Original: Deformable Vision Transformer Based Single-Stage Pedestrian Detector
Revised: Deformable Vision Transformer Based Single-Stage Pedestrian Detector

Original: Anonymous Author(s)
Revised: Anonymous Author(s)

Original: AfﬁliationAddress email
Revised: AffiliationAddress email

Original: Abstract
Revised: Abstract

Original: Pedestrian detection is a challenging ﬁeld in computer vision, which requires bothfast inference and high accuracy.
Revised: Pedestrian detection is a challenging field in computer vision, which requires bothfast inference and high accuracy.

Original: Single-stage detectors are faster than region ofinterest based two-stage detectors, but the latter categorys decreased accuracy ismainly due to the lack of spatially adaptive features.
Revised: Single-stage detectors are faster than region ofinterest based two-stage detectors at the expense of accuracy mainly due to the lackof spatially adaptive features.

Original: To this end, we proposea single-stage anchor-free pedestrian detector with enhanced spatial and multiscale features based on the deformable vision transformer aiming to achieve thebalance between speed and accuracy.
Revised: To this end, we propose a single-stage anchor-freepedestrian detector with enhanced spatial and multi-scale features based on thedeformable vision transformer aiming to achieve the balance between speed andaccuracy.

Original: Thearchitecture design  is investigated indepth and comprehensive comparisons with state-of-the-art single- and two-stagedetectors on various pedestrian datasets are performed.
Revised: The design of the architecture is investigated in depth. Comprehensivecomparisons with state-of-the-art single- and two- stage detectors on various pedestrian datasets are performed.

Original: The proposed detectorachieves leading performance on both Caltech and Citypersons datasets amongsingle- and two-stage methods using less parameters compared to the baseline.
Revised: The proposed detector achieves leading performanceon both Caltech and Citypersons datasets among single- and two- stage methodsusing less parameters compared to the baseline.

Original: The  miss rates for R (3.8%) and HO (36.5%) are decreased to 2.6% and 28.0% on Caltech, and 10.6% and 36.7% on Citypersons validation datasets respectively.
Revised: The log-average miss rates for Reasonable (3.8%) and Heavy (36.5%) are decreased to 2.6% and 28.0% on Caltech, and 10.6% and 36.7% on Citypersons validation datasets respectively.

Original: Iteven outperforms SOTA two-stage detectors in HO subset by 3% on Citypersonsvalidation set.
Revised: It evenoutperforms SOTA two-stage detectors in Heavy subset by 3% on Citypersonsvalidation set.

Original:  Introduction
Revised:  Introduction

Original: Pedestrian detection is a popular task subordinate to object detection in computer vision.
Revised: Pedestrian detection is a popular task subordinate to object detection in computer vision.

Original: This taskaims at accurately locating and classifying pedestrians in images or videos obtained via visible-lightand infrared cameras.
Revised: This taskaims at accurately locating and classifying pedestrians in images or videos obtained via visible-lightcameras, infrared cameras etc.

Original: In this paper, we focus on  detection using RGB images.
Revised: In this paper, we focus on the detection from RGB images.

Original: Pedestrian detectionis a signiﬁcant procedure as it is serving as the prerequisite for various vision tasks [1], such ashuman-centric tasks (person re-identiﬁcation [2, 3], person search [4], human pose estimation [5])and more generic multi-object tracking [6].
Revised: Pedestriandetection is very important as it serves as the prerequisite of various vision tasks [1], such as humancentric tasks (person re-identification [2, 3], person search [4], human pose estimation [5] etc.) andmore generic multi-object tracking [6].

Original: In this case, it plays an important role in autonomousdriving [7, 8], video surveillance [9] and action tracking.
Revised: It has been applied to autonomous driving [7, 8], videosurveillance [9] and action tracking.

Original: Pedestrian detection is a challengingtask, mainly due to the varying appearances resulting fromthe signiﬁcant occlusions andthe varying scales.
Revised: Pedestrian detection is very challenging mainly due to the varying appearances resulted from thesignificant occlusion and varying scales.

Original: On the one hand, signiﬁcant intra- or inter- classocclusion occurs when a pedestrian is occluded by other pedestrians or objects like cars and bicyclesin busy areas.
Revised: On the one hand, significant intra- and inter- class occlusionoccur when a pedestrian is occluded by other pedestrians or objects like cars, bicycles etc., in busyareas.

Original: Due to  heavy occlusions, only partial human body is visible which signiﬁcantlyreduces the discriminative features.
Revised: Due to the heavy occlusion, only a small part of the human body is visible which significantlyreduces the discriminative features.

Original: On the other hand, when the target is far away from the camera,its height and width decrease in the image, and vice versa.
Revised: On the other hand, when the target is far away from the camera,its height and width reflected in the image decrease, and vice versa.

Original: Large pedestrians tend to have moreinformative features but are difﬁcult to be fully extracted from a vast region. Small targets arecompact but relatively ambiguous with less preserved features.
Revised: Large pedestrians tend to havemore informative features but are difficult to be fully extracted from a vast region while featuresof small targets are compact but relatively ambiguous with less preserved details.

Original: In summary, the ﬂuctuating amountand varying shape of effective features are the key problems.
Revised: In summary, thefluctuating amount and varying shape of effective features are the root problems.

Original: These intrinsic problems challenge thecapabilities of the  feature extraction modules, which act as a long-standing bottleneck in pedestriandetection and almost all other object detection tasks.
Revised: These intrinsicproblems challenges the capabilities of feature extraction modules, which act as a long-standingbottleneck in pedestrian detection and almost all other object detection tasks.

Original: To deal with these problems, deep Convolutional Neural Networks (CNNs)-based two-stage methods [10, 11, 12, 13, 14, 15] have pervaded pedestrian detection tasks owing to the high detection accuracy.
Revised: To deal with these problems, deep Convolutional Neural Networks (CNNs) based approaches,especially two-stage methods [10–15] based on Fast and Faster R-CNN [16, 17] have pervaded pedestrian detection tasks owing to the high detection accuracy.

