AdaPKC: PeakConv with Adaptive Peak Receptive Field for Radar Semantic Segmentation

Authors: teng  li
liwen  zhang
youcheng  zhang
zijun  hu
pengcheng  pi
zongqing  lu
qingmin  liao
zhe  ma
Venue: NeurIPS_cc_2024_Conference
Published: 2024-05-14 13:16:28

Abstract: Deep learning-based radar detection technology is receiving increasing attention in areas such as autonomous driving, UAV surveillance, and marine monitoring. Among recent efforts, PeakConv (PKC) provides a solution that can retain the peak response characteristics of radar signals and play the characteristics of deep convolution, thereby improving the effect of radar semantic segmentation (RSS). However, due to the use of a pre-set fixed peak receptive field sampling rule, PKC still has limitations in dealing with problems such as inconsistency of target frequency domain response broadening, non-homogeneous and time-varying characteristic of noise/clutter distribution. Therefore, this paper proposes an idea of adaptive peak receptive field, and upgrades PKC to AdaPKC based on this idea. Beyond that, a novel fine-tuning technology to further boost the performance of AdaPKC-based RSS networks is presented. Through experimental verification using various real-measured radar data (including publicly available low-cost millimeter-wave radar dataset for autonomous driving and self-collected Ku-band surveillance radar dataset), we found that the performance of AdaPKC-based models surpasses other SoTA methods in RSS tasks. The code is available at https://github.com/lihua199710/AdaPKC.

