This slide is about detecting concealed object from images using Search Identification Network (SINet) just like predator searches its prey and identifies it.

1. Concealed Object Recognition using
Search Identification Network
(SINet)
ADARSHA DHAKAL
PUL078MSCSK003

2. PROBLEM DOMAIN
i. Some objects that are concealed in images
are difficult to find.
ii. High intrinsic similarities between object
and background.
iii. COR is for detecting concealed objects
from image.
2

3. OBJECTIVES
• Study on object detection from a concealed vision perspective.
• To detect the concealed part of an image using Search Identification Network
(SINet).
12/11/2022 3

4. LITERATURE REVIEW
• Two remarkable studies on concealed animals from Abbott Thayer [2] and
Hugh Cott [3] are still hugely influential.
• COD Datasets. CHAMELEON [4] is an unpublished dataset that has only
76 images with manually annotated object-level ground-truths (GTs).
• Another contemporary dataset is CAMO [5], which has 2.5K images (2K
for training, 0.5K for testing) covering eight categories.
12/11/2022 4

5. PROPOSED METHODOLOGY
5
Feature
Extraction
TEM NCD
Search Phase
GRA GGO
Reverse
Guidance
Identification Phase
SINet Framework

6. Search Phase
• Feature Extraction: The Res2Net represents multi-scale features at a granular
level and increases the range of receptive fields for each network layer [6].
• Texture Enhanced Module(TEM): Used to capture fine-grained textures with
enlarged context cues. It includes four parallel residual branches with
different dilation rates [7].
• Neighbor Connection Decoder(NCD): Provides the location information of
concealed object [8].
12/11/2022 6

7. Identification Phase
• Reverse Guidance: Principled strategy to mine discriminative concealed
regions by erasing objects by using sigmoid and reverse operation.
• Group Guidance Operation(GGO): It can explicitly isolate the guidance
prior and candidate feature before the subsequent refinement process [9].
• Group Reversal Attention(GRA): Multistage refinement is done with the
assistance of both reverse guidance and GGO to improve performance.
12/11/2022 7

8. EXPECTED OUTCOME
i. Identify objects that are embedded in their background.
ii. Distinguish high intrinsic similarities between the concealed object
and their background.
12/11/2022 8

9. EXPECTED OUTCOME
12/11/2022 9

10. TIME SCHEDULE
12/11/2022 10

11. THANK YOU
12/11/2022 11

12. REFERENCES
1. Fan, D.-P., Ji, G.-P., Cheng, M.-M., & Shao, L. (2021). Concealed Object Detection.
2. G. H. Thayer and A. H. Thayer, Concealing-coloration in the Animal Kingdom: An Exposition of the Laws of Disguise Through Color and Pattern:
Being a Summary of Abbott H. Thayer’s Discoveries. Macmillan Company, 1909.
3. H. B. Cott, Adaptive coloratcottion in animals. 1940.
4. P. Skurowski, H. Abdulameer, J. Błaszczyk, T. Depta, A. Kornacki, and P. Kozieł, “Animal camouflage analysis: Chameleon database,”
2018, unpublished Manuscript
5. T.-N. Le, T. V. Nguyen, Z. Nie, M.-T. Tran, and A. Sugimoto, “Anabranch network for camouflaged object segmentation” Comput. Vis.
Image Underst., vol. 184, pp. 45–56, 2019
6. S. -H. Gao, M. -M. Cheng, K. Zhao, X. -Y. Zhang, M. -H. Yang and P. Torr, "Res2Net: A New Multi-Scale Backbone Architecture" in IEEE
Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 2, pp. 652-662, 1 Feb. 2021
7. https://samirkhanal35.medium.com/relationships-between-pixels-neighbours-and- connectivity-d38e473cd994
8 . Chen, S., & Fu, Y. (2020). Progressively Guided Alternate Refinement Network for RGB Salient Object Detection (version 1)
12/11/2022 12