Original: These methods ﬁrst make a coarse prediction of targets in the Region Proposal Network(RPN), features inside which are then utilized to reﬁne the bounding boxes and predict the ﬁnal score.
Revised: These methods first make coarsepredictions of targets in the Region Proposal Network (RPN), then refine the bounding boxes andpredict the final scores based on the features inside the proposal regions.

Revised: In addition to methodsfor general object detection, pedestrian detectors take the advantage of unique characteristics oftargets, such as the mask of visible parts [49, 10] and key-points of human bodies [25].

Original: However, the repeated prediction requires longer training/inference time.
Revised: However,the inference speed of these methods is limited by the repeated prediction. This requires longertraining time per epoch and is hard to be applied to some real-world scenes.

Original: Secondly, the design ofthe anchors in RPN depends substantially on the choice of parameters.
Revised: Secondly, the design ofthe anchors in RPN is seriously parameter depended.

Original: Lastly,  jointly training twonetworks is complicated.
Revised: Lastly, the procedures of jointly training twonetworks are complicated.

Original: To achieve faster inference and easier training procedures, single-stage approaches, which only makea single round prediction, are developed [16] andhave been applied in pedestrian detection [17, 18,19, 20, 21] prediction.
Revised: To achieve faster inference and easier training procedures, single-stage approaches, which only makea single round prediction, are developed [18] and applied to pedestrian detection [19–23].

Original: Single-stage methods are promising; however, they suffer from decreaseddetection accuracy.
Revised: However,they suffer from decreased detection accuracy.

Original: The miss rates of two-stage methods in reasonable subset on Caltech are smallerthan 4%
Revised: The miss rates of two-stage methods in Reasonablesubset on Caltech are smaller than 4%

Original: [14][10, 22, 23], while those for single-stage methods are larger than 4.5% [19].
Revised: [14][10, 24, 25], while those for single-stage methods arelarger than 4.5% [21].

Original: For Citypersons validation set, the miss rates for the former is less than 40% in HO subset which ismuch lower than the latter (best result 42% [21]).
Revised: For Citypersons validation set, the miss rates for the former is less than 40%in Heavy subset, which is much lower than the latter (42% [23]).

Original: Our research purpose is to improve the detectionperformance ofthe single-stage pedestrian detectors, especially in R and HO subsets,  to narrowerthe gap between single- and two-stage methods.
Revised: Our research purpose is to improvethe detection performance of single-stage pedestrian detectors, especially the detection accuracy of Reasonable and Heavy subsets, and to narrow the gap between single- and two- stage methods.

Original: Massive studies have been carried outaiming to enhance the single-stage pedestrian detectors basedon Center and Scale Prediction (CSP) [19], a typical single-stage and anchor-free pedestrian detectorwhich signiﬁcantly accelerates the inference [1].
Revised: Massive studies have been carried out to enhance the single-stage pedestrian detectors based on Center and Scale Prediction (CSP) [21], a typical single-stage and anchor-free pedestrian detectorwhich significantly accelerates the inference [1].

Original: In the past year, most research were focusing on thefusion of representative features at multiple levels [24, 25, 26, 27, 28, 29].
Revised: In the past year, most research focuses on the fusionof representative features [26–31].

Original: [27] enhanced featuresat different levels via increasing semantic information atthe low-level and enriches the localizationinformation atthe high-level.
Revised: For example, [29] enhances the features via increasing semanticinformation at low-level and enriches the localization information at high-level.

Original: Similarly, [29] fused multi-level feature maps  in adequate proportions.[26, 28] also explored multi-level feature aggregation.
Revised: Similarly, [31] fusesthe feature maps with different scales in adequate proportions. [28, 30] also explore new strategies toaggregate multi-level features.

Original: These  enhancements make sense for theintrinsically unbalanced feature information between shallower  and deeper feature maps.
Revised: These feature enhancements are mainly performed along the featuremap level dimension, which makes sense because of the intrinsically unbalanced feature informationbetween shallower feature maps and deeper ones.

Original: However,this unbalance problem also exists in two-stage detectors.
Revised: However, this unbalance problem also exists intwo-stage detectors.

Original: This means that it is not the root cause tothe poorer performance of single-stage methods in comparison to two-stage methods.
Revised: This means that it is not the root cause of the poorer accuracy of single-stagemethods.

Original: To ﬁgure out the root cause,we compared the general architecture of single- and two-stage detectors  in Figure 1.
Revised: To address the root cause, the general architecture of single- and two- stage detectors are compared in Figure 1.

Original: Assume that the training strategies and the detection head are the same for bothmethods, the difference in performance lies in the information fed into the detection head.
Revised: Assume that the training strategies and the detection head are the same for both methods,the difference in the architechture lies in the information fed into the detection head.

Original: For twostage methods, boththe proposal region positions and features within this domain are fed into thedetection head.
Revised: For two-stagemethods, both positions and features of the proposal regions are fed into the detection head.

Revised: Theseproposals contain potential pedestrians.

Original: Thereby, the detection head classiﬁes via spatially target-focused features with lessbackground interference and reﬁnes the bonding boxes based on region positions.
Revised: Thereby, the detection head classifies via spatially targetfocused features with less background interruptions and refines the bonding boxes by predictingsmall offsets from the proposal positions.

Original: For single-stagemethods, each pixel in the feature map serves as the proposal regionwithout pre-estimated positions.
Revised: For single-stage methods, each pixel in the feature mapserves as the ‘proposal region’ with no pre-estimated positions.

Original: Features with these proposal regions share the same receptive ﬁeld size. This may be too small toinclude sufﬁcient information of large targets, orthe receptive ﬁeld is so large that the backgroundinformation submerges the visible pedestrians.
Revised: These pixels share the same receptivefield size which may be too small to include sufficient information for large targets or so largethat the background information overwhelms the useful features for detection.

Original: Obviously, this is more challenging forclassiﬁerscompared to the  two-stage method.
Revised: Obviously, thisis more challenging for the classifier compared to two-stage methods.

Original: As for the proposal region positions, single-stage methods haveto regress from scratch, which isalso more difﬁcult than simple reﬁnement in two-stage methods.
Revised: Additionally, single-stagemethods have to regress from scratch, which is more difficult than simple refinement in two-stagemethods.

