Fetching the paper…
Reading the bibliography…
Concealed scene understanding (CSU) is a hot computer vision topic aiming to perceive objects exhibiting camouflage.
A. Tankus and Y. Yeshurun, “Detection of regions of interest and camouflage breaking by direct convexity estimation,” in IEEE WVS , 1998
1998
Earlier work this paper cites.
T. E. Boult, R. J. Micheals, X. Gao, and M. Eckmann, “Into the woods: Visual surveillance of noncooperative and camouflaged targets in complex outdoor settings,” Proceedings of the IEEE , vol. 89, no. 10, pp. 1382–1402, 2001
2001
Earlier work this paper cites.
——, “Convexity-based visual camouflage breaking,” CVIU , vol. 82, no. 3, pp. 208–237, 2001
2001
Earlier work this paper cites.
M. Galun, E. Sharon, R. Basri, and A. Brandt, “Texture segmentation by multiscale aggregation of filter responses and shape elements.” in ICCV , 2003
2003
Earlier work this paper cites.
A. Mittal and N. Paragios, “Motion-based background subtraction using adaptive kernel density estimation,” in CVPR , 2004
2004
Earlier work this paper cites.
A. Malhi and R. X. Gao, “Pca-based feature selection scheme for machine defect classification,” IEEE TIM , vol. 53, no. 6, pp. 1517–1525, 2004
2004
Earlier work this paper cites.
A. Kumar, “Computer-vision-based fabric defect detection: A survey,” IEEE TIE. , vol. 55, no. 1, pp. 348–363, 2008
2008
Earlier work this paper cites.
D. Conte, P. Foggia, G. Percannella, F. Tufano, and M. Vento, “An algorithm for detection of partially camouflaged people,” in IEEE AVSS , 2009
2009
Earlier work this paper cites.
H.-K. Chu, W.-H. Hsu, N. J. Mitra, D. Cohen-Or, T.-T. Wong, and T.-Y. Lee, “Camouflage images,” ACM TOG , vol. 29, no. 4, pp. 51–1, 2010
2010
Earlier work this paper cites.
J. Y. Y. H. W. Hou and J. Li, “Detection of the mobile object with camouflage color under dynamic background based on optical flow,” Procedia Engineering , vol. 15, pp. 2201–2205, 2011
2011
Earlier work this paper cites.
H. Y. Ngan, G. K. Pang, and N. H. Yung, “Automated fabric defect detection—a review,” IVC , vol. 29, no. 7, pp. 442–458, 2011
2011
Earlier work this paper cites.
Z. Liu, K. Huang, and T. Tan, “Foreground object detection using top-down information based on em framework,” IEEE TIP , vol. 21, no. 9, pp. 4204–4217, 2012
2012
Earlier work this paper cites.
A. Borji and L. Itti, “State-of-the-art in visual attention modeling,” IEEE TPAMI , vol. 35, no. 1, pp. 185–207, 2012
2012
Earlier work this paper cites.
J. Masci, U. Meier, D. Ciresan, J. Schmidhuber, and G. Fricout, “Steel defect classification with max-pooling convolutional neural networks,” in IJCNN , 2012
2012
Earlier work this paper cites.
S. Ghorai, A. Mukherjee, M. Gangadaran, and P. K. Dutta, “Automatic defect detection on hot-rolled flat steel products,” IEEE TIM , vol. 62, no. 3, pp. 612–621, 2012
2012
Earlier work this paper cites.
P. Ochs, J. Malik, and T. Brox, “Segmentation of moving objects by long term video analysis,” IEEE TPAMI , vol. 36, no. 6, pp. 1187–1200, 2013
2013
Earlier work this paper cites.
K.-C. Song, S.-P. Hu, Y.-H. Yan, and J. Li, “Surface defect detection method using saliency linear scanning morphology for silicon steel strip under oil pollution interference,” ISIJ International , vol. 54, no. 11, pp. 2598–2607, 2014
2014
Earlier work this paper cites.
S. Kim, “Unsupervised spectral-spatial feature selection-based camouflaged object detection using vnir hyperspectral camera,” TSWJ , vol. 2015, 2015
2015
Earlier work this paper cites.
J. S. Kulchandani and K. J. Dangarwala, “Moving object detection: Review of recent research trends,” in IEEE ICPC , 2015
2015
Earlier work this paper cites.
