High-dimension Prototype is a Better Incremental Object Detection Learner
Authors: Yanjie Wang
Liqun Chen
Tianming Zhao
Tao Zhang
Guodong Wang
Luxin Yan
Sheng Zhong
Jiahuan Zhou
Xu Zou
Venue: ICLR 2025 Poster (2025)
Keywords: Object Detection; Incremental Learning; Prototype Learning
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Incremental object detection (IOD), surpassing simple classification, requires the simultaneous overcoming of catastrophic forgetting in both recognition and localization tasks, primarily due to the significantly higher feature space complexity. Integrating Knowledge Distillation (KD) would mitigate the occurrence of catastrophic forgetting. However, the challenge of knowledge shift caused by invisible previous task data hampers existing KD-based methods, leading to limited improvements in IOD performance. This paper aims to alleviate knowledge shift by enhancing the accuracy and granularity in describing complex high-dimensional feature spaces. To this end, we put forth a novel higher-dimension-prototype learning approach for KD-based IOD, enabling a more flexible, accurate, and fine-grained representation of feature distributions without the need to retain any previous task data. Existing prototype learning methods calculate feature centroids or statistical Gaussian distributions as prototypes, disregarding actual irregular distribution information or leading to inter-class feature overlap, which is not directly applicable to the more difficult task of IOD with complex feature space. To address the above issue, we propose a Gaussian Mixture Distribution-based Prototype (GMDP), which explicitly models the distribution relationships of different classes by directly measuring the likelihood of embedding from new and old models into class distribution prototypes in a higher dimension manner. Specifically, GMDP  dynamically adapts the component weights and corresponding means/variances of class distribution prototypes to represent both intra-class and inter-class variability more accurately. Progressing into a new task, GMDP constrains the distance between the distribution of new and previous task classes, minimizing overlap with existing classes and thus striking a balance between stability and adaptability. GMDP can be readily integrated into existing IOD methods to enhance performance further. Extensive experiments on the PASCAL VOC and MS-COCO show that our method consistently exceeds four baselines by a large margin and significantly outperforms other SOTA results under various settings.

Published as a conference paper at ICLR 2025
HIGH-DIMENSION PROTOTYPE IS A BETTER INCRE- MENTAL OBJECT DETECTION LEARNER
Yanjic Wang', Liqun Chen', Tianming Zhao', Tao Zhang', Guodong Wang', Luxin Yan', Sheng Zhong', Jiahwan Zhou Xu Zou'
NNational Key Laboratory of Multispectral Information Intelligent Processing Technology,
Huazhong University of Science and Technology, Wuhan 430074, China
"College of Computer Science and Technology. Qingdao University, Qingdao 266071, China "Wangxuan Institute of Computer Technology. Peking University, Beijing 100871, China awai, chen1 doctorwgdégma com, j jiahuanzhoulpku. edu.cn, zx@zoux.me
poeniigu quu, tmi ng, zhangtao, ganiuxin, zhongsheng @uust edu.
ABSTRACT
Incremental object detection (IOD), surpassing simple classification, requires the simultaneous overcoming of catastrophic forgetting in both recognition and local- ization tasks, primarily due to the significantly higher feature space complexity. Integrating Knowledge Distillation (KD) would mitigate the occurrence of catas- trophic forgetting. However, the challenge of knowledge shift caused by invisible previous task data hampers existing KD-based methods, leading limited im- provements in IOD performance. This paper aims to alleviate knowledge shift by enhancing the accuracy and granularity in describing complex high-dimensional feature spaces. To this end, we put forth a novel higher-dimension-proiiee prototype learning approach for KD-based IOD, enabling more flexible, accurate, and fine-grained representation of feature distributions without the need to retain any previous task data. Existing prototype learning methods calculate feature cen- troids or statistical Gaussian distributions prototypes, disregarding actual ir- regular distribution information or leading to inter-elass feature overlap, which is not directly applicable to the more difficult task of TOD with complex feature space. To address the above issue, we propose Gaussian Mixture Distribution- based Prototype (GMDP), which explicitly models the distribution relationships of different classes by directly measuring the likelihood of embedding from new and old models into class distribution prototypes in higher dimension manner. Specifically, GMDP dynamically adapts the component weights and correspond- ing means/variances of class distribution prototypes to represent both intra-class and inter-class variability more accurately. Progressing into a new task, GMDP constrains the distance between the distribution of new and previous task classes, minimizing overlap with existing classes and thus striking balance between sta- bility and adaptability. GMDP can be readily integrated into existing IOD methods to enhance performance further. Extensive experiments on the PASCAL VOC and MS-COCC show that our method consistently exceeds four baselines by large margin and significantly outperforms other SOTA results under various settings.
INTRODUCTION
In recent years, deep learning methods [366 have witnessed remarkable advancements in various visual tasks, particularly in object detection 58) However, these methods typically learn defined labeled classes from static datasets, limiting their applicability in dynamic real-world scenarios. Incremental Object Detection (IOD) methods have emerged address this issue. In contrast to human incremental learning, deep learning often suffers from the problem of catastrophic forgetting [45,, where new information interferes with previous knowledge. This phenomenon poses a significant challenge for IOD, complicating the retention and integration of new data without disrupting previously learned information.
exciting and challenging task to
*Corresponding author.

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Published as a conference paper at ICLR 2025
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Dobii dea Conker
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Figure 1: The comparison of different prototypes of in- cremental learning. (a) Many methods 68 59 21 con-
Figure 2: Confidence weighting maps and t-SNE (60] visualization of embed- ding for classification and object de- tection. (top) In contrast to classifica- tion focusing on the local salient region, detection also emphasizes global shape and boundary features. (bottom) Classi- fication tasks reveal clear feature sepa- ration, while detection tasks present dis- tinct, crowded distributions, rendering detection features more complex.
sider feature centroids as prototypes but overlook distri- butional information, causing significant forgetting. (b) Calculating statistical Gaussian distributions as proto- enhances the reliability of prototype modeling, but simple modeling brings about overlap- ping feature spaces, leading to misclassification. (c) Our higher dimension Gaussian mixture distribution-based prototype can more accurately characterize detection fea- ture distribution, resulting in a better IOD performance.
types (69] 73 23
Rehearsal- and Knowl- 4 34 35) aims to mitigate catastrophic forgetting by reutilizing previously training samples. The efficacy of these methods hinges upon the 48
Existing IOD methods can be roughly divided into two paradigms: edge Distillation (KD))based IOD. Rehearsal-based IOD
72), the most commonly employed paradigm, leverages features and responses from a teacher model's 's previous class to guide student model in acquiring new knowledge. Nonetheless, distilling new data could introduce knowledge bias due to the invisible previous task data, leading to limited improvements in IOD performance.
replay strategy of the samples. In contrast, KD-based IOD
Recently, several works have explored Prototype Learning getting, which characterizes classes by learning the distribution of features within classes without keeping any previous task feature representations, thus compensating for the deficiency of KD. Ex- primarily rely on feature centroids as prototypes (Figure (a)). However, this approach overlooks distributional information, resulting in significant forgetting due to insufficient modeling of previous data. As illustrated in Figure (b), some meth- ods 669 73 23 calculate statistical Gaussian distributions as prototypes to enhance the reliability of prototype modeling by considering class distributions, which to some extent improves detec- tion performance. Due to limitations in modeling capabilities, the prototype based on a statistical Gaussian distribution fails to accurately estimate complex detection feature distributions, leading to confusion between current and prior knowledge since estimated distributions may overlap. From the perspective of confidence weighting maps, the features for classification in Figure 2 (a) are predom- inantly concentrated in local salient regions, whereas detection features (Figure (b)) also attend to
to address catastrophic for-
isting incremental learning methods 68 (9 22
global boundaries or shape characteristics ( For t-SNE visualizations of classificationFiiuree 2 (c)). features from different classes often exhibit clear delineation, while those from the same classes demonstrate a Gaussian-like clustered distribution. Conversely, for detection tasks (Figure 2 (d), features from different classes tend to be relatively crowded and exhibit distinct distribution pat- terns. Therefore, the above methods are tailored for incremental classification and are not directly applicable to the more challenging task of IOD, which involves more complex feature space.
