SiamCAN:Simple yet Effective Method to enhance Siamese Short-Term Tracking
Authors: Yue Zhao
Zhibin Yu
Venue:  (2021)
Keywords: Siamese trackers
cross-attention
light structure
anchor-free

Abstract: Most traditional Siamese trackers are used to regard the location of the max response map as the center of target. However, it is difficult for these traditional methods to calculate response value  accurately when face the similar object, deformation, background clutters and other challenges. So how to get the reliable response map is the key to improve tracking performance. Accordingly, a simple yet effective short-term tracking framework (called SiamCAN),by which bridging the information flow between search branch and template branch, is proposed to solve the above problem in this paper. Moreover, in order to get more accurate target estimation, an anchor-free mechanism and specialized training strategy are applied to narrow the gap between the predicted bounding box and groundtruth. The proposed method achieves state-of-the-art performance on four visual tracking benchmarks including UAV123, OTB100, VOT2018 and VOT2019, outperforming the strong baseline, SiamBAN, by 0.327 $\displaystyle \rightarrow$ 0.331 on VOT2019 and 0.631 $\displaystyle \rightarrow$ 0.638 success score, 0.833 $\displaystyle \rightarrow$ 0.850 precision score on UAV123.

Under review as a conference paper at ICLR 2021
SIAMCAN: YET EFFECTIVE METHOD TO EN- HANCE SIAMESE SHORT-TERM TRACKING
Anonymous authors Paper under double-blind review
ABSTRACT
Most traditional Siamese trackers are used to regard the location of the re- sponse map as the center of target. However, it is difficult for these traditional methods to calculate response value accurately when face the similar object, de- formation, background clutters and other challenges. So how to get the reliable response map the key to improve tracking performance. Accordingly, simple yet effective short-term tracking framework (called SiamCAN),by which bridging the information flow between search branch and template branch, proposed solve the above problem in this paper. Moreover, in order to get more accurate target estimation, an anchor-free mechanism and specialized training strategy are applied to narrow the gap between the predicted bounding box and groundtruth. The proposed method achieves state-of-the-ar art performance on four visual track- ing benchmarks including UAV123, OTB100 VOT2018 and VOT2019, outper- forming the strong baseline, SiamBAN, by 0.327 + 0.331 on VOT2019 and 0.631 0.638 success score, 0.833 0.850 precision score on UAV 123.
INTRODUCTION
Visual object tracking is the fundamental task of computer vision, aiming at tracking unknown ob- ject of which the information is given by the first frame. Although great progress has been achieved in recent years, a robust tracker is still in desperate demand due to tricky challenge such as scale vari- ation, appearance deformation and similar object with complex background which can deteriorate tracking performance (Wu et al. (2013); Zhang et al. (2014))
Recently, Siamese Network based trackers have taken vital place in SOT field due to its accuracy and speed. Since (Tao et al. (2016)) and (Bertinetto et al. (2016) introduced Siamese networks in visual tracking, Siamese structure has been adopted as baseline for researchers to design efficient trackers (Li et al. (2018); Zhu et al. (2188)); Zhang & Peng (219 Li al. (2019); Xu et al. (2020); Chen et al. ).20)). After siamRPN (Li et (2018)) being proposed to gain more accurate anchor boxes, region proposal network has become essential part of tracker. However, the anchor scales are manual-set which go against the fact that the tracking target is unknown. Besides, the perfor- mance of the Siamese based trackers depends greatly on offline training by using massive frame pairs. Therefore, it highly increases the risk of tracking drift when facing significant deformation, similar object distractors, or complex background, due the undiscriminating feature learned from the target when the category of the target is excluded from the training dataset.
In these years, the attention mechanism has become the spotlight in computer vision which inspires the relative works not only in detection task but also in visual tracking (He et al. 2018); Abdelpakey al. (2018): ; Wang et al.) (2018); Zhuu al.) (2018b)). The attention mechanism includes channel attention and spatial attention, the former tends to generate set of -hannel-weights for modeling interdependencies between channels while the latter focuses on finding the informative part by uti- lizing the inter-spatio relationship of features. Considering these benefits, Siamese based trackers try to introduce attention module to distinguish target from complex background. Nevertheless, the performance of these trackers is not satisfactory for exploiting the expressive power of the attention mechanism inappropriately.
