Interference-Isolated Elastic Weight Consolidation and Knowledge Calibration for Incremental Object Detection
Authors: De Cheng
Mingyue Zeng
Zhipeng Xu
Di Xu
Nannan Wang
Xinbo Gao
Venue: ICLR 2026 Poster (2026)
Keywords: continual learning
object detection
Primary area: transfer learning, meta learning, and lifelong learning

Abstract: Incremental Object Detection (IOD) enables AI systems to continuously learn new object classes over time while retaining knowledge of previously learned categories. This capability is essential for adapting to dynamic environments without forgetting prior information. Although existing IOD methods have made progress in mitigating catastrophic forgetting, they usually lack explicit and quantitative modeling of information conflicts during knowledge preservation, making task boundaries ambiguous. Such conflicts often stem from the fact that a single image can contain objects belonging to previous, present, and future tasks, where unlabeled past and future objects are often mistakenly treated as background. In this paper, we propose a novel approach grounded in Elastic Weight Consolidation (EWC) to alleviate conflict knowledge preservation caused by task interference. Specifically, we introduce the Interference Knowledge Isolated Elastic Weight Consolidation (IKI-EWC) framework for IOD, which leverages the mispredictions of the old detector on new task data to estimate task conflicts and suppresses them at the parameter level. By reformulating the Bayesian posterior of model parameters, we derive a mathematical relationship between previously learned knowledge and interference knowledge, enabling targeted elimination of conflicts during model weight updates. In addition, we also propose a prototype-based knowledge calibration (PKC) mechanism to further preserve old knowledge during the training of the objector's classification head. This method employs a learnable projection layer to compensate semantic drift in old class prototypes, and then jointly trains the classification head using both calibrated prototypes and current task features, thereby mitigating forgetting caused by classifier updates. Extensive experiments on PASCAL VOC and MS-COCO benchmarks demonstrate the effectiveness of the proposed method, outperforming state-of-the-art approaches in various settings.

Published as a conference paper at ICLR 2026
INTERFERENCE-ISOLATTH ELASTIC WEIGHT CONSOL- IDATION AND KNOWLEDGE CALIBRATION FOR INCRE- MENTAL OBJECT DETECTION
; De Cheng', Mingyue Zeng', Zhipeng Xu', Di Xu²; Nannan Wang', Xinbo Gao 1 Xidian University, HHaaei Technologies Co. Ltd dcheng@xi i dian.eee di...ddu........... zengmi ntu.eestuuuuuueegggogoo.g xidian..... .dd..cn en
Xinbo
ABSTRACT
Incremental Object Detection (IOD) enables AI systems to continuously learn new object classes over time while retaining knowledge of previously learned categories. This capability is essential for adapting to dynamic environments without forgetting prior information. Although existing IOD methods have made progress in mitigat- ing catastrophic forgetting, they usually lack explicit and quantitative modeling of information conficts during knowledge preservation, making task boundaries ambiguous. Such conflicts often stem from the fact that single image contain objects belonging to previous, present, and future tasks, where unlabeled past and future objects propose a novel approach grounded in Elastic Weight Consolidation (EWC) to alleviate conflict knowledge preservation caused by task interference. Specifically, we introduce the Interference wwwiecrrenoe--ooleeeeeeeeeee Knowledge- Isolaee Elastic Weight Consolidation (IKI-EWC) framework for IOD, which leverages the mispredictions of the old detector on new task data to estimate task conflicts and suppresses them at the parameter level. By reformulating the Bayesian posterior model parameters, we derive mathematical relationship between previously learned knowledge and interference knowledge, enabling targeted elimination of conflicts during model weight updates. In addition, also propose prototype- based knowledge cali- bration (PKC) mechanism to further preserve old knowledge during the training of the detector's ' classification head. This method employs learnable projection layer to compensate semantic drift in old class prototypes, and then jointly trains the classification head using both calibrated prototypes and current task features, thereby mitigating forgetting caused by classifier updates. Extensive experiments on PASCAL VOC and MS-COCO benchmarks demonstrate the effectiveness of the proposed method, outperforming tate-of-the-ant approaches in various settings.
often mistakenly treated as background. In this paper, we
INTRODUCTION
Object detection Felzenszwalb et al. and important computer vision task Kirillov al. (2023); Croitoru et (2023), Cheng al. (2024); Wang et al. (2025)); Xu et al. (2025); ); He et al. (025a): Cheng et al. (2025 2026) that has seen remarkable advancement in recent years. However, static detectors, which learn predefined classes from static datasets, cannot adapt to dynamic settings of real-world scenarios. Incremental Object Detection (IOD) methods, which aim to continually detect new objects without forgetting previously learned categories, have emerged as an exciting and challenging task to address this issue. Unlike humans, who can incrementally acquire knowledge with minimal disruption, deep learning models are prone to overwriting prior information when exposed to new data. This limitation is key challenge for IOD, where the model must integrate new categories without forgetting the old ones.
(2099); y; Tompson et al. (015)) Tian et al. (2019) is a fundamental
Existing Incremental Object Detection (IOD) methods can be categorized into knowledge-distillationn based methods Hao et al. (2019a): Liu et al. (2020b): [eng et al. (2020); Zhou al. 2200 Mo al. (2024) and parameterrrqullaiizzttoo---
main types:
methods Liu al. (2020a) The
"Corresponding authors.

---

Published as a conference paper at ICLR 2026
Stage
Mislatcting
Detector
Stage
(Isterioenne
R
Stage t:
Radkgread propasal Iree the
delectir es curvest dats
Delector
Stage
I
inlnffrrnne repions)
p.Deesheepp)
R2
Revisiting the data partitioning between adjacent tasks, the old task data D,-1 contains regions where object classes from the new task were mistakenly labeled background, due the absence their class labels during training on task 1. We retrospectively estimate the conflicting knowledge using the new task data D,. The red boxes represent the ground-truth labels, while the dashed red boxes highlight regions that are excluded from the forward pass during model inference.
Figure I:
showledge-dissillation distillatiinnn---ee based approaches typically leverage features or predictions from previous models transfer knowledge, while parameter-regulatizaii based approaches impose constraints on model parameters to preserve knowledge from previous tasks. Beyond catastrophic forgetting caused by conventional model parameter updates, IOD poses an additional challenge due to the presence of unlabeled objects from both past and future classes in current training data. These objects are usually misclassified as background, introducing knowledge conflicts between tasks. While the issue of past-class objects can be partially mitigated by generating pseudo labels with the old model, future-class objects remain entirely unlabeled and thus harder to address. This inter-task interference undermines model performance and complicates the stability of incremental learning.
To address these issues, we propose a novel Interference- Interference-Koollddgee -nowledge- Isolaaed Elastic Weight Con- solidation and Knowledge Calibration (IIKC) framework for IOD. Unlike existing work Mo al. 20224), our method avoids the additional cost of training separate teacher model on the current task data. Instead, address knowledge conflict from the perspective of parameter regularization. We theoretically build the Interference- SereeencceKnowlede Knowledge-Isolated Elastic Weight Consolidation (IKI-EWC) module, bridging the gap between the traditional class incremental learning and IOD tasks on top of the Elastic Weight Consolidation (EWC) Kirkpatrick et al. (2017) method. While the traditional EWC method mitigates catastrophic forgetting by constraining important parameters, our method focuses on isolating interference caused by misclassified past- and future-class objects. Specifically, as shown in Figure [ at stage - 1, the absence annotations for future objects leads to their background. At stage f, we estimate this interference by applying the previous detector to current data, and then reformulate the Bayesian parameter estimation to explicitly account for this conflict, enabling effective isolation of task interference. Our proposed method effectively removes the erroneous information (i.c., treating future objects as background) in the parameter importance estimation, thereby alleviating catastrophic forgetting while minimizing the negative impact of knowledge conflicts during new task learning. This Interference- Knowledge-Isole Elastic Weight Consolidation module is designed mitigate interference while minimizing impact on subsequent learning, thereby improving knowledge consolidation for IOD.
misclassification
In addition, we introduce novel Prototype- Knowledge Calibration (PKC) module further mitigate catastrophic forgetting in the classification head of object detectors. Inspired by Li et al. (2024), PKC addresses semantic drift, which occurs due to inconsistencies between feature represen- tations from old and new tasks. The module employs trainable projection layer align the features from previous tasks with those the current task. This alignment helps preserve the knowledge from old classes while accommodating new ones, ultimately reducing feature drift and alleviating forgetting. By calibrating the prototypes of old classes, PKC improves the model's ability to maintain consistent performance across incremental tasks.
RELATED WORK
Incremental Learning techniques can be broadly classified into three categories: memory- Liu
al. (2021) Luo al. (2023) Prabhu et al. (2020): Qi al. 2023). regularization-basedl Kirkpatrick
2

