Robust Object Detection Using Knowledge Graph Embeddings

1. Google Knowledge Graph. https://developers.google.com/knowledge-graph

2. Aggarwal, C.C., Hinneburg, A., Keim, D.A.: On the surprising behavior of distance metrics in high dimensional space. In: Van den Bussche, J., Vianu, V. (eds.) ICDT 2001. LNCS, vol. 1973, pp. 420–434. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44503-X_27

   Chapter  Google Scholar 

3. Bansal, A., Sikka, K., Sharma, G., Chellappa, R., Divakaran, A.: Zero-shot object detection. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 397–414. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01246-5_24

   Chapter  Google Scholar 

4. Caesar, H., Uijlings, J., Ferrari, V.: Coco-stuff: thing and stuff classes in context. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1209–1218 (2018)

   Google Scholar 

5. Cai, Z., Vasconcelos, N.: Cascade R-CNN: delving into high quality object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6154–6162 (2018)

   Google Scholar 

6. Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213–229. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_13

   Chapter  Google Scholar 

7. Dai, X., et al.: Dynamic head: unifying object detection heads with attentions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7373–7382 (2021)

   Google Scholar 

8. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

   Google Scholar 

9. Du, Y., Wei, F., et al.: Learning to prompt for open-vocabulary object detection with vision-language model. arXiv preprint arXiv:2203.14940 (2022)

10. Frome, A., et al.: Devise: a deep visual-semantic embedding model. In: Proceedings of the Conference on Neural Information Processing Systems, vol.26 (2013)

    Google Scholar 

11. Ghiasi, G., et al.: Simple copy-paste is a strong data augmentation method for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2918–2928 (2021)

    Google Scholar 

12. Gosala, N., Valada, A.: Bird’s-eye-view panoptic segmentation using monocular frontal view images. arXiv preprint arXiv:2108.03227 (2021)

13. Gu, X., Lin, T.Y., Kuo, W., Cui, Y.: Open-vocabulary object detection via vision and language knowledge distillation. arXiv preprint arXiv:2104.13921 (2021)

14. Gupta, A., Dollar, P., Girshick, R.: LVIS: a dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2019)

    Google Scholar 

15. Gupta, D., Anantharaman, A., Mamgain, N., Balasubramanian, V.N., Jawahar, C., et al.: A multi-space approach to zero-shot object detection. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1209–1217 (2020)

    Google Scholar 

16. Hayat, N., Hayat, M., Rahman, S., Khan, S., Zamir, S.W., Khan, F.S.: Synthesizing the unseen for zero-shot object detection. In: Proceedings of the Asian Conference on Computer Vision (2020)

    Google Scholar 

17. He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask R-CNN. In: International Conference on Computer Vision, pp. 2980–2988 (2017)

    Google Scholar 

18. 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)

    Google Scholar 

19. Hurtado, J.V., Mohan, R., Burgard, W., Valada, A.: MOPT: multi-object panoptic tracking. arXiv preprint arXiv:2004.08189 (2020)

20. Kornblith, S., Lee, H., Chen, T., Norouzi, M.: Why do better loss functions lead to less transferable features?. arXiv preprint arXiv:2010.16402 (2021)

21. Kuznetsova, A., et al.: The open images dataset v4: unified image classification, object detection, and visual relationship detection at scale. Int. J. Comput. Vis. 128(7), 1956–1981 (2020)

    Article  Google Scholar 

22. Law, H., Deng, J.: CornerNet: detecting objects as paired keypoints. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11218, pp. 765–781. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01264-9_45

    Chapter  Google Scholar 

23. Li, Q., Zhang, Y., Sun, S., Zhao, X., Li, K., Tan, M.: Rethinking semantic-visual alignment in zero-shot object detection via a softplus margin focal loss. Neurocomputing 449, 117–135 (2021)

    Article  Google Scholar 

24. Li, Y., Li, P., Cui, H., Wang, D.: Inference fusion with associative semantics for unseen object detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 1993–2001 (2021)

    Google Scholar 

25. Li, Y., Wang, T., Kang, B., Tang, S., Wang, C., Li, J., Feng, J.: Overcoming classifier imbalance for long-tail object detection with balanced group softmax. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 10991–11000 (2020)

    Google Scholar 

26. Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)

    Google Scholar 

27. Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2980–2988 (2017)

    Google Scholar 

28. 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

29. Liu, S., Huang, D., Wang, Y.: Adaptive NMS: refining pedestrian detection in a crowd. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6459–6468 (2019)

    Google Scholar 

30. Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)

31. Ma, Z., Luo, G., et al.: Open-vocabulary one-stage detection with hierarchical visual-language knowledge distillation. arXiv preprint arXiv:2203.10593 (2022)

32. Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Empirical Methods in Natural Language Processing, pp. 1532–1543 (2014)