Main paper: 
 introduction: 
 As a common remote sensing device, radar exhibits superior robustness in complex environments (e.g., varying weather and lighting conditions) compared to cameras, and it is more cost-effective and resilient in extreme weather scenarios compared to LiDARs. Benefiting from the physical advantages of radar sensors and the powerful capabilities of deep learning techniques, modern deep learning-based radar signal interpretation has become a hot research topic in the field of radio frequency detection technology. It has been extensively explored in autonomous driving [30, 19, 34, 6] , UAV surveillance [9, 17] , sea monitoring [27, 21, 28] , etc. Considering the similar dense representations between radar frequency maps and optical images, most of these works directly transfer convolution networks or modules developed for optical signals to radar perception tasks, such as radar object detection (ROD) and radar semantic segmentation (RSS), and they have achieved impressive performance. Nevertheless, without specific design for the inherent characteristics of radar signals, these approaches fail to fully liberate the potential of deep learning techniques.Recently, PKCIn-Net [32] introduced an innovative convolution operator named PeakConv (PKC), tailored for the efficient analysis of radar signals, and this operator seamlessly integrates the advan- tages of classic radar detectors [22] with common convolution networks [19, 6] . For radar signals, the frequency responses of objects comprise target echoes and interference, and share a distinct peak-shaped pattern, thus most classic radar detection methods [22, 10, 25, 23] build peak detection algorithms upon constant false alarm rate (CFAR) criteria. Extending from cell averaging-CFAR (CA-CFAR) [22] , PKC explicitly embeds a similar band-pass peak enhancement mechanism in a standard convolution operator for better characterising target signatures in radar signals. Concretely, following the guard-reference policy of CA-CFAR, it first estimates interfering signals with the center unit/cell and reference units outside predefined guard bands. Then, with estimated interference, it finishes noise suppression for each cell under test (CUT) in feature space and enhances peak frequency response associated with objects of interest.Despite its superior suitability for radar data than alternative convolution operators [31, 5, 33] , there exists even greater potential for PKC to learn peak frequency response of radar signals. Research on CFAR detectors [10, 25, 23, 11] reveals that there exist significant variations in target signature and associated interference within radar signals, rendering the predefined reference cells in CA-CFAR inadequate for precisely locating interfering signals, and this limitation can also be observed in PKC. To provide a clearer depiction, let us delve deeper into these variations present within radar signals, as illustrated in Fig. 1 . On the target side, since multi-dimensional radar tensors are generated through a sequence of cascading fast Fourier transformations (FFTs), target signatures along different dimensions exhibit distinct degrees of frequency response tailing (broadening). Additionally, the broadening degrees of different instances would also be influenced by target categories or states, i.e., the relative distances, azimuth or velocity from the radar. On the interference side, the noise or clutter distribution commonly exhibits non-homogeneous and time-varying characteristics. However, since the PKC kernel always gathers the reference units at fixed locations for noise estimation, i.e., the predefined peak receptive field (PRF), the dynamic variations in both targets and interference degrade its performance. In short, the fixed PRF essentially limits the learning ability of PKC, thus, it hinders the RSS model from obtaining better performance.Motivated by the adaptive selection of reference cells in classic CFAR detectors [10, 25, 23] , in this work we introduce two novel data-adaptive band-pass filtering mechanisms aimed at upgrading PKC to adaptively adjust its PRF for each CUT in a data-driven manner, namely adaptive PeakConv (AdaPKC). Concretely, both versions of AdaPKC first measure the correlation between CUT and its alternative reference units in high-dimensional feature representations, then select proper reference units and integrate them seamlessly with PKC to effectively take care of the fluctuating dynamics of radar signals. The main contributions of our work are:• We present the first attempt specially tailored for radar signal processing to dynamically adjust the receptive field for convolution operators. Concretely, we propose a novel updated version of PKC, termed as AdaPKC, which can adaptively adjust the PRF (or reference units) at cell-level granularity. And two different implementation versions are provided, which both exhibit enhanced flexibility and robustness in handling fluctuating radar signals compared to original PKC. • To better release the learning ability of AdaPKC, a fine-tuning technology with a thresholding on-line switch is presented. With such technology, the same AdaPKC-based model can even achieve better performance with less computational cost. • To verify the effectiveness of AdaPKCs, quantitative and qualitative experiments are conducted on various real-measured large scale radar datasets including CARRADA [20] collected from a low-cost FMCW (≈ 77GHz) radar in autonomous driving scenario and self-collected dataset recorded from a Kurz-under (Ku) band (≈ 17GHz) radar for UAV surveillance and sea monitoring.Results show that AdaPKC-based models achieve SoTA RSS performance and our fine-tuning strategy further brings visible improvements, verifying the scope of application of AdaPKCs. 
related work: 
 Receptive field (RF) adjustment. RF is crucial for modern deep convolution models, affecting the granularity of modeling primitives, computation architecture and their representation capabilities, etc. The rational use of RF can directly improve the representation ability of the models, e.g. enlarging the scope [31] , multi-scale modeling [2] , and dynamically changing shapes [5, 33] . Beyond that, the concept of RF has also been applied to Transformers [7, 16] . These methods are proposed for vision tasks and have achieved significant results. However, compared with conventional convolution, the improvement is not satisfactory enough on radar signals. Recently, by fully considering the characteristics of radar signals, the concept of PRF has been proposed, which makes the original convolution have the ability of band-pass filtering and noise suppression [32] . However, the bandwidth of filtering is pre-set, which hinders the adaptive ability of PKC to radar data. To this end, this paper attempts to study a data-driven PRF adjustment method, and introduces the concept of adaptive PRF (AdaPRF), so as to further improve the RSS performance. Considering that the adaptive adjustment of the suppression (guard) bandwidth and the non-suppression (reference) units is essentially a dynamic adjustment of RF, and from this point of view, the content studied in this paper is related to deformable convolution (DefConv) [5, 33] . Unfortunately, the dynamic RF technology of DefConvs cannot solve the problem in hand, for the following reasons: i) Differences of signaling mechanism. DefConv uses the visual prior information that the target is visually deformed geometrically, which cannot be directly corresponded to the radar signal. ii) Mismatched prediction method. DefConv generates new RF through prior prediction, i.e., the regular RF is still used to infer the sampling point outside regular RF. However, in radar signal, the interference (noise/clutter) with large entropy, is often difficult or even impossible to predict. At present, a better way is under the premise of observation, i.e., posterior measurement or statistics. iii) Different mechanisms of representation. Noise suppression is not required to be considered by DefConv, thus it does not need to distinguish between the center unit and surroundings during calculation. To this end, a novel adaptive RF adjustment