---

Published as a conference paper at ICLR 2026
et al. b; Smith al. (202 1 ): Sun al. (2023) Toldo & Ozay (2022). and network architecture- based methods Hu et al. (2023): y Wang et al. 2002a by: Zhou et al. (2022); He et al. (2025b). Memory- based approaches store small set of samples for replay generate new samples to compensate for data in subsequent stages. Regularization-
methods aim to stabilize model parameters by controlling feature changes, helping reduce the tendency for forgetring. In practice, this is commonly realized either via knowledge distillation Li & Hoiem (2017), RRbbufi et al. (2017) that aligns logits or intermediate features between the old and new models or via parameter regularization Kirkpatrick et al. (2017); Zenke et al. (2017) Alluudi et al. (2018) that penalizes updates to parameters deemed important for past tasks. Network rrchitecture-based methods dynamically adjust network structures or design specific parameters for each stage to accommodate the changing data stream. In this study,
we focus on the parameter regularization approach.
Incremental Object Detection (IOD) faces challenges beyond standard class-incrementte learning. Besides catastrophic forgetting, unlabeled old or future categories each stage are often treated as background. creating knowledge conflicts that harm both old and new classes. ILOD SSmellov et al. (2017) pioneered the knowledge distillation (KD) method for IOD, and many works extended KD to Faster R-CNN to curb forgetting Hao et al. (2019b); Chen et al. (2019); Li al. 2019). BPF Mo et al. (202)) distills from the combined class probabilities of two teachers to reduce old old-new new conflicts, and GMDP Wang al. (2025b) uses Gaussian- mixttre prototypes align feature distributions. In parallel, al. (2020a), and rehearsal approaches such as ABR Liu et al. (2023) keep a small class-balanced exemplar set to counter background shift. In this work, we reformulate Bayesian parameter estimation tailored IOD task, which reduces the impact of knowledge conflicts by incorporating parameter importance. To further mitigate forgetting in classification head, we retrain it using calibrated class prototypes with new task features, where prototypes are calibrated via a trainable projection layer designed to compensate semantic drift between tasks.
www.eeter-regllaiiaatii methods constrain important weights to stabilize updates Liu
3 METHOD
3.1 PRELIMINARIES
Problem Definition. Incremental Object Detection (IOD) focuses on detecting objects over sequence of learning steps, where each step introduces new subset of object classes. Let c represent the complete set of object classes be learned by the detector M across T steps, such that c = _h_1 & and 6 Ce = 9 for all # j. where i, E (I....,T') T.. Let D denote the dataset composed of images x and their corresponding annotations y, i.e., {(xx y) (x, v) E D}. At step t, the model is only provided with annotations for the current class subset Ct, even though the images may contain objects from previously seen categories C1a-1- The available annotations V, are thus limited to instances belonging to q. The key challenge in IOD is to incrementally adapt the model from M to Mr by learning the new class subset ct, without access the previous training data (Di..... D J. while still retaining high detection performance on all previously learned classes.
General Detector Training Pipeline. We adopt the two-stage Faster R-CNN Ren et (0016).
comprising a backbone M an RPN MMP", and a detection head Mhead, The head pools features on region proposals and uses FC layers produce region features, which feed a classifier fch and box regressor frr, At incremental stage t, we write the model as
M, ME (f.h }:²),
(1)
where the region feature extractor M, includes the backbone. RPN, and the ROI-pooling+FC part of the head (i.c.. M, = M o M ROI/FC). Here, denotes sequential (left-to-right) composition.
Elastic Weight Consolidation. Elastic Weight Consolidation (EWC) Kirkpatrick al. (2017) is regularization- based method designed to mitigate catastrophic forgetting in continual learning. The key idea is to preserve knowledge from previous tasks by penalizing large updates to parameters that are deemed important.
From the Bayesian perspective, when data from multiple tasks is learned sequentially, the goal is to maximize the posterior distribution over model parameters given all observed data. Specifically, after observing data from tasks D, D., the posterior is written as:
p ( O Di ( D1.-1)
(2)
3

---

Published as a conference paper at ICLR 2026
IKI-EWC module
Regios
Region
Protuospe
Pooling
lay
Sample
C
Projectioas al!
Ratrin
Projection
-
Classteer
D.
Backbose
RPN
BCJ Pooling
layers
A
Region Feature
Old Regint Fsatueesf...
Calibration
Parameler Importance Estimatiio Interference Stage &
Interference-Carrnttt Importunce from Previous Tacks:
Staget
gradd
I
grad
MM.
:
geeal
M.
Compute
0
Importance
of
pysshoog
Robin
Ry
Trainoblee
Frozzo
* A Replon Features
Old Ervsorype
O Stappled regicn Statiees de 1.2 norm Regalanantien
Cuncsierste
Beckeward
-- Elaskic Repularization
Parumeters of
Elastic Weight Consolidation (IKIEEWC) module, which alleviates catastrophic forgetting while resolving knowledge conflicts between and new tasks. (II) illustrates Prototype-baser Knowledge Calibration (PKC) module, which retrains the classification head by combining current task features with old prototypes, effectively preventing catastrophic forgetting. The lower part deseribes the implementation of (I), where the old model M,-1 used to calculate the importance Iz over the interference region set Rig-1- 1- This then combined with the previous rrevious-tasks importance I to compute the final parameter importance I for the old parameters.
Figure 2: Overview of proposed IIK.. (I) introduces the -eneereeeee--ooooddeeeeeeeeeeeee
In the standard EWC setting, an additional assumption of conditional independence is made: p(D:|0, D 1)) (D), This simplifies equation p(0)Dia)) x p(D,(0) p(0
3.2 OVERALL FRAMEWORK
To address knowledge conflict and catastrophic forgetting, we propose novel Interference-Isllated elastic weight consolidation and Knowledge Calibration (IIKC) framework for IOD, as shown Elastic Weight
in Fig. 2 It contains two main components: Consolidation (IKI-EWC) module and Prototype- Knowledge Calibration (PKC) module. First, use high-confidence predictions from the previous model M, pseudo-labels to simulate previous task scenarios. This helps eliminate conflict knowledge Ri related to previous tasks for estimation of the parameter importance of Mr-1, thus improving the retention of the actual important knowledge. Second, to mitigate the severer catastrophic forgetting in the classification head, introduce projection layer for knowledge calibration that compensates for semantic drift of the stored old-class prototypes, enabling better integration with current task features during retraining.
3.3 INTERFERENCE, CICAFFERENC--KNOWII.N KNOWLEDGE- ISOLADDEEEE-SSSEEEDD ELASTIC WEIGHT CONSOLIDATION
We address catastrophic forgetting in IOD task through the lens of parameter regularization. To this end, we revisit the EWC method, which aims preserve knowledge by constraining updates to important parameters. However, directly applying EWC to IOD raises some issues: due to the absence of future class annotations during earlier-stage model training, the old model M,- mistakenly learns to classify new objects as background. EWC then inadvertently preserves this interference knowledge, intensifying the conflict between learning new knowledge and retaining old, ultimately worsening catastrophic forgetting. This interference manifests in the violation of the conditional independence assumption, where the factorization from equation 2 to equation no longer holds. To address this, method, IKI-EWC, identifies and isolates this interference knowledge.
4

---

Published as a conference paper at ICLR 2026
Rethinking Posterior Factorization for IOD. In Faster R-CNN, the detection loss L comprises the RPN (C)Pm) and ROI (Cro)) terms, each with a classification loss L and a regression loss ~
edle
Σ C (s). JER
C
Σ
SmoothLl(y, - -vg",
TR
JER
where R denotes all region proposals and R$ the positives. For proposal j. Sy are the classification scores with label Mj. and Vj, vi are the predicted and target box parameters. Here, L is the cross-entropy loss on class scores, and SmoothL1(.) is robust loss for bounding-box regression.
As shown in equation 4 both Çals and Crey are computed per proposal and then averaged. This naturally motivates a region-based view in which image-level data (with multiple objects) are mapped to proposal-leve data for fine ane-grained analysis. At stage t, we treat the current dataset Dr as a set of proposals R2 (foreground and background). Proposals accumulated from stages 1 to t- 1 are denoted R1a-11 = - R2. Let R S R1 be the background proposals from earlier stages. We define the interference set Ria-1 S R those earlier negatives that actually correspond to stage-/ foreground:
Ri---1 = R : 3g E 9r, loU(r,g) 2 27}
(5)
where G. is the set of ground-trutt boxes for the new classes at stage and y is the IoU threshold. These proposals encode lask-confticting knowledge: unlabeled in earlier stages, they were learned as background yet now conflict with the stage-l foreground semantics.
We consider tasks 1:t as our study scope. Following prior work Mo et al. (202)), we assume that pseudo-labeling D, with old classes prevents the current proposal set R2 from containing elements of the past positive set R Under this assumption, the data Di. decompose as
Di. (R1:-1 Ri=-1) U R2 R1 Ri,
(6)
where Ry R11-11 Ri collects the non-confficting proposals from past stages.
Rederiving the posterior in the IOD setting. Based on the separation in equation we re-derive the posterior p(0 (Di:)) for IOD. Assuming approximate independence between non-conflicting past proposals and current proposals, the posterior factorizes as
p(0 DI:t) x p(R, 0) p(0 Ría--),
(7)
where pRR, θ) corresponds to the stage-t detection objective, and p(0 R( acts EWC-style consolidation term built from non-conflicting past proposals.
IKI-EWC Method in Practice. For equation 6. Ri==11 cannot be computed in isolation during either the 1:t-1 stages or stage t. In the former, G, is not yet available; in the latter, R1:-1 is no longer accessible. Importantly, the interfering subset Ri.--1 relies evidence across stages: it needs the background set R from 1:t and the labels Gr at stage identify past negatives that are actually new-class foreground. Thus R cannot be obtained during 1:/-1 1 alone. We therefore approximate it at stage t using Dj and the old model M,-1, and denote the estimate by Ria-11
Concretely, we run M,- on D. to obtain old-class pseudo labels D (new classes masked), and construct a simulated past set D1. using D, images with Di:1-1 as ground truth. Feeding D1:-1 back into M yields proposals R and their background subset R R We then define
R { ( R : 3g E Gc, loU(r.g) 27
At stage f, we estimate the interference set Ri Because the raw past proposals R1:-1 are unavailable, R/ cannot be formed at the data level. By definition, the non-conficting part would satisfy
Ri = R1+ Ri
Viewing R1-01 1 as a mixture of clean subset R and an interference subset Ri and letting R
k
denote their relative mass, we can solve for the clean posterior (see Appendix B.1 R
p(0 R(..-1) (1 (1+h)p(C p(0 R1a-1)
kp(O
| R(
(10)
5