    Google Scholar 

33. Perez-Rua, J.M., Zhu, X., Hospedales, T.M., Xiang, T.: Incremental few-shot object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 13846–13855 (2020)

    Google Scholar 

34. Qiao, S., Chen, L.C., Yuille, A.: Detectors: detecting objects with recursive feature pyramid and switchable atrous convolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 10213–10224 (2021)

    Google Scholar 

35. Radford, A., et al.: Learning transferable visual models from natural language supervision. arXiv preprint arXiv:2103.00020 (2021)

36. Rahman, S., Khan, S., Barnes, N.: Improved visual-semantic alignment for zero-shot object detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 11932–11939 (2020)

    Google Scholar 

37. Rahman, S., Khan, S.H., Porikli, F.: Zero-shot object detection: joint recognition and localization of novel concepts. Int. J. Comput. Vis. 128(12), 2979–2999 (2020)

    Article  Google Scholar 

38. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Proceedings of the Conference on Neural Information Processing Systems, pp. 91–99 (2015)

    Google Scholar 

39. Shmelkov, K., Schmid, C., Alahari, K.: Incremental learning of object detectors without catastrophic forgetting. In: International Conference on Computer Vision, pp. 3400–3409 (2017)

    Google Scholar 

40. Singh, K.K., Mahajan, D., Grauman, K., Lee, Y.J., Feiszli, M., Ghadiyaram, D.: Don’t judge an object by its context: learning to overcome contextual bias. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 11070–11078 (2020)

    Google Scholar 

41. Sirohi, K., Mohan, R., Büscher, D., Burgard, W., Valada, A.: EfficientLPS: efficient lidar panoptic segmentation. arXiv preprint arXiv:2102.08009 (2021)

42. Speer, R., Chin, J., Havasi, C.: ConceptNet 5.5: an open multilingual graph of general knowledge. In: Proceedings of the AAAI Conference on Artificial Intelligence (2017)

    Google Scholar 

43. Sun, P., et al.: Sparse R-CNN: end-to-end object detection with learnable proposals. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 14454–14463 (2021)

    Google Scholar 

44. Tan, J., Lu, X., Zhang, G., Yin, C., Li, Q.: Equalization loss v2: a new gradient balance approach for long-tailed object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1685–1694 (2021)

    Google Scholar 

45. Tian, Z., Shen, C., Chen, H., He, T.: FCOS: fully convolutional one-stage object detection. In; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9627–9636 (2019)

    Google Scholar 

46. Valverde, F.R., Hurtado, J.V., Valada, A.: There is more than meets the eye: self-supervised multi-object detection and tracking with sound by distilling multimodal knowledge. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 11612–11621 (2021)

    Google Scholar 

47. Wang, F., Liu, H.: Understanding the behaviour of contrastive loss. arXiv preprint arXiv:2012.09740, pp. 2495–2504 (2020)

48. Wang, X., Ye, Y., Gupta, A.: Zero-shot recognition via semantic embeddings and knowledge graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6857–6866 (2018)

    Google Scholar 

49. Woo, S., Kim, D., Daejeon, K., Cho, D.E., So Kweon, I.E., Kweon, I.S., Cho, D.E.: LinkNet: relational embedding for scene graph. In: Proceedings of the Conference on Neural Information Processing Systems, pp. 560–570 (2018)

    Google Scholar 

50. Xie, J., Zheng, S.: ZSD-YOLO: zero-shot yolo detection using vision-language knowledge distillation. arXiv preprint arXiv:2109.12066 (2021)

51. Yan, C., Chang, X., Luo, M., Liu, H., Zhang, X., Zheng, Q.: Semantics-guided contrastive network for zero-shot object detection. IEEE Trans. Patt. Anal. Mach. Intell. pp. 1–1 (2022)

    Google Scholar 

52. Yang, J., et al.: Focal self-attention for local-global interactions in vision transformers. arXiv preprint arXiv:2107.00641 (2021)

53. Yatskar, M., Ordonez, V., Farhadi, A.: Stating the obvious: extracting visual common sense knowledge. In: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 193–198 (2016)

    Google Scholar 

54. cui Zhang, C., et al.: A simple and effective use of object-centric images for long-tailed object detection. arXiv preprint arXiv:2102.08884 (2021)

55. Zhang, L., Xiang, T., Gong, S.: Learning a deep embedding model for zero-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2021–2030 (2017)

    Google Scholar 

56. Zheng, Y., Huang, R., Han, C., Huang, X., Cui, L.: Background learnable cascade for zero-shot object detection. In: Proceedings of the Asian Conference on Computer Vision (2020)

    Google Scholar 

57. Zhou, X., Koltun, V., Krähenbühl, P.: Probabilistic two-stage detection. arXiv preprint arXiv:2103.07461 (2021)

58. Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019)

59. Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable DETR: deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159 (2020)

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