S. He, R. W. Lau, W. Liu, Z. Huang, and Q. Yang, “Supercnn: A superpixelwise convolutional neural network for salient object detection,” IJCV , vol. 115, no. 3, pp. 330–344, 2015
2015
Earlier work this paper cites.
G. Li and Y. Yu, “Visual saliency based on multiscale deep features,” in CVPR , 2015
2015
Earlier work this paper cites.
L. Wang, H. Lu, X. Ruan, and M.-H. Yang, “Deep networks for saliency detection via local estimation and global search,” in CVPR , 2015
2015
Earlier work this paper cites.
A. Borji, M.-M. Cheng, H. Jiang, and J. Li, “Salient object detection: A benchmark,” IEEE TIP , vol. 24, no. 12, pp. 5706–5722, 2015
2015
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in CVPR , 2015
2015
Earlier work this paper cites.
S. Xie and Z. Tu, “Holistically-nested edge detection,” in ICCV , 2015
2015
Earlier work this paper cites.
C.-Y. Lee, S. Xie, P. Gallagher, Z. Zhang, and Z. Tu, “Deeply-supervised nets,” in AISTATS , 2015
2015
Earlier work this paper cites.
L. Cui, Z. Qi, Z. Chen, F. Meng, and Y. Shi, “Pavement distress detection using random decision forests,” in ICDS , 2015
2015
Earlier work this paper cites.
X. Zhang, C. Zhu, S. Wang, Y. Liu, and M. Ye, “A bayesian approach to camouflaged moving object detection,” IEEE TCSVT , vol. 27, no. 9, pp. 2001–2013, 2016
2016
Earlier work this paper cites.
J. Kim and V. Pavlovic, “A shape-based approach for salient object detection using deep learning,” in ECCV , 2016
2016
Earlier work this paper cites.
N. Liu and J. Han, “Dhsnet: Deep hierarchical saliency network for salient object detection,” in CVPR , 2016
2016
Earlier work this paper cites.
G. Li and Y. Yu, “Deep contrast learning for salient object detection,” in CVPR , 2016
2016
Earlier work this paper cites.
Y. Tang and X. Wu, “Saliency detection via combining region-level and pixel-level predictions with cnns,” in ECCV , 2016
2016
Earlier work this paper cites.
S. S. Kruthiventi, V. Gudisa, J. H. Dholakiya, and R. V. Babu, “Saliency unified: A deep architecture for simultaneous eye fixation prediction and salient object segmentation,” in CVPR , 2016
2016
Earlier work this paper cites.
L. Wang, L. Wang, H. Lu, P. Zhang, and X. Ruan, “Saliency detection with recurrent fully convolutional networks,” in ECCV , 2016
2016
Earlier work this paper cites.
F. Perazzi, J. Pont-Tuset, B. McWilliams, L. Van Gool, M. Gross, and A. Sorkine-Hornung, “A benchmark dataset and evaluation methodology for video object segmentation,” in CVPR , 2016
2016
Earlier work this paper cites.
P. Bideau and E. Learned-Miller, “It’s moving! a probabilistic model for causal motion segmentation in moving camera videos,” in ECCV , 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016
2016
Earlier work this paper cites.
A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,” in ICLR , 2016
2016
Earlier work this paper cites.
Y. Shi, L. Cui, Z. Qi, F. Meng, and Z. Chen, “Automatic road crack detection using random structured forests,” IEEE TITS , vol. 17, no. 12, pp. 3434–3445, 2016
2016
Earlier work this paper cites.
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid scene parsing network,” in CVPR , 2017
2017
Earlier work this paper cites.
S. Li, D. Florencio, Y. Zhao, C. Cook, and W. Li, “Foreground detection in camouflaged scenes,” in ICIP , 2017
2017
Earlier work this paper cites.
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in ICCV , 2017
2017
Earlier work this paper cites.
P. Zhang, D. Wang, H. Lu, H. Wang, and B. Yin, “Learning uncertain convolutional features for accurate saliency detection,” in ICCV , 2017
2017
Earlier work this paper cites.
L. Wang, H. Lu, Y. Wang, M. Feng, D. Wang, B. Yin, and X. Ruan, “Learning to detect salient objects with image-level supervision,” in CVPR , 2017
2017
Earlier work this paper cites.
D. Zhang, J. Han, and Y. Zhang, “Supervision by fusion: Towards unsupervised learning of deep salient object detector,” in ICCV , 2017
2017
Earlier work this paper cites.