Original: These suggest that the lack of spatially target-focused feature representation and the position prediction from scratch  as two key bottlenecks hindering the progress of single-stage detectors.
Revised: According to the analysis, the lack of spatially target-focused feature representation and theprediction of bounding boxes from scratch serve as two key bottlenecks hindering the improvementof single-stage detectors.

Original: Thus, to focus features  on the targets or other useful information automatically without the assistance of region proposals, we take the advantage of vision transformers.
Revised: To make the features fed into the detection head concentrating on the targets or other useful information automatically without the assistance of proposal regions, we take the advantage of visiontransformers.

Original: Vision transformersuseattention weights to describe the pairwise dependency of each entity in the feature map.
Revised: Vision transformers describe the pairwise dependency of each entity in the featuremap with attention weights.

Original: They outputthe weighted averaged feature which aggregates important entities  whilesupressing the disturbinginformation.
Revised: The output weighted averaged feature is the adaptive aggregation ofimportant entities (with higher attention weights) while the disturbing information is suppressed(with lower attention weights).

Original: With attention mechanism on top of the backbone  spatially adaptive features are supplemented which beneﬁts the subsequent classiﬁcation and regression tasks.
Revised: Using such attention mechanism on top of the backbone enables thesingle-stage detector to supplement spatially filtered features easily for subsequent classification andregression tasks.

Original: In this case, the enhanceddetector can make the best use ofboth the fast inference originated from single-stage methods andmore effective and cleaner features.
Revised: Thus, the modified detector makes the best use of the fast inference originated fromsingle-stage methods and more effective and cleaner features.

Original: Our main contributions are as follows:
Revised: Our main contributions are as follows:

Original: • Propose a novel multi-scale deformable feature extractor based on a deformable visiontransformer. The features fed into the detection head is enhanced along both scale and
Revised: • Proof the effectiveness of using the deformable vision transformer to improve the accuracy of single-stage pedestrian detectors

Original: [Table] spatial dimensions. [Table]
Revised: while maintaining fast inference.

Original: • Propose a new single-stage detector based on the proposed feature extractor  and CSP.
Revised: • Propose a novel feature extractor which enhance features along both scale and spatial

Original: Com- [Table]
Revised: dimensions.

Original: prehensive experimentson multiple pedestrian datasets show that the performance gap be- [Table] tween the presented method and two-stage methods is narrowed. [Table]
Revised: Comprehensive experiments show that the performance gap between thepresented method and two-stage methods is narrowed on various pedestrian datasets.

Original: • Extend the application of vision transformers on top of the backbone in pedestrian detectiontasks.
Revised: • Extend the application of vision transformers on top of the backbone in pedestrian detection tasks.

Original:  Related works
Revised:  Related works

Original: The vision transformer in Computer Vision (CV) originates from the transformers widely utilizedin Natural Language Processing (NLP).
Revised: Vision transformers in Computer Vision (CV) originates from the transformers widely utilizedin Natural Language Processing (NLP).

Original: Thanks to the attention mechanism, vision transformers areapplied to image feature extraction asthe backbone (like ViT [30] and Swin Transformer [31]) or ontop of the backbone (like DETR [32]).
Revised: Thanks to the attention mechanism, vision transformersare applied to image feature extraction as (like ViT [32] and Swin Transformer [33]) or on topof the backbone like DETR [34].

Original: Our detector belongs to the latter category.
Revised: Our detector belongs to the latter category.

Original: DETR consists ofthe convolutional backbone, six encoder-decoder pairs and the prediction head.
Revised: DETR consists ofthe convolutional backbone, six encoder-decoder pairs and the prediction head.

Original: It is an inspiring endto-end detector which has aroused plentyof studies based on it.
Revised: It is an inspiringend-to-end detector which has aroused plenty studies based on it.

Original: One typical variant is deformable DETR [33] which outperforms Faster R-CNN [34] and DETRon COCO 2017 validation set [35].
Revised: One typical variant of DETR is thedeformable DETR [35].

Original: Itis known that DETR requires massive memory to store the self-attention weights within each MultiHead Self-Attention (MHSA) layer.
Revised: It is known that DETR requires massive memory to store the self-attentionweights within each Multi-Head Self-Attention (MHSA) layer.

Original: The memory cost is linear to the number of attention heads andis square to the number of pixels in the  feature map.
Revised: The memory cost is linear to thenumber of attention heads and is square to the number of pixels in the down sampled feature map.

Original: Additionally, ﬁrst- and second-order momentumin optimizers introduce in further memory cost in training.
Revised: Additionally, first and second order momentum in optimizers introduce in further memory cost intraining.

Original: For accuratepedestrian detection, moreattention heads andthe relatively large feature map are preferred, which results in memory explosion.
Revised: To achieve accurate detection, more attention heads and relatively large down sampledfeature map are preferred, which results in memory explosion.

Original: To  this end, the fast-converging and memory-saving deformable DETR is proposed.
Revised: To overcome this problem, thefast-converging and memory-saving deformable DETR is proposed.

Original: It  calculates theattention weights at several sampling locations rather than each pixel in the feature map. Its memorycost is linear to the number of pixels, which makes it possible to train with high resolutions butlimited memory.
Revised: It only calculates the attentionweights at several sampling locations rather than each pixel in the feature map, so that the memoryis linear to the number of pixels, which makes training with high resolutions and limited memoryresources possible.

Revised: Experiments show that deformable DETR outperforms Faster R-CNN [17] and

Revised: DETR on COCO 2017 validation set [36].

Original: So far, vision transformer seems inspiring and potential, but it is rarely applied in pedestrian detection in the form of DETR or its variants.
Revised: So far, vision transformers seem inspiring and potential, but it is rarely applied in pedestrian detectionin the form of DETR or its variants.

Original: [36] found that deformable DETR not only behaves worsethan Faster R-CNN on CrowdHuman dataset [37], but also costs tenfold training time.
Revised: [37] found that deformable DETR not only behaves worsethan Faster R-CNN on CrowdHuman dataset [38], but also costs tenfold training time.