In this paper, we propose a novel higher- dimensioon protttttoon-oottppeeeee learning approach for KD-based IOD to alleviate the knowledge shift. This approach enables a more flexible. accurate, and fine-gra grained representation of feature distributions without retaining any previous task data. To address the above issue of existing prototype learning methods, we propose Gaussian Mixture Distribution-base
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Prototype (GMDP), which explicitly models the distribution relationships between different classes by directly measuring the likelihood of embedding from new and old models into class distribution prototypes in more complex manner to fit nigh-dimensional feature spaces of object detection, as shown in Figure | (c). Specifically, the GMDP dynamically adjusts the component weights and corresponding means and variances of class distribution prototypes to represent intra-class and inter-class variability accurately. Progressing into new task, GMDP would constrain the distance between the distribution of new task classes and previous classes, minimize overlap with existing class features, and strike a balance between stability and adaptability. In addition, implementing GMDP in detection tasks involves unique challenges, particularly in managing the complexities of high-dimensional features during the learning process. To address this issue, we leverage Length Scaling Progressive Learning (L.SPL) method, which gradually learns complex, high-dimensional features by first focusing on the discriminative characteristics of new classes. To enhance the plas- ticity of class prototypes, we present a Dynamic Adaptive Prototype Optimization (DAPO) strategy for IOD. This strategy aims to enhance the adaptability and efffcttivnness of GMDP modeling by improving the separation of class features through inter-class mean dispersion, intra-class compo- nent cohesion, and prototype variance minimization. Simultaneously, our approach improve performance further with Rehearsal- methods. In summary, our contributions are as follows:
To alleviate the knowledge shift, we propose a novel prototype learning approach for KD- based IOD, which enables a more flexible, accurate, and fine-grained representation of feature distributions without keeping any previous task data.
A novel GMDP is introduced to explicitly model the distribution relationships between different classes suitable for more complex feature spaces. A Length Scaling Progressive Learning method is proposed to address the learning difficulties of GMDP. To enhance the plasticity of class prototypes, we present a DAPO strategy for IOD during the incremental process, which could enhance the adaptability and effectiveness of GMDP modeling.
GMDP can be readily integrated into existing IOD methods to enhance performance fur- ther. Methods with our idea on the PASCAL VOC and MS COCO consistently outperform 4 baselines significantly and achieve state-of-the-ae results in different settings.
2 RELATED WORK
2.1 INCREMENTAL LEARNING
Incremental learning 39 g 44 aims to continuously update a model by introducing different sub- sets of the label space without retraining the model on old data. Previous methods could be roughly categorized into rehearsal-based
fashion. Rehearsal- based fashion methods mitigate catastrophic forgetting induced by new classes by periodically revis- iting and rehearsing previously learned samples 4 (28 49). or generating new samples [26] 36 64]. However, these methods suffer from challenges related to privacy protection and high storage costs. segregate parameters for different tasks to ensure that learning new tasks does not interfere with previously learned tasks. Regularization- based meth- ods [39; 10 [1 22 impose constraints the features and parameters balance learning between new and previous classes, thereby maintaining stability and generalization during the model updat- ing process. In this work, we focus on regularization methods based Knowledge Distillation.
parameter-isolation, and regularization-bas
Parameter-isolation methods [37 43
42
67
2.2 CONTINUAL LEARNING WITH PROTOTYPES
Recently, various prototype-based class incremental learning (CIL) methods have been proposed to continually learn new classes without preserving any historical exemplars [55 68 7
Some
methods [ 62. 74 treat the prototype of each class as a learnable embedding vector. Mean Feature Prototypes calculate the mean feature of all samples from the same class to form the prototype (2 73). However, using a single feature center can lack informative distribution. To address this, the latest methods [16 calculate both the mean feature vector and its variance from all samples to better represent class distribution. These approaches, however, can be susceptible to outliers and class overlap, which may undermine stability due to misclassification risks 770 7 In this paper, we introduce the GMDP to constrain the distance between the distribution of new and previous classes, and minimize overlap with existing class features.
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Figure 3: Histogram visualizations of the statis- tical distribution of detection features.
Figure 4: The confusion matrix of detection fea- ture divergence across different classes.
2.3 INCREMENTAL OBJECT DETECTION
Differing from general object detection 52
14
51
15 50 with static training data, Incremental
Object Detection (IOD) (57 24 65
5 3 aims dynamically adapt new tasks introduced over
time, while mitigating the risk of catastrophic forgetting, which could be roughly categorized into the Rehearsal-bait -aased fashion and knowledge distillation- based fashion. Rehearsal-
fashion (36
38 involves storing a balanced set of examples, such as images [36] or sample instances [38 and periodically revisiting previously learned samples at each incremental step to finetune the model. For knowledge distillation-base fashion, after Shmelkov et al. (57 introduced the first knowledge
distillation- based incremental detector ILOD, most researchers [1] 8 47 [27] have designed various knowledge distillation-base IOD methods on many object detection architectures.
Despite the KD-based IOD being the most commonly employed paradigm, distilling new data could introduce knowledge bias due to the invisible previous task data, leading to limited IOD perfor- mance. Conversely, prototype-base discriminative class decision boundaries of new and old models. However, inaccurate prototype dis- tribution modeling may result in overlapping feature distributions among classes in complex high- dimensional feature spaces. Given the complementary advantages of KD and prototype methods, we focus on a KD approach based on class distribution prototypes, balancing detector stability and adaptability by explicitly modeling complex detection features without keeping previous task data.
incremental learning methods encourage the model to learn
3 PROPOSED METHOD
3.1 MOTIVATION
In contrast to existing works 49
that only adopt recognition features for prototype compu- tation, our method leverages both recognition and location- brased detection features generated from the linear layer of Rol. Thus, we aim to capture the multi-faceted class characteristics as shown in Figure (b), enabling a more comprehensive and nuanced representation of discriminative features. To illustrate the distribution of IOD detection features across diverse dimensions, we exemplify the
features from class 1 and class across various dimensions shown in Figure 3 These features are extracted from the linear layer of Rol trained on the PASCAL VOC. Each subplot indicates the distribution of detection features across distinet dimensions. Employing Shapiro-Wilk (54) and Scostino-Pearson [6]] tests, we evaluate the normality of each dimension feature, revealing devi- ations from Gaussian distribution. Consequently, IOD detection features are inherently in a more complex feature space, diverging from the simplicity of recognition features, posing challenges for Moreover, variations are observed between the dimensions of different classes. That is, each dimension of the features can be considered inde- pendently distributed, enabling more complex modeling. This encourages us to further validate the feasibility of applying more precise prototype representations of IOD. We obtained more suitable and accurate probability density estimates by fitting each dimensional feature adopting a Gaussian Mixture Distribution (Red dashed line) compared to single Gaussian Distribution. Therefore, introduce Gaussian Mixture Distribution-bar Prototype (GMDP) as a more flexible and refined approach represent IOD feature distribution by introducing distinct components for each class.
single Gaussian distribution modeling 669
73 23
The effectiveness of GMDP plays a crucial role representing class prototypes that overcome catastrophic forgetting and enhance model plasticity. To validate this, we follow the definition of distribution distance proposed by Liu et al. [2 [3] to distinguish classes and adopt the Jensen- Shannon (JS) divergence metric quantify the distance between class distributions. The distance
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Task 1-/
Reg
C
GMDP
Label: Person
Delect: Person
Link
Liagit Reg
Task
GMDP
Cls
Reg
Label: Horse
Detect: Person, Horse
GMDP prediction of Task t-|
GMDP prediction Task
Ccc
Lepk
MPP
LMD
Distribution Relationship keep Prototype Cons is tency
The overall framework. The Gaussian Mixture Distribution-base Prototype (GMDP) introduces a flexible, accurate feature characterization in more complex manner to fit high- dimensional feature spaces. GMDP would transfer old prototype knowledge by constraining the distribution relationships of the prototype to maintain consistency, thereby alleviating catastrophic forgetting. To enhance the plasticity of prototypes, we further present a dynamic adaptive prototype optimization strategy (Cran,, Lic and LMP to improve the modeling adaptability of GMDP.
Figure 5:
between the distribution of two classes can be computed as follows:
dss D(Pl(k) + +2
where d is the Jensen-Shanee Shannon divergence, D refers to the KL divergence, P and Q are the Two distribution-basn class prototypes, and μ (P + Q)/2 is the midpoint distribution. Figure il- lustrates the distance between class prototypes guided by the statistical Gaussian distributions- prototype (SGDP) (Ieft) and our GMDP (right) in the 10-10 task. 10-10 task means that the first stage trains annotations containing 10 classes and then trains images containing the remaining 10 classes in the second stage on PASCAL VOC. A smaller divergence value indicates less distinguishability between prototypes, potentially resulting in poorer detection performance. Notably, class pro- totypes guided by SGDP (Figure gleft) exhibit smaller disparities with the 10 classes the second task compared to other prototypes. Conversely, classes in both stages with our GMDP display better discriminability. Thus, our GMDP can more accurately describe the class distributions in complex feature space compared to SGDP, promoting the modeling of inter-class and intra-class differences.
3.2 GAUSSIAN MIXTURE DISTRIBUTION BASED PROTOTYPE
To alleviate the knowledge shift issue, propose novel prototype learning approach for KD- based IOD as Figure 5 which can more flexibly, and accurately represent feature distributions without keeping any previous task data. The key idea is, based on the above analyses, to leam set of Gaussian mixture prototypes that capture the essential features according to their distribution characteristics and adopt these prototypes to transfer knowledge from old model the new one.