method is required.Radar semantic segmentation. Benefiting from the reliable perceptual capabilities, convolutional neural networks (CNNs) play an indispensable role in existing RSS networks for radar frequency maps processing, whether in pure CNN models [13, 8, 19, 32] or transformer-assisted CNN models [34, 12, 6] . RSS-Net [13] utilizes a fully convolutional neural network with encoder-decoder structure to recognize targets in radar scans, and it incorporates an atrous spatial pyramid pooling (ASPP) [2] module to gather multi-scale spatial information. RAMP-CNN [8] employs parallel branches to extract features from multiple views and adopts 3D convolutions to better capture temporal information. TMVA-Net [19] leverages these techniques to develop a multi-view RSS model, which is capable of making semantic predictions across multiple views simultaneously. T-RODNet [12] integrates Swin Transformer [16] modules into a CNN-based RSS model to strengthen its modeling capability. TransRSS [34] and TransRadar [6] introduce attention blocks into the multi-view feature fusion stage to enhance the fusion effectiveness. Recently, as the first fundamental convolution operator tailored for radar signal processing, PKC [32] is proposed. Compared to Dilated Convolution [31] and Deformable ones [5, 33] , PKC demonstrates superior RSS performance. However, it is inherently constrained by its fixed peak receptive field, posing challenges in achieving consistent interference (noise/clutter) suppression under the dynamic and time-varying nature of radar signals. By contrast, our AdaPKCs overcome this limitation well by implementing novel data-adaptive band-pass filtering mechanisms. 
method: 
 In this section, the proposed two versions of AdaPKC with different AdaPRF mechanisms are first introduced in § 3.1. Then, the proposed fine-tuning strategy to further uncover the potential of AdaPKC-based RSS models is presented in § 3.2. We design these models using both multiview [19, 32] and single-view frameworks, which are comprehensively described in Appendix A.2 and A.3 for saving space. [10, 25, 23] , we first demonstrate how to estimate AdaPRF in an explicitly measuring way, referred to as metric-based AdaPRF (AdaPKC ξ ). To illustrate the mechanism of AdaPKC ξ , we begin by defining the search space of alternative PRFs. Reviewing the PRF definition in previous work [32] we can see that, the PRF for center unit x c encompasses x c itself and a set of sampled reference units {x(i)r } Nr i=1 , and the area of reference units is governed by horizontal-and verticalsymmetry guard bandwidth b G ≜ {b Gx , b G y } and reference bandwidth b R ≜ {b R x , b R y }, as illustrated in Fig. 2-(a) . Following this definition, the PRF adjustment corresponds precisely to the adjustment of the reference unit set, thus we can define the PRF search space by defining the candidate sets of reference units with the adjustment ranges for the guard bandwidth and reference bandwidth. Given that adjusting the reference bandwidth leads to a drastic change in the number of sampled reference units compared to adjusting the guard bandwidth, in AdaPKC ξ we keep anytime-fixedb R ≜ {b R x = 1, b R y = 1}and denote the set of K guard bandwidth candidates asΩ G ≜ {b G k } K k=1 = {b G | b G min |x ≤ b G x ≤ b G max |x , b G min |y ≤ b G y ≤ b G max |y }, generating K sets of reference units as {{x (i) r|k } N k i=1 } K k=1 correspondingly.As a result, AdaPRF estimation in AdaPKC ξ is equivalent to selecting an appropriate reference unit set from these candidate sets for each CUT (or center unit), as illustrated in Fig. 2-(b) .For better explanation, we divide the observed radar signals into three subsets: i) signals reflected directly from a target, S t ; ii) the target-interfering noise, i.e., the noise coupled with the signal that partially leaks out of the target, S t-n ; and iii) the target-independent noise, S n . In practice, it is the part that from S t-n really causes misjudgment. Therefore, classic CFAR detectors focus on filtering out such noise either by the extreme value [10, 25] or the median value [23] in amplitude domain of signals. Motivated by this idea, our AdaPKC ξ centers its attention on collecting reference units predominantly occupied by such noise.However, as a learnable module, AdaPKC ξ needs to process the representation tensors of radar signals, implying that the measurement of the reference units should be conducted on feature space. For some CUT x c = ψ(s; W) and its candidate reference unit x r = ψ(s ′ ; W), where s ∈ S t , s ′ ∈ S t ∪ S t-n ∪ S n , and ψ(•; W) ∈ R C denotes a convolution layer with shared weights, W. Then, AdaPKC ξ should be responsible for transforming these features into a metric space that explicitly delineates their correlation with the target. This transformation is achieved by utilizing the inner product of representations between CUT and its candidate reference unit, i.e., x c x ⊤ r , sharing a similar spirit with the matched filter concept in conventional radar signal processing [22] and attention in [26] . Reference unit under confirmation Under this definition, these measures exhibit the following statistical properties,𝒃𝒃 𝐦𝐦𝐦𝐦𝐦𝐦|𝒙𝒙 𝐆𝐆 𝒃𝒃 𝐦𝐦𝐀𝐀𝐦𝐦|𝒙𝒙 𝐆𝐆 Metric 𝑘𝑘 Selection • • • • • • • • • • • • • • • • 𝑏𝑏𝑥𝑥 G 𝑏𝑏𝑦𝑦 G 𝑏𝑏𝑦𝑦 R 𝑏𝑏𝑥𝑥 R 𝑘𝑘 † (a) Fixed PRF in PKC • ★ Cell Under Test (CUT), 𝐦𝐦 𝒄𝒄 Guard unit Sampled reference unit, 𝐦𝐦 𝒓𝒓 𝜉𝜉1, 𝜉𝜉2, … , 𝜉𝜉K • • • • • • • • • • • • • • • • 𝑏𝑏𝑥𝑥 G 𝑏𝑏𝑦𝑦 G 𝑏𝑏𝑦𝑦 R 𝑏𝑏𝑥𝑥 R 𝐦𝐦𝑟𝑟↑ Reference bandwidth: 𝐛𝐛 R ≜ 𝑏𝑏𝑥𝑥 R , 𝑏𝑏𝑦𝑦 REQUATIONwhere, E(•) is the expectation and ∥ • ∥ 2 denotes the L 2 norm. From Eq. ( 1), we can see that the inner product transformation assigns three statistical boundaries to x r from S t , S t-n and S n : for s ′ ∈ S t-n , the expectation E x c x ⊤ r consistently exhibits smaller value than case for s ′ ∈ S t and larger value than s ′ ∈ S n , with a notable separation between their respective magnitudes. This attribute significantly serves to facilitate the subsequent localization of reference units from target-interfering noise.Then we elucidate the process of translating the K available sets of reference units (or PRFs) into the previously discussed metric space. Since different sets may comprise varying numbers of units, we uniformly sample N (16 by default) units as representatives, as illustrated in Fig. 2-(b) . For the center unit x c and its k th reference unit set {xEQUATIONr|k } N i=1 } denote its corresponding PRF. Then the correlation value (or metric score), ξ k for the k th PRF w.r.t. x c is formulated asEQUATIONwhere, C is the feature dimension; σ(•) is the sigmoid function which normalizes ξ k to (0, 1).With the correlation values Ξ = {ξ k } K k=1 for all alternative PRFs, we can select the appropriate PRF R † , which effectively encompasses target-interfering noise. In view of the attribute presented in Eq. 1, we employ the maximum value of the first-order gradient of Ξ as the selection criterion and please refer to Appendix B.1 for the detailed analysis. Then, we have final selection strategy as follows,EQUATIONwhere arg max operator retrieves the index corresponding to the maximum value in g [sort (Ξ)]; g is the difference function; sort is the descending sort operator. After obtaining R † , AdaPKC performs a convolution operation similar to PeakConv, detailed in Appendix A.1.In AdaPKC ξ , a measuring criterion is established in Eq. 3 based on prior knowledge of radar signals, providing a non-parametric way to achieve AdaPRF. Differing from AdaPKC ξ , AdaPKC θ learns to build the criterion in a task-driven manner, employing a small network to estimate AdaPRF, i.e., a parametric way. Thus, given center unit x c and its K candidate PRFs, {R k } K k=1 , the natural way to locate AdaPRF R † , is to define a function f (•; θ) with learnable parameters θ, which is used to produce the likelihood of each R k being R † , then we can haveEQUATIONEQUATIONAt a rough glance, there is no obvious difference between Eq. 4 and Eq. 3. However, AdaPKC θ involves joint optimization of estimation network parameters (i.e., θ) and segmentation model parameters. The use of arg max operation will lead to the loss of gradient information