Original: Althoughdense object queries and the rectiﬁed attention ﬁeld are proposed to narrow the performance gap,the modiﬁed deformable DETR still shows a limited advantage over the traditional Fast R-CNN.
Revised: Althoughit narrowed the performance gap using dense object queries and the rectified attention field, themodified deformable DETR still show limited advantage over the traditional Faster R-CNN.

Original: Different from the use of six encoder-decoder pairs in deformable vision transformer,BoTNet [38]achieves competing performacne on ImageNet validation set [39] with much simpler usage of attention machanism. It substitutes the convolutional layers  in the last stage of ResNet [40] for MHSAlayers.
Revised: Thisimplies that rigidly putting the whole six encoder-decoder pairs into the pedestrian detector maynot be cost-effective. As an example, BoTNet [39] only substitutes the convolutional layers inresidual bottleneck blocks in the last stage of ResNet [40] for MHSA layers, but it produces strongperformance on ImageNet validation set [41].

Original: This implies that rigidly utilizing the whole six encoder-decoder pairs in the pedestrian detector may not be cost-effective. Inspired by this, our work  utilizes one deformable encoder  to enhancethe single-stage pedestrian detector.
Revised: Inspired by this and the aforementioned limitation,our work only utilizes a single encoder of the deformable DETR as an adaptive feature extractor andapplies it to the single-stage anchor-free CSP.

Original:  Method
Revised:  Method

Original: Deformable transformer encoder : Figure 2 shows the deformable encoder in the multi-scale deformable vision transformer.
Revised: Deformable Transformer Encoder : Figure 2 shows the deformable encoder in the multi-scaledeformable vision transformer.

Original: This encoder takes the multi-scale feature maps { z l } L l =1 ( L = 3 isthe number of feature maps) with height H l and width W l at scale l and reference points, which arethe positions of grid centers of the  maps, as inputs.
Revised: This encoder takes the multi-scale feature maps (cid:8) z l (cid:9) L l =1 ( L = 3 isthe number of feature maps) with height H l and width W l at scale l and reference points, which are the positions of grid centers of the feature maps, as inputs.

Original: Itoutputs the enhanced feature maps with thesame resolution as the input.
Revised: It the enhanced feature maps with the same resolution as the input.

Original: The input feature maps are initially added by ﬁxed encoded positional[32] and learnable level information [33] to disambiguate spatial and scale positions. Then they areprojected to the query feature map z q via linear projection.
Revised: The input feature maps are first added by fixed encoded positional[34] and learnable level information [35] to disambiguate spatial and scale positions, then projected tothe query feature map z q via linear projection.

Original: Theinput feature maps  generate valuefeature maps z v with a linear layer but nopositional or level encodings.
Revised: The feature maps also generate the value feature mapsz v with a linear layer but no encodings.

Original: The query feature map z q ,value feature map z v together with the pre-generated reference points are sent to the Multi-Scale Deformable Attention (MSDA) layer to produce enhanced spatially adaptive features. The MSDAlayer is followed by a Fee-Forward Network (FFN) which is a ﬁxed module in common transformerencoders.
Revised: The query feature map z q , value feature map z v togetherwith the pre-generated reference points are sent to the Multi-Scale Deformable Attention (MSDA)layer to enhance spatially adaptive features, followed by a Fee-Forward Network (FFN).

Original: In summary, the encoder supplements the semantic information of input feature maps viathe embedded attention layer and FFN.
Revised: In summary, the encoder supplements the semantic information of input feature maps via the embedded attention layer and FFN.

Original: Multi-scale deformable attention : The MSDA layer sums theentities selected from the value feature map z v. Selected feature entities locate atpredicted sampling locations with deformableattention weights.
Revised: Multi-Scale Deformable Attention : The MSDA layer sums the selected entities in the value featuremap z v at sampling locations with predicted attention weights for each corresponding query entity.

Original: The deformable attention weights Win MSDA are the linear projection ofquery features z q followed by a softmax operator alongthe scale and sampling point dimensions.
Revised: These attention weights W are the linear projection of query features z q followed by a softmaxoperator along scale and sampling point dimensions.

Original: For a single query entity, it only needs N h N l N p attention weights, representing the signiﬁcanceof selected value features at different attention heads, scales, and points.
Revised: For a single query entity, it only needs N h N l N pattention weights, representing the significance of selected value features at different attention heads,scales, and points.

Original: Sampling locations arethe summation of reference points p and sampling offsets ∆ p which is thelinear embedding ofthe query features.
Revised: Selections are decided by the sampling locations which are the summation ofreference points p and sampling offsets ∆ p which is the embedding of the query features.

Original: For ﬂoat sampling locations, selected value feature is bilinear interpolatedfor accuracy and training offset predictor.
Revised: At eachfloat sampling location, selected value feature is bilinear interpolated for accuracy and training offsetpredictor.

Original: The q -th feature vector of the separate output featurez so ,h ∈ R N q × c v ( N q = 
Revised: With weight, sampling locations and selected value features prepared, the q -th element ofthe separate output feature z so ,h ∈ R N q × c v ( N q = [Equation]

Original: ∑ Ll =1 H l W l ) at attention head h
Revised: Ll =1 H l W l ) at attention head h

Original: (total N h heads) is
Revised: (total N h heads)is

Original: [Equation]
Revised: [Equation]

Original: 1) where p , q , l and h index the sampling offsets, feature vectors of the deformable attention feature z o ,the scale of value v and attention head.
Revised: where p , q , l and h index the sampling offsets, elements of the deformable attention feature z o ,the scale of value v and attention head.

Original: W plhq is an element of the weight W ∈ R N q × N h
Revised: W plhq is a value from the weight W ∈ R N q × N h

Original: × L × N p .p ∈
Revised: × L × N p .p ∈

Original: R N q × L × 2 and ∆ p ∈ R N q × N h
Revised: R N q × L × 2 and ∆ p ∈ R N q × N h

Original: ×
Revised: ×

Original: L × N p × 2 are the reference points and sampling offsets.
Revised: L × N p × 2 are the reference points and sampling offsets.