S. He, J. Jiao, X. Zhang, G. Han, and R. W. Lau, “Delving into salient object subitizing and detection,” in ICCV , 2017
2017
Earlier work this paper cites.
G. Li, Y. Xie, L. Lin, and Y. Yu, “Instance-level salient object segmentation,” in CVPR , 2017
2017
Earlier work this paper cites.
W. Wang, J. Shen, and L. Shao, “Video salient object detection via fully convolutional networks,” IEEE TIP , vol. 27, no. 1, pp. 38–49, 2017
2017
Earlier work this paper cites.
T.-N. Le and A. Sugimoto, “Deeply supervised 3d recurrent fcn for salient object detection in videos.” in BMVC , 2017
2017
Earlier work this paper cites.
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in CVPR , 2017
2017
Earlier work this paper cites.
D.-P. Fan, M.-M. Cheng, Y. Liu, T. Li, and A. Borji, “Structure-measure: A new way to evaluate foreground maps,” in ICCV , 2017
2017
Earlier work this paper cites.
D.-P. Fan, M.-M. Cheng, J.-J. Liu, S.-H. Gao, Q. Hou, and A. Borji, “Salient objects in clutter: Bringing salient object detection to the foreground,” in ECCV , 2018
2018
Earlier work this paper cites.
G. Li, Y. Xie, and L. Lin, “Weakly supervised salient object detection using image labels,” in AAAI , 2018
2018
Earlier work this paper cites.
C. Cao, Y. Huang, Z. Wang, L. Wang, N. Xu, and T. Tan, “Lateral inhibition-inspired convolutional neural network for visual attention and saliency detection,” in AAAI , 2018
2018
Earlier work this paper cites.
J. Zhang, T. Zhang, Y. Dai, M. Harandi, and R. Hartley, “Deep unsupervised saliency detection: A multiple noisy labeling perspective,” in CVPR , 2018
2018
Earlier work this paper cites.
M. A. Islam, M. Kalash, and N. D. Bruce, “Revisiting salient object detection: Simultaneous detection, ranking, and subitizing of multiple salient objects,” in CVPR , 2018
2018
Earlier work this paper cites.
W. Wang, J. Shen, X. Dong, and A. Borji, “Salient object detection driven by fixation prediction,” in CVPR , 2018
2018
Earlier work this paper cites.
X. Li, F. Yang, H. Cheng, W. Liu, and D. Shen, “Contour knowledge transfer for salient object detection,” in ECCV , 2018
2018
Earlier work this paper cites.
D. Zhang, H. Fu, J. Han, A. Borji, and X. Li, “A review of co-saliency detection algorithms: fundamentals, applications, and challenges,” ACM TIST , vol. 9, no. 4, pp. 1–31, 2018
2018
Earlier work this paper cites.
T.-N. Le and A. Sugimoto, “Video salient object detection using spatiotemporal deep features,” IEEE TIP , vol. 27, no. 10, pp. 5002–5015, 2018
2018
Earlier work this paper cites.
G. Li, Y. Xie, T. Wei, K. Wang, and L. Lin, “Flow guided recurrent neural encoder for video salient object detection,” in CVPR , 2018
2018
Earlier work this paper cites.
H. Song, W. Wang, S. Zhao, J. Shen, and K.-M. Lam, “Pyramid dilated deeper convlstm for video salient object detection,” in ECCV , 2018
2018
Earlier work this paper cites.
D.-P. Fan, C. Gong, Y. Cao, B. Ren, M.-M. Cheng, and A. Borji, “Enhanced-alignment measure for binary foreground map evaluation,” in IJCAI , 2018
2018
Earlier work this paper cites.
M. Wang and W. Deng, “Deep visual domain adaptation: A survey,” NC , vol. 312, pp. 135–153, 2018
2018
Earlier work this paper cites.
T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of gans for improved quality, stability, and variation,” in ICLR , 2018
2018
Earlier work this paper cites.
P. Bergmann, S. Löwe, M. Fauser, D. Sattlegger, and C. Steger, “Improving unsupervised defect segmentation by applying structural similarity to autoencoders,” in VISIGRAPP , 2018
2018
Earlier work this paper cites.
L. Liu, R. Wang, C. Xie, P. Yang, F. Wang, S. Sudirman, and W. Liu, “Pestnet: An end-to-end deep learning approach for large-scale multi-class pest detection and classification,” IEEE Access , vol. 7, pp. 45 301–45 312, 2019
2019
Earlier work this paper cites.