---

Published as a conference paper at ICLR 2026
Here & quantifies the severity of interference as the ratio of estimated interfering proposals to non- conflicting ones. The term p(0) R1:0-1) is the EWC posterior computed at the end of stage t 1, whereas p(O Ri=-11 is evaluated at stage on the estimated interference set. In this way, we account for interference without constructing Rí the data level.
We further adopt the Laplace approximation MacKay 1992) to model both p(0 and p(0(R),,-1) as Gaussian distributions centered at the converged parameters 0 with variances of and o derived from their respective Hessian matrices Kirkpatrick et al. (0177) H and H" More details are provided in Appendix [3.2]
Finally, by maximizing the posterior probability (0 in equation 7 using Bayesian estima- tion, the loss at stage t can be expressed as:
( = Edet (0) + (0. (
(
(11)
where À a hyperparameter that balances the plasticity and stability of the current model Mr. 07-1. represents the parameter of the old region feature extractor M 0, represents the parameter of the current region feature extractor M,, which we aim to learn on the new data D, The importance score I, for i-th parameter 0 is computed as:
1,
I. (1 k)) 2, 12,
(12)
with 1, -H, denoting interference-carryinn importance from previous tasks, capturing the sensitivity of θ; to old knowledge and encouraging its preservation. In contrast, I2,; = --.=-H; - denotes parameter importance for interference at stage reflecting how strongly 0, is influenced by conflicting knowledge in the interference regions. This formulation helps preserve essential past-task parameters while relaxing constraints on those affected by interference. More details are in Appendix B.2
3.4 PROSOTYPE-BASED KNOWLEDGE CALIBRATION
Although parameter regularization helps limit changes in the region feature extractor Mf. it is enough to prevent catastrophic forgetting, especially in the classification head fea( This head vulnerable to semantic drift, where the feature space shift causes old class prototypes to lose their original meaning. As the region feature space of the updated model O, changes, previously learned prototypes drift, making harder for them to represent historical class features accurately. This misalignment degrades the performance of the classification head fch(-). To address this, we introduce a lightweight projection layer that compensates for semantic drift between the old feature space and the new feature space.
Specifically, after training the old model, we extract set of region features Q (Q11, Q12..... 2mm) from the output of the ROI FC layers on the old data D,-1- Here, de- notes the number old classes, and m is the number region features sampled per class. Based on these features, we compute set of class prototypes C {C1, C2...., ). The feature distribution of each class is modeled as a Gaussian, where the mean μ4; is computed by Σ)= Qij, and the variance of is estimated from the diagonal of the covariance matrix Σ;
Let f, and f,-1 denote region features from the current task, extracted by the new and old feature extractors, respectively. The goal is to learn projection that maps f--1 into the feature space of f. To this end, we optimize a projection loss C where the projection function sp(-) is a learnable linear transformation parameterized by weight matrix W and bias vector b:
(ff(-1) WF-1 +b,
(13)
Σ
(1p((fr 12
(14)
TTTopK
where (p(f)-1,)) denotes the projection of the old feature f,-1. using the projection layer, and TopK refers to the K region pairs per class with the smallest L2 distances between old and new features. By minimizing (20o), we learn transformation between the old and new region feature extractors.

---

Published as a conference paper at ICLR 2026
Table 1: mAP@0.55 results on single incremental step on PASCAL VOC 2007. The best performance in each presented with bold. Methods marked with + use exemplars.
5-15
Method
Veuse
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
76.4
76.4
76.4
78.3
70.7
76.4
76.9
76.0
76.4
73.6
77.4
76.4
Fine-tuning
72.5
11.2
73.4
ORE
Josept el
2021a
2V
69.4
60.1
68.9
71.8
59.7
66.55
00.4
68.8
64.6
OW-DEIX cupprr
CVPR 22
70.2
62.0
69.8
72.2
59.8
69:1
63.5
67.9
65.7
Meta Dosspt
TFAMU221
20.9
57.6
ABR-
el MIPA
2CCV
71.0
69.7
70.9
73.0
65.
71.0
71.2
72.8
72.0
64.7
71.0
69.4
GMDPXBX Wang et al.
2025b
ICLR'25
74.8
70.1
74.6
75.8
65.5
73.2
72.1
73.2
72.7
67.1
71.9
70.7
Faster Pon
3000
PR1:20
61.1
68.5
37.1
PPAS/Zhou 2iel A140
70.5
53.0
69.2
63.5
50.0
61.8
MVC
Tingel 20221
PR'22
70.2
60.6
69.7
69.4
57.9
66.5
66.2
66.0
66.1
PRO
CVPR 23
73.9
48.5
72.6
73.5
60.8
70.1
66.0
67.2
66.5
PreudeKA Ciite
MM'23
72.9
67.3
73.4
70.3
09.1
68.6
MMA Centonn of Ail-et
CVPR222
71.1
63.4
70.7
73.0
00.5
69.9
09.3
63.9
66.6
06.8
57.2
59.6
Wa et
Wa 2024
TIP
72.5
61.4
71.9
73.6
62.0
70.7
70.3
68.7
69.5
BFF
B0CV
GMDF1DDI
Wingel
20256
ICLR'25
742
67.9
73.9
74.6
63.5
71.8
71.9
69.7
70.8
63.2
60.5
61.7
Ours
76.0
63.2
75.4
76.9
64.2
73.7
75.9
75.5
75.7
69.7
77.5
75.6
After the projection layer is trained, we sample features f for old classes from a Gaussian distribution estimated from class prototypes. These features are then projected through (p()) to obtain drift- compensated representations. We concatenate the projected features with current task features ft, and feed them into the classification head (c) of the detection module Mhead to produce logits z. The classification head is then retrained using a cross-entropy loss _ with corresponding labels y.-
4 EXPERIMENTS
Datasets and Evaluation Metrics. We evaluate our method on the PASCAL VOC 2007 Everingham ct al. (2010) and MS COCO 2017 Lin et al. (2014) datasets, following prior works Wang et al. (2025b): Mo al. )2024): Feng et al. (2022 Dong et (221): Zhou et al. (2020). PASCAL VOC 2007 contains 9,963 images across 20 categories, while COCO 2017 includes 118k training images and 5k validation images spanning 80 categories. For evaluation, we use the mean Average Precision at 0.5 IoU (mAP@0.5) for VOC, and the COCO-style mAP averaged over IoU thresholds from 0.5 to 0.95 for COCO. In each incremental setting (A-B), denotes the number of classes in the initial stage, and B represents the number of new classes added at each subsequent stage. Columns with gray background in the results tables report the average AP over all classes.
Implementation Details. Similar to previous works, our incremental object detector is built upon Faster R-CNN with a Res He et al. (2016) backbone, initialized with ImageNet pre-trained weights Deng et al. (2009). In the VOC (10-10) setting, and sample two features per class in the PKC module. VOC experiments are conducted on a single NVIDIA GeForce RTX 4090 GPU with batch size of 16, using PyTorch and the SGD optimizer.
set γ 0.5, A 20, and K 32,
4.1 COMPARISON WITH THE STATE-OF-THE-AN
PASCAL VOC 2007. We evaluate our method on the PASCAL VOC 2007 dataset using both two-stage and multi-stage incremental task settings. In the two-stage setting, tasks are divided into 19-1, 15-5, 10-10, and 5-15, where the number of new classes added each stage increases by 1, 5, 10, and 15 classes, respectively. In the multi-stage setting, tasks are divided into 10-5, 5-5, 10-2, and 15-1, with 5, 5, 2, and classes added in each step until all classes are included.
Two-stage Incremental Setting, In Table [ we compare our method with existing approaches. Our method consistently outperforms previous methods. Specifically, in the 19-1, 15-5, 10-10, and 5-15 settings, our method improves the average AP (Average AP among all classes) over BPF Mo et al. (2024) by 1.3%, 1.0%, 2.8% and 2.6%, respectively, outperforms the rehearsal -ased method GMDP- AMDP-ABRE PABR Wang et al. (2025b) by 0.8%, 0.5%, 3.0%. and 4.9% respectively, and further surpasses GMDP-ILOD Wang et al. (2025b) by 1.5%. 1.9%, 4.9%, and 13.9% respectively. The improvements in the 1-20 show that method achieves better stability and plasticity than most other methods.
7