Original: p ql and∆ p plhq denote the position of a single reference point and one of its corresponding sampling offsetsrespectively.
Revised: p ql and∆ p plhq denote the position of a single reference point and one of its corresponding sampling offsetsrespectively.

Original: The separate output features from N h attention heads are projected to the q -th featurevector of the ﬁnal output  z o by a linear layer expressed as
Revised: The separate output features from N h attention heads are projected to the q -th elementof the final output deformable attention feature z o by a linear layer expressed as

Original: [Equation]
Revised: [Equation]

Original: where W ′ h ∈ R c
Revised: where W ′ h ∈ R c

Original: ×
Revised: ×

Original: c
Revised: c

Original: v denotes the learnable weight for the h -th attention head.
Revised: v denotes the learnable weight for the h -th attention head.

Original: Center and scale prediction : The architecture of CSP is simple: It concatenates multi-scale featuremaps and fed them to the detection head.
Revised: Center and Scale Prediction : The architecture of CSP is simple. It concatenates multi-scale featuremaps and fed them to the detection head.

Original: Two main innovations of CSP are the anchor-free structureand predicting the center heatmap, height  map and the optional offsets w.r.t.
Revised: Two main innovations of CSP are the anchor-free structureand predicting the center heatmap, height ("scale" is used in the original work, however, we use"height" in case of confusion with feature map scale) map and the optional offsets w.r.t.

Original: the centers instead ofthe traditional bounding boxes.
Revised: the centersinstead of the traditional bounding boxes.

Original: Note that "scale map" is used in the original work, however, weuse "height map" in case of confusion with the feature map scale.

Original: With no anchor applied, lesshyper-parameters are required which simpliﬁes training and design.
Revised: With no anchors applied, less hyper-parameters are required which simplifies training and design.

Original: The sample center and scaleground truths are shown in Figure 3.
Revised: The sample center and scale ground truths are shown in Figure3.

Original: Centers of all pedestrians are assigned as positives in the centerheatmap, while negatives for other positions.
Revised: Centers of all pedestrians are assigned as positives in the center heatmap, while negatives for otherpositions.

Original: For the height map, these points are assigned with thelogarithm of the pedestrian height.
Revised: For the height map, these points are assigned with the logarithm of the object height.

Original: Proposed single-stage detector with enhanced features : The proposed detector consists of the
Revised: Proposed Single-stage Detector with Enhanced Features : The proposed detector consists of the

Original: ResNet 50 backbone, the detection neck, andthe head as shown in Figure 3.
Revised: ResNet 50 backbone, the detection neck, and head as shown in Figure 3.

Original: The detection head predicts the center and scale (height) maps and offsets to slightly adjust the position of pedestrians.
Revised: The detection head predictsthe center and scale (height) maps and offsets to slightly adjust the positions of pedestrians.

Original: The features are enhanced in the detection neck where we take the advantage of the deformable transformer encoder, particularly the embedded MSDA mechanism to supplement the spatially adaptivefeatures across different scales.
Revised: Thefeatures are enhanced in the detection neck where we take the advantage of the deformable transformerencoder, particularly the embedded MSDA mechanism to supplement the spatially adaptive featuresacross multiple scales.

Original: In the neck, input feature maps are ﬁrst upsampled with deconvolutional layers or encoded with the convolutional layer to generated multi-scale feature maps 165 { z l } 3 l =1 .
Revised: In the neck, input feature maps are first upsampled with deconvolutional layersor encoded with the convolutional layer to generated multi-scale feature maps (cid:8) z l (cid:9) 3 l =1 .

Original: They are followed by group normalization to prevent the Internal Covariate Shift (ICS)that might be inducted by subsequent linear operations in the deformable encoder.
Revised: They arefollowed by group normalization to prevent the Internal Covariate Shift (ICS) that might be inductedby subsequent linear operations in the deformable encoder.

Original: The encoderyields enhanced multi-scale feature maps.
Revised: The encoder yields enhanced multi-scalefeature maps.

Original: Enhanced features are upsampled to keep the resolutionas ( H/ 4 , W/ 4) for accurate detection.
Revised: Enhanced features are upsampled to keep the resolution as ( H/ 4 , W/ 4) for accuratedetection.

Original: They are normalized via L2Norm [19] before concatenationalong the channel dimension.
Revised: They are normalized via L2Norm [21] before concatenation along the channel dimension.

Original: This force the features at different scalesto contribute equally tothe feature representation.
Revised: This makes the features at different scales contribute equally to the final feature representation fedinto the detection head.

Original: Concatenated features are compressed along the channel dimension to reduce the network parameters.
Revised: Concatenated features are compressed along the channel dimension to reducethe network parameters.

Original: Compared to the architecture of CSP, the proposed detector adopts thenovel multi-scale deformable attention mechanism in the detection neck.
Revised: Compared to the architecture of CSP, the proposed detector adopts the novelmulti-scale deformable attention mechanism in the detection neck.

Original: Furthermore, an additionalconvolutional layer is applied in CSP detection head ahead of the branches.
Revised: Other parts maintain the sameexcept for an additional convolutional layer is applied in CSP detection head before the branches.

Original: Training : Same ground truth and loss functions as [19] are utilized.
Revised: Training : Same ground truth and loss functions as [21] are utilized.

Original: The overall loss consists ofthree parts:
Revised: The overall loss consists of threeparts as:

Original: [Equation]
Revised: [Equation]

Original: 3) where the center heatmap loss L center , height map loss L height and offset loss L offset are given inthe supplementary.
Revised: ) where the center heatmap loss L center , height map loss L height and offset loss L offset are given inthe supplementary.

Original: Weights for each loss λ c , λ s and λ o are set as 0.01, 1 and 0.1.
Revised: Weights for each loss λ c , λ s and λ o are set as 0.01, 1 and 0.1 [21].

Original: Inference : Predicted scores above 0.01 are kept.
Revised: Inference : Predicted scores above 0.01 are kept.

Original: The predicted pedestrian centers are the summationof the offsets and positions of those qualiﬁed scores.
Revised: The predicted pedestrian centers are the summationof the offsets and positions of those qualified scores.