T.-N. Le, T. V. Nguyen, Z. Nie, M.-T. Tran, and A. Sugimoto, “Anabranch network for camouflaged object segmentation,” CVIU , vol. 184, pp. 45–56, 2019
2019
Earlier work this paper cites.
Y. Zeng, P. Zhang, J. Zhang, Z. Lin, and H. Lu, “Towards high-resolution salient object detection,” in ICCV , 2019
2019
Earlier work this paper cites.
Z. Wu, L. Su, and Q. Huang, “Cascaded partial decoder for fast and accurate salient object detection,” in CVPR , 2019
2019
Earlier work this paper cites.
Q. Hou, M.-M. Cheng, X. Hu, A. Borji, Z. Tu, and P. H. S. Torr, “Deeply supervised salient object detection with short connections,” IEEE TPAMI , vol. 41, no. 4, pp. 815–828, 2019
2019
Earlier work this paper cites.
Y. Liu, Q. Zhang, D. Zhang, and J. Han, “Employing deep part-object relationships for salient object detection,” in ICCV , 2019
2019
Cited alongside, same era.
Q. Qi, S. Zhao, J. Shen, and K.-M. Lam, “Multi-scale capsule attention-based salient object detection with multi-crossed layer connections,” in ICME , 2019
2019
Cited alongside, same era.
B. Li, Z. Sun, and Y. Guo, “Supervae: Superpixelwise variational autoencoder for salient object detection,” in AAAI , 2019
2019
Cited alongside, same era.
Y. Zeng, Y. Zhuge, H. Lu, L. Zhang, M. Qian, and Y. Yu, “Multi-source weak supervision for saliency detection,” in CVPR , 2019
2019
Cited alongside, same era.
Y. Zeng, Y. Zhuge, H. Lu, and L. Zhang, “Joint learning of saliency detection and weakly supervised semantic segmentation,” in ICCV , 2019
2019
Cited alongside, same era.
H. Bi, C. Zhang, K. Wang, J. Tong, and F. Zheng, “Rethinking camouflaged object detection: Models and datasets,” IEEE TCSVT , vol. 32, no. 9, pp. 5708–5724, 2022
2022
Later among the works it cites.
S. Caijuan, R. Bijuan, W. Ziwen, Y. Jinwei, and S. Ze, “Survey of camouflaged object detection based on deep learning,” IFCST , vol. 16, no. 12, p. 2734, 2022
2022
Later among the works it cites.
J. Pei, T. Cheng, D.-P. Fan, H. Tang, C. Chen, and L. Van Gool, “Osformer: One-stage camouflaged instance segmentation with transformers,” in ECCV , 2022
2022
Later among the works it cites.
T.-N. Le, Y. Cao, T.-C. Nguyen, M.-Q. Le, K.-D. Nguyen, T.-T. Do, M.-T. Tran, and T. V. Nguyen, “Camouflaged instance segmentation in-the-wild: Dataset, method, and benchmark suite,” IEEE TIP , vol. 31, pp. 287–300, 2022
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
W. Wang, S. Zhao, J. Shen, S. C. Hoi, and A. Borji, “Salient object detection with pyramid attention and salient edges,” in CVPR , 2019
2019
Cited alongside, same era.
J.-J. Liu, Q. Hou, M.-M. Cheng, J. Feng, and J. Jiang, “A simple pooling-based design for real-time salient object detection,” in CVPR , 2019
2019
Cited alongside, same era.
J.-X. Zhao, J.-J. Liu, D.-P. Fan, Y. Cao, J. Yang, and M.-M. Cheng, “Egnet: Edge guidance network for salient object detection,” in ICCV , 2019
2019
Cited alongside, same era.
J. Su, J. Li, Y. Zhang, C. Xia, and Y. Tian, “Selectivity or invariance: Boundary-aware salient object detection,” in ICCV , 2019
2019
Cited alongside, same era.
L. Zhang, J. Zhang, Z. Lin, H. Lu, and Y. He, “Capsal: Leveraging captioning to boost semantics for salient object detection,” in CVPR , 2019
2019
Cited alongside, same era.
R. Fan, M.-M. Cheng, Q. Hou, T.-J. Mu, J. Wang, and S.-M. Hu, “S4net: Single stage salient-instance segmentation,” in CVPR , 2019
2019
Cited alongside, same era.