Based on the limitations discussed above, we design simple Cross coossaatention Guided Siamese net- work (SiamCAN) based tracker with anchor-fin frre strategy which performs better than the state-of- the-art trackers when facing the similar object challenge. SiamCAN takes template channel attention

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R135
63.66
R160
Sanit
Stam
Greandruh
Figure Tracking results on the challenges three aspects: scale variation (book), similar dis- tractors (crabs) and fast motion (dinosaur). Compared with other two state-of-the-art trackers, our tracker (SiamCAN) performs better.
to guide the feature extraction of search image by which can strengthen the ability of tracker to over- come distractors and complex backgrounds, performing better than most Siamese-base trackers, as shown in Figure 1. The main contributions of this work are:
We formulate cross-attention guided Siamese framework (SiamCAN) including cross- channel attention and self-spatial attention. The cross-channel attention builds an inter- active bridge between the target template and search frame share the identical channel weights. The self-spatial attention focuses on the discriminative part of the correlated fea- ture map, which complementary to the cross-channel attention.
The proposed tracker adaptive box regression, without numerous hyper-pan paremmerrsssssss set- ting. In order to get more accurate bounding box, we adopt the proper strategy to utilize the merits of anchor-free at the stage of training.
SiamCAN achieves state-of-the-arr results on four large tracking benchmarks, including OTB (Wu et al. (2013),, UAV123 [Mueller et al. (2016)), VOT2018 (Kristan et al. (2018) and VOT2019 (Kristan al. [2019)). The speed of tracker can also achieve 35 FPS.
RELATED WORK
In this section, we briefly review the recent Siamese based trackers, the anchor-free approaches and attention mechanism in both tracking and detection filed.
2.1 SIAMESE NETWORK BASED TRACKER
The pioneering works, SINT Tao et al. the siamese network in tracking filed. Due to its fast speed with light structure, Siamese network draws great attention from the visual tracking community. SiamFC tries to use siamese network learn the feature of both target template and search frame, and compare the similarity of them to find the most confident candidates. Although tracks fast, it cannot handle the scale variation problem by applying several scales of feature map. Inspired by Faster-RCNN (Ren et all (2015) from object detection, SiamRPN (Li al. (2018) draws on the region proposal network to get more various scale ratio bounding boxes. Since then, the RPN module has become an essential part of the tracker (Zhu et al. (2018)); Zhang & Peng 22019; Li al. (2019); Dong & Shen (2018); Fan & Ling (2019)). However, the complexity of anchor design makes the performance of trackers depend greatly the effect of anchor training.
2016)) and SiamFC (Bertinetto et al. (2016)), first introduce
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2.2 ANCHOR-FREE APPROACHES
In recent time, Anchor-free approaches have developed fast. The achor-free work be divided into two categories. The first one (Kong et 20.9)) Law & Deng (2018) 2018) aims to estimate the keypoints of the objects, while, the other (Redmon et al. (2016); Tian box for each pixel which can avoid presetting the scale ratio of anchors. Not only is anchor- approach popular in detection field, but is suitable for target estimation tracking field due to its high efficiency. SiamFC++ takes example by FCOS (Tian et al. (2019)) to design regression subnetwork and add centerness branch to eliminate the low quality samples. SiamCAR (Guo et al. (2200) changes the basic network structure additionally, merging the multi-layers features before correlation. Different from SiamCAR, SiamBAN (Chen et al. (2020)) puts emphasis on the label assignment which improves the tracking performance. Our method differs from the above trackers in details (Sectior4.33
(201 Itends to predict the bounding
2.3 ATTENTION MECHANISM
Attention mechanism has been the focus of the detection filed, on account to its powerful ability of enhancing deep CNNs. SE-Net (Hu et al. (2018) firstly puts forward the mechanism to gener- channel weights return to direct the learning of channel attention. After that, CBAM (Woo) itt al. (2018) utilizes both max-pooling and average-pooling to generate the merged attention, in- cludes channel and spatial attention. Recently, ECA-Net (Wang et al.) (2020b)) finds that avoiding dimensionality is of great importance for channel attention learning, and propose a cross- cross-channel in- teraction strategy which performs better than SE-Net. In the tracking field, the recent trackers began to equip with the attention mechanism to get better performance. SA_Siam (He et al. (2018) simply combines the SE-Net and SiamFC to get both discriminative and general features which boost the tracking performance. RASNet (Wang et al. 2018)) designs residual attention, general attention and channel attention to learn target feature better. SATIN (Gao et al. (2200) uses hourglass net- work as backbone and designs cross-attention module for exemplar branch to combine the channel and spatial attention from shallow and deep layers. However, these trackers only calculate along- side each branch and neglect the information flow between them, result, the ability of attention mechanism can not be fully utilized.