Specifically, we introduce a Gaussian Mixture Distribution-bast Prototype (GMDP) for IOD, which explicitly models the distribution relationships of different classes by directly measuring the likelihood of embedding from new and old models into class distribution prototypes. Given a set of input features X 3i, T2 -. D. from the linear layer of ROI, where each feature 2; RDD
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represents object proposal, aim to learn a set of prototypes P = (P1, P2: ..., Pe). where each prototype P. = (Me.1..... He,k: Oc.1, Oc,k; Re) represents a Gaussian mixture distribution with K components and a mixing coefficient Te E EK Each component is composed of Gaussian distri- bution with a mean Me,k I and covariance matrices σı E RNN' For object detection, compute the likelihood probability of each input feature I; under each prototype distribution p.:
P(r:(po)= Σ sof (imma(TTT))
exp (-2 (o,k) o ((okt))Me))))))))))))))) (2)
(eck))
where K is the component number of the Gaussian mixture distribution-based prototype. The so lomaa function is adopted to ensure that the mixing coefficients sum 1. Thus the GMDP loss LGMDP definition for optimizing each class prototype through I; is as follows:
C No. ΣΣ yr. log(P(e.))))) + Σ 1011.. c=1
LGMDP = N
(3)
where N and C' are the number of all features in the batch and total classes. Vi. is the ground- truth label for the i-th sample and the e-th class. No is the number of c-th class feature. And the regularization weight. Ee ||@|] is weight decay term that tends to decrease the magnitude of the weights (0) of GMDP and penalizes large parameter values to prevent overfitting. LOMOP measures the difference between the predicted scores and the ground-truth labels. The prediction score of GMDP is computed by taking the maximum likelihood across all prototypes:
SGMDP(æ:) = max ((xy)po).
(4)
Finally, we aggregate weighted between the GMDP prediction SGMDP and the IOD detector clas- sification result Sp, final classification scores as follows:
Stotal
SCMDP(ylz) + a) (y(l)),
(5)
where a is set to 0.5 to balance the weight between two confidence. During the incremental stage, we transfer old knowledge from the old model to the new model by measuring the divergence between the likelihood probability distributions generated from our GMDP of the old and new models. We can define transfer prototype knowledge loss by Kullback-Leibler (KL) divergence:
(P.teePotele) log Po(zlc) Pal(le)
Lipk DKI((P - (z(zlc)
dz,
(6)
where (z]c) and Pr, are the likelihood probabilities of the old and new models, respectively, conditioned the set of old classes Co. is a high-dimensional feature vector extracted from each proposal in the ROI layer. By employing prototypes to represent the feature distributions, our method can adapt to data without requiring access to the previous task data, thereby overcoming the knowledge shift problem inherent in existing KD-based IOD methods.
Introducing GMDP makes network convergence difficult. To address this, we leverage Length Scaling Progressive Learning (L.SPI.) method that simplifies the learning process by scaling the length of the learned features and progressively in- corporating the GMDP. Formally, if æ E RRD is the original feature vector, the LSPL reduces its dimensionality help the model focus on discriminative information, which is expressed as:
Length scaling progressive learning method:
= z=o b),
where W and are the weight matrix and bias, respectively. o(-) denotes the activation function. This transforms x into E Rd where < D, retaining crucial feature information. Besides, we adopted an initial training phase to relieve cold start, first conducting N training steps and then employing our GMDP construct high-dimensional prototypes.
3.3 DYNAMIC ADAPTIVE PROTOTYPE OPTIMIZATION STRATEGY
To enhance the plasticity of class prototypes, present Dynamic Adaptive Prototype Optimiza- tion (DAPO) strategy for IOD during the incremental process, which comprises three core principles aimed at enhancing the adaptability and effectiveness of GMDP modeling.

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Published as a conference paper at ICLR 2025
Inter-Class Mean Dispersal:
The first principle emphasizes maximizing the separation between the means of different classes, thereby promoting greater dissimilarity among prototypes. By increasing inter-class mean dispersion of GMDP, the distance between distinct prototypes is maximized, facil- itating better discrimination between classes. The Inter-Class Mean Dispersal (IMD) loss measures the distance between means of different classes of GMDP:
LIMD = LOND=E>
1
(8)
1=1 | M,ll2 +
where C' the number of classes, μ4 is the mean of class and is small constant.
The second principle focuses on minimizing the distances be- tween components of GMDP within the same class. This ensures that the components of class's GMDP are closely clustered, enhancing the coherence and representativeness of the class distribu- tion prototype. The Intra-Class Component Cohesion (ICC) loss measures the distance between means of different components within the same class:
Intra-Class Component Cohesion:
LICe ΣΣ ||l Mi,ell2
(9)
i=1 sij=kk=jt]
where M the number of components, and Pi,j the mean j-th component of i-th class.
The third principle aims mitigate the variance of GMDP across disparate classes. By minimizing intra-class prototype variance, the distributions become more compact, providing more structured and discriminative feature space that facilitates superior performance in subsequent tasks. The Minimization of Prototype Variance (MPV) loss aggregates the variances of all components follows:
Minimization of Prototype Variance:
CMPV
(10)
1=
where oi. is the variance of the j-th component of class i. By incorporating these principles into the optimization process, our approach enables prototypes dynamically adapt task requirements.
Table 1: m.P@0.5% results on the two-stage incremental setting on Pascal VOC. The best and the second best results are highlighted in bold and underlined. Two methods equipped with our idea consistently outperform baselines by large margin and achieve state-of-the-ar results compared with existing methods in different incremental settings.
19-1
15.5
10-10
5-15
Method
1-19
20
1-20
1-15
16-20
1-20
1-10
11-20
1-20
1-5
6-20
1-20
Joint Training
70.1
75.7
76.4
67.8
74.3
75.5
73.0
74.3
75.7
74.3
Fine-tuning
11.8
64.7
14.4
15.9
54.2
25.5
2.6
63.4
32.9
6.9
63.1
49.1
Faster ILOD
PRL 20
70.9
63.2
70.6
73.1
57.3
69.2
70.3
53.0
61.7
62.0
37.1
43.3
MVC 66
PR 22
70.2
60.6
69.7
69.4
57.9
66.5
66.2
66.0
66.1
ORE
CVPR 21
69.4
60.1
68.9
71.8
58.7
68.5
60.4
68.8
64.6
Meta-
TPAMI 21
70.9
$7.6
71.7
55.9
67.8
68.4
64.3
66.3
OW-DETR
CVPR 22
70.2
62.0
69.8
72.2
59.8
69.1
63.5
67.9
65.7
MMA 5
CVPR 22
70.9
62.9
70.5
72.7
60.6
69.7
69.8
63.9
66.8
66.8
57.2
59.6
CAT 40
CVPR 23
74.5
61.1
73.8
76.5
59.3
72.2
70.0
67.4
67.7
Wuef al.
TIP 24
72.5
61.4
71.9
73.6
62.0
70.7
70.3
68.7
69.5
CVPR 17
69.8
72.5
58.5
68.9
53.7
61.7
43.2
GMDP-ILOD
Ours
74.2
67.9
73.9
74.6
63.5
71.8
71.9
69.7
70.8
65.2
60.5
61.7
ABR 38
ICCV 23
71.0
69.7
70.9
73.0
65.1
71.0
71.2
72.8
72.0
64.7
71.0
69.4
GMDP-ABR
Ours
74.8
70.1
74.6
75.8
65.5
73.2
72.1
73.2
72.7
67.1
71.9
70.7
4
EXPERIMENTS
4.1
DATASETS AND EVALUATION
38 to adopt two widely used incre- mental object detection (IOD) datasets evaluate the effectiveness of our method: PASCAL VOC
For a fair comparison, we followed the previous work [57

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Published as a conference paper at ICLR 2025
Table mAP@0.5% results on the multiple-stage incremental setting on Pascal VOC. The best and the second best results are highlighted in bold and underlined. Two baselines with our idea are able to forget less about the knowledge of previous tasks and learn better new information.
