Grad-CAM++: generalized gradient-based visual explanations for deep convolutional networks. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 839–847. IEEE (2018)
Petsiuk, V., Das, A., Saenko, K.: Rise: randomized input sampling for explanation of black-box models. arXiv preprint arXiv:1806.07421 (2018)
Nguyen, A., Yosinski, J., Clune, J.: Multifaceted feature visualization: uncovering the different types of features learned by each neuron in deep neural networks. arXiv preprint arXiv:1602.03616 (2016)
Materzyńska, J., Torralba, A., Bau, D.: Disentangling visual and written concepts in clip. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16410–16419 (2022)
Wang, H., et al.: Score-CAM: score-weighted visual explanations for convolutional neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 24–25 (2020)
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115, 211–252 (2015)
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
He, J., et al.: Partimagenet: a large, high-quality dataset of parts. arXiv preprint arXiv:2112.00933 (2021)
Chefer, H., Gur, S., Wolf, L.: Transformer interpretability beyond attention visualization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 782–791 (2021)
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: CVPR (2016)
Ribeiro, M.T., Singh, S., Guestrin, C.: “Why should i trust you?” explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144 (2016)
Agarwal, C., Nguyen, A.: Explaining image classifiers by removing input features using generative models. In: Proceedings of the Asian Conference on Computer Vision (2020)
Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034 (2013)
Nourelahi, M., Kotthoff, L., Chen, P., Nguyen, A.: How explainable are adversarially-robust CNNs? arXiv preprint arXiv:2205.13042 (2022)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Recht, B., Roelofs, R., Schmidt, L., Shankar, V.: Do imagenet classifiers generalize to imagenet? In: International Conference on Machine Learning, pp. 5389–5400. PMLR (2019)
Choe, J., Oh, S.J., Lee, S., Chun, S., Akata, Z., Shim, H.: Evaluating weakly supervised object localization methods right. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3133–3142 (2020)
Gupta, T., Vahdat, A., Chechik, G., Yang, X., Kautz, J., Hoiem, D.: Contrastive learning for weakly supervised phrase grounding. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12348, pp. 752–768. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58580-8_44
Gildenblat, J., contributors: Pytorch library for cam methods (2021). https://github.com/jacobgil/pytorch-grad-cam
OpenAI: openai/clip: Contrastive language-image pretraining (2022). https://github.com/openai/CLIP. Accessed 06 July 2022
Radin, L., Osher, S., Fatemi, E.: Non-linear total variation noise removal algorithm. Phys. D 60, 259–268 (1992)
Fu, R., Hu, Q., Dong, X., Guo, Y., Gao, Y., Li, B.: Axiom-based grad-CAM: towards accurate visualization and explanation of CNNs. arXiv preprint arXiv:2008.02312 (2020)
Jiang, P.T., Zhang, C.B., Hou, Q., Cheng, M.M., Wei, Y.: Layercam: exploring hierarchical class activation maps for localization. IEEE Trans. Image Process. 30, 5875–5888 (2021)
Zhang, Q., Rao, L., Yang, Y.: Group-CAM: group score-weighted visual explanations for deep convolutional networks. arXiv preprint arXiv:2103.13859 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, P., Li, Q., Biaz, S., Bui, T., Nguyen, A. (2023). gScoreCAM: What Objects Is CLIP Looking At?. In: Wang, L., Gall, J., Chin, TJ., Sato, I., Chellappa, R. (eds) Computer Vision – ACCV 2022. ACCV 2022. Lecture Notes in Computer Science, vol 13844. Springer, Cham. https://doi.org/10.1007/978-3-031-26316-3_35
Download citation
DOI: https://doi.org/10.1007/978-3-031-26316-3_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-26315-6
Online ISBN: 978-3-031-26316-3
eBook Packages: Computer ScienceComputer Science (R0)