---

Published as a conference paper at ICLR 2026
Table 2: mAP@0.5 results on multiple incremental steps on PASCAL VOC 2007. The best performance each presented with bold. Methods marked with + use exemplars.
10-5
5-5
10-2
15-1
Method
Venue
I-10
11:20
1-20
1-5
6-20
1-20
1-10
11-20
1-20
1-15
16-20
1-20
Joint Training
76.9
76.0
73.6
77.4
76.4
76.9
76.0
76.4
78.3
70.7
76.4
Pine-runing
5.3
30.6
18.0
0.5
18.3
13.8
3.8
13.6
8.7
0.0
10.5
5.3
ABR-Liuca ct
2025
ICCCV
68.7
67.1
67.9
64.7
56.4
58.4
67.0
58.1
62.6
68.7
56.7
65.7
GMD Wallg ct
2025h
ICLR'25
69.9
67.8
68.9
66.3
59.3
61.1
67.6
58,9
63.3
69.5
58.9
66.9
Haster Peng et
20200
PRE:20
68.3
55.7
16.0
25.9
64.2
48.6
56.4
66.9
44.5
61.3
MMA Cemmlli e Ni723
MMA.Cemell
CVPR
66.7
61,8
64.2
62.3
31.2
38.9
65.0
53.1
59.1
68.3
54.3
64.1
HPH-Mo
BCCV"24
GMDP-1L0 Wang et nl.
2025b
ICLR'25
68.1
62.3
65.2
61.1
35.8
42.
64.2
53.3
58.8
66.8
50.4
62.7
69.7
584
75.8
42.2
Table 3: mAP results MS COCO 2017. Methods marked with exemplars.
Table 4: Ablation study of various val- ues of haper-parameters.
Method
Venne
AP
AP50
40-40
AP75
AP AP50 AP75
70-10
Hyper-parameter Value
1-10
VOC(10-10)
11-20
1-20
Joint Training
40.2 61.5
44.2
40.2
61.5
44.2
73.4
75.2
74.3
Fine-tuning
21.6
33.4
23.1
5.6
8.6
6.2
10
74.6
74.8
74.7
20
75.0
75.2
75.1
ILOD-Meta* Joseph et
2021b
T1AMI
23.8
40.5
24.4
50
75.4
73.0
74.2
Ai a
2023
ICCV'23
34.5
57.8
35.2
31.1
52.9
32.7
100
75.3
72.4
73.9
GMD Wang el
2025b
ICLR'25
36.8
59.6
36.7
32,5
53.8
33.9
2
75.8
75.5
75.7
Faster 1LOD Peng et
2020
PRL'20
20.6
40.1
21.3 39.9
PscadoRM Yang et al
2023
MM
25.3 44.4
K
32
75.9
75.5
75.7
Cermelli Cemnlll el
CVPR'22
33.0 56.6
34.6
30.2 52.1 31.5
128
75.7
75.5
75.6
BPF al. 2021
ECCV'24
34.4 51.3
37.3
36.2
56.8
38.9
512
Ours
35.9 55.8 38.8
33.1
57.6
40.6
Multi-stage Incremental Setting. To further demonstrate the effectiveness of our IOD method,
also show the performance in multi-step incremental learning in Table 2 The catastrophic forgetting problem becomes more critical in longer incremental settings. Our method improves over GMDP-
ABR Wang et al. 2025b) by 2.6%, 4.3%, 0.7%, and 0.5%, and outperforms BPFMMo et al. (2024) by 2.8%, 2.9% % and 0.5% in the 10-5, 5-5, 10-2, and 15-1 settings, respectively. It further surpasses GMDP-ILOD Wang et al. (202bb) by 6.3%, 23.3%, 5.2%, and 4.7% in the same settings. In the 15-1 setting, the dominance of old task categories leads our regularization-basee method assign higher importance to old task parameters, limiting the model's 's plasticity and hindering adaptation to new categories.
MS COCO 2017. On the challenging COCO 2017 dataset, we conduct experiments under the 40-40 and 70-10 settings, where 40 and 10 new classes are added, respectively, following the setup in Mo et al. (2024). As shown in Table 3 our method consistently outperforms the recent wate-of-the--ad non-rehearsal method BPF Mo et al. (2024). Under the 40-40 and 70-10 settings, our method achieves performance gain of 1.5% and 0.9% on average AP, respectively, compared to BPF. The results demonstrate the effectiveness of our proposed method in selectively suppressing parameter-level task interference, which becomes particularly crucial as the number of classes increases.
4.2 ANALYSIS AND ABLATION STUDY
Ablation Study on each component. We evaluate the individual contributions of the IKI-EWC and PKC modules through ablation studies under the VOC 10-10, 10-5, and 5-5 settings, as shown in T Using pseudo-labeling as the baseline, IKI-EWC recalculates parameter importance in the context of incremental object detection. Specifically, pseudo labels are generated by the previous model M,-1 on current task data D, to approximate annotations for old classes. IKIEEWC mitigates interference between old and new knowledge during regularization, enhancing model plasticity while effectively reducing forgetting. Compared to the baseline (rows and 2), it improves performance by 1.3%, 4.5%, and 4.0% on the 10-10, 10-5, and 5-5 settings, respectively. To further reduce forgetting in the classification head, the PKC module retrains it using semantically calibrated prototypes after the final task. Compared models without PKC (rows and 3), it brings additional gains of 0.5%,
8

---

Published as a conference paper at ICLR 2026
Table 5: Ablation study on each component.
VOC(10-10)
VOC(10-S)
VOC(5-S)
Idx
IKI-EWC
PKC
11-20
11-15
16-20
1-5
73.0
74.6
73.8
64.6
74.2
62.2
66.4
47.2
64.0
76,3
56.4
75.0
75.2
75.1
72.4
76.4
62.5
70.9
62.9
66.6
75.0
55.6
65.0
3
73.8
74,8
74.3
66.2
75.6
63.5
67.9
49.4
63.2
75.8
57.1
61.4
75.9
75.5
75.7
72.6
77.8
62.8
71.5
63.8
66.6
74.7
56.7
65.4
Table 6: Ablation study the effect of IKI-EWC module.
VOC(10-10)
VOC(10-5)
VOC(5-5)
Method
1-10
11-20
1-20
1-10
11-15
16-20
1-20
1-5
6-10
11-15
16-20
1-20
buseline
73.0
74.6
73.8
64.6
74.2
62.2
66.4
47.2
64.0
76.3
56.4
61.0
1.2++
75.0
42.5
58.8
63.1
60.6
34,5
55.3
51.8
47.2
43.6
23.2
44.4
EWC++
75.0
73.9
74.5
70.7
74.9
61.4
69.4
56.0
68.0
76.9
53.6
63.6
IKI-EWC
75.0
75.2
75.1
72.4
76.4
62.5
70.9
62.9
75.0
55.6
65.0
1.5%, and 0.4% the three respective splits. When combined, IKI-EWC and PKC form the full IIKC framework, which consistently achieves the best performance across all settings.
Effectiveness of the IKI-EWC Module. We compare our parameter regularization method with EWC and 1.2 methods on the VOC dataset under the 10-10, 10-5, and 5-5 settings. The 1.2 method imposes uniform penalties all parameters, without regard their importance for previous tasks. Since pseudo-labeling is commonly used in prior work (e.g., Det et al. (2020a)), excluding pscudo- labels from EWC and L2 would lead to a significant performance drop and an unfair comparison. To address this, we introduce pscudo-labels into both EWC and L2, denoted as EWC++ and L2++, respectively. As shown in Table 6 our method consistently outperforms EWC++ by 0.6%, 1.5%, and 1.4%, and surpasses L2++ by 16.3%, 15.6%, and 20.6% across the three settings, respectively. These consistent performance gains highlight the effectiveness of our regularization strategy and its superiority over traditional EWC methods, even when enhanced with pseudo-labels.
Effectiveness of the PKC Module. To isolate the effect of semantic drift correction, we compare the full PKC module with variant excluding only the projection component, denoted as PKC-NPC (Table 7 The baseline refers to model without prototype retraining and semantic alignment. Retraining with aligned prototypes yields improvements of 0.4%, 0.4%, and 1.1% the 10-10, 10-5, and 5-5 settings, respectively, highlighting the effectiveness of correcting semantic drift in preserving performance across incremental tasks.
Analysis Of Hyper-parameters À and K. We analyze the impact of two key hyper-parameters: and K. The regularization strength À (equation 1T) balances model plasticity and stability: smaller favors plasticity but increases forgetting. while larger À enhances stability at the cost of learning new tasks. As shown Table 4 setting À 20 achieves the best trade-off in the 10-10 setting. The hyper-parameter K (equation 14) controls the number of top-matched region feature pairs selected from the current data Dr, based on similarity between features from the old model M,-1 and the new model Mr. These pairs are used to train the projection layer and correct feature drift. While performance varies slightly with different K values, the model is generally robust, with the best results observed at K 32. Additional analyses are given in Appendix C and Appendix
5 CONCLUSION
This paper presents novel framework for Incremental Object Detection (IOD) that combines Interference Knowledg Isolated Elastic Weight Consolidation (IKI-EWC) with a Knowledge Cal- ibration (PKC) strategy. Theoretically, it bridges the gap between traditional class-incremental learning and the unique challenges of IOD by building on the weight consolidation paradigm. This approach leverages new task data to identify and remove conflicting knowledge, so as to mitigate catastrophic forgetting through importance-based parameter regularization. In addition, we further propose the Prototype-basee Knowledge Calibration module for region feature compensation to reduce the severity of catastrophic forgetting problem in the classification head of the object detector. Extensive experiments demonstrate that our approach effectively reduces catastrophic forgetting while maintaining high adaptability, making it significant contribution to Incremental Object Detection.