of θ, as a result, the optimization of the estimation network cannot be driven by the segmentation task. To this end, we transform the discrete estimation problem into a continuous form. Firstly, following the setup of anytime-fixed b R ≜ {b R x = 1, b R y = 1} in AdaPKC ξ , estimating R † can be equivalently translated into estimating the optimal guard bandwidth b G † . Subsequently, to ensure that the estimation of b G † retains gradient information, the estimation network is designed to generate the continuousvalued b G † , instead of the likelihoods for alternative guard bandwidths. Finally, the derivable linear interpolation is used to associate the continuous-valued b G † with discrete spatial coordinates.Concretely, given an input feature map X ∈ R C×H×W , our AdaPKC θ is responsible for obtaining an appropriate guard bandwidthB G † = {b G † h,w } H,W h=1,w=1 ∈ R 4×H×W .For enhancing the expressive capability of AdaPKC, the horizontal-and vertical-symmetry b G design in the original PRF isextended to a quadruple form, b G = {b G ↑ , b G ↓ , b G ← , b G → }, so that the shape of PRF would enjoy free change in four directions, i.e., top, bottom, left, right, as illustrated in Fig. 3-(a) , resulting in more diverse band-pass filters. As shown in Fig. 3-(b) , we use a small network with two paralleled conv blocks, g Hrz (•) : R C×H×W → R 2×H×W and g Vtc (•) :R C×H×W → R 2×H×W to estimate b G ∈ R 4for each unit of X in horizontal (←→) and vertical (↑↓) directions, respectively. To ensure the directional consistency, the horizontal branch possesses kernels with a size of 1 × (2b G max + 1), and the kernel size of the vertical branch is (2b G max + 1) × 1, correspondingly. All four directions have the same lower bound, b G min = 1 and the same upper bound, b G max = 3 by default.Then, for each x c , its AdaPRF R † = {x c , {x(i) † r } N i=1 } (N = 16by default) can be obtained by the following two steps:Step 1. Guard bandwidth estimation:EQUATIONwhere, each b G † h,w in B G † is the learned guard bandwidth for x c of the spatial coordinate (h, w);together with a constant b G max -b G min , the sigmoid activation σ(•) can modulate the input values to (0, b G max -b G min ); β(•)and ⊕ denotes the BatchNorm and Concat operator, respectively; 1 ∈ Z 4×H×W is an all-one cube.Step 2. Reference unit sampling: for better illustration, we first define the default PRF with guard bandwidths equal to 1 in [32] asR 0 = {x c , {x (i) r } N i=1 }, which is shown in Fig. 2-(a). let p c = (h c , w c ) and p (i) r = (h (i) r , w (i)r ) denote the spatial coordinate of the current center unit x c and one of its reference unit x (i) r in R 0 , respectively. Then we split {xEQUATIONr← } M j=1 and {x (j) r→ } M j=1 , according to the four directions, where M = N/4, as indicated by the braces drawn in Fig. 2-(a) . By using b G † obtained from Step 1, we can sampleR † = {x c , {x (i) † r } Ni=1 } through linear interpolation, as exemplified in Fig. 3-(a) . Taking the top direction as an example, each x (j) † r↑ can be obtained by xEQUATION)Sampling values for the rest directions and back-propagation of gradients in linear interpolation are demonstrated in Appendix B.2.To further release the learning ability of AdaPKC, a fine-tuning strategy is proposed. To improve interpretability, we focus mainly on optimizations for the explicit AdaPRF estimation version, i.e., AdaPKC ξ . The motivation comes from two perspectives. i) Model confidence: the representation ability and confidence of the model gradually improve with training, hence, during early training phase the less representative features may result in unreliable metric scores calculated in Eq. 2, consequently affecting the selection of AdaPRF and misleading the subsequent learning process. ii) Data sparsity: AdaPKC is responsible for the PRF adaptation for both background units (x c / ∈ S t ) and target units (x c ∈ S t ). However, due to the sparsity of target occupation w.r.t. the radar detection range, most units in the input feature map are background units, thus the heavy class-imbalance hinders the model's concentration on target points and their AdaPRF optimization.To address the above issues, we propose to fine-tune AdaPKC ξ with a thresholding on-line switch (FiTOS), and please refer to Appendix C for a visual illustration. Firstly, a pre-trained PKC model with pre-defined PRFs is used to initialize the AdaPKC-based model to be optimized, so that AdaPKC can have a warm-start before PRF adjustment. Thus, the risk of obtaining unreliable metric scores is greatly reduced. Secondly, considering that the majority of background units are occupied by locally similar noise, i.e., their monotonic correlation/metric curves, sort(Ξ), are relatively flat, FiTOS introduces a confidence threshold τ to filter out these background units in spatial dimension on-the-fly. Specifically, if the steepness of the metric curve, i.e., max{ g [sort (Ξ)]}, is below the threshold τ , then the corresponding unit is considered a background unit, and we retain the initial PRF, i.e., switch off PRF adjustment. Otherwise, we adopt the newly estimated AdaPRF, i.e., switch on PRF adjustment. As a result, the PRF selection criterion in Eq. ( 3) is modified as follows,EQUATIONwhere k 0 denotes the index of pre-defined PRF in the pre-trained model. 
experiments: 
 To verify the effectiveness of our methods, we conduct quantitative and qualitative experiments on two public multi-view radar datasets and our self-collected single-view radar dataset. For simplicity of notations, AdaPKC ξ -based and AdaPKC θ -based multi-view RSS model is denoted as AdaPKC ξ -Net and AdaPKC θ -Net, respectively. The proposed single-view baseline model is named KuRALS-Net. Additionally, we append "FiT" in the upper right corner of the models to indicate the use of FiTOS.CARRADA [20] dataset is recorded by a low-cost FMCW radar in millimeter wave band (≈ 77GHz).It comprises camera-radar synchronised multi-view radar recordings in various scenarios, and contains four categories of objects: pedestrian, cyclist, car and background. The dimensions of provided range-angle-Doppler (RAD) tensors are 256 × 256 × 64 and support multi-view (RA and RD views) RSS task. The dataset splits are the same as in [32, 19] . CARRADA-RAC [32] dataset is derived from CARRADA and mainly calibrates the original RA annotations, see Appendix D.1 for details.KuRALS dataset is self-collected by a Kurz-under band (≈ 17GHz) surveillance Radar, which is recorded in multiple scenarios, including Aerial vehicles, Land targets and ships on the Sea surface. Different from other public datasets, e.g., CARRADA [20] and CRUW [29] , KuRALS aims at exploring the performance of deep models in the field of monitoring radar, hence offering a greater range field (≦ 6.4km) andTo investigate the working mechanism of AdaPKCs, a series of comparison analysis is conducted on CARRADA benchmark. We first compare AdaPKCs with PeakConv and a manual PRF adjustment method. Results in Tab. 1 demonstrate that, both versions of AdaPKC exhibit significant enhancements in RSS performance compared to PeakConv, especially in the RA view, and they incur affordable additional computational complexity and inference speed overhead. Considering the severer signal tailing effect in RA view, this suggests that AdaPKC can better handle situations with frequency response ambiguity. For better illustration, we analyze the distribution of guard bandwidths in Appendix E.1. Additionally, since manually adjusting PRFs directly affects the RSS performance of PeakConv-based models, as discussed in [32] , in this work we undertake a similar exploration experiment within a broader range of guard bandwidths. Specifically, different guard bandwidths in range dimension, b G R ∈ {1, 2, 3, 4, 5}, are tested, while the guard bandwidths in angle and Doppler dimensions are fixed at 1 for controlling variables. To ensure consistent parameter counts under different bandwidth settings, we adopt the same strategy of uniform sampling as in AdaPKC ξ . As shown in Tab. 2, presetting a proper PRF globally in a hyper-parameter way can indeed help the PeakConv-based model achieve better