OUR APPROACH
Attention Module
Mas/AugPool
Regression
25x25
127-127x3
Feature Extraction Backbone
ConV
1x1x226
Classifaaion 25x25x2
25x25x256
Anchar-free Network
Crop
7x7x356
Template branch
T-Fectures
258-255-3
17x17-256
31x31-2256 256
Search branch Cross-correlation
5-Features
Matric Mutiolication
Convolution Layers
Figure 2: Illustration of the proposed tracking framework, consisting of feature extraction, cross- attention module and anchor-free network. In the feature extraction part, the features of the third, fourth and fifth blocks are sent to the cross-attention module. The cross-attention module uses the channel attention from the template branch combine with the feature from the search branch. The right side shows the classification and anchor free regression network, which is taken to localize the target and predict the size of bounding box.
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As shown in Figure 2, the proposed framework mainly consists of three components, the feature extracted Siamese network, the cross apsss-ttttntion module and anchor-fres bounding box regression subnetwork with foreground classification subnetwork.
3.1 FEATURE EXTRACTED SIAMESE NETWORK
Like the most Siamese based tracker, we adopt the fully convolution network without padding, which guarantees the accurate location calculation. Feature extracted network composes of two parts, template branch and search branch. Both of them share the same layer parameters of backbone, by this mean. CNNs can learn the relative feature for them calculate similarity in the subsequent operation. The template branch intends to encode the exemplar feature in the first frame while the other branch aims to encode the candidates feature which may involve target in the follow-up frames. Set the input in template branch as L. the subsequent frames in search branch as Lg. We feed the It and I, into backbone, and get the feature σi (1t) and Φi (1,) from different I-th backbone layers. Next, the given features
sent to the corresponding branch after having convolution with neck layer to reduce feature channel size to 256 and get the template feature (I)( with the search feature vis (T.). At last, crop the 7x7 patch from the center of template feature.
3.2 CROSS-ATTENTIO NETWORK
Attention mechanism is created for the purpose that enforce CNNs to focus on the special parts which is of great importance, i.e., channels information and spatial information. The channel atten- tion is designed to explore the interdependencies between channels while the spatial attention tends to make CNNs pay more attention to the most critical areas of the feature. Different from (He et al.) 2018): Wang et al. (018)). ). the channel attention is used between two branches not being applied as self-attention. Moreover, SATIN (Gao al. (2020) designs module also called cross-attention, but the 'cross' means the combination of different layers which is different from our method. In this paper, the target branch feature (4+) is sent to global average pooling to get aggregated feature Y,, i.e.,
1
Y WH Ei=
W.H
Given the aggregated feature, the channel weight is obtained by performing a ID convolution of size i.c..
VÚ OE w wo)) 2
Where is Sigmoid function, indicates the parameters of 1D convolution and 2 indicates the set adjacent channels of yi- To let search branch learns the information from target template, multiply the channel weights with the search feature, i...
V (1,) vo(s) * V
3.3 CLASSIFICATION AND ANCHIOR-FREE REGRESSION SUBNETWORK
As shown in Figure 2, the correlation feature map is calculated by the depth-wise correlation opera- tion between (T,) and t/a (T+), i.e.,
pels wxhxe (I.) vI((I))
(4)
Freg Fr = (1.)) x (le()))
(5)
where denotes depth-wise convolution operation. Then, we apply self-spatial attention to the feature map in order to focus on discriminative part automatically, i.c.,
pella
Fxxkxe = a ([Aog P hxx)) M Max P(Fa (x))))))))
(6)
Freg
exhxx = (((((ggP(FFre (x wx M Max Mar ((F
(7)
After that, we two convolution layers with kernel size 1x1 reduce the number of channel from 256 to 2 and 4 respectively for each branch and concatenate the feature maps from different layers of backbone by the trainable weights a, i.e..