---

Published as a conference paper at ICLR 2026
Table 7: Ablation study on effect PKC module.
Method
VOC(10-10)
VOC(10-5)
VOC(S-5)
11-15
11-15
baseline
75.2
75.1
72.4
76.4
62.5
70.9
62.9
66.6
75.0
55.6
65.0
PKC-NPC
75.6
75.0
75.3
72.2
77.6
62.3
71.1
61.1
65.1
76.8
54.1
64.3
PKC
75.9
75.5
75.7
72.6
77.8
62.8
71.5
63.8
66.6
74.7
56.7
65.4
In the future, this unified, architecture-agnostie framework could be extended to detectors such as DETR, enabling broader applicability in incremental object detection.
ACKNOWLEDGMENTS
This work was supported in part by the National Key R&D Program of China under Grant No.2023YFAI00880 in part by the National Natural Science Foundation China under Grants 62576262, U22A2096, in part by the Key Research and Development Program of Shaanxi Province under grant 2024GX- YBXM135, for the Central Universities under Grant QTZX25083, QTZX23042.
in part by the Fundamental Research Funds
REFERENCES
Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, and Tinne Tuytelaars. Memory aware synapses: Learning what (not) to forget. In Proceedings of the European conference computer vision (ECCV), pp. 139 134-154, 2018.
Fabio Cermelli, Antonino Geraci, Dario Fontanel, and Barbara Caputo. Modeling missing annotations for incremental learning in object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 3700-3710, 2022.
Li Chen, Chunyan Yu, and Lvcai Chen. A new knowledge distillation for incremental object detection. In 2019 international joint conference on neural networks (IJCNN) pp. 1-7. IEFE, 2019.
De Cheng, Zhipeng Xu, Xinyang Jiang, Nannan Wang, Dongsheng Li, and Xinbo Gao. Disentangled prompt representation for domain generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, pp. 23595-23600 2024.
De Cheng, Lingfeng He, Nannan Wang. Dingwen Zhang, and Xinbo Gao. Semantic-aligned learning with collaborative refinement for unsupervised vi-reid: D. cheng et al. International Journal of Computer Vision, 133(9))5992-600) 6014, 2025.
De Cheng, Zhipeng Xu, Xinyang Jiang, Dongsheng Li, Nannan Wang, and Xinbo Gao. Prompt disentanglement via language guidance and representation alignment for domain generalization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2026.
Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor lonescu, and Mubarak Shah. Diffusion models in vision: A survey. IEEE transactions on pattern analysis and machine intelligence, 45(9):
2023.
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pp. 248-255. Ieee, 2009.
Na Dong, Yongqiang Zhang, Mingli Ding, and Gim Hee Lee. Bridging co-occurrence with unlabeled in-the-wild data for incremental object detection. Advances in Neural Information Processing Systems, 34:30492-30503, 2021.
Mark Everingham, Lue Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision, 88:303-338, 2010.
Pedro F Felzenszwalb. Ross B Girshick, David McAllester, and Deva Ramanan. Object detection with discriminatively trained part-based models. JEEE transactions on pattern analysis and machine intelligence, 3279):167--1645 2009.
10

---

Published as a conference paper at ICLR 2026
Tao Feng, Mang Wang, and Hangjie Yuan. Overcoming catastrophic forgetting in incremental object detection via elastic response distillation. In Proceedings of the IEEE/CVE Conference on Computer Vision and Pattern Recognition, pp. 9427-9436, 2022.
Akshita Gupta, Sanath Narayan, KJ Joseph, Salman Khan, Fahad Shahbaz Khan, and Mubarak Shah. Ow-detr: Open-world detection transformer. In Proceedings of the IEEE/CVE conference on computer vision and patlern recognition. pp. 9235-9244, 2022.
Yu Hao, Yanwei Fu, Yu-Gang Jiang, and Qi Tian. An end-to-end architecture for class-incrementte object detection with knowledge distillation. In 20/9 IEEE International Conference on Multimedia and Expo (ICME), pp. 1-6, 2019a. doi: 10.1 10110/M.2222010000000
Yu Hao, Yanwei Fu, Yu-Gang Jiang, and Qi Tian. An end-to-end architecture for class-incremental object detection with knowledge distillation. In 2019 IEEE International Conference on Multimedia and Expo (ICME), pp. 1-6. IEEE, 2019b.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEFE conference on computer vision and pattern recognition, pp. 770-778, 2016.
Lingfeng He, De Cheng, Nannan Wang, and Xinbo Gao. Exploring homogeneous and heterogeneous consistent label associations for unsupervised visible-infrared person reid. International Journal of Computer Vision, (3199)) 2025a.
Lingfeng He, De Cheng, Di Xu, Huaijie Wang, and Nannan Wang. Harnessing textual semantic priors for knowledge transfer and refinement in clip-driven continual learning. arXiv preprint arXiv:2508.00 01579, 2025b.
Zhiyuan Hu, Yunsheng Li. Jiancheng Lyu, Dashan Gao, and Nuno Vasconcelos. Dense network expansion for class incremental learning. In Proceedings the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11858-1887, 2023.
KJ Joseph. Salman Khan, Fahad Shahbaz Khan, and Vineeth N Balasubramaniaa. Towards open world object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 5830-5840, 2021a.
KJ Joseph, Jathushan Rajasegaran, Salman Khan, Fahad Shahbaz Khan, and Vineeth N Balasubrama- nian. Incremental object detection via meta-learning. IEEE Transactions Pattern Analysis and Machine Intelligence, 44(12) 21:9299-92211 2021b.
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Yen Lo, et al. Segment anything. In Proceedings of the IEEF/CVF international conference on computer vision, pp. 4015-4026, 2023.
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Barnskaaaaaiikka,, et al. Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences, 114 (13):3521-3526 2017.
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 Proceedings of the 4th ACM/IEEE Symposium on Edge Computing, pp. 113-126, 2019.
Qiwei Li, Yuxin Peng, and Jiahuan Zhou. Fes: Feature calibration and separation for non-exemplar class incremental learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 28495 28504, June 2024.
Zhizhong Li and Derek Hoiem. Learning without forgetting. IEEE transactions pattern analysis and machine intelligence, 40(12):2935-22)) 2017.
Tsung- Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitmick. Microsoft coco: Common objects in context. In Computer vision- ECCV 2014: 13th European conference, zurich, Switzerland, September 62, 2014, proceedings, part 13, pp. 740-755. Springer, 2014.
11