performance. However, the manual adjustment way cannot cater to each unit, and the computational cost of traversing the guard bandwidth in all dimensions and directions is quite large. In contrast, AdaPKC completely automates the PRF adjustment, and the adjustment granularity reaches the unit-level. With this unit-level PRF adaptation capability, AdaPKCs demonstrate superior RSS performance compared to the manual adjustment way. 134.3M@3 54.7% 66.3% 34.5% 40.9% U-Net [24] 17.3M@3 55.4% 68.0% 32.8% 38.2% DeepLabv3+ [1] 59.3M@3 50.8% 61.6% 32.7% 38.3% RSS-Net [13] 10.1M@3 32.1% 36.9% 32.1% 37.8% RAMP-CNN [8] 106.4M@9 56.6% 68.5% 27.9% 30.5% TMVA-Net [19] 5.6M@5 56.1% 68.0% 37.7% 46.2% TransRadar [6] 4.9M@5 57.2% 69.1% 39.9% 49.5% T-RODNet [12] 162.0M@4 --43.5% 53.6% PKCIn-Net [32] 6.3M@5 60.7% 72.6% 43.1% 53.7%AdaPKC ξ -Net 6.3M@5 61.2% 73.1% 44.1% 55.1% AdaPKC θ -Net 6.3M@5 61.5% 73.6% 43.6% 54.5% AdaPKC ξ -Net F iT 6.3M@5 62.1% 74.0% 44.3% 55.5% Results on CARRADA-RAC dataset are shown in Tab. 5. Similar to the trend on CARRADA, the evaluation results exhibit the superior performance of our methods than these RSS baseline models. AdaPKC θ -Net still shows a relatively balanced performance improvement in both views. AdaPKC ξ -Net demonstrates a more pronounced improvement in the RD view, while AdaPKC ξ -Net F iT shows a greater enhancement in the RA view. We speculate that this might be attributed to the collaborative training of different views in the multi-view segmentation task.To verify the effectiveness of AdaPKCs in other application scenarios, we conduct comparative experiments on KuRALS dataset. Quantitative results are shown in Tab. 6 and the qualitative comparisons are presented in Appendix F.5. Compared to conventional visual segmentation models, our proposed KuRALS-Net offers a stronger baseline and AdaPKCs further boost the RSS performance of KuRALS-Net, validating their application potential in surveillance radar detection scenarios.Since the threshold τ plays a vital role in FiTOS, we study the impact by testing different values of τ from 0.1 to 0.9. The summary results on mDice are shown in Fig. 4 and the corresponding mIoU results are presented in Appendix F.6. The optimal outcome for AdaPKC ξ -Net F iT in RD and RA view is obtained with τ = 0.6 and 0.7, respectively. It is observed that the performance of AdaPKC ξ -Net F iT declines when τ is either too large or too small. When τ goes larger, fewer target units adjust their PRFs during the fine-tuning stage, rendering AdaPKC ξ less effective. Conversely, when τ is small, AdaPKC ξ tends to focus on background units with random fluctuations, resulting in training confusion. Compared to PKCIn-Net, AdaPKC ξ -Net F iT consistently demonstrates superiority under different τ s, highlighting the essentiality of PRF adaptation for the original PKC. When compared to AdaPKC ξ -Net, AdaPKC ξ -Net F iT exhibits superior performance with most values of τ , demonstrating the efficacy of the fine-tuning strategy. Especially, in RD view, the RSS performance shows significant improvement within a broad range of τ . Taking mDice as an example, it consistently surpasses 73.5% for τ ∈ [0. RSS performance of AdaPKC ξ -Net F iT with different values of τ ∈ {0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9}. AdaPKC ξ -Net and PKCIn-Net actually corresponds to the case where τ of AdaPKC ξ -Net F iT equals to 0 in whole training process and 1 in the fine-tuning stage, respectively.Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?Answer: [Yes] Justification: This paper fully discloses all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper. 
conclusion: 
 This work delves deeply into the convolution operator for radar signals, PKC, and improves upon it. Due to the design of PRF, PKC filters out the reference units corresponding to interfering signals in a band-pass filtering manner. Compared to other convolution operators in deep learning, PKC obtains a more robust representation by using the center unit and the reference unit to cancel each other out and then weighted fusion. However, the fixed suppression bandwidth (i.e., guard bandwidth) setting limits the adaptability of PKC to signal diversity. Based on this, we propose a method for adaptive adjustment of the PRF, and provide two effective solutions based on metrics and learning, i.e., AdaPKC ξ and AdaPKC θ . In addition, to further boost the learning ability of AdaPKC, a novel fine-tuning strategy is presented. To fully verify the effectiveness of AdaPKC, different real-measured radar datasets are used for experimental analysis. The results show the superior performance of AdaPKC in RSS tasks. attribute, we might adopt the median value of Ξ as the selection criterion. However, while the use of median value can locate ξ k where it is desired in some case, it is clear that the maximum value of the first order gradient is more robust for a variety of scenarios, as illustrated in Fig. S3 . Therefore, to promote the robustness of this selection, we finally employ the maximum value of the first-order gradient.Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?Answer: [Yes] Justification: In abstract, this paper provides an link to the data and code, with sufficient instructions to faithfully reproduce the main experimental results.Guidelines:• The answer NA means that paper does not include experiments requiring code. Guidelines:• The answer NA means that the paper does not include experiments.• The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them. • The full details can be provided either with the code, in appendix, or as supplemental material. 
experiment statistical significance: 
 Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?Answer: [Yes] Justification: This paper reports error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments.Guidelines:• The answer NA means that the paper does not include experiments.• The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.• The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions). • The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.) • The assumptions made should be given (e.g., Normally distributed errors). • It should be clear whether the error bar is the standard deviation or the standard error of the mean. • If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics. • The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction). 10. Broader Impacts Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed? Answer: [Yes] Justification: The paper discuss the potential societal impacts of our work in Section G. Guidelines:• The answer NA means that there is no societal impact of the work performed.• If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact. • Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations. • The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster. • The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology. • If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML). 
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 Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?Answer: [NA] Justification: This paper poses no such risks.Guidelines:• The answer NA means that the paper poses no such risks.• Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters. • Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images. • We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.12. Licenses for existing assets Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?Answer: [Yes] Justification: The creators or original owners of assets, used in the paper, are properly credited and the license and terms of use are explicitly mentioned and properly respected.Guidelines:• The answer NA means that the paper does not use existing assets.• The authors should cite the original paper that produced the code package or dataset.• The authors should state which version of the asset is used and, if possible, include a URL. • The name of the license (e.g., CC-BY 4.0) should be included for each asset.• For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.• We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution. • For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review. 
 