pels wxhx2
N
Σi= On
Fill
Flwxxxx2
(8)
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P wxxx1 =E -4=1 Freg Flwxixd
(9)
where N denotes the total number of the backbone layers we use. The classification feature map has two channels, the one represents the foreground and the points (i.j) on Pale 2x, i, re- fer to the probability scores of target, the other represents the background and the points ( on Palx (ex,xx(1,,,3) refer to the probability scores of background. The regression feature map has four channels, each of them represents the four direction distances from the points location in search branch input to the four sides of the bounding box respectively, that is say, each point (i.j) in P j. is a vector which can denoted as (l, (t,,t,,)
Classification label and regression label. For anchor based methods, the positive sample and the negative one are classified by the value of Intersection over Union between anchor and groundtruth. In this paper, We use ellipse and circle figure region to design label for points (i.j) in feature map, which is inspired by (Chen et al. (2020)). The ellipse E₁ center and axes length are set by groundtruth center (g 9yc) of groundtruth size 9 ) We also get the circle C2 with 0.5g 0.5gh
-
2
as radius, i.c.
2 )2 + )2))
(B(pi) g 8e )2
(B(p,) - Iue )2
(10)
)2
B(p,)² +(p)) p2
(11)
where B denotes the calculation for the location of points (i,j) in feature map Pale wxxx2 back to the search frame. If the point B(p; Pý) falls within the C2 region, it will be given a positive label, and if it falls outside the E1 area, it will be given negative label, i...
1, (fC2(p(i.5) <
label
(fEi(((i))))
(12)
0, otherwise
For regression branch, the regression targets can be defined by:
d(s,)) pt Iz.: d(a.)) = Pi 9y0
(13)
(((,)) ym ps, ((..) 9y Pj
(14)
where (gza> Iv.). (gu, In.) denote the left-top and right-bottom coordinates location of the groundtruth.
Loss function. We employ cross entropy loss to train the classification network. To predict more accurate bounding box, we adopt the DIoU loss (Zheng et al. (2020) to train the regression network, i...
I Treg = ToU pp
(15)
where p(-) is the Euclidean distance, and Pyt denote the central points of predicted box and groundtruth and c is the diagonal length the smallest enclosing box covering the two boxes. For regression branch training, DIoU loss optimize the bounding faster than GloU loss (Rezatofighi et al. (2019)). The overall loss function is: Aot X2
(16)
where constants A1 and A weight the classification loss and regression loss. During model training, we simply set 1, X2 without tyyper-paramend searching.
3.4 TRAINING AND INFERENCE
Training. We train model by using image pairs, 127 x 127 pixels template patch and a 255 x 255 pixels search patch. The training datasets include ImageNet VID (Russakovsky et al. (2015)) ), COCO (Lin et al. (2014)). YouTubb-BBunddng Boxes (Real et al. (0117), ImageNet Det (Real et al. (017)) and GOTTOk (Huang et al. (2019)). Due to the numbers of negative samples are more than the positive samples, we set at most 16 positive samples and 48 negative samples from the search image.
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Besides, in order to get more accurate regression information, adopt the DIoU loss to optimize the regression branch.
Inference. We feed the cropped first frame and the subsequent frames to the feature abstract network as template image and search image. Next, the features are sent to the cross-attention module and pass the classification branch and regression branch. After that, we get the classification map. The location of the highest score represents the most probable center the tracking target. Then, we use the scale change penalty and the cosine window as that introduced in (Li et al. (2018)) to guarantee the smooth movements of target. According to the location p of the final score, we can get the predicted boxes B, i.e.,
ba Pi d/0 byt Pj de
(17)
ba2 = + d= by2 + de
(18)
where d'y,,, denotes the predicted values of the regression targets on the regression map, (bat, by1) and (byz, byz) are the top-left corner and bottom-left corner of the predicted box.
4 EXPERIMENTS
4.1 IMPLEMENTATION DETAILS
Our approach is implemented in Python with Pytorch on RTX2080Ti. The backbone is modified Re in (He et al. (2016)), and the weights pre-trained ImageNet (Russakovsky al. (215)). During the training phase, the model is optimized by the stochastic gradient descent (SGD), at the meantime, the total number of epochs is 20 and the batch size is set 28. For the first 10 epochs, we frozen the parameters of the backbone and only train the heads structures, for the last 10 epochs, we unfrozen the last 3 blocks of backbone to be trained together. Besides, we warm up the training during the first 5 epoch with warmup learning rate of 0.001 to 0.005, and in the last 15 epochs, the learning rate exponentially decayed from 0.005 to 0.00005.
4.2 RESULTS ON THRER BENCHMARKS
To affirm the effect of our method, we evaluate tracker performance with the recent trackers
MAML Waag et al. (2020a)) PriDiMP (Danelljan et al. (2020)) SiamBAN (Chen et al. (2020).