A. Borji, “Saliency prediction in the deep learning era: Successes and limitations,” IEEE TPAMI , vol. 43, no. 2, pp. 679–700, 2019
2019
Cited alongside, same era.
L. Jiao, R. Zhang, F. Liu, S. Yang, B. Hou, L. Li, and X. Tang, “New generation deep learning for video object detection: A survey,” IEEE TNNLS , vol. 33, no. 8, pp. 3195–3215, 2022
2022
Later among the works it cites.
X. Cheng, H. Xiong, D.-P. Fan, Y. Zhong, M. Harandi, T. Drummond, and Z. Ge, “Implicit motion handling for video camouflaged object detection,” in CVPR , 2022
2022
Later among the works it cites.
G. Shin, S. Albanie, and W. Xie, “Unsupervised salient object detection with spectral cluster voting,” in CVPR , 2022
2022
Later among the works it cites.
X. Tian, K. Xu, X. Yang, B. Yin, and R. W. Lau, “Learning to detect instance-level salient objects using complementary image labels,” IJCV , vol. 130, no. 3, pp. 729–746, 2022
2022
Later among the works it cites.
D.-P. Fan, T. Li, Z. Lin, G.-P. Ji, D. Zhang, M.-M. Cheng, H. Fu, and J. Shen, “Re-thinking co-salient object detection,” IEEE TPAMI , vol. 44, no. 8, pp. 4339–4354, 2022
2022
Later among the works it cites.
R. Cong, K. Zhang, C. Zhang, F. Zheng, Y. Zhao, Q. Huang, and S. Kwong, “Does thermal really always matter for rgb-t salient object detection?” IEEE TMM , 2022
2022
Later among the works it cites.
K. Fu, Y. Jiang, G.-P. Ji, T. Zhou, Q. Zhao, and D.-P. Fan, “Light field salient object detection: A review and benchmark,” CVMJ , pp. 1–26, 2022
2022
Later among the works it cites.
G.-P. Ji, D.-P. Fan, K. Fu, Z. Wu, J. Shen, and L. Shao, “Full-duplex strategy for video object segmentation,” CVMJ , vol. 8, p. 155–175, 2022
2022
Later among the works it cites.
X. Qin, H. Dai, X. Hu, D.-P. Fan, L. Shao, and L. Van Gool, “Highly accurate dichotomous image segmentation,” in ECCV , 2022
2022
Later among the works it cites.
K. Wang, H. Bi, Y. Zhang, C. Zhang, Z. Liu, and S. Zheng, “D 2
2022
Later among the works it cites.
2022
Later among the works it cites.
C. Zhang, K. Wang, H. Bi, Z. Liu, and L. Yang, “Camouflaged object detection via neighbor connection and hierarchical information transfer,” CVIU , vol. 221, p. 103450, 2022
2022
Later among the works it cites.
W. Zhai, Y. Cao, H. Xie, and Z.-J. Zha, “Deep texton-coherence network for camouflaged object detection,” IEEE TMM , 2022
2022
Later among the works it cites.
G. Chen, S.-J. Liu, Y.-J. Sun, G.-P. Ji, Y.-F. Wu, and T. Zhou, “Camouflaged object detection via context-aware cross-level fusion,” IEEE TCSVT , vol. 32, no. 10, pp. 6981–6993, 2022
2022
Later among the works it cites.
M. Zhuge, X. Lu, Y. Guo, Z. Cai, and S. Chen, “Cubenet: X-shape connection for camouflaged object detection,” PR , vol. 127, p. 108644, 2022
2022
Later among the works it cites.
G.-P. Ji, L. Zhu, M. Zhuge, and K. Fu, “Fast camouflaged object detection via edge-based reversible re-calibration network,” PR , vol. 123, p. 108414, 2022
2022
Later among the works it cites.
Q. Zhang, Y. Ge, C. Zhang, and H. Bi, “Tprnet: camouflaged object detection via transformer-induced progressive refinement network,” TVCJ , pp. 1–15, 2022
2022
Later among the works it cites.
Y. Cheng, H.-Z. Hao, Y. Ji, Y. Li, and C.-P. Liu, “Attention-based neighbor selective aggregation network for camouflaged object detection,” in IJCNN , 2022
2022
Later among the works it cites.