---

Published as a conference paper at ICLR 2026
Liyang Liu, Zhanghui Kuang. Yimin Chen, Jing-Hao, Xue, Wenming Yang, and Wayne Zhang. Incdet: In defense of elastic weight consolidation for incremental object detection. IEEE transactions on neural networks and learning systems. 32(6):2306-233 -3199, 2020a.
Xialei Liu, Hao Yang, Avinash Ravichandran, Rahul Bhotika, and Stefano Soatto. Multi-task incremental learning for object detection, 2020b.
Yaoyao Liu, Bernt Schiele, and Qianru Sun. Rmm: Reinforced memory management for class-
incremental learning. Advances neural information processing systems, 34:3478-3490, 2021.
Yuyang Liu, Yang Cong, Dipam Goswami, Xialei Liu, and Joost Van De Weijer. Augmented box replay: Overcoming foreground shift for incremental object detection. In Proceedings of the IEEF/CVF international conference on computer vision, pp. 11367-11377, 2023.
Zilin Luo, Yaoyao Liu, Bernt Schiele, and Qianru Sun. Class-incremental exemplar compression for class-incremental learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11371-11380, 2023.
David JC MacKay. A practical bayesian framework for backpropagation networks. Neural compula- tion, 4(3):448 472, 1992.
Qijie Mo, Yipeng Gao, Shenghao Fu, Junkai Yan, Ancong Wu, and Wei-Shi Zheng. Bridge past and future: Overcoming information asymmetry in incremental object detection. In European Conference on Computer Vision, pp. 463-480. Springer, 2024.
Can Peng, Kun Zhao, and Brian C Lovell. Faster ilod: on faster renn. Pattern recognition letters, 140:109-115, 15, 2020.
Incremental learning for object detectors based
Ameya Prabhu, Philip HS Torr, and Puneet K Dokania. Gdumb: A simple approach that questions our progress in continual learning. In Computer Glasgow, UK, August 23-28, 2020, Proceedings, Part II 16, pp. 524-540. Springer, 2020.
2020: 16th European Conference,
Daiqing Qi, Handong Zhao, and Sheng Li. Better generative replay for continual federated learning. arXiv preprint 13000 2023.
Sylvestre- Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert. icarl: Incremental classifier and representation learning. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 2001-2010, 2017.
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE transactions on pattern analysis and machine intelligence, 19(6):1137-111) 2016.
Konstantin Shmelkov, Cordelia Schmid, and Karteek Alahari. Incremental learning of object detectors without catastrophic forgetting. In Proceedings of the IEEE international conference on computer vision, pp. 3400-3409, 2017.
James Smith, Yen-Chang Hsu, Jonathan Balloch, Yilin Shen, Hongxia Jin, and Zsolt Kira. Always be dreaming: A new approach for data-free class-incremental learning. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 9374-9384, 2021.
Zhicheng Sun, Yadong Mu, and Gang Hua. Regularizing second-order influences for continual learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, pp. 20166-20119 2023.
Zhi Tian, Chunhua Shen, Hao Chen, and Tong He. Fcos: Fully convolutional one-stage object detection. In Proceedings of the IEEE/CVF international conference computer vision, pp. 9627-9636, 2019.
Marco Toldo and Mete Ozay. Bring evanescent representations life in lifelong class incremental learning. In Proceedings the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16732-16741, 2022.
12

---

Published as a conference paper at ICLR 2026
Jonathan Tompson, Ross Goroshin, Arjun Jain, Yann LeCun, and Christoph Bregler. Efficient object localization using convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 648-656, 2015.
Yun Wang, Da-Wei Zhou, Liu Liu, Han-Jia Ye, Yatao Bian, De-Chuan Zhan, and Peilin Zhao Beef: Bi-compatible class-incremental learning via meeggy-based expansion and fusion. In The eleventh international conference on learning representations, 2022a.
Fu- Yun Wang, Da-Wei Zhou, Han-Jia Ye, and De-Chuan Zhan. Foster: Feature boosting and compression for fass-incremental learning. In European conference computer vision, pp. 398-414. Springer, 2022b.
Huaijie Wang, De Cheng, Lingfeng He, Yan Li, Jie Li, Nannan Wang, and Xinbo Gao. Ekpe: Elastic knowledge preservation and compensation for caas-innrremenal learning. arXiv preprint arXiv:2506. /2351, 2025a.
Yanjie Wang, Liqun Chen, Tianming Zhao, Tao Zhang, Guodong Wang, Luxin Yan, Sheng Zhong, Jiahuan Zhou, and Xu Zou. High-dimension prototype is a better incremental object detection learner. In The Thirteenth International Conference on Learning Representations, 2025b.
Aming Wu, Cheng Deng, and Wei Liu. Unsupervised out-of-distribution object detection via pca- driven dynamic prototype enhancement. IEEE Transactions on Image Processing, 33:2431-2446, 2024.
Zhipeng Xu, De Cheng, Xinyang Jiang, Nannan Wang. Dongsheng Li, and Xinbo Gao. Adversarial domain prompt tuning and generation for single domain generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18584-18595, 2025.
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: 108863, 2022.
Dongbao Yang, Yu Zhou, Xiaopeng Hong, Aoting Zhang, Xin Wei, Linchengxi Zeng. Zhi Qiao, and Weipinng Wang. Pseudo object replay and mining for incremental object detection. In Proceedings of the 31st ACM International Conference on Multimedia, pp. 153-162, 2023.
Friedemann Zenke, Ben Poole, and Surya Ganguli. Continual learning through synaptic intelligence. In International conference on machine learning, pp. 3987-3995, PMLR, 2017.
Da-Wei Zhou, Qi-Wei Wang, Han-Jia Ye, and De-Chuan Zhan. A model or 603 exemplars: Towards memory-efficient class-incremental learning. arXiv preprint Ttt.:22055/32/16 2022.
Wang Zhou, Shiyu Chang, Norma Sosa, Hendrik Hamann, and David Cox. Lifelong object detection. arXiv preprint arXiv:2009.0/ /29, 2020.
OrT Zohar,
Wang, and Serena Yeung. Prob: Probabilistic objectness for open world object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1144-1111 11453, 2023.
13

---

Published as a conference paper at ICLR 2026
A TRAINING PIPELINE FOR IKI-EWC MODULE
Algorithm Training Procedure of the Proposed IKI-EWC Module
Input: Task sequence .., Dr } with corresponding labels (Vi..... Vr), threshold y, projec- tion TopK K, regularization weight À
1: for t = 1 to I" do
2:
Redefine Di as R2 3: if' = then
4:
Train detection model M₁ with general detection loss: L()) Liee
5: else
6:
Step Pseudo-labeling for Interference Isolation
7:
Generate pseudo labels 11 D, using model Mr-1
8:
Simulate prior task data D1:t-1 1 by masking new-class ground truths in Dr Identify background regions RT.--1 in Dist-1
9:
Estimate conflicting regions Ri E RI--1: Aj Gr (oU(i)) 2 7
11:
Define R Ria-1 \ Ri
Step 2: Compute Regularization Importance (IKI-EWC)
13:
for each parameter 0; of 0, do
Compute frequency ratio k P(
num i
14:
PR
WURT RR
15:
Compute importance 2,, from conflicting knowledge Ri
27
16:
I2. = (x..))
0 80;
log egp(yylz, 9)
0 1.1
Compute final importance: I, end for
T +
18:
19:
// Step 3: Train Detection Model with IKI-WWC Loss Optimize: L(O) Ddet (0) Σ, 1, 1,,1,
20:
21:
end if
Get parameters 0f of model M, // Step 4: Update the old importance
23:
for each parameter 0 of 0 do
( 80:
25:
Compute Regularization Importance: I, ((,)))
log p2 0)
0,=0i,
26:
Update 1,s = I, + I
27:
end for
28:
end for
Algorithm I summarizes the full training pipeline of our proposed IKI-EWC method across in- cremental tasks. For the first task, the model is trained using standard detection loss without any regularization, as there is no prior knowledge to preserve. Starting from the second task, we introduce three-step process to mitigate knowledge interference: (1) Pseudo-labeling for interference isola- tion, where the old model generates labels to estimate conflict regions based on loU with new-task ground truths: (2) Importance estimation, where we compute parameter-wise regularization strengths that balance old knowledge retention (I1,) and suppression of conflicting gradients (I2,)) using the frequency- based ratio k; and (3) Regularized optimization, where the model is trained using detection loss plus an IKI-EWC regularization term weighted by the fused importance I, After training, update the stored importance values for use in future incremental steps. This approach allows the model to dynamically isolate interference and adaptively regulate parameter updates based on task conflict, ultimately reducing catastrophic forgetting.
14

---

Published as a conference paper at ICLR 2026
B THEORETICAL ANALYSIS
B. REGION-LEVEL POSTERIOR FACTORIZATION FOR IOD
Elastic Weight Consolidation (EWC) Kirkpatrick et al. (017) adopts probabilistic view of neural networks and aims to learn model M, that performs well on data from all tasks 1 } where D, the current-task data and D comprises previous tasks that are no longer accessible at stage I. From a Bayesian perspective, optimizing M, is equivalent to maximizing the posterior p(0 D1:)).
In incremental object detection (IOD), shown in Figure the current data Dj contain both future and previously seen objects yet lack complete annotations for all classes. The same limitation applics to earlier data D1.-11, where future-class instances unlabeled and thus treated background. Consequently, the standard EWC factorization
p(0 Di:)) x p(P 0) p(0 D1:--)
(15)
does not hold because D1 and D; are no longer (approximately) conditionally independent. Instead, we use the general form of Bayes' theorem:
p(0 Dia) = p(0, Di.))
((D
p(0, 11)
p(D Dr)
(16)
p(D: 0,D1.111 p(0-1)p(@p0 D1a-1) ((Da--1)
p(D; (D:t-1) 10p1ppppp) p(D
p(D: 0,P1.111) p(0) D1a-1)
p(D; DI=-1)
Following Sec. 3.3 adopt region-based view that maps image-level data to proposal-level data for fine-grained analysis:
D. Rss
D1a-1 R1--1-
Under this setting, issues arise:
1. Stage t. We have labels for the new classes but not for old ones. Hence R, contains new-class foreground regions and background regions that include old-class objects. Old- class objects are then misclassified background, breaking independence between R, and R and causing the same type of mismatch when training the new task.
2. Stages 1:t-1.
We have labels for old classes but not for the new ones. Thus R1a-1 contains old-class foreground regions and background regions that may actually include new-class objects (interference). During proposal generation, these new-class objects are learned as background, breaking independence between R1:-1 and R,. Applying EWC here would incorrectly regularize new-class objects as background.
To address the first point, we use the old model M,-1, trained on D1a-11. to generate pseudo-labels D' for old-class foreground on D,, These pseudo-labels prevent old-class objects from being treated background in R., thereby alleviating the non-independence between R, and R1:-1 caused by missing old labels at stage l. Accordingly, all subsequent derivations are based on this setup; we do not consider cases where R₁ contains elements from the past positive R
B. FOR THE SECOND POINT (IDEAL ISOLATION).
If the new-class labels G. were available for past data, we would isolate the interfering regions by removing
Ri {r R 3g E G. IoU(r, 2
from the past pool, yielding the non-conflicting subset
Rí R1--1 1 Ri
(17)
15