SiamCAR (Guo et al. (0200). ROAM (Yang et al. (2020). SiamFC++ (Xu et al. (2020). STN
(Tripathi et al. (2019)) ARTCS Krristan et al. (2019)). 9,, PN++ (Li et al. (2019)) (S11I, (aao
al. (0200). ATOM (Danelljan
al. (2019). DIMP-18 (hhat al. (2019)). DaSiamRPN (Zhu
et al. (0188)), SPM (Wang et al. (2019)ECO Damelljan et al. (2017)) CFNet Vaalmarre et al.
(2017)) SiamRPN (Li et al. (2018). DeepSRDCF (Danelljan et al. (2015a)). SRDCF (Danelljan
et (2.15b)) on four benchmarks UAV123 (Mueller al. (2016)), VOT20T8 (Kristan al. (0018)
VOT2019 (Kristan al. (2019) and OTB100 (Wu et al. 2013 (details in Appendix A.I
4.2.1 RESULTS ON UAV
UAV123 contains 123 challenging video sequences, which can be divided into categories ac- cording to their attributes. The performance of the tracker is evaluated by two evaluation metrics, the precision scores and the AUC scores. The AUC scores reflect the overlap between the predict bounding box and ground-truth box, while the precision scores are relative with the distance between the center the predict bounding box and ground-truth box. In Figure 3, our method achieves the best performance in precision scores, i.c., 0.850, and the second best AUC scores 0.678. As for the 11 categorics of the challenge, SiamCAN ranks 1st or 2nd in Scale Variation, Similar Object, Fast Motion and Low Resolution, in Appendix A.2 The results demonstrate that tracker can handle the similar object and scale change challenge, due to the learning of gross-attention subnetwork and anchor-free mechanism.
4.2.2 RESULTS ON VOT2018
VOT2018 consists of 60 challenging videos. The evaluation metries to rank the performance of the tracker based on EAO (Expected Average Overlap) which depends on the accuracy and the robustness. As shown in Table our tracker outperforms SiamRPN++ by 2.8 points by introducing

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Buccesa plats of OPE on UAV123
Procision plots of OPE UAV128
0.5
0.5
0.5
8.5
0.5
[B-054 Dura
BOmBAN
(6.412 GammAAr
18.2
61
DOG
(6.741 EG3
10
611
112
1.5
1.0 011
07
05
11.
10
38
31
Ovarip op Trosheld
Localion- throshold
Figure Success and precision plots on UAV123
Table 1: Comparison with state-of-the-art trackers on VOT2018. Red, blue and green fonts indicate the top-3 trackers.
SATIN
MAML
ATOM
SiamFC++
SiamRPN+
PrDiMP
SiamBAN
Ours
EAOT
0.294
0.392
0,401
0.400
0.417
0.442
0.452
0.445
At
0.635
0.590
0.556
0.604
0.618
0,597
0.591
R
0.220
0.204
0.183
0.234
0.165
0.178
0.173
the anchor-free regression network. Compared with SiamFC++, we achieve EAO improvements of 4.5 points. Although our method fails achieve the best EAO, the robustness scores of listed Siamese-basee trackers are higher than our method, which affect the EAO scores, in other words, SiamCAN is robust Siamese- based tracker.
4.2.3 RESULTS ON VOT2019
VOT2019 video sequences 20% different from VOT2018, adding more fast motion and similar object videos. Table the evaluation results on VOT2019 compared with the recent trackers. We see that the recent proposed MAML gets the highest accuracy scores, while our SiamCAN surpassing MAML by 2.5 points in terms of EAO. Besides, our robustness scores also rank 2nd.
4.3 ANALYSIS OF THE PROPOSED METHOD
Discussion on effective sample selection. Anchor-free method has the weakness that network may produce low-quality predicted bounding boxes far away from the center of target, even though the predicted box is accurate. To address this issue, SiamFC++ and SiamCAR introduce centerness branch get high quality sample, forcing the network focus the target center. While, SiamBAN uses ellipse figure region to design label which has the same effect. Accordingly, we do several experiments find which method performs better. As shown in Table 3 baseline tracker consists of cross-attention module and anchor-free network. Ellipse label does better than Circle label (2) vs (I. while the centerness branch with ellipse label even have worse effects (2) 3). Based on
Table 2: Comparison with state-of-the-art trackers VOT2019. Red, blue and green fonts indicate the top-3 trackers.