Original: The predicted width is the height multiplied bythe constant 0.41 [19].
Revised: The predicted width is the height multiplied bythe uniform aspect ratio 0.41 [21].

Original: The predicted sizes are remapped to the original image size and fed to NMS.
Revised: The predicted sizes are remapped to the original image size andfed to NMS.

Original:  Experiments
Revised:  Experiments

Original: 4.1 Settings
Revised: 4.1 Settings

Original: Datasets : The proposed detector is evaluated on two commonly used public pedestrian datasets:
Revised: Datasets : The proposed detector is evaluated on two commonly used public pedestrian datasets:

Original: Caltech [41] and Citypersons [42].
Revised: Caltech [42] and Citypersons [43].

Original: The Caltech dataset is an approximately 10 hours of 480x640video taken in a single urban city.
Revised: The Caltech dataset is an approximately 10 hours of 480x640video taken in a single urban city.

Original: The standard training set contains about 250,000 frames with350,000 bounding boxes.
Revised: The standard training set contains about 250k frames with 350kbounding boxes.

Original: In our experiments, the training data augmented by 10 folds containing42782 images with 13674 persons and the standard test set containing 4024 images with corresponding new annotations [43] and ﬁxed aspect-ratio for bounding boxes [44] are used.
Revised: In our experiments, the training data augmented by 10 folds containing 42782 images with 13674 persons and the standard test set containing 4024 images with corresponding newannotations [44] and fixed aspect-ratio for bounding boxes [45] are used.

Original: Citypersonstraining set was recorded across 18 different cities, 3 seasons and various weather conditions with19654 persons in 2975 high resolution images (1024x2048).
Revised: Citypersons training setrecorded across 18 different cities, 3 seasons and various weather conditions with 19654 persons in2975 high resolution (1024x2048) images.

Original: The validation set contains 500 imagesacross 3 cities.
Revised: The validation set contains 500 images across 3 cities.

Original: Training details : Our network is established on [44].
Revised: Training Details : Our network is based on the CSP reproduction provided by Pedestron [45].

Original: ResNet 50 pretrained on ImageNet withAdam, moving average weights [45] and step learning rate schedule are applied.
Revised: ResNet50 pretrained on ImageNet, Adam with moving average weights [46] and step learning rate scheduleare applied.

Original: Data augmentation[Table] [Table] techniques including random horizontal ﬂip and scaling are applied.
Revised: Data augmentation techniques including random horizontal flip and scaling are applied.

Original: For Caltech, additional randomcolor distortion and cropping are implemented.
Revised: For Caltech, additional random color distortion and cropping are implemented.

Original: The input images are rescaled to 336x448 for Caltech and 640x1280 for Citypersons dataset.
Revised: The input imagesare rescaled to 336x448 for Caltech and 640x1280 for Citypersons dataset.

Original: The proposed network is trained with a single NVIDIA GeForce RTX 3090 GPU for 10 and 75 epochs with batch size 16 and 4 on Caltech and Citypersonsrespectively.
Revised: The proposed network istrained with a single NVIDIA GeForce RTX 3090 GPU for 10 and 75 epochs with batch size 16 and4 on Caltech and Citypersons respectively.

Original: The learning rate and related hyperparameters will be presented afterwards accordingto different designs of architectures.
Revised: The learning rate and related hyperparameters will bepresented afterwards according to different designs of architectures.

Original: All the training is performed with a randomly chosen and ﬁxedseed.
Revised: All the training is performedwith a randomly chosen and fixed seed.

Original: Metrics : Log-average Miss Rate (denoted as MR − 2 ) over False Positive Per Image (FPPI) rangingin [10 − 2 , 10 0 ] is calculated over Reasonable (R), Reasonable Small (RS), Heavily Occluded (HO),and All (A) subsets deﬁned in Table 1.
Revised: Metrics : Log-average Miss Rate (denoted as MR − 2 or miss rate in this paper) over False Positive Per Image (FPPI) in the range [10 − 2 , 10 0 ] is calculated over Reasonable, Small, Heavy, and Allsubsets defined in Table 1.

Original: The lower the MR − 2 the better.
Revised: The lower the MR − 2 the better.

Revised: [Table]

Original: 4.2 Ablation studies
Revised: 4.2 Ablation studies

Original: In this section, ablation studies are conducted to investigate the inﬂuence of network architectureson the detection performance.

Original: All studies are performed on the Caltech dataset.

Original:  Scalesof feature maps : Different combinations of multi-scale feature maps { z l } 3 l =1 with threedownsample ratios ( 1 / 4 , 1 / 8 and 1 / 16 ) are compared in Table 2.
Revised: Feature Maps Scales : Different combinations of multi-scale feature maps (cid:8) z l (cid:9) 3 l =1 with threedownsampling ratios ( 1 / 4 , 1 / 8 and 1 / 16 ) are compared in Table 2.

Original: In this comparison, only z o 3 whichhas the same resolution as z 3 upsampled to ( H/ 4 , W/ 4) and fed into the detection head withoutintermediate concatenation and L2Norm.
Revised: In this comparison, only z o 3which has the same resolution as z 3 upsampled to ( H/ 4 , W/ 4) ,and is fed into the detection headwithout intermediate concatenation and L2Norm.

Original: Results show that feature maps with ratios 1 / 4 , 1 / 8 , and1 / 6 for each scale produce the best performance. In this case, we choose to upsample c 1 and c 2 bytwo times while maintain the scale of c 3 in the proposed detector and the following experiments.
Revised: Results show that feature maps with ratios 1 / 4 ,1 / 8 , and 1 / 6 for each scale produce the best performance, which is yield by upsampling c 1 and c 2by two times while maintaining the scale of c 3 .

Original:  Scalesof enhanced feature maps : Different combinations of multi-scale enhanced feature maps{ z o l } 3 l =1 are fed into the detection head.
Revised: [Table] Enhanced Feature Map Scales : Fix the downsampling ratios of (cid:8) z l (cid:9) 3 l =1 as 1 / 4 , 1 / 8 , and 1 / 16 ,feed different collections of multi-scale enhanced feature maps { z o l } 3 l =1 to the detection head.