10-5 (3 tasks)
5-5 (4 tasks)
10-2 (6 tasks)
15-1 (6 tasks)
10-1 (11 tasks)
Method
1-10
11-20
1-20
1-5
6-20
1-20
1-10
11-20
1-20
1-15
16-20
1-20
1-10
11-20
1-20
Joint Training
75.5
73.0
74.3
70.1
75.7
74.3
75.5
73.0
74.3
76.4
67.8
74.3
75.5
73.0
74.3
Sine-tuning
5.3
30.6
18.0
0.5
18.3
13.8
3.79
13.6
8.7
0.0
10.47
5.3
0.0
5.1
2.55
Faster ILOD
[47] PRL 20
68.3
57.9
63.1
55.7
16.0
25.9
64.2
48.6
56.4
66.9
44.5
61.3
53.5
41.0
47.3
MMA 5
CVPR 22
67.4
60.5
64.0
62.3
31.2
38.9
65.7
52.5
59.1
67.2
47.8
62.3
57.9
44.6
51.2
LLOD 57
CVPR 17
67.2
59.4
63.3
58.5
15.6
26.3
62.1
49.8
55,9
65.6
47.6
60.2
52.9
41.5
47.2
GMDP-ILOD
Ours
68.1
62.3
65.2
61.1
35.8
42.1
64.2
53.3
58.8
66.8
50.4
62.7
56.2
50.7
53.5
ABR [38
23
68.7
67.1
67.9
56.4
58.4
67.0
58.1
62.6
68.7
56.7
65.7
62.0
55.7
58.9
GMDP-ABR
Ours
69.9
67.8
68.9
66.3
59.3
61.1
67.6
58.9
63.3
69.5
58.9
66.9
63.3
57.5
60.4
2007 [2] and MS-COCO [32. PASCAL VOC 2007 comprises 9,963 images across 20 categories, with 50% of them training and validation sets. The remaining half is reserved for testing. The evaluation metric employs mean Average Precision (mAP) using a 0.5 loU threshold. MS-COCO a challenging dataset containing 80 object categories. We report the COCO-style Average Precision (AP) at different IoU ranging from 0.5 to 0.95 (mAP@75 the evaluation.
0.50 IoU (mAP@ 50), and 0.75 loU
4.2 IMPLEMENTATION DETAILS
In this paper, our experiments conducted upon four robust baselines: ILOD (57]. ERD 13] CL-
DETR (36) and ABR 38). Comprehensive results of Transformer baselines ERD and CL-DETR
provided in the Appendix. Following prior work [57] [3] 38), we employ the ResNet-50 (0) as
the backbone, initializing ILOD and ABR baselines with pre-trained models from ImageNet [53].
Fitting validation based on each feature length in Section 3.1 confirms that the maximum number of
components for the Gaussian Mixture Distribution is 2. Adjusting the weights of the components can also be applied a single Gaussian Mixture Distribution. All experiments are carried out on Nvidia GeForce RTX 3090 GPUs with a batch size of 16 implemented by PyTorch.
4.3
QUANTITATIVE EVALUATION
4.3.1 RESULTS ON PASCAL VOC.
We partitioned the datasets into distinct incremental task sequences in class-incremental object de- tection. PASCAL VOC 2007 was split into two-stage and multi-stage incremental task settings. For the two-stage incremental setting, tasks are divided into 19-1, 15-5, 10-10, and 5-15, incrementing by 5, 10, and 15 classes, respectively. The multi-stage incremental setting divides tasks into 10-5, 5-5, 10-2, 15-1, and 10-1, comprising 3, 4. 6, 6, and 11 tasks, respectively.
Two-stage increments. In Table we compare our method to state-of-the-ar approaches. We ob- served that employing the fine-tuning method across all settings led to catastrophic forgetting (45). This resulted in significant hindrances in learning new tasks due to forgetting prior knowledge, lead- ing to knowledge confusion. Compared to ILOD [57], our approach demonstrates stable improve- ments across both old and new tasks, with notable 15% improvement in the 10-10 setting and 43% improvement in the 15-5 setting. GMDP-ILOD achieves competitive performance compared methods under the classic IOD method from CVPR in 2017. Based on
with recent advanced ABR [38, our method outperforms the baseline by large margin and achieves state-of- the-art performance, further validating the effectiveness of our method.
Multi-stage increments. To demonstrate the performance of our IOD method, we also show the performance of multi-step incremental learning on Pascal VOC shown in Table 2 It can be observed that fine-tuning exhibits a more severe catastrophic forgetting over multiple steps, where the forgetting of old knowledge also leads to the limited performance of new classes. Our IOD method surpasses the two baselines by a large margin and performs well on each incremental step. Moreover, our method improves both old and new class performance against the baseline model. This is attributed to the feature explicit modeling for more accurate GMDP, capturing the essential features according to their distribution characteristies.
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Table 3: mAP results on MS COCO 2017 different IoU, where the best among columns in bold.
40-40 mAP@
70-10 mAP@
Method
50
50
75
[50
95]
75
Joint Training
35.9
60.5
38.0
35.9
60.5
38.0
Fine-tume tuning
19.0
31.2
20.4
5.6
6.2
Faster ILOD [47]
20.6
40.1
21.3
39.9
MMA (5]
33.0
56.6
34.6
30.2
52.1
31.5
ABR (38
34.5
57.8
35.2
31.1
52.9
32.7
GMDP-ABR
36.8
59.6
36.7
32.5
53.8
33,9
Table 4: Ablation study of different components of method for IOD Pascal- VOC 2007. After training with the GMDP, our model achieves a significant improvement over the baseline, especially in previous tasks. The performance of new tasks could be further enhanced by DAPO strategy.
DAPO
19-1
15-5
10-10
Baseline GMDP
Speed
LIND Lice LMPV
1-19
20
1-20
1-15
16-20
1-20
1-10
11-20
1-20
Joint Training Fine-tuning
70.1
75.7
74.3
76.4
67.8
74.3
75.5
73.0
74.3
11.8
64.7
14.4
15.9
54.2
25.5
2.6
63.4
32.9
69.8
64.5
69.6
72.5
58.5
68.9
69.8
53.7
61.7
27.2fps
73.8
65.2
73.4
73.3
60.2
70.0
72.0
66.3
69.2
25.3fps
74.0
66.9
73.6
73.9
62.1
71.0
71.9
69.1
70.5
25.3fps
V
73.7
65.7
73.3
73.7
61.8
70.7
72.1
67.2
69.5
25.3fps
73.9
67.1
73.6
74.0
62.3
71.1
71.8
70.4
25.3fps
V
74.2
67.9
73.9
74.6
63.5
71.8
71.9
69.7
70.8
25.3fps
4.3.2 RESULTS ON MS-COCO.
As indicated in Table 3 conduct the experiments MS-COCO compare the proposed GMDP with existing methods under 40-40 and 70-10 incremental settings. Fine-tuning also suffers from catastrophic forgetting across all settings. The performance of our method exceeds its baseline. Under 40-40 and 70-10 settings, our method achieves 7%, 5% performance gain compared with ABR 38 on mAP@ [50:95] respectively. After applying the GMDP and DAPO strategy, our method outperforms all other methods significantly. The results show that our prototype modeling method for IOD performs better on the more difficult MS-COCO dataset.
4.4 ABLATION STUDIES AND DISCUSSION
To prove the effectiveness of each component, perform ablation studies shown in Table All experiments are conducted on PASCAL VOC 2007 with ILOD (57] as the base detector. We con- ducted experiments with GMDP based on ILOD, observing significant performance improvements (Table[4rom vs row 4) attributed to GMDP's s ability to explicitly model distributional characteris- ties, resulting in improved IOI) performance and generalization capabilities. Importantly, the model retained valuable information according to their distribution characteristics from previous tasks, thereby mitigating catastrophic forgetting. We also evaluated the impact of the three loss functions, LIMD. Licc. and PV of DAPO on model performance. Adopting the complete DAPO strategy notably enhanced adaptability and performance on new tasks, indicating its efficacy in facilitating knowledge transfer and adaptation by dynamically optimizing GMDP.
In Figure 6 we compare the effectiveness of various training methods for IOD through visualization of feature distributions. Joint Training illustrates the feature distributions for different classes are separttea, maximizing the model's performance. The first 5 classes of Fine-tuning are ran- domly scattered and mixed with other classes, resulting in catastrophic forgetting. The Statistical Gaussian Distribution-based Prototype (SGDP) mitigates some aspects of catastrophic forgetting, but notable overlap exists between old and new tasks with many outliers. This indicates that SGDP ineffective in preserving prior knowledge under complex high- nigh-dimmnsional feature spaces. Our GMDP effectively maintains the separation of different classes, with minimal overlap. This demonstrates its capability to preserve prior knowledge and integrate new tasks seamlessly, achiev- ing superior IOD) performance.

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(a) Joint Training
(b) Fine tuming
(G) SGDP
(d) Ours
Figure 6: The t-SNE visualization results with different learning methods under a 5-15 task setting. (a) Joint Training is trained on all tasks simultaneously, clearly separating classes. (b) Fine-tuning method is sequentially trained on new tasks, leading to severe catastrophic forgetting. (c) SGDP mitigates some forgetting but still shows significant overlap between old and new task features, along with many outliers, indicating inadequate retention of previous knowledge. (d) Clusters generated by our method are more compact and can be well separated from each other.
Table 5: (Left) Analysis of different numbers of Gaussian components in GMDP. (Right) Visualiza- tion of decision boundary and the I-SNE for different classes of GMDP with 4 components.