H. Zhu, P. Li, H. Xie, X. Yan, D. Liang, D. Chen, M. Wei, and J. Qin, “I can find you! boundary-guided separated attention network for camouflaged object detection,” in AAAI , 2022
2022
Later among the works it cites.
T. Zhou, Y. Zhou, C. Gong, J. Yang, and Y. Zhang, “Feature aggregation and propagation network for camouflaged object detection,” IEEE TIP , vol. 31, pp. 7036–7047, 2022
2022
Later among the works it cites.
P. Li, X. Yan, H. Zhu, M. Wei, X.-P. Zhang, and J. Qin, “Findnet: Can you find me? boundary-and-texture enhancement network for camouflaged object detection,” IEEE TIP , vol. 31, pp. 6396–6411, 2022
2022
Later among the works it cites.
M.-C. Chou, H.-J. Chen, and H.-H. Shuai, “Finding the achilles heel: Progressive identification network for camouflaged object detection,” in ICME , 2022
2022
Later among the works it cites.
J. Liu, J. Zhang, and N. Barnes, “Modeling aleatoric uncertainty for camouflaged object detection,” in WACV , 2022
2022
Later among the works it cites.
Y. Sun, S. Wang, C. Chen, and T.-Z. Xiang, “Boundary-guided camouflaged object detection,” in IJCAI , 2022
2022
Later among the works it cites.
M. Zhang, S. Xu, Y. Piao, D. Shi, S. Lin, and H. Lu, “Preynet: Preying on camouflaged objects,” in ACM MM , 2022
2022
Later among the works it cites.
Z. Liu, Z. Zhang, Y. Tan, and W. Wu, “Boosting camouflaged object detection with dual-task interactive transformer,” in ICPR , 2022
2022
Later among the works it cites.
Y. Pang, X. Zhao, T.-Z. Xiang, L. Zhang, and H. Lu, “Zoom in and out: A mixed-scale triplet network for camouflaged object detection,” in CVPR , 2022
2022
Later among the works it cites.
Y. Zhong, B. Li, L. Tang, S. Kuang, S. Wu, and S. Ding, “Detecting camouflaged object in frequency domain,” in CVPR , 2022
2022
Later among the works it cites.
Q. Jia, S. Yao, Y. Liu, X. Fan, R. Liu, and Z. Luo, “Segment, magnify and reiterate: Detecting camouflaged objects the hard way,” in CVPR , 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
J. Xie, W. Xie, and A. Zisserman, “Segmenting moving objects via an object-centric layered representation,” in NeurIPS , 2022
2022
Later among the works it cites.
M. Kowal, M. Siam, M. A. Islam, N. D. Bruce, R. P. Wildes, and K. G. Derpanis, “A deeper dive into what deep spatiotemporal networks encode: Quantifying static vs. dynamic information,” in CVPR , 2022
2022
Later among the works it cites.
L. Li, T. Zhou, W. Wang, L. Yang, J. Li, and Y. Yang, “Locality-aware inter-and intra-video reconstruction for self-supervised correspondence learning,” in CVPR , 2022
2022
Later among the works it cites.
B. Yan, Y. Jiang, P. Sun, D. Wang, Z. Yuan, P. Luo, and H. Lu, “Towards grand unification of object tracking,” in ECCV , 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie, “A convnet for the 2020s,” in CVPR , 2022
2022
Later among the works it cites.
W. Wang, E. Xie, X. Li, D.-P. Fan, K. Song, D. Liang, T. Lu, P. Luo, and L. Shao, “Pvt v2: Improved baselines with pyramid vision transformer,” CVMJ , vol. 8, no. 3, pp. 415–424, 2022
2022
Later among the works it cites.
J. Qi, Y. Gao, Y. Hu, X. Wang, X. Liu, X. Bai, S. Belongie, A. Yuille, P. H. Torr, and S. Bai, “Occluded video instance segmentation: A benchmark,” IJCV , vol. 130, no. 8, pp. 2022–2039, 2022
2022
Later among the works it cites.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in CVPR , 2022
2022
Later among the works it cites.
D.-P. Fan, J. Zhang, G. Xu, M.-M. Cheng, and L. Shao, “Salient objects in clutter,” IEEE TPAMI , vol. 45, no. 2, pp. 2344–2366, 2023
2023
Closest in time.
M. Rizzo, M. Marcuzzo, A. Zangari, A. Gasparetto, and A. Albarelli, “Fruit ripeness classification: A survey,” AIA , vol. 7, pp. 44–57, 2023
2023
Closest in time.