SPM
ARTCS
SiamRPN+
MAML
ATOM
STN
SiamBAN
Ours
EAO1
0.275
0.287
0.292
0.295
0.301
0.314
0.327
0.331
At 0.577
0.602
0.580
0.637
0.603
0.589
0.602
0.595
R,
0.507
0.482
0.446
0.421
0.411
0.349
0.396
0.376

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Table 3: Results of different sample select methods VOT2208.
#Num
tracker
method
EAO
1
baseline
Circle label
0.428
baseline
Ellipse label
0.440
3
baseline
Ellipse tcettrecesss
0.436
baseline
Ellipse&circle label
0.445
Table 4: Results of each Components of our tracker on VOT2018.
#Tracker
Components
Regression loss
EAO
baseline
GIoU Loss
0.351
baseline
DIoU Loss
0.378
cross cross-attention
GloU Loss
0,411
cross-attention
DIoU Loss
0,421
spatial-attention
DIoU Loss
0.445
the performance of ②, we visualize the tracking of 2 and compare with D details in Figure 4. At the training stage, 2 gives the positive label to the points fall within Eg region, while 4 gives the positive label to the points fall within the C2 region, the more central position. In this aspect, the comparision in Table
④ can be explained.
Num:
Num:
Figure Comparision of different label assignments. Both 2 and ! predict accurate boundingbox, but the latter focuses more on the target center.
Discussion on components of tracker. To verify the role of each components of our tracker, we the results of components on VOT2018 to analyze the details. As shown in Table the baseline model can get an EAO of 0.351, which consists of regular backbone, i.c., ResNer50, a classifica- tion and an anchor-free regression network. Exchanging DIoU Loss for GloU Loss during training can get higher scores, due to the more accurate predicted bounding box (2) vs I). Adding cross- attention module can obtain a large improvement, i.e., 4.3 points on EAO strates the information interaction between the template branch and the search branch is of great significance. Finally, tracker utilizes the self-spatial attention can gain another improvement of 2.4 points EAO ( vs 5).
vs 2 This demon-
Feature visualization. We visualize the features extracted from tracker (,, tracker a and tracker ) in Figure 5. On the left side of Figure 5, tracker (I) vs tracker 3 shows the tracker not equipped with cross-attention module is easier to lose target and focus on the wrong object when appear similar object and the visualized feature demonstrates that cross-attention module can enable tracker to tell target from similar distactors. On the right side of Figure 5, tracker ( shows the power cross- attention module combined with proper training strategy.
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Framer
Visualize
Frame:6
Visualize
Frame:207
Visualize
Tracker:
Tracker:
Tracker:
Tracker:
Tracker:
Tracker:
Frame:5
Visualize
Frame:6
Visualize
Frame:354
Visualize
Tracker:
Tracker:
Tracker:
Tracker
Tracker:
Tracken:
Figure 5: Visualization of confidence map. The left: the performance of tracker ! and tracker 3 The right: the performance tracker (
CONCLUSION
In this paper, we propose a simple Siamese-based tracker called SiamCAN, which combined with cross-attention and anchor-free mechanism. The cross-attention module utilizes the target template channel attention to guide the feature learning of search frame, bridging the information flow be- tween each branch. The anchor-free regression discards the fussy design of the anchors, and ad- justs the scale ratio of the bounding box automatically. In order to use them to their fullest poten- tial, choosing appropriate label assignment strategy and suitable loss funtion boost the tracking performance with limited laboratory equipments. Extensive experiments are conducted on four benchmarks, which demonstrate our trackers with light structure yet achieves the state-of-the-a performance, especially in scale variation, background cluster, deformations and similar distractors challenges.
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A APPENDIX
A.1 EXPERIMENT RESULTS ON OTB
ОТВ100 contains 100 challenging video sequences, and the evaluation metrics are same with UAV123. In Figure 5, our method achieves the best performance in precision scores, i.., 0.913, and the second best success scores 0.684. As for the categories of the challenge, SiamCAN ranks 1st 2nd in Deformation, Background Clutters, Scale Varation and Out-of-Plane Rotation. The sults demonstrate that our trackers can handle the deformation, background clutters, scale variation and out-of-plane rotation, in Figure 6.
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Figure 6: Success and precision plots on OTB100.