Num of Component
19-1
13-5
1-19
20
1-20
1-15
16-20
1-20
69.8
64.5
69.6
72.5
58.5
68.9
73.8
67.2
73.5
73.5
63.1
70.9
74.2
67.9
73,9
74.6
63.5
71.8
73.5
66.9
73.2
73.1
62.7
70.5
71.3
65.9
71.0
71.7
61.1
69.0
In addition, we have carefully explored the impact of increasing the number of components in GMDP under the PASCAL VOC dataset in Table 5 (Left). We can see that the GMDP with 2 components consistently improves performance compared to component, allowing for more ac- curate representation of higher-dimensionall prototypes. Although theoretically, more components can better fit complex feature distributions (The decision boundary the 4 components is more accurate in Table 5 (Right (a)), in practice, this poses significant challenges to the network's ' fitting capability. 4 components result in a decrease the detection performance of the proposed method, evidenced by the t-SNE visualization in Table (Right (b)), which displays many outliers. This difficulty in the convergence of more components is a critical issue. Our Length Scaling Progressive Learning method addresses these challenges by simplifying the learning process, enabling optimal performance under 2 components setting. Exploring the potential of adopting more components is an avenue for our future work.
4.5 LIMITATION
The Introduced GMDP increases the model's complexity, making end-to-end training lenging and requiring an initial training phase before integrating our method. This step makes it easier for the model to converge. Meanwhile, shown in the last column of Table we also evalu- ate testing speed, where our method is nearly 7% slower than the baseline (row 3 vs row 8). Despite this, our method demonstrates significant performance improvements under 4 baselines for all tasks.
chal-
CONCLUSION
In this paper, we propose novel prototype learning approach for KD-based IOD to alleviate the knowledge shift, which enables a more flexible, accurate, and fine- -na-ggraied representation of fea- ture distributions without keeping any previous task data. A Gaussian Mixture Distribution-bart Prototype (GMDP) is introduced to explicitly model the distribution relationships between different classes suitable for more complex feature spaces. To enhance the plasticity of class prototypes, we present Dynamic Adaptive Prototype Optimization Strategy (DAPO) strategy for IOD during the incremental process, which could enhance the adaptability and effectiveness of GMDP modeling. The proposed method can be readily integrated into existing IOD methods to enhance performance further. Methods with our idea on the PASCAL VOC and MS COCO consistently outperform four baselines significantly and achieve state-of-the-art results in different incremental settings.
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ACKNOWLEDGEMENT
This work is supported by the National Natural Science Foundation of China (NSFC) grants 62176100, U24B20139, and 62376011. The computation is completed in the HPC Platform of Huazhong University of Science and Technology.
REFERENCES
[1] Nader Asadi, MohammadReza Davari, Sudhir Mudur, Rahaf Aljundi, and Eugene Belilovsky. Prototype-sample relation distillation: towards replay-free continual learning. In International Conference on Machine Learning (ICML), 2023.
[2] Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Percira. Analysis of representa- tions for domain adaptation. Advances in Neural Information Processing Systems (NeurIPS), 2006.
[3] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In European Conference on Computer Vision (ECCV), 2020.
[4] Francisco M Castro, Manuel J Jimééni Nicolás Guil, Cordelia Schmid, and Karteek Alahari. End-to-end incremental learning. In European Conference on Computer Vision (ECCV), 2018.
[5] Fabio Cermelli, Antonino Geraci, Dario Fontanel, and Barbara Caputo. Modeling missing annotations for incremental learning in object detection. In Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
[6] Aditya Chattopadhay,, Anirban Sarkar, Prantik Howlader, and Vineeth N Balasubramanian. Grad-cam++: Generalized gradient- gradient-based visual explanations for deep convolutional networks. In Winter Conference Applications of Computer Vision (WACV), 2018.
[7] Guobin Chen, Wongun Choi, Xiang Yu, Tony Han, and Manmohan Chandraker. Learning efficient object detection models with knowledge distillation. Advances in neural information processing systems (NeurIPS), 2017.
[8] Li Chen, Chunyan Yu, and Lvcai Chen. A new knowledge distillation for incremental object detection. In International Joint Conference on Neural Networks (IJCNN), 2019.
[9] Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Aleš Leonardis, Gre- gory Slabaugh, and Tinne Tuytelaars. continual learning survey: Defying forgetting in classification tasks. Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 44 (7):3366-3385, 2021.
[10] Arthur Douillard, Matthieu Cord, Charles Ollion, Thomas Robert, and Eduardo Valle. Podnet: Pooled outputs distillation for small-tasks incremental learning. In European Conference on Computer Vision (ECCV), 2020.
[11] Arthur Douillard, Alexandre Ramé, Guillaume Couairon, and Matthieu Cord. Dytox: Trans- formers for continual learning with dynamic token expansion. In Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
[12] Mark Everingham, S. M. Eslami, Lue Van Gool, Christopher Williams, John Winn, and An- drew Zisserman. The pascal visual object classes challenge: A retrospective. IJCV, 2015.
[13] Tao Feng, Mang Wang, and Hangjie Yuan. Overcoming catastrophic forgetting in incremental object detection via elastic response distillation. In Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
[14] Ross Girshick. Fast r-cnn. In International Conference on Computer Vision (ICCV), 2015. 2015.
[15] Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014.

---

Published as a conference paper at ICLR 2025
[16] Dipam Goswami, Yuyang Liu, Bartlomiej Twardowski, and Joost de Weijer. Fecam: Ex- ploiting the heterogeneity of class distributions in exemplar-fin free continual learning. Advances in Neural Information Processing Systems (NeurIPS), 2024.
[17] Akshita Gupta, Sanath Narayan, KJ Joseph, Salman Khan, Fahad Shahbaz Khan, and Mubarak Shah. Ow-detr: Open-worth detection transformer. In Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
[18] Yu Hao, Yanwei Fu, Yu-Gang Jiang, and Qi Tian. An end-to-end architecture for class- incremental object detection with knowledge distillation. In International Conference on Mul- timedia and Expo (ICME), 2019.
[19] Tyler L Hayes and Christopher Kanan. Lifelong machine learning with deep streaming linear discriminant analysis. In Conference on Computer Vision and Pattern Recognition workshops (CVPRW). 2020.
[20] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
[21] Michael Hersche, Geethan Karunaratne, Giovanni Cherubini, Luca Benini, Abu Sebastian, and Abbas Rahimi. Constrained few-shot class-incremental learning. In Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
[22] Saihui Hou, Xinyu Pan, Chen Change Loy, Zilei Wang, and Dahua Lin. Learning uni- fied classifier incrementally via rebalancing. In Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
[23] Zitong Huang, Ze Chen, Zhixing Chen, Erjin Zhou, Xinxing Xu, Rick Siow Mong Goh, Yong Liu, Chunmei Feng, and Wangmeng Zuo. Learning prompt with distribution- based feature replay for few-shot class-incremental learning. arXiv preprint arXiv:2401.01598, 2024.
[24] KJ Joseph, Salman Khan, Fahad Shahbaz Khan, and Vineeth N Balasubramanian. Towards open world object detection. In Conference Computer Vision and Pattern Recognition (CVPR), 2021.
[25] KJ Joseph, Jathushan Rajasegaran, Salman Khan, Fahad Shahbaz Khan, and Vineeth N Bala- subramanian. Incremental object detection via meta-learning. Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 44(12):920--2216 2021.
[26] Ronald Kemker and Christopher Kanan. Fearnet: Brain-inspired model for incremental learn- ing. In International Conference on Learning Representations (ICLR), 2018.
[27] Dawei Li, Serafettin Tasci, Shalini Ghosh, Jingwen Zhu, Junting Zhang, and Larry Heck. Rilod: Near real-time incremental learning for object detection at the edge. In Symposium on Edge Computing, pp. 113-126, 2019.
(28) Mingxue Li, Yang Cong, Yuyang Liu, and Gan Sun. Class-incremental gesture recognition learning with out-of-distribution detection. International Conference on Intelligent Robots and Systems (IROS), 2022.
[29] Xiang Li, Wenhai Wang, Lijun Wu, Shuo Chen, Xiaolin Hu, Jun Li, Jinhui Tang, and Jian Yang. Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems (NeurIPS), 33:21002- 21012, 2020.
[30] Xiang Li, Wenhai Wang, Xiaolin Hu, Jun Li, Jinhui Tang, and Jian Yang. Generalized focal loss v2: Learning reliable localization quality estimation for dense object detection. In conference on computer vision and patlern recognition (CVPR), 2021.
[31] Zhizhong Li and Derek Hoiem. Learning without forgetting. Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 40 (00(1))::235299. 2017.
12

---

Published as a conference paper at ICLR 2025
[32] Tsung- Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Pi- otr Dollár, and C Lawrence Zitmick. Microsoft coco: Common objects in context. In European Conference Computer Vision (ECCV), 2014.