---

Published as a conference paper at ICLR 2026
Consequently,
Di:: R1a = R1 U R.
(18)
By isolating the interference subset Ri from the past proposals, we explicitly remove conflicting knowledge. If catastrophic forgetting did not occur, a model trained sequentially-finn on Ria-1 and then R.wwo behave similarly to one trained jointly on R1.- Hence, treat R and R2 approximately independent subsets. in the spirit of EWC. Applying Bayes' theorem to the region-level representation R1t yields
p(0 (i.))= p(0 R1:e)
p(0, R1:e)
p(R1:1
p(0, 1.)))
p(R RRt)
(19)
p(R, 0, 0.R(,,-1) p p(t) ((R
p(R, R2:t-1) (=)
p(R, 0, R1 p R2 p(R, 1(
Assuming approximate independence between Ri and R₂ i.e. (RR, 0, Ri =p(Re 0), p(R, R(aa1) = p(Re).
(20)
we obtain
p00 Dia)) p(R, p() R1==1)
x p(R; p(0 p(0 R(.1-)),
(21)
p(R,)
which is the region-level analogue of the original EWC posterior and underpins our framework.
Finally, using the decomposition in equation 17
DI:-1 : R1#-1 Ria-1 U
1 0,
(22)
we view R1--1 as a mixture of the non-conflicting subset Ri and the interference subset Ri:--1- Accordingly, the posterior over I R1;--1 can be written weighted average:
p(R(-=1)
p(ii.-1) p(0 R(411) + ((R1:11) p(0 Ri
p(0 R p(R
(23)
Since p(R = (( + p( (Ri:--)) rearranging gives
p(0 R(--1)== = p(R(:c-1)
p(0 R1i--1) ((R p(0 Ri:--1) D(R(.111)
p(R p(R(,111)
(24)
1 pl ((Rjaa1))
p(Rj
p(O R1i--1)
p(0 R
p(R
p(R(
B.1.2 FOR THE SECOND POINT (IN PRACTICE).
During stages 1:t-1, is unavailable, so Ri (and thus Ria-1) cannot be formed at the data level. We therefore estimate the interfering subset at stage by running M on D, to obtain old- class pseudo labels y1.- 1, constructing simulated past set Diz-1 (images from Dr with ) 1), and passing it through M,-1 to get proposals R14-1 with background subset R We then define
Ria-1 {r E R 3g E G., ToU(r, g) 2 }
(25)
and take the surrogate non-conflicting set as
Ri = R1S-1 Ri
(26)
Let
RRim-1l
4:
(27)
1R
16

---

Published as a conference paper at ICLR 2026
Viewing the past proposals mixture of non-conflicting and interfering parts leads to the surrogate reweighting:
p(0 (1) = (1+ R1:-11 - le p(O Ri
(28)
Here p(0) R1a-1) is the EWC posterior frozen at the end of stage t-1, and p(0 Ri:-1) is computed at stage on the estimated interference set.
B.2 PARAMETER IMPORTANCE ESTIMATION WITH ISOLATED INTERFERENCE
To approximate the posterior p(0 11), we begin by applying the Laplace approximation MacKay (1992), which models probability distributions as Gaussians centered at the optimal parameters. Specifically, we approximate both the prior (1x-1) and the interference posterior p(0 as Gaussian distributions with shared mean 0-- and variances derived from their respective Hessian matrices Kirkpatrick et al. (2017):
1 H
of
(29)
H
According to equation 28 the posterior (( can also be approximated Gaussian distribu- tion with the same mean and a combined variance:
HH'
o. =
= (1+ + k2H
(30)
We define the importance scores as I H and Ig H" leading to the interference-aware importance:
I k1)+ + 12
i=
(31)
The Hessians are estimated by:
27
0 log logply/z, 0,=07 1.1
H, -E(r,))-Daa
00,
(32)
27
H - (e.g)
log poyll, 0)
a0:
0:-0
where these Hessians computed different task stages. Specifically, the Hessian H; is estimated based on data from previous tasks D1=-1. capturing the sensitivity of model parameters to previously learned knowledge. In contrast, the Hessian H; computed at the current task stage I, using the interference region set R This set includes regions from the current task data that were incorrectly labeled as background by old model M,-1, thus representing parameter sensitivity specifically to interference knowledge.
The log-posterior over R1 can then be expressed as:
log p(0 R1 - -
(33)
where C' is a constant. Meanwhile, the log-likelihood of the current task data is represented as:
log l)g -ddd _det
(34)
Combining the two terms, the posterior maximization becomes:
17

---

Published as a conference paper at ICLR 2026
Diject Counts Frediuus Channes per Tudt
1432
4230
2223
V0039-0
Figure 3: Object counts from previous and future task classes observed in each task under the VOC 5-5 setting. Each bar indicates the number of object instances belonging class sets introduced in earlier tasks (blue) and those introduced in later tasks (red). This reveals inter-task interference in incremental object detection, where objects from future classes may appear before their annotations are introduced.
Table 8: Oracle analysis on VOC (15-5). mAP@0.5 is reported.
Method
1-15
16-20
1-20
EWC
76.8
61.3
72.9
IKI-EWC
76.7
63.1
73.3
Oracle-EWC
76.8
63.5
73.5
0 arg max
log ((x)()))))))) + log (ogr(((((1..)))
(35)
ALI
arg min
Edet (0) + 2(1 + I + 2
(0 0 1
where À is regularization coefficient. The final loss function at stage becomes:
A Σ I,(0, 0 0;
=(0)= L rdet (8)
(36)
2
C ADDITIONAL ANALYSIS
C.I EMPIRICAL EVIDENCE OF WIDESPREAD INTERFERENCE
In IOD, the data each incremental stage may contain objects belonging to both previous and future task classes. Objects from previous tasks appear without annotations but can be partially recovered through pseudo labeling using old detectors. contrast, objects from future tasks remain unlabeled and are mistakenly treated as background, introducing harmful interference during training.
To quantify this phenomenon, we analyze the VOC 5-5 task split and visualize the number object instances from both previous and future task classes encountered in each stage. As shown in Figure 3 first two tasks observe significant amounts of future-class objects, while the latter stages receive many instances from previously seen classes. This highlights the presence of inter-task interference caused by incomplete annotations, supporting the motivation for our interference isolation strategy.
C.2 ANALYSIS OF IDEALIZED INTERFERENCE REMOVAL
We validate interference isolation with an oracle study on VOC (15-5), where we assume perfect future labels stage t-1 and thus construct the ideal non-conflicting R RI i
18

---

Published as a conference paper at ICLR 2026
Table 9: Gradient angle statisties (first 100 iterations, batch size 16) on VOC (10-10)
(9), 9x) /
Norm(g)
Norm(g>)
mean
89.8 99.814
0.030
0.046
std
12.730
0.059
0.101
Table 10: Class laass-vvergged confusion and accuracy on VOC (10-10).
Method
New (%) New >Old (%) Overall Mís. (%) New Acc. (%) Old Acc. (%) Overall Acc. (%)
baseline
11.0
11.2
11.1
60.5
57.3
58.9
EWC
12.2
10.9
11.5
61.6
59.0
60.3
IIIEEWC
11.3
11.4
11.3
63.2
59.6
61.4
IKC
10.1
11.4
10.7
63.1
61.5
62.3
exactly (Oracle-EWC). We compare this IKI-EWC, which estimates Ri stage without revisiting past data.
As shown in Table IKI-EWC improves over plain EWC on the new classes (16-20) and on overall mAP, while trailing Oracle-EWC by 0.4 (new-class) and 0.2 (overall) mAP. Old-class accuracy (1-15) remains essentially unchanged across methods.
C.3 ASSESSING INDEPENDENCE BETWEEN OLD AND NEW CLASSES
An important assumption Sec. 3.3 is that, after isolating interference, updates from past (old-class) knowledge are approximately independent of those required by the current (new-class) task. To probe this on VOC (10-10), measure the angle between gradient directions without any extra regularization.
Strictly proving absolute independence between two real-world data sources is infeasible: both subsets are RGB images drawn from the same visual world, so exact independence cannot be guaranteed even in classification. We therefore test for approximate independence by checking whether the update directions for old and new objectives are nearly orthogonal in practice.
Let 91 denote the gradient of the old-task losses (classification and regression computed with old- class pseudo-labels) and it the gradient of the new-task losses (classification and regression on new classes). Over the first 100 iterations (batch size 16), we compute the angle (g), 92) (degrees) and the norms || ll. ||g]|. The statistics summarized in Table 9 the mean angle is 89.8 with standard deviation 12.7%, and the mean norms are [[ 0.030 and || 0.046 (std 0.059 and 0.101).
Angles near 90 indicate that the two update directions are close to orthogonal, implying weak mutual interference. These results support our working assumption that, once interference regions isolated, R1 and R, can be treated as approximately independent for posterior factorization.
C.4 CONFUSION AND ACCURACY ANALYSTS BETWEEN OLD AND NEW CLASSES
Using Table TO we assess whether EWC amplifies old-new foreground confusion and whether PKC mitigates it. Overall, plain EWC produces only minor changes the aggregate misclassification rate, suggesting it does not substantially worsen cross-task confusion. In contrast, adding PKC within our full system (IIKC) lowers the overall confusion between old and new foreground categories and is accompanied by clear improvement in both new-class and old-class accuracy. In short, EWC has a limited effect on old-new foreground confusion, while PKC effectively reduces it and improves accuracy.
19