 Appendix: The sampling values of reference units in down, left and right directions are obtained by the following forms of linear interpolation, accordingly:x (j) † r↓ = 1 -b G † ↓ -⌊b G † ↓ ⌋ • x h (j) r↓ +⌊b G † ↓ -1⌋,w (j) r↓ + b G † ↓ -⌊b G † ↓ ⌋ • x h (j) r↓ +⌈b G † ↓ -1⌉,w (j) r↓ , (S2) x (j) † r← = 1 -b G † ← -⌊b G † ← ⌋ • x h (j) r← ,w (j) r← -⌊b G † ← -1⌋ + b G † ← -⌊b G † ← ⌋ • x h (j) r← ,w (j) r← -⌈b G † ← -1⌉ , (S3) sort(Ξ) sort(Ξ) sort(Ξ)Figure S3 : The comparison between different selection criteria across three representative scenarios. The median value fails to capture target-interfering noise in the first and last scenarios, whereas our introduced selection criterion demonstrates robustness in locating it.EQUATIONr→ +⌈b G † → -1⌉ .For back-propagation of gradients in these linear interpolations, we compute the gradient of Eq. S1w.r.t. b G † = {b G † ↑ , b G † ↓ , b G † ← , b G † → } as ∂V ec N i=1 w (i) j • (x c -x (i) † r ) ⊤ Cout j=1 ∂b G † = -V ec       N i=1 w (i) j ∂x (i) † r ∂b G † ⊤    Cout j=1    . (S5) Since {x (i) † r } N i=1have been split into four subsets based on their directions, and eachx (i) † ris solely determined by the guard bandwidth offset in its corresponding direction, there only remain four nonzero items in∂x (i) † r ∂b G † N i=1 , i.e., ∂x (j) † r↑ ∂b G † ↑ , ∂x (j) † r↓ ∂b G † ↓ , ∂x (j) † r← ∂b G † ← and ∂x (j) † r→ ∂b G † → , where j ∈ [1, M ].For simplicity, we demonstrate the calculation procedure with ∂x (j) † r→ ∂b G † → as an example, and the remaining items follow a similar approach. From Eq. S4, we can get∂x (j) † r→ ∂b G † → = -x h (j) r→ ,w (j) r→ +⌊b G † → -1⌋ + x h (j) r→ ,w (j) r→ +⌈b G † → -1⌉ . (S6)With the combination of Eq. S5 and S6, we can obtain the desired gradients. The detailed process of FiTOS strategy is illustrated in Fig. S4 .The CARRADA-RAC [32] dataset is derived from CARRADA and mainly calibrates the original RA annotations. For the generation of both RD and RA annotations, CARRADA adopts a semiautomatic method relying on the Mean-Shift clustering [3] . However, in CARRADA, the clustering performance is seriously degraded by unreliable centroid initialization from optical images and inaccurate candidate search space in RA representation. To alleviate these issues, CARRADA-RAC proposes an RD association strategy to refine the initial centroid, and introduces a regionalized CFAR for readjusting the search space.Multi-view RSS. The input sizes of RA, AD and RD views are 256 × 256, 256 × 64 and 256 × 64, respectively. Both AdaPKC-Nets leverage a sequence of 5 input frames for temporal information aggregation, consistent with PKCIn-Net [32] . We train all these models on two NVIDIA-3090 GPUs and use the Adam optimizer [14] for training. The initial learning rate is 1e -4, and decays in a cosine manner by default. We train these models for 300 epochs with a batch size of 6. For FiTOS, the 300 epochs are evenly distributed between the pre-training and fine-tuning stages, and we set τ = 0.6 for the fine-tuning stage by default. We train these models using a combination of weighted cross-entropy loss, Dice loss and coherence loss, configured with the recommended parameters outlined in [19] .Single-view RSS. In contrast to the multi-view RSS task, the experimental configurations for KuRALS dataset maintain consistency with the following two exceptions: i) the input includes only a single view of RD tensor after 0-Doppler frequency elimination, with shape of 2048 × 124; ii) the weighted cross-entropy loss in multi-view RSS is substituted with weighted focal loss [15] to tackle the severer target-background imbalance in KuRALS.We analyze the guard bandwidth distribution in AdaPKC ξ -Net from two perspectives: different categories and different views. The distribution histogram of guard bandwidths in AdaPKC ξ -Net is illustrated in Fig. S5 . From the perspective of different categories, we observe that the guard bandwidth of the background class tends toward a uniform distribution, while that of the foreground class exhibits a more distinctly concentrated trend. For a better illustration, we present the variance values of different categories in Tab. S1, and the remarkable difference in variance between foreground and background classes confirms our observation. This observation aligns with the characteristics of our proposed metric score, indicating that AdaPKC ξ consistently enhances features of foregroundclass targets, thus improving their discriminability from the background class. From the perspective of different views, this concentrated trend appears more apparent in the RA view compared to the RD view. Concretely, RA view shows a clear concentration on the guard bandwidth of (2, 1), while RD view exhibits considerable proportions on all guard bandwidths except (1, 1). Moreover, this concentrated guard bandwidth in the RA view remains consistent across different AdaPKC layers, while that of the RD view transitions from (2, 3) in the first layer to (1, 2) in the second layer. This comparative analysis of different views indicates that the guard bandwidth selection in RA view demonstrates more confidence and ease for AdaPKC, which explains the better performance improvement of AdaPKC on the RA view. We compare the performance of AdaPKC and dynamic CFAR detectors, and the results are shown in Tab. S2. The comparison reveals several significant limitations of CFAR methods: i) They can only detect foreground targets without the ability to categorize them; ii) They show poor target identification performance, struggling with complex target and interference scenarios; iii) They rely on manual parameter tuning and lack adaptive learning capabilities. Therefore, it is evident that enhancing radar target perception paradigms through deep learning is both necessary and practical, serving as one of the key motivations for PKC and AdaPKC. We demonstrate the exploration of adaptive sampling strategies for reference band. Sampling for reference band plays an indispensable role in multiple applications of peak convolutions: i) for PKC with different guard bandwidth settings, sampling fixed N reference points ensures consistent parameter counts; ii) for AdaPKCs, different x c within the feature map might claim diverse PRFs, necessitating reference points sampling to meet the requirement of convolution operations; iii) for future research on reference bandwidth adjustment, this sampling mechanism operates in a manner akin to that in AdaPKCs. For simplicity of exploration, we take the mentioned application in PKC as the subject of our experiment. In addition to the common uniform sampling, we explore two adaptive sampling strategies based on the similar principle of AdaPKC ξ : For the candidates of reference points {x(i)r } Nr i=1 in a fixed reference band, we first compute their inner product (similarity) with x c , i.e., x c x ⊤ r . Then, the first version (v1) of adaptive sampling selects the N least similar reference points to locate target-interfering noise, while the second version (v2) of adaptive sampling chooses the N reference points with intermediate similarity instead. To evaluate the effectiveness first frame, where the target is interfered with minor noise/clutter, results indicate that all methods can accurately locate and classify targets in relatively clean RD view. However, in the RA view, methods without peak convolution struggle to identify target regions completely in the presence of stronger interference. The interference suppression capability equips PKCIn-Net with superior identification performance, while AdaPKC ξ -Net F iT further strengthens this capability and recognizes complete target regions. In the second frame, as clutter interference on targets intensifies, PKCIn-Net can still accurately locate targets in both RD and RA views but faces challenges in differentiating target categories in the highly cluttered RA view. Nevertheless, AdaPKC ξ -Net F iT correctly classifies targets in both views. In the third frame, where the signal of the distant car target is weak and the clutter interference is strong, existing methods miss the car target whereas AdaPKC ξ -Net F iT can still identify it in the RD view. These results suggest that AdaPKC ξ -Net F iT exhibits stronger interference suppression and target recognition capabilities compared to PKCIn-Net and other RSS methods.Ω G 0 = {{b G x , b G y } | b G x ∈ Ω G x|0 , b G y ∈ Ω G y|0 }; Number of training rounds: N . Initialization: The maximum selection frequency of b G x and b G y : {C * x = 0, C * y = 0}. for i ← 0 to N -1 do AdaPKC ξ -Net* ← Getting AdaPKC ξ -Net trained from scratch with Ω G i ; F i ← Getting the feature map calculated by AdaPKC ξ -Net*; for d in {x, y} do for b G d in Ω G d|i do C d ← Getting the cumulative frequency of b G d being selected across all units in F i ; if C d ≥ C * d then C * d = C d ; b G* d = b G d ; end end C * d = 0; end b G * = {b G* x , b G* y }; Ω G i+1 ← Getting a new local search space extending from b G * ; end Output: Return AdaPKC ξ -Net*.Considering the limited samples of both ship and land vehicle classes, we merge the two classes into a single vehicle class to mitigate the negative impact of class-imbalance on model training. This class merging is primarily based on two observations: i) both ships and land vehicles belong to rigid transportation vehicles, whose reflected radar signals share considerable similarities; ii) ships and land vehicles are primarily used for marine monitoring tasks and land detection tasks, respectively, and there exist nearly no overlapping application scenarios between them, which minimizes the risk Limitations. We have collected a Ku-band continuous wave radar dataset to validate the effectiveness of our proposed method in surveillance radar detection scenarios. Nevertheless, pulse-Doppler radar is also commonly used in these scenarios and investigation on such radar datasets would further uncover the potentials and issues of our methods in practical applications. However, there is currently a lack of publicly available datasets for pulse-Doppler monitoring radar. To alleviate this issue, we will try to collect and release a pulse-Doppler radar dataset to enable more comprehensive validation and analysis of our methods and other works.Societal Impacts. Our approach is applicable to various practical applications, such as perceptions for autonomous driving, UAV surveillance and marine monitoring. However, inappropriate usage may lead to decreased reliability, potentially resulting in safety and other issues.Justification: For each theoretical result, this paper provides the full set of assumptions and a complete (and correct) proof.• The answer NA means that the paper does not include theoretical results.• All the theorems, formulas, and proofs in the paper should be numbered and crossreferenced. • All assumptions should be clearly stated or referenced in the statement of any theorems.• The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch to provide intuition. • Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material. • Theorems and Lemmas that the proof relies upon should be properly referenced.• The answer NA means that the paper does not include experiments.• If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not. • If the contribution is a dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable. • Depending on the contribution, reproducibility can be accomplished in various ways.For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed. • While NeurIPS does not require releasing code, the conference does require all submissions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. , with an open-source dataset or instructions for how to construct the dataset). (d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility.In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results. 