H. Mei, X. Yang, Y. Zhou, G.-P. Ji, X. Wei, and D.-P. Fan, “Distraction-aware camouflaged object segmentation,” SCIS , 2023
2023
Closest in time.
G.-P. Ji, D.-P. Fan, Y.-C. Chou, D. Dai, A. Liniger, and L. Van Gool, “Deep gradient learning for efficient camouflaged object detection,” MIR , vol. 20, pp. 92–108, 2023
2023
Closest in time.
Y. Lv, J. Zhang, Y. Dai, A. Li, N. Barnes, and D.-P. Fan, “Towards deeper understanding of camouflaged object detection,” IEEE TCSVT , 2023
2023
Closest in time.
G. Sun, Z. An, Y. Liu, C. Liu, C. Sakaridis, D.-P. Fan, and L. Van Gool, “Indiscernible object counting in underwater scenes,” in CVPR , 2023
2023
Closest in time.
M. Zhuge, D.-P. Fan, N. Liu, D. Zhang, D. Xu, and L. Shao, “Salient object detection via integrity learning,” IEEE TPAMI , vol. 45, no. 3, pp. 3738–3752, 2023
2023
Closest in time.
R. Cong, W. Song, J. Lei, G. Yue, Y. Zhao, and S. Kwong, “Psnet: Parallel symmetric network for video salient object detection,” IEEE TETCI , vol. 7, no. 2, pp. 402–414, 2023
2023
Closest in time.
2023
Closest in time.
Q. Zhai, X. Li, F. Yang, Z. Jiao, P. Luo, H. Cheng, and Z. Liu, “Mgl: Mutual graph learning for camouflaged object detection,” IEEE TIP , vol. 32, pp. 1897–1910, 2023
2023
Closest in time.
J. Lin, X. Tan, K. Xu, L. Ma, and R. W. Lau, “Frequency-aware camouflaged object detection,” ACM TMCCA , vol. 19, no. 2, pp. 1–16, 2023
2023
Closest in time.
J. Ren, X. Hu, L. Zhu, X. Xu, Y. Xu, W. Wang, Z. Deng, and P.-A. Heng, “Deep texture-aware features for camouflaged object detection,” IEEE TCSVT , vol. 33, no. 3, pp. 1157–1167, 2023
2023
Closest in time.
H. Xing, Y. Wang, X. Wei, H. Tang, S. Gao, and W. Zhang, “Go closer to see better: Camouflaged object detection via object area amplification and figure-ground conversion,” IEEE TCSVT , 2023
2023
Closest in time.
D. Zheng, X. Zheng, L. T. Yang, Y. Gao, C. Zhu, and Y. Ruan, “Mffn: Multi-view feature fusion network for camouflaged object detection,” in WACV , 2023
2023
Closest in time.
R. He, Q. Dong, J. Lin, and R. W. Lau, “Weakly-supervised camouflaged object detection with scribble annotations,” in AAAI , 2023
2023
Closest in time.
X. Hu, D.-P. Fan, X. Qin, H. Dai, W. Ren, Y. Tai, C. Wang, and L. Shao, “High-resolution iterative feedback network for camouflaged object detection,” in AAAI , 2023
2023
Closest in time.
Z. Huang, H. Dai, T.-Z. Xiang, S. Wang, H.-X. Chen, J. Qin, and H. Xiong, “Feature shrinkage pyramid for camouflaged object detection with transformers,” in CVPR , 2023
2023
Closest in time.
C. He, K. Li, Y. Zhang, L. Tang, Y. Zhang, Z. Guo, and X. Li, “Camouflaged object detection with feature decomposition and edge reconstruction,” in CVPR , 2023
2023
Closest in time.
N. Luo, Y. Pan, R. Sun, T. Zhang, Z. Xiong, and F. Wu, “Camouflaged instance segmentation via explicit de-camouflaging,” in CVPR , 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
E. Meunier, A. Badoual, and P. Bouthemy, “Em-driven unsupervised learning for efficient motion segmentation,” IEEE TPAMI , vol. 45, no. 4, pp. 4462–4473, 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
G.-P. Ji, M. Zhuge, D. Gao, D.-P. Fan, C. Sakaridis, and L. V. Gool, “Masked vision-language transformer in fashion,” MIR , 2023
2023
Closest in time.