[33] Jiaming Liu, Senqiao Yang, Peidong Jia, Ming Lu, Yandong Guo, Wei Xue, and Shanghang Zhang. Vida: Homeostatic visual domain adapter for continual test time adaptation. arXiv preprint arXiv:2306. 043444 2023.
[34] Xialei Liu, Chenshen Wu, Mikel Menta, Luis Herranz, Bogdan Raducanu, Andrew D Bag- danov, Shangling Jui, and Joost van de Weijer. Generative feature replay for class-incremental learning. In Conference on Computer Vision and Pattern Recognition workshops (CVPRW), 2020.
[35] Yaoyao Liu, Bernt Schiele, and Qianru Sun. Rmm: Reinforced memory management for juss-incrremenal learning. Advances in Neural Information Processing Systems (NeurIPS), 2021.
[36] Yaoyao Liu, Bernt Schiele, Andrea Vedaldi, and Christian Rupprecht. Continual detection transformer for incremental object detection. Conference on Computer Vision and Patlern Recognition (CVPR). 2023.
[37] Yuyang Liu, Yang Cong, Gan Sun, Tao Zhang, Jiahua Dong, and Hongsen Liu. L3doc: Life- long 3d object classification. IEEE Transactions on Image Processing, 30:7486-7498, 2021.
[38] Yuyang Liu, Yang Cong, Dipam Goswami, Xialei Liu, and Joost van de Weijer. Augmented box replay: Overcoming foreground shift for incremental object detection. In International Conference Computer Vision (ICCV), 2023.
[39] David Lopez-Paz and Marc' Aurelio Ranzato. Gradient episodic memory for continual learn- ing. Advances in Neural Information Processing Systems (NeurIPS), 2017.
[40] Shuailei Ma, Yuefeng Wang, Ying Wei, Jiaqi Fan, Thomas H Li, Hongli Liu, and Fanbing Lv. Cat: Localization and identification cascade detection transformer for open-world object detection. In Conference on Computer Vision and Pattern Recognition (CVPR). 2023.
[41] Zheda Mai, Ruiwen Li, Hyunwoo Kim, and Scott Sanner. Supervised contrastive replay: Re- visiting the nearest class mean classifier in online class-incremental continual learning. In Conference on Computer Vision and Puttern Recognition (CVPR), 2021.
[42] Arun Mallya and Svetlana Lazebnik, Packnet: Adding multiple tasks to a single network by iterative pruning, In Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
[43] Arun Mallya, Dillon Davis, and Svetlana Lazebnik. Piggyback: Adapting a single network to multiple tasks by learning to mask weights. In European Conference on Computer Vision (ECCV), 2018.
[44] Mare Masana, Xialei Liu, Bartlomiej Twardowski, Mikel Menta, Andrew D) Bagdanov, and Joost van de Weijer. Class-incremental learning: survey and performance evaluation. Trans- actions on Pattern Analysis and Machine Intelligence (TPAMI), 45(5) 2022.
[45] Michael McCloskey and Neal J Cohen. Catastrophic interference in connectionist networks: The sequential learning problem. In Psychology of learning and motivation, volume 24, pp. 109-165. Elsevier, 1989.
[46] Egon S Pearson, Ralph B D " AGOSTINO, and Kimiko O Bowman. Tests for departure from normality: Comparison of powers. Biometrika, 64(2):231-246 1977.
[47] Can Peng, Kun Zhao, and Brian C Lovell. Faster ilod: Incremental learning for object detectors based on faster renn. Pattern recognition letters, 140:109-115, 11000111,0000000 2020.
[48] Can Peng, Kun Zhao, Sam Maksoud, Meng Li, and Brian C Lovell. Sid: incremental learning for mmchor-free object detection via selective and inter-related distillation. Computer vision and image understanding, 200:10 2021.
13

---

Published as a conference paper at ICLR 2025
[49] Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert. icarl: Incremental classifier and representation learning. In Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
[50] Joseph Redmon and Ali Farhadi. Yolov3: An incremental improvement. arXiv preprint 1804::180..0276,, 2018.
[51] Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Uni- fied, real-time object detection. In CVPR, 2016.
[52] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems (NeurIPS), 28, 2015.
[53] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, al. Imagenet large scale visual recognition challenge. International journal of computer vision, 1 11:2211252,, 2015.
[54] Samuel Sanford Shapiro and Martin B Wilk. An analysis of variance test for normality (com- plete samples). Biometrika. 5213-1): 1965.
[55] Wuxuan Shi and Mang Ye. Prototype reminiscence and augmented asymmetric knowledge ag- gregation for non-exemplar class-incremental learning. In International Conference on Com- puter Vision (ICCV), 2023.
[56] Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim. Continual learning with deep generative replay. Advances in Neural Information Processing Systems (NeurIPS), 2017.
[57] Konstantin Shmelkov, Cordelia Schmid, and Karteek Alahari. Incremental learning of ob- ject detectors without catastrophic forgetting, In Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
[58] Zhi Tian, Chunhua Shen, Hao Chen, and Tong He. Feos: Fully convolutional one-stage object detection. In International Conference on Computer Vision (ICCV). 2019.
[59] Marco Toldo and Mete Ozay. Bring evanescent representations to life in lifelong class incre- mental learning. In Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
[60] Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of Machine Learning Research, 9(11), 2008
[61] Yanjie Wang, Zhijun Zhang, Wenhui Xu, Liqun Chen, Guodong Wang, Luxin Yan, Sheng Zhong, and Xu Zou. Learning oriented object detection via naive geometric computing. IEEE Transactions on Neural Networks and Learning Systems, 2023.
[62] Yujie Wei, Jiaxin Ye, Zhizhong Huang, Junping Zhang, and Hongming Shan. Online proto- type learning for online continual learning. In International Conference on Computer Vision (ICCV), 2023.
[63] Aming Wu, Cheng Deng, and Wei Liu. Unsupervised outtt--disttibution object detection via pea-driven dynamic prototype enhancement. IEEE Transactions Image Processing, 2024.
[64] Chenshen Wu, Luis Herranz, Xialei Liu, Yaxing Wang, Joost van de Weijer, and Bogdan Radu- canu. Memory replay gans: Learning to generate new categories without forgetting, Advances in Neural Information Processing Systems (NeurIPS), 2018.
[65] Binbin Yang. Xinchi Deng, Han Shi, Changlin Li. Gengwei Zhang, Hang Xu, Shen Zhao, Liang Lin, and Xiaodan Liang. Continual object detection via prototypical task correla- tion guided gating mechanism. In Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
[66] Dongbao Yang, Yu Zhou, Aoting Zhang, Xurui Sun, Dayan Wu, Weiping Wang, and Qixiang Ye. Multi-view correlation distillation for incremental object detection. Pattern Recognition, 131: 131:10 2022.
14

---

Published as a conference paper at ICLR 2025
[67] Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang. Lifelong learning with dynam- ically expandable networks. International Conference on Learning Representations (ICLR), 2018.
[68] Lu Yu, Bartlomiej Twardowski, Xialei Liu, Luis Herranz, Kai Wang, Yongmei Cheng, Shangling Jui, and Joost van de Weijer. Semantic drift compensation for class-incremental learning. In Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
[69] Baoquan Zhang, Xutao Li, Yumming Ye, Zhichao Huang, and Lisai Zhang. Prototype comple- tion with primitive knowledge for few-shot learning. In Conference Computer Vision and Pattern Recognition (CVPR), 2021.
[70] Baosheng Zhang, Yuchen Guo, Yipeng Li, Yuwei He, Haoqian Wang, and Qionghai Dai. Mem- ory recall: A simple neural network training framework against catastrophic forgetting. IEEE Transactions on Neural Networks and Learning Systems, 33(5):2010-2022, 2021.
[71] Mengmi Zhang, Tao Wang, Joo Hwee Lim, Gabriel Kreiman, and Jiashi Feng. Variational prototype replays for continual learning. arXiv preprint arXiv: 09447, 2019.
[72] Bowen Zhao, Xi Xiao, Guojun Gan, Bin Zhang, and Shu-Tao Xia. Maintaining discrimination and fairness in class incremental learning. In Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
[73] Fei Zhu, Xu-Yao Zhang, Chuang Wang, Fei Yin, and Cheng-Lin Liu. Prototype augmentation and self-supervision for incremental learning. In Conference Computer Vision and Pattern Recognition (CVPR), 2021.