Original: Note that the downsample ratios for them are 1 / 4 , 1 / 8and 1 / 16 respectively.
Revised: Notethat the deformable encoder keeps the resolutions of the input feature maps, the downsampling ratiosfor { z o l } 3 l =1 are 1 / 4 , 1 / 8 and 1 / 6 respectively.

Original: All the enhanced feature maps are ﬁrst upsampled to ( H/ 4 , W/ 4) , if needed,followed by normalization if multiple feature maps are utilized.
Revised: All the enhanced feature maps are first upsampled to( H/ 4 , W/ 4) , if needed, followed by normalization when multiple feature maps are utilized.

Original: They are then processed in threeways: 1.
Revised: They arethen processed in three ways: 1.

Original: Cat: Concatenate  along the channel dimension followed by a compression layer to reducethe number of channels to 256. 2.
Revised: Cat: Concatenate them along the channel dimension followed by acompression layer to reduce the number of channels to 256. 2.

Original: Add: Add them elementwise.
Revised: Add: Implement elementwise sum ofthe enhanced feature maps.

Original: Sep: No fusion  across multiscales but send them separately to the detection head which doubles the number of predictions.
Revised: Sep: No fusion operations across multi-scales; send them separatelyto the detection head which doubles the number of predictions.

Original: Table3 compared various strategies to fuse the multi-scale enhanced feature maps.
Revised: Table 3 compares various strategiesto fuse the multi-scale enhanced feature maps.

Original: 1 train boldly withoutany learning rate schedules.

Original: It shows that concatenation followed by L2Norm produces the bestresults in both R and HO subsets on Caltech test set.
Revised: It shows that concatenation followed by L2Normproduces the overall best results in both Reasonable and Heavy subsets on Caltech test set.

Original: Choice of normalization method applied to { c l } 3 l =1 : As Table 3 shows, GN performs best in Rsubset while L2N in HO subset.
Revised: Choice of the Normalization Method Applied to (cid:8) c l (cid:9) 3 l =1 : As Table 3 shows, GN performs best in Reasonable subset while L2N in Heavy subset.

Original: Considering that the miss rate of GN is 11.6% lower in R subsetand only 1.2% higher in HO subset compared to L2N, GN is utilized in our experiments as indicatedin Figure 3.
Revised: Considering that the miss rate of GN is 11.6% lowerin Reasonable subset and only 1.2% higher in Heavy subset compared to L2N, GN is utilized in ourexperiments as the blue blocks above enhanced feature maps in Figure 3 indicate. [Table]

Original: Number of encoders : Different numbers of encoders are applied in the proposed architecture.
Revised: Number of Encoders : Based on the settings in the last part, different numbers of encoders are tested.

Original: These encoders are connected in series.
Revised: These encoders are connected in series.

Original: Table 4 presents that although more encoders can provide higher-level semantic information, the best result is observed when  a single encoder is applied.
Revised: Table 4 presents that although more encoders can providehigher level semantic information, the best result is observed when only a single encoder is applied.

Original: This phenomenon supports the design of BotNet [38] to some extent, which indicates that followingthe whole set of six encoder-decoder pairs in (deformable) DETR may not be suitable for speciﬁc [Table] [Table] object detection tasks.
Revised: This phenomenon supports the design of BotNet [39] to some extent, which indicates that followingthe whole set of six encoder-decoder pairs in (deformable) DETR may not be suitable for specificobject detection tasks.

Original: A single encoder can  work effectivelyin pedestrian detection task with theleast number of learnable parameters which prevents overﬁtting.
Revised: A single encoder can also work effectively with least number of learnableparameters, which prevents overfitting.

Original: Signiﬁcance of enhanced feature maps : We evaluated the baseline CSP detector and the proposeddetector which improve CSP by adopting the deformable encoder-based feature enhancement module in the detection neck.
Revised: [Table] Importance of Enhanced Feature Maps : We evaluated the baseline CSP detector and the proposeddetector which improves CSP by adopting the deformable encoder-based feature enhancementmodule in the detection neck.

Original: Both the single  enhanced feature map and the concatenation of two mapsare assessed.
Revised: Both the single and concatenated enhanced feature map(s) are assessed.

Original: Evaluation is conducted on Caltech (Table 5) and Citypersons (Table 6) respectively.
Revised: Evaluation is conducted on Caltech (Table 5) and Citypersons (Table 6) respectively.

Original: Results from both datasets indicate that with the enhanced feature map(s), the miss rates are decreased signiﬁcantly in R, HO and A subsets, in particular, the HO subset witnesses a decrease upto 7.9%, and R subset up to 3.1%.
Revised: Results fromboth datasets indicate that with the enhanced feature map(s), the miss rates are decreased significantlyin Reasonable, Heavy and All subsets, in particular, the Heavy subset witnesses a decrease up to 7.9%,and Reasonable subset up to 3.1%.

Original: This means that the idea of extracting spatially adaptive features works effectively for the single-stage detector.
Revised: This means that the idea of extracting spatially adaptive featuresworks effectively for the single-stage detector.

Original: With the concatenation of multi-scale enhancedfeature maps (row three), the performance is further improved comprehensively on Caltech datasetcompared to the single feature map (row two).
Revised: With the concatenation of multi-scale enhancedfeature maps (third row), the performance is further improved comprehensively on Caltech datasetcompared to the single feature map (second row).

Revised: This owes to the deformable sampling locationsand attention weight maps allowing to adaptively extract useful features, such as visible features,for the task.

Revised: Furthermore, the feature map at scale 1 focuses on the visible part of the pedestrianwhen the reference point locates on the visible part of the target, complementing the lack of occludedtarget’s information with the sole use of the feature map at scale 3 as shown in the supplementary.