Decision: This paper received mixed reviews: one Weak Accept, two Borderline Accepts, and one Reject. Reviewers who leaned toward acceptance appreciated the approach but raised concerns regarding the clarity of the technical novelty and the significance of performance improvements compared to baseline results.

In the rebuttal and follow-up discussion, the authors effectively clarified the technical contributions and justified the performance gains, addressing nearly all the reviewers' concerns. As a result, three reviewers agreed on accepting the paper.

The reviewer 'qbsg' (who gave a Reject) provided a brief and insufficiently detailed review. Although the AC requested additional feedback to justify the rating, the reviewer did not respond. Given that the rebuttal adequately addressed all of the reviewer’s initial concerns and no further comments were provided, the AC recommends disregarding this rating.

Based on reviewer comments and authors rebuttal and feedback, the AC recommends paper acceptance. 
Authors are requested to incorporate the reviewers' comments in the final version.

Reviews: review: paper_summary: paper_summary: This paper works on the improvement of Radar semantic segmentation. Motivated by the limitation of learning ability of the SoTA PKC method due to the fixed peak receptive field (PRF), an adaptive version named AdaPKC is proposed.  The method can be metric-based and learning-based.  The advantages of the proposed method are validated by the extensive experiments  on two datasets.
strength_weakness: strength_weakness: - The novelty of the proposed method is limited, considering the existing PKC work.
- Although the method is practical and lightweight, the performance gain is not significant.
- I also have concerns about the practicality of the problem setting for radar semantic segmentation in this work. It appears to be more similar to the object detection task found in other datasets. Additionally, other modalities such as cameras or LiDAR are usually available and can provide complementary information even in adverse weather conditions. Moreover, scanning Radar and 4D Radar sensors (such as ORR, Radiate, and K-Radar datasets), which offer much higher resolution, are also becoming increasingly popular.
questions: questions: - How are the frame rates for $AdaPKC^θ$, $AdaPKC^ξ$ and PKC?
- Is any relationship between the guard bandwidth and the specifics of the Radar sensor?
limitations: limitations: The limitation of the evaluation of the method for pulse-Doppler radar is discussed.
paper_summary: paper_summary: This paper proposes an idea of adaptive peak receptive field, and upgrades PKC to AdaPKC based on this idea. Beyond that, a novel fine-tuning technology to further boost the performance of AdaPKC-based RSS networks is presented.
strength_weakness: strength_weakness: The adaptive version of PeakConv (PKC) is considered to be incremental work. The numerical results also indicated the AdaPKC performance improvement over PKC is very limited. 