[74] Kai Zhu, Yang Cao, Wei Zhai, Jie Cheng, and Zheng-Jun Zha. Self self-promoted prototype refine- ment for few-shot mass-incrremetal learning. In Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
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A APPENDIX
A.1 IMPLEMENTATION DETAILS
Our method is based on ILOD [57 with the SGD optimizer, setting the initial learning rate to 0.001, decaying to 0.0001 after 30,000 iterations, and incorporating a momentum of 0.9. During the incremental phase, the learning rate is adjusted to 0.0001. Following the original paper, other settings are the same as the original paper. Our GMDP-ERD based on ERD [3] employs the GFLVI [29] detector with ResNet-50 as the backbone. Following the original paper, we adopt the same settings to train the detector. 12 epochs train the method for each incremental step. Our method based on CL-DETR [36] adopts a pre-trained ResNet-50 as the backbone. We train the model with 50 epochs under different incremental stages. Our GMDP-ABR follows the baseline ABR [38] train model with the SGD optimizer, weight decay of 0.0001, and momentum of 0.9. The first stage has learning rate of 0.005, and the subsequent stages have learning rate of 0.002. For tasks involving incremental addition of 5 or 10 classes, we employed 15,000 iterations, while for the inclusion of or 2 new classes, regimen of 5,000 iterations was applied.
In summary, the total loss (Ctotat) of the training model under the incremental stage is the weighted sum of the original baseline loss LDt, GMDP loss LGMDP, transferring prototype knowledge loss Lipk. and DAPO strategy loss. The Ltogit and freg in Figure [5) are part of Det, represent the loss functions in the baseline model, corresponding to the distillation loss for class classification and bounding box regression, respectively. The DAPO strategy loss consists of three parts: Inter- Class Mean Dispersal loss LIMD, Intra-Class Component Cohesion loss Lice, and Minimization of Prototype Variance loss LMPV. The total loss of the incremental object detection network is as follows:
Liotal = Det + LI + A2Lipk + Aa(Crann + Lice + LMPV),
(1I)
where the parameters A1 0.05, A2 0.1, and A3 0.05 represent the weight of different losses, thereby regulating the tradeoff between them. These parameters primarily aim to standard- ize the range of loss values within the same range rather than implementing intricate adjustments or adding bells and whistles. Adopting GMDP introduced additional parameters, leading to slower network convergence. To address this issue, we leverage Length Scaling Progressive Learning (LSPL) method that enhances the network convergence speed by reducing the dimensionality of the detection features 256. Sufficient experimental results indicate that despite reducing feature di- mensionality, our detection performance remained unaffected, and we maintained the same network iteration settings as the original baseline model. Besides, during the initial stage of learning new classes, the feature distribution is inherently unstable. Thus, significant challenges have been posed for the learning and convergence of GMDP, due to the lack of stable feature dependency relation- ships. To address this issue, we adopted initial training phase, first conducting 1000 training steps and then employing our GMDP to construct the Gaussian distribution prototype.
A.2 ADDITIONAL EXPREIMENTS
In this section, we conducted IOD experiments on the Ms-COCO 2017 dataset based two dif- ferent baselines, ERD 13] and CL-DETR (36] to evaluate our method. As shown in Table [ our experiments are conducted on two-stage task settings, adding 40 and 10 categories in 40-40 and 70-10 setups, respectively. Each experiment was conducted three times, with the classes and data order randomized in each phase. Our approach consistently outperforms both two baselines across various IOD settings, demonstrating sigmificant improvements. Furthermore, our results are compet- itive with the current state-of-the of-the--rt (SOTA) methods. indicating the effectiveness and robustness of our proposed method in diverse IOD) settings. This improvement can be attributed to GMDP's ability to explicitly model the distributional characteristics of detection features, allowing for more accurate and discriminative representations. By dynamically optimizing prototype representations, the DAPO strategy enables the model to quickly adapt to new task requirements, improving IOD performance and generalization capabilities.
We also validated the effectiveness of our method on the Ms-COCO dataset under the multi-stage incremental setting. Table 7 displays 40+ 10x4, 40+20x2) to provide a comprehensive demonstration of our method's performance under various incremental settings., our method achieves consistent performance improvement compared
results with additional multi-stage experiments (c.g.,
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Published as a conference paper at ICLR 2025
Table 6: mAP results on MS-COCO 2017 different IoU. We run experiments for three different classes and data orders.
Setting
Method
Baseline
AP
AP50
AP75
APS
APM
API.
LwF 31
TPAMI
GFLVI
7.1
12.4
7.0
4.8
9.5
10.0
ERD
13
CVPR
GFLVI
34.9
51.9
37.4
18.7
38.8
45.5
GMDP-ERD
Ours
GFLVI
36.4100
53.2.105
39.1
19.4.10.
39.7.10.
46,9
CL-DEET 36
CVPR 23
UP-DETR
37.6
56.5
39.4
20.5
39.1
49.9
70+10 10
LwF 31
7PAMT 18
Deform-ETT DETTR
24.5
36.6
26.7
12.4
28.2
35.2
iCaRL 49
CVPR 17
Deform- DETR
35.9
52.5
39.2
19.1
39.4
48.6
CL-DETR 36
CVPR 23
Deform-DET
40.1
57.8
43.7
23.2
43.2
52.1
GMDP-CL-DEL
Ours
Refomm-DETI
42.6
60.4
45.2
24.1
45.0
55.6
LwF
TPAMI
GFLVI
17.2
25.4
18.4
ERD
CVPR
GFLVI
36.9
$4.5
47.5
GMDP-ERD
Ours
GFLVI
38.4=a1
55.1=03
40.3+01
22.9+0.3
41.7+62
49.8=04
CL-DETR 36
CVPR
UP-DETR
37.0
56.2
39.1
20.9
38.9
49.2
40+40
LwF B1
TPAMI Deform-DEII
23.9
41.5
25.0
12.0
26.4
33.0
iCaRL 49
CVPR
Deform- DDATR
33.4
52.0
36.0
18.0
36.4
45.5
CL-DETR 36
CVPR 23
37.5
55.1
40.3
20.9
40.8
50.7
GMDP-CI-DDER
Ours
Deforn-DETRR
40.8=04 58.5
43.3:102
23.611.5
43.5
53.5=04
to the baseline and performs well on each incremental step. This is attributed to the feature explicit modeling for more accurate GMDP, capturing the essential features according their distribution characteristics. It is noteworthy that due to the time limitation, we can not implement our idea of transformer- baasedd methods. only Bester-RCNN-Fo RCNN-based methods are used for fair comparisons.
Table 7: GMDP result (AP/AP50) on the multiple-stage incremental setting on MS COCO. We reproduced the results of ABR* with the same setting.
40+ 10x4
40+20x2
Method
T1 (1-40)
T2 (40-50)
T3 (50-60)
T4 (60-70)
T5 (70-80)
T2 (40-60)
T3 (60-80)
ERD [13
45.7/66.3
36.4/53.9
30.8/46.7
26.2/39.9
20.7/31.8
36. 76.7/54.6
32.4/48.6
RILOD 27
45.7/66.3
25.4/38.9
11.2/17.3
10.5/15.6
8.4/12.5
27.8/42.8
15.8/4.0
SID 48
45.7/66.3
34.6/52.1
24.1/38.0
14.6/23.0
12.6/23.3
34.0/51.8
23.8/36.5
ABR 38
45.7/66.3
37.9/56.3
35.5/52.4
31.5/47.6
28.5/43.5
38.0/57.5
34.1/50.3
GMDP-ABR
46.3/67.5
39.2/59.1
36.8/55.4
33.2/49.3
29.7/45.1
39.6/60.2
35.7/52.9
Table Analysis of different number of Gaussian component in GMDP under multiple-stage incre- mental settings.
10-5 (3 tasks)
5-5(3 tasks)
15-1(6 tasks)
Num of Component
1-10
11-20
1-20
1-5
6-20
1-20
1-15
16-20
1-20
68.7
67.1
67.9
64.7
56.4
58.4
68.7
56.7
65.7
68.9
67.5
68.2
66.0
58.7
60.5
69.1
58.3
66.4
2
69.9
67.8
68.9
66.3
59.3
61.1
69.5
58.9
66.9
69.4
67.2
68.3
65.7
58.5
60.3
68.9
58.1
66.2
68.6
67.0
67.8
64.9
57.2
59.1
68.3
57.0
65.5
We have carefully analyzed the influence of setting different numbers of Gaussian components in GMDP under multiple-stage incremental settings. Illustrated in different numbers of Gaussian components are used in the experiment on the PASCAL VOC dataset. We can see that the GMDP with components under multiple-stage incremental settings consistently improves performance compared to component, with results similar to 2-stage incremental settings. The proposed method insensitive and often achieves performance improvement when the threshold changes from to 3.
Although theoretically, more components can better fit complex feature distributions, in practice, this poses significant challenges to the network's fitting capability. components result decrease in the detection performance of the proposed method. This difficulty in the convergence of more components is a critical issue. Our Dimension Scaling Progressive Learning method addresses these challenges by simplifying the learning process, enabling optimal performance under 2 components
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Published as a conference paper at ICLR 2025
setting. The above results also demonstrate the effectiveness and consistency of our method in two- stage and stati-stage incremental settings.