A.2 MORE EXPERIMENT RESULTS ON UAV 123
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Figure 7: Comparisons on OTB-100 with challenges: Deformation, Background Clusters, Scale Variation and Out-of-Plane Rotation. Our SiamCAN performance ranks in top two.
COPLTTA
Figure 8: Comparisons on UAV123 with challenges: Scale Variation, Similar Object, Fast Motion and Low Resolution. Our SiamCAN performance ranks in top two.
13


Reviews:
[{"review_id": "hVJ3L5xXl-P", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "#### 1.summary\nIn this paper, the authors introduce an cross-channel attrention mechanism and the anchor-free box regression branch with Diou-loss to deal with the clutters during the tracking procedure.\n#### 2.strengths\nThe experimental results show that this method has excellent performance on several public benchmark datasets.\n#### 3.weaknesses\n- The novelty of the paper is deficient , the proposed method such as cross-attention mechanism [1], anchor-free regression [2, 3]  have been previously exploited in existing models.\n- The proposed method is obviously based on the SiamBAN[2], however, there exist much reduplicate content which has already mentioned in [2].\n- In experiments part, there are missing some important algorithms to compare, such as SiamBAN which is the baseline method for this paper.\n- the analysis of the proposed method is deficient.\n\n  *[1] Y. Yu, Y. Xiong, W. Huang, M. R. Scott, Deformable siamese attention networks for visual object tracking, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 6728– 6737.*\n\n  *[2] Zedu Chen, Bineng Zhong, Guorong Li, Zhang Shengping, and Ji Rongrong. Siamese box adaptive network for visual tracking. In IEEE Conference on Computer Vision and Pattern Recognition, pages 6668–6677,  2020.*\n\n  *[3] Yinda Xu, Zeyu Wang, Zuoxin Li, Ye Yuan, and Gang Yu. SiamFC++: Towards robust and accurate visual tracking with target estimation guidelines. In AAAI, pages 12549-12556, 2020.*\n\n\n#### 4. Correctness\nThe claims, method, and empirical methodology are correct.\n#### 5. clarity\nThe paper writing is clear and easy to follow. But there exist some grammar faults. Moreover ,in sec 3.3, there are two same subtitles and similar content, which may be confusing. Besides, in figure 4, the arrangement of the pictures does not seem to match the description text.\n#### 6. Relation to prior work\nIt is an incremental work based on the prior work.\n#### 7. Reproducibility\nYes.\n\n\n", "extra_scores": {}, "extra_text": {}}, {"review_id": "UkdXZncUWJ", "reviewer": "AnonReviewer4", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "## Summary\n\nThis paper proposes a siamese network for visual tracking, namely SiamCAN, which utilizes cross channel attention, spatial attention, and anchor-free regression head. The method achives state-of-the-art performance on four visual tracking benchmarks. \n\n## Strengths\n\n +. The design of the network is reasonable, including using template's channel information to help the search branch to learn more specific feature, using spatial attention to aggregate location information from the depth-wise correlation, and utilizing anchor-free regression head to locate the object. \n\n+. The final results are good, and ablation study shows the effectiveness of these modules.\n\n## Weaknesses\n\n-. The technical contribution is weak. The main components, including channel attention, spacial attention and the anchor free network, are not new. This proposed method is quite similar to SiamBAN, especially the multi-head fusion and the anchor free network.  Furthermore,  the cross channel attention is more like a cross-correlation between the search branch feature and the pooling template vector. In this case, are the 1D conv layer and the sigmoid function necessary?  \n\n-. The description of the method is not clear. In Figure 2, there is only one correlation map but in Sec. 3.3 the author sad it has N correlation maps from different layers of the backbone. Furthermore, the index \"i\" of these N layers is conflict with the position index \"point(i, j)\", which would lead to misunderstanding. Also in Figure 2, the classification map's size is 25x25x1, but it is 25x25x2 in Sec. 3.3. The description of spatial attention is not clear. We can find Max/AvgPool in the figure but find no description. So how is the spatial attention used? The only word I can find is in related works \"CBAM (Woo et al. (2018)) utilizes both max-pooling and average-pooling to generate the merged attention, includes channel and spatial attention\". Is that the same? In Sec 3.2, \"Inspired by (Wang et al. (2020b)), we add channel attention and spatial attention into our network. \". However, Wang et al. (2020b) did not use spatial attention module. \n\n## Overall Rating\n\nDue to the weakness described above, I rate a \"Marginally below acceptance threshold\" score for the paper. \n", "extra_scores": {}, "extra_text": {}}, {"review_id": "EDJFGJcngeG", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This paper presents a Siamese-based single object tracking method using attention mechanism both in channel-wise and spatial-wise for learning deep correlation between exemplar and candidate images. Extensive experiments on UAV123, VOT2018 and VOT2019 demonstrate the effectiveness of the proposed method, and the 35 FPS running speed indicates its competitive efficiency.