The comparison with classical CFAR with adaptive reference cell selection is not included in the validation part.
questions: questions: It is not clear how many annotated radar data is sufficient to train the proposed network. 
What is complexity of the proposed network? Is it running faster than CFAR with adaptive reference cell selection?
limitations: limitations: Limitation of the proposal is not discussed in the submission.
paper_summary: paper_summary: This paper presents a radar semantic segmentation method, AdaPKC, which combines PeakConv and Adaptive Peak Receptive Field (APRF) concepts. The author demonstrates extensive applicability of AdaPKC in radar perception including autonomous driving, drone surveillance, and ocean monitoring. The method significantly enhances radar semantic segmentation performance with robustness and scalability.
strength_weakness: strength_weakness: This method builds upon existing methods and is an improved version of the existing PeakConv (PKC). It combines them in a novel way. The novelty is limited for NeurIPS.
questions: questions: 1.Can you provide a more detailed analysis of the computational complexity?
2.Can you provide a more detailed analysis of the impact of the proposed AdaPKC on real-time performance?
limitations: limitations: NA
paper_summary: paper_summary: The PeakConv (PKC) model specialized for radar signal analysis effectively characterizes the target signatures of radar signals. However, the fixed predefined peak receptive field limits the performance of PKC due to significant variations in target features and associated interference within radar signals. To solve this problem, the authors propose AdaPKC, aimed at adaptively adjusting the peak receptive fields in PKC through two data-adaptive band-pass filtering mechanisms. The experimental results show that the proposed method is advanced to some extent.
strength_weakness: strength_weakness: 1.The explanation of Figure 1 is unclear, and the captions for Figures 2 and 3 do not sufficiently summarize the contents depicted, resulting in poor readability.
2.The experiments lack an evaluation of the model's computational efficiency
3.The effectiveness of the proposed method requires further validation.
questions: questions: 1.Please provide a clearer explanation of the content presented in Figure 1. Which part of the Figure 1(a) does the red ellipse in the first row of radar frequency map represent? Why does it disappear in the second radar frequency map of the first row? Why does the purple ellipse reappear in the second row? What do the corresponding image frames look like, and how are they related to these radar frequency maps? Please also supplement the captions for Figures 2 and 3 to facilitate reader comprehension.
2.In Section 3.1.1, "...we divide the observed radar signals into three subsets...". How are these subsets divided? How are the expressions for each segment in Equation 1 derived?
3.Please add an analysis of the computational efficiency of the model. How much computational cost is added by the two modules proposed?
4.Dilated convolution can also adaptively adjust the receptive field of the convolution kernel. What are the advantages of your method compared to dilated convolution techniques?
5. Would the method proposed in this paper still work if other baselines similar to PKC was used?
limitations: limitations: See Weakness and Questions.

Comments: title: Final reminder for LAST 3 Hrs of Discussion
We sincerely hope you could take the time to review our responses!
We sincerely hope you could take the time to review our responses!
We look forward to further improving the clarity and depth of our work with your valuable input!
We look forward to further improving the clarity and depth of our work with your valuable input!
We sincerely appreciate your decision to upgrade the score!
Replying to Official Comment by Reviewer i1nF
Replying to Official Comment by Reviewer mTDV
Reply for rebuttal
comment: Dear Reviewers, 

Sorry to bother you, but this is the last reminder from the authors. **We sincerely invite all reviewers to review and evaluate our responses**. We thank you for your hard work in the early stage and hope to get your short attention again.

Since **there are only three hours left for discussion**, we attach great importance to this submission and would like to buy you a little time at the end.

Warm regards,

9766 Authors
Dear Reviewer ixjc,

We have thoroughly analyzed your questions and concerns you raised in your reviews, dedicating substantial time and effort to provide comprehensive explanations and corresponding revisions. We believe our responses should effectively address the issues you raised. **We sincerely hope you could take the time to review our responses**. If our response is **adequate**, we kindly ask you to give a **fair score upgrade**. Should you have any **other concerns**, we are eager to engage in **further discussions with you**. Thank you for your valuable input in enhancing the quality of our paper, and we also appreciate your respect for our hard work.

9766 Authors
Dear Reviewer qbsg,

We have thoroughly analyzed your questions and concerns you raised in your reviews, dedicating substantial time and effort to provide comprehensive explanations and corresponding revisions. We believe our responses should effectively address the issues you raised. **We sincerely hope you could take the time to review our responses**. If our response is **adequate**, we kindly ask you to give a **fair score upgrade**. Should you have any **other concerns**, we are eager to engage in **further discussions with you**. Thank you for your valuable input in enhancing the quality of our paper, and we also appreciate your respect for our hard work.

9766 Authors
Dear Reviewer,

We want to express our sincere gratitude for the time and effort you've dedicated to reviewing our paper. Your feedback has proven to be invaluable in elevating the quality of our work. We're pleased to inform you that we've taken great care in addressing each of the questions and concerns you raised in your reviews. ***Please do not hesitate to reply if you have any further questions or suggestions!***  We look forward to improving the clarity and depth of our work with your valuable input!

Warm regards,

Paper 9766 Authors
Dear Reviewer,

We want to express our sincere gratitude for the time and effort you've dedicated to reviewing our paper. We're pleased to inform you that we've taken great care in addressing each of the questions and concerns you raised in your reviews. ***Please do not hesitate to reply if you have any further questions or suggestions!***  We look forward to further improving the clarity and depth of our work with your valuable input!

Warm regards,

Paper 9766 Authors
Dear Reviewer mTDV,

We sincerely appreciate your decision to upgrade the score to a borderline accept! Your insightful feedback will be incorporated into the revision.

Wishing you a wonderful day!

Paper 9766 Authors
Dear Reviewer i1nF,

We are genuinely appreciative of your decision to upgrade the score to a weak accept and to recommend our work for final acceptance! Your insightful feedback will be incorporated into the revision. 

Additionally, we would like to make an effort to see if we can earn an even higher evaluation from you. ***If you have any further questions or suggestions, please don't hesitate to reach out!*** We are eager to provide any additional clarification needed and look forward to continuing discussions that will enrich the revision of our paper.

Warm regards,

Authors of Paper 9766
Dear Reviewer mTDV,

We are delighted that our rebuttal has effectively clarified the novelty of our work, and we greatly appreciate your recognition of the better balance AdaPKC achieved between computational complexity and inference speed overhead.

We are pleased to inform you that we have thoroughly addressed all the questions and concerns you raised in your reviews. However, **we are puzzled as to why our work did not fully gain your recognition, resulting in a missed opportunity for a higher evaluation**. 

***Please do not hesitate to reply if you have any further questions or suggestions!***  We look forward to improving the clarity and depth of our work with your valuable input!

Warm regards,

Paper 9766 Authors
Thanks the authors for their explaination. I suggest the current version can be considered for fianl acceptance.
Thank you for your response and clarification on the novelty of the work, particularly the introduction of AdaPRF. 

Your explanation effectively highlights the innovative aspects of AdaPKC, including the integration of radar signal principles with deep learning techniques, and the unique challenges your method addresses. I appreciate your further explanation of AdaPKC and the additional analysis of computational complexity, which demonstrates a better balance between extra computational complexity and inference speed overhead compared to PKC.

 Overall, the author has addressed some of the issues raised during the review process, reinforcing the significance of AdaPKC as an innovative approach in the field of radar signal processing. I look forward to seeing the proposed improvements.