18


Reviews:
[{"review_id": "9qSAGxnsSl", "reviewer": "Reviewer_ytH7", "summary": "This paper mainly focuses on the incremental object detection task. To effectively describe complex detection feature spaces, the authors propose to learn class prototypes based on Gaussian Mixture Distribution, which may better model the distribution relationships of different classes, thus facilitating the knowledge distillation procedure and incremental learning process. Furthermore, a dimension scaling progressive learning strategy and three additional loss functions are introduced to enhance the training stability and adaptability of the proposed prototype modeling method.", "questions": "1. For the confusion matrix in Figure 4, I am confused about the values calculated for the proposed GMDP method. Since the class prototypes of GMDP are Gaussian mixture distributions, I am not sure how to calculate the Jensen-Shannon (JS) divergence between them. Based on my knowledge, it can only get an upper bound value of this metric for a pair of Gaussian mixture distributions, so could the authors provide more details about such calculation process?\n\n2. In the appendix A.2, I have noticed that the proposed method can also be integrated into a CL-DETR detector. Since this query-based method does not generate the proposal feature vectors, I am not very clear on how to produce the Gaussian Mixture Distribution prototypes for it. Could the authors provide more details for such integrating process?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The motivation of this paper is clearly stated, and the proposed solution of modeling Gaussian Mixture Distribution based prototypes sounds reasonable.\n2. The experiments under different settings are conducted and the ablation studies are comprehensive, which indicate the effectiveness of the proposed method and show the effects of introduced DSPL and DAPO strategies. \n3. The implementation details, including training/optimization details and hyper-parameter settings, are provided, which can increase the reproducibility of the proposed method.", "weaknesses": "1. The authors claim that their method learns higher-dimension prototypes. However, I cannot understand the definition of **\"high-dimension\"** here, does it mean that the representation of the ROI proposal feature vector $x$, where the prototypes are built on, is of high dimensionality? But as stated in the 306th-310th rows, the introduced dimension scaling progressive learning strategy transforms the original feature vector $x$ into a **lower dimensional** vector $z$, and calculates the prototypes based on $z$. This operation seems to conflict with the concept of **\"high-dimension\"**, could the authors make more discussions about this?\n\n2. In Eq. (3), the details of how to assign the ground-truth label $y_{i,c}$ for each proposal feature vector should be clarified, can we find the details of such label assignment operation in previous works? \n\n3. From Table 5, it can be seen that the performance of the proposed method is quite sensitive to the number of Gaussian mixture components, which may increase the difficulty of tuning this hyper-parameter for different application scenarios.\n\n4. In Section 3.2, there are some notations are confused. For example:\n     1) In the 268th row, both the dimensionality of proposal feature $x_i$ and the size of input feature set are denoted as $N$.\n     2) In the 276th to 277th rows, the component number of Gaussian mixture distribution is denoted as $K$, but it is also indicated by $M$ in Eq. (9) and (10).\n     3) The notations of the proposal feature vector $x_i$ are bold in Eq. (4) and Eq. (7), but not in Eq. (2), (3) and (5). They should be consistent.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "g2YNM4Mrce", "reviewer": "Reviewer_A3Cy", "summary": "This paper introduces a new higher-dimension-prototype learning approach for knowledge distillation-based incremental object detection, which uses Gaussian mixture distributions to model the feature distributions of classes more effectively. This approach helps address the issue of knowledge shift caused by the introduction of new classes without access to previous task data. Dynamic Adaptive Prototype Optimization strategy improves the separation of class features and enhances the plasticity of class prototypes. Comprehensive experimental results support the efficacy of the proposed method.", "questions": "Please address my concerns in the 'Weakness' section.", "limitations": "", "rating": 5, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. This paper leverages the flexibility and expressiveness of Gaussian mixtures to capture the complex distributions of object features in high-dimensional spaces, alleviating the knowledge shift caused by the lack of previous data.\n\n2. The proposed Dynamic Adaptive Prototype Optimization (DAPO) strategy is a well-reasoned enhancement that improves the plasticity and stability of class prototypes. By optimizing the separation and cohesion of class features, the strategy provides a robust mechanism for managing the intricacies of incremental learning.\n\n3. The paper is well-organized and clearly written, which facilitates readers' understanding of the proposed approach.\n\n4. The method is validated through extensive experiments, demonstrating consistent superiority over existing state-of-the-art methods.", "weaknesses": "1. Gaussian Mixture Distribution-based prototypes have been previously explored in class incremental learning[1-4]. Specifically, [1] highlights that embedding distributions in feature space are not convex or isotropic in practice. Thus, the author’s emphasis on the complexity of detection features compared to classification features may not provide substantial new insights. The innovation level is thus moderate.\n\n2. This method is not specifically tailored for incremental object detection (IOD) tasks, which appears more suited for general class-incremental learning for modeling class distributions.\n\n3. The integration of GMDP and DAPO at the base stage of the model may inherently enhance the detector’s feature representation capacity. Given that the upper bound of Faster R-CNN has been exceeded in certain settings (e.g., the 19-1 setting), the comparisons with existing methods may be skewed. They are not designed for incremental scenarios.\n\n[1] Steering Prototypes With Prompt-Tuning for Rehearsal-Free Continual Learning.\n\n[2] Contrastive Learning of Multivariate Gaussian Distributions of Incremental Classes for Continual Learning.\n\n[3] Class-Incremental Mixture of Gaussians for Deep Continual Learning.\n\n[4] Saving 100x Storage: Prototype Replay for Reconstructing Training Sample Distribution in Class-Incremental Semantic Segmentation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "UQ0aMsAJuH", "reviewer": "Reviewer_sEU9", "summary": "The paper focuses on incremental object detection (IOD) and aims to address knowledge shift using enhanced high-dimensional prototype learning. Specifically, the authors propose a Gaussian Mixture Distribution-based Prototype (GMDP) to model the complex feature distribution based on both new and old models. To facilitate GMDP modeling, they further introduce a Dynamic Adaptive Prototype Optimization Strategy (DAPO). The proposed method can be readily integrated into existing IOD approaches, consistently improving their results.", "questions": "1. Do all classes share the same number of components in GMDP? Are there insights into how to determine the number of components for different classes? Intuitively, a unimodal Gaussian distribution may be sufficient for easily distinguished classes, while a multiple-component Gaussian distribution is more suitable for harder classes.\n2. What are the weights of the losses in the DAPO? How are these values determined across different settings?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The investigation into why accurate modeling of the complex distribution for IOD is necessary and compelling.\n2. The experiments are convincing and comprehensive.\n3. The paper is easy to follow, and the visualizations are helpful, making it easy for readers to grasp the main idea.", "weaknesses": "1. Lack of discussion regarding method complexity and training time.\n2. As the core contribution involves adopting Gaussian Mixture Distribution, it would be beneficial to conduct ablation studies on the number of components in both two-stage and multiple-stage incremental settings. \n3. Minor: some of the best results are not boldfaced in Table 4.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "DpX7O65piQ", "reviewer": "Reviewer_vCwD", "summary": "This paper introduces a higher-dimension prototype learning method that incorporates GMDP to address issues such as knowledge shift present in KD-based IOD methods. Additionally, a DAPO strategy is designed to enhance the adaptability and effectiveness of GMDP modeling. The authors conducted experiments on PASCAL VOC and MS-COCO, demonstrating the validity of the proposed method.", "questions": "Some descriptions need to be more precise and clear. For example, in line 303, \"the old class C_o\" should refer to a \"class set\". The variable z in Equation (6) has not been introduced; it should refer to proposals generated from the ROI layer. Additionally, how are the means and variances of the K components of GMD calculated? Are they based on the features x? Is the computation of these means and variances confirmed before or after training for incremental learning tasks?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 1, "contribution": 3, "strengths": "1. This paper proposes a novel approach by introducing a Gaussian Mixture Distribution-based Prototype(GMDP) to alleviate the knowledge shift.\n2. GMDP can be readily integrated into existing IOD methods for better performance.\n3. Experiments show that this method achieves SOTA results on PASCAL VOC and MS COCO.\n4. The motivation is clearly articulated, and it includes figures that illustrate the issues with current IOD methods as well as the differences in feature spaces for classification and detection tasks.", "weaknesses": "1. The quality and clarity of the figures need further improvement. The legend for Figure 1 is inconsistent, and Figure 3 is blurry and has a low resolution.\n2. Some descriptions need to be more precise and clear. For example, in line 303, \"the old class C_o\" should refer to a \"class set\". The variable z in Equation (6) has not been introduced; it should refer to proposals generated from the ROI layer. Additionally, how are the means and variances of the K components of GMD calculated? Are they based on the features x? For incremental learning tasks, is the computation of these means and variances confirmed before or after training?\n3. As mentioned in line 515, \"this poses significant challenges to the network’s fitting capability.\", it lacks an experiment on the selection of the number of Gaussian componenets for larger models.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)