\n\nStrengths:\n1. The idea of multiplying the weights of aggregated template feature to the search feature is interesting and rather intuitive, the experimental results on several datasets confirmed the effectiveness.\n2. This paper is well-organized and easy to follow.\n3. Comprehensive experiments including the appendix are carried out, and convincing.\n\nWeaknesses:\n1. Lacking important literature reviews on highly relevant works.\nThe proposed SOT method belongs to 1) anchor-free and 2) attention-based pipelines. There are several latest and popular works the author should mention in Related Works:\nSiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines   https://arxiv.org/pdf/1911.06188.pdf\nSiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking    https://arxiv.org/abs/1911.07241v2\nSATIN: Siamese Attentional Keypoint Network for High Performance Visual Tracking\nhttps://arxiv.org/pdf/1904.10128.pdf\nAll three works above are anchor-free methods, and the third paper SATIN adopts attentional mechanism for SOT.\nThe author compared the proposed method with SiamFC++ and SiamCAR in experimental results, but the review of these methods and explanation of difference in methodology are missing in this paper.\nAs a matter of fact, the most relevant work to this paper is SATIN which also adopted channel and spatial attention in Siamese architecture. The only difference is multiplying the attentive channel weights of exemplar to candidate, which is called cross-attention in this paper. \nThe author should give a more thorough review and explain the difference between these works.\n\n2.  The contribution of this work is rather limit.\nThe paper describes three contributions in section 1, however,  the second point (anchor-free and DIoU) is hardly the contribution of this work  but reimplementation of previous works, and the third point is only a report of experimental results. In fact, the only contribution of this work is introducing cross-channel attention to explicitly learn the correlation of template and search images, which is a small trick in network design. Compared to SATIN, which also adopts channel attention to template feature and spatial attention later,  this paper does not bring a substantial contribution to visual tracking community. \n\n3. The comparison with SOTA methods are not comprehensive.\nThe experimental results on UAV123 do not include SOTA method SiamFC++, similarly, results on VOT2018 do not include SiamCAR, and results on VOT2019 do not include SiamFC++ and SiamCAR. Moreover, the author should also compare the results with SATIN on both OTB and VOT datasets.\n\n4. The presentations of Figure 1 and Figure 4 are terrible.\nThe resolution of Figure 1 is rather low, and the author did not even explain which one corresponds to the challenge situation of three aspects in figure caption. It is hard to tell which one is better in case 1 (first row) and case 2 (second row) due to the low resolution.\nFigure 4 is rather confusing. The author did not explain the visualized confidence map belongs to which layer, and this attentive map is whether from template feature or search feature. I assume the second row on left side is from the subsequent frames, then what’s the meaning of both rows on right side, and why there is a blank gap between left and right?\nThe author should pay more attention to improve the figures in this paper, and the captions should be more thorough without ambiguity.", "extra_scores": {}, "extra_text": {}}, {"review_id": "676Tf4Gf46Z", "reviewer": "AnonReviewer3", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "The architecture of the tracker is standard siamese. The novelty is at a technical level, modules of the \"cross-guided\" type have been proposed. It does bring an improvement, but not to the state-of-the-art level.  There is no significant insight, training, updating novelty or theoretical. Recent short-term trackers output segmentation, the proposed tracker outputs a bounding box.\n\nThe performance of the tracker is evaluated on UAV, VOT 2018 and VOT 2019. UAV is saturated. The performance VOT 2019 is worse than state-of-the-art, which is not reported -- the trackers selected for comparison do not include the best performing ones. It is not clear why  VOT 2020 was not included. \n\nOverall, this is \"yet another siamese tracker\", which is not sufficient for ICLR acceptance.\n\n", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: All three reviewers initially recommended reject.  The main concerns were:
1) weak technical contribution and insight [R1, R2, R3, R4];
2) incremental novelty (another variation of SiamFC) [R1, R2, R3];
3) unconvincing experiment results against missing SOTA [R1, R2, R3];

The author's response did not assuage these concerns.
Decision: Reject