---

Published as a conference paper at ICLR 2026
Table 11: Impact Different Values on IKI-EWC Performance.
Value of
VOC(10-10)
1-10
11-20
1-211
75.0
0.0087
75.0
75.2
75.]
868183.68 183,4375
74.3
74,4
74.3
Table 12: Ablation study the IoU threshold y under VOC (10-10).
Hyper-parnmeter
1-10
11-20
1-20
0.20
74.7
75.2
75.0
0.50
75.0
75.2
75.1
0.65
75.1
74.8
75.0
C.5 ANALYSIS OF SCALING FACTOR k AND SYPER-PARAMETER
C.5.1
THE SCALING FACTOR
The interference-aware regularization term in equation 35 is defined as:
0 = arg min
Cdet (0) + 2(1 2 (0 0 1
(37)
0
p(i pR
where the scaling factor k:
reflects the relative prevalence of interference in the old data. Intuitively, larger k implies more contamination by future-class knowledge, thus requiring stronger dein-weighting of corresponding parameters during regularization.
To validate the theoretical impact of k, we conduct experiments under the VOC 10-10 setting, comparing three configurations: (1) the computed k, (2) k 0 (ignoring interference), and (3) an excessively large & scaled by 10 As illustrated in Figure 4 and Table [1] the computed yields the best performance across all class splits, while both extreme values degrade results. This confirms that captures the trade-off between stability and interference suppression, validating the effectiveness of our formulation.
C.5.2 THE HYPER- PHEERR-PRAMEEERR
The IoU threshold y (see equation 8 determines whether a current foreground region is treated as background by the old detector. As shown in Table [2] the metries change only slightly as y varies.
D COMPUTATIONAL OVERHEAD ANALYSIS
Table 13 summarizes efficiency on VOC (10-10) with single RTX 4090. We compare plain EWC, our two modules used separately (IKI-EWC as module I and PKC module 2. lightweight projection layer removed after training), the full IIKC system, and strong non-replay baseline (BPF; the first row shows our re-implementation). GFL.OPs are reported per iteration (forward plus backward) with inputs of size xx608 x 928, and "Additional GPU" denotes the extra memory beyond the stored checkpoint. The results indicate that IKI-EWC and PKC each add only modest computation and memory while maintaining overall efficiency, and the combined IIKC system achieves the highest accuracy with only small cost increase relative to EWC. We also report runtime COCO in Table 14
E
LIMITATION
limitation IKI-EWC its reliance on the old model for generating pseudo labels, which may lead to imprecise annotations and residual interference. This issue could potentially be addressed
20

---

Published as a conference paper at ICLR 2026
Table 13: Efficiency comparison under VOC (10-10) on single NVIDIA 4090.
Medhod
1-20
GFLOPs
Rumning (min)
Additiinal GPU (MB)
4Params (M)
BPF (BCCV"24)
72.9
369
364
252.07
33.2
74.5
173
4144
EXI-EWCON dimtt module)
73.1
333
174
313.78
41.4
PKC (scondd module)
74.3
15
4,01
1,05
IIC (full
75.7
339
189
317.79
42.5
Table 14: Running time on COCO (70-10) with Two RTX 4090 GPUs.
Method
Running time (minutes)
IKI EWC
2499.14
76.0
Value
1-2.0
8-0.0037
8-808360.4376
75.0
75.0
75.3
75.0
m3p
24.5
26.4
14.3
M4
74.0
15.9
73.5
1:30
11:20
VOC110-10
Figure 4: Ablation study of the scaling factor l: in IKI-EWC. We compare the computed optimal value with two extreme settings: a minimal value = and an excessively large value scaled by 10 Results show that properly estimated i leads to the best performance, validating its theoretical derivation.
in the future by incorporating memory replay techniques to better preserve old task representations and integrating attention mechanisms reduce misclassification errors between foreground and background regions.
F POTENTIAL IMPACT
Our method enhances the adaptability of object detection systems in dynamic environments, benefiting applications such as autonomous driving, assistive robotics, and privacy-preserviee surveillance by reducing the need to store or retrain on historical data. However, with many machine learning techniques, may be misused in unethical surveillance or military contexts, and remains susceptible to bias, imbalance, or adversarial data if deployed without adequate safeguards.
G LARGE LANGUAGE MODEL USAGE STATEMENT
We relied on large language model (LLM) only for language refinement (grammar, wording, and clarity). The LLM did not contribute the conception of ideas, study design, data collection, analysis, figure/table generation, and it did not write technical content. All methodological and experimental contributions, as well as the interpretation results and final decisions, are by the authors.
21


Reviews:
[{"review_id": "ej43fs7Khk", "reviewer": "Reviewer_w6Bq", "summary": "This paper tackles the interference of knowledge between past/present knowledges in Incremental dense prediction problems. To mitigate this problem, the authors conduct quantative modeling based on bayesian analysis and propose novel algorithms called IKI-EWC. Also, the authors propose prototype based classifier correction algorithm to prevent the drift at the classfier level", "questions": "See weaknesses", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The authors well analysed the existing problem and then conducted rigorous mathematical analysis on that. This makes the motivation of the proposed method very strong\n- The paper is well written and easy to follow. \n-This paper provides extensive experimental results that can empirically support the authors' claim as well", "weaknesses": "- Authors approximated non-interference knowledge only by using current data. I agree that this would be the best/practical way of doing it under this setting but I wonder does it actually approximate the goal well? Is there any way of computing ground truth goal even by using whole ground data? \n\n- I wonder why the authors used parameter regularisation methods. It is generally know to be poor compared to distillation methods. Can we use the IKI concept for distillation based methods as well?\n\n- Prototype based classifier retain is quite common concept in classification CIL methods. Is it entirely novel in dense prediction tasks?\n\n- Doesn't not regarding IKI concept on PKC levels conflicts with the other module (IKI-EWC)?\n\n- Although the motivation is quite strong, improvements look marginal, especially when each module is solely applied.", "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": "smiEJCN66f", "reviewer": "Reviewer_V8s4", "summary": "The authors propose IIKC, a two-part framework for Incremental Object Detection (IOD): (1) Interference-Knowledge-Isolated Elastic Weight Consolidation leverages the old model’s mispredictions on new-task data to eliminate interference caused and rebuild the Bayesian posterior and parameter importance; and (2) Prototype-based Knowledge Calibration applies a learnable linear projection to compensate for semantic drift of old-class prototypes and then jointly retrains the classification head with current features. The approach outperforms strong baselines—covering both no-rehearsal and small-exemplar rehearsal regimes—across multiple stepwise and multi-step protocols on PASCAL VOC and MS-COCO.", "questions": "See above.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The proposed IKI-EWC internalizes the IOD-specific “future-class- background” interference into computable posterior correction and importance fusion, yielding an end-to-end implementable path for conflict isolation. Compared with heuristic reweighting or soft masking, it offers an explicit probabilistic formulation with closed-form solutions (Eqs. 10 and 12).Which provides a systematic extension of EWC (new decomposition, a quantified $k$ , and a new fused importance $\\tilde{I}$ ) that is both theoretically grounded and engineering-ready.\n\n2. PKC addresses semantic drift using a lightweight projection together with prototype-based retraining, with small overhead and yielding clear gains (as confirmed by ablations). Moreover, compared with certain dual-teacher distillation schemes, the proposed method is simpler in both computation and implementation.\n\n3. The paper is well structured and clearly written. Figures 1 and 2 intuitively illustrate the core idea and overall framework, enabling rapid understanding. It also provides an overall framework diagram and complete training pseudocode—explicitly specifying the input parameters γ, Top-K, and λ—which facilitates re-implementation and comparative ablation studies.\n\n4. The