Original: Due to the additional interpolation operation in  deformable self-attention layer, the proposed detectors require longer inference time compared tothebaseline, however, the proposed detector contains less learnable parameters as shown in Table 7. 
Revised: Due to the additional interpolation operation in the deformable self-attention layer, the proposeddetectors require longer inference time compared to the baseline, however, they contain less learnableparameters as shown in Table 7. [Table] [Table] [Table]

Original: 4.3 Comparison with the state-of-the-art method
Revised: 4.3 Comparison with the state-of-the-arts

Original: We compared two proposed architectures, namely the single enhanced feature map z
Revised: We compared two proposed architectures, namely the single enhanced feature map z

Original: o 3 and the concatenation of z o 3 and z o 1 , with the state-of-the-art single- and two-stage detectors on Caltech and
Revised: o 3 and theconcatenation of z o 3 and z o 1 , with the state-of-the-art single- and two- stage detectors on Caltech and

Original: Citypersons datasets.
Revised: Citypersons datasets.

Original: Proposed detectors are trained with the hyperparameters presented in Table 8.
Revised: Proposed detectors are trained with the hyperparameters presented in Table 8.

Original: As Table 9 and Table 10 show, the proposed detectors achieve lowest miss rates in R and HO subsetson both datasets among listed single-stage detectors, and leading performances in A subset.
Revised: Table 9 and Table 10 show that the proposed detectors achieve lowest miss rates in Reasonable and Heavy subsets and leading performance in All subset on both datasets among presented single-stagedetectors.

Original: For
Revised: [Table]

Original:  Caltech dataset, the lowest miss rate (3.7%) of the proposed detector in R subset is 0.2% smallerthan that of the two-stage KGSNetas presented in the upper part of Table 9.
Revised: For Caltech dataset, the lowest miss rate (3.7%) of the proposed detector in Reasonable subset is 0.2%smaller than that of the two-stage KGSNet presented in the upper part of Table 9.

Original: With pretraining on

Original:  Citypersons dataset, the miss rates on all three subsets of Caltech  areconsiderably reduced and reachthe lowest compared to other pretrained detectors  shown in the bottom part of Table 9. For Cityper- [Table] [Table]
Revised: With pretrainingon Citypersons dataset, the miss rates on all three subsets of Caltech dataset are reduced significantlyand reach the lowest compared to other pretrained detectors as shown in the bottom part of Table9.

Original: [Table]

Original: [Table]

Original: part of the table. [Table] [Table] sons dataset, the proposed two detectors even outperform the competitive two-stage detectors in R,and especially HO subset with miss rate 36.7% which is 3% lower than that of the best of two-stagedetectors.
Revised: For Citypersons dataset, the proposed two detectors even outperform the competitive two-stagedetectors in Reasonable and especially Heavy subset with miss rate 36.7% which is 3% lower thanthat of the best of two-stage detectors.

Original: Overall, it can be observed that with the enhanced spatial adaptive and multi-scale features, the gap between single- and two-stage detectors in R and HO subsets on different datasetshas been narrowed.
Revised: Overall, it can be observed that with the enhanced spatially adaptive and multi-scale features, thegap between single- and two- stage detectors in Reasonable and Heavy subsets on different detectorshas been narrowed.

Original: Surprisingly, the proposed single-stage method can produce better results thanaccurate two-stage methods on certain pedestrian dataset, such as Citypersons dataset.
Revised: Surprisingly, the proposed single-stage method can produce better results thanaccurate two-stage methods on certain pedestrian dataset, such as Citypersons dataset.

Original: In this case,our enhanced single-stage  detector not only predicts quickly, but also with accuracy comparable tocomplicated two-stage methods.
Revised: Therefore,our enhanced single-stage pedestrian detector not only predicts quickly, but also achieves accuracycomparable to complicated two-stage methods.

Original:  Conclusion
Revised:  Conclusion

Original: We propose a single-stage anchor-free pedestrian detector with enhanced spatial and multi-scale features.
Revised: The paper proposes a single-stage anchor-free pedestrian detector with enhanced spatial and multiscale features.

Original: This is fulﬁlled by the combination of deformable self-attention mechanismof the deformablevision transformer and Center and Scale Prediction (CSP).
Revised: This is fulfilled by the combination of deformable self-attention mechanism and Center and Scale Prediction (CSP).

Original: Comprehensive experiments over  architecture design in the detection neck and comparison with the state-of-the-arts are performed.
Revised: Comprehensive experiments over the architecture design in thedetection neck and comparison with the state-of-the-arts are performed.

Original: Morethan 40% parameters are reduced in the detection neck compared to CSP; the detector  achieves thebest results in R and HO subsets among all the listed single-stage detectors on both Caltech and
Revised: More than 40% parametersare reduced in the detection neck compared to CSP; however, this detector still achieves the best [Table] [Table] results in Reasonable and Heavy subsets among presented single-stage detectors on both Caltech and

Original: Citypersons datasets.
Revised: Citypersons datasets.

Original: With pretraining, the miss rates for these subsets can be decreased to 2.6%and 28.0% which is far better than other single-stage methods and comparable to two-stage methods (2.3% for R).
Revised: With pretraining, the miss rates for these subsets can be decreased to 2.6% and28.0% which are far better than other single-stage methods and comparable to two-stage methods.

Original: It even outperforms SOTA two-stage detectors in HO subset by 3%miss rate on Citypersons validation set.
Revised: Iteven outperforms SOTA two-stage detectors in Heavy subset by 3% on Citypersons.

Original: The proposed detector signiﬁcantly improves the accuracy of the singlestage method with slightly decreased inference speed.
Revised: The proposeddetector significantly improves the accuracy of the single-stage method with slightly decreasedinference speed.

Original: Theseresults demonstrate that single-stagedetectors can do better if spatially adaptive and multi-scale features are jointly adopted, thus makingthem cost-effective and efﬁcient.
Revised: These demonstrate that single-stage detectors can do better if spatially adaptive andmulti-scale features are jointly adopted, thus making them cost-effective and promising.

Revised: It shouldbe mentioned that, false positives are introduced if the attention points of a negative reference pointextend to the target areas.

Revised: Although the performance has been improved with current deformabletransformer, the generation of attention weights and sampling locations can be carefully designed tosuppress the false positives for further improvements.

Original: Checklist
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Original: [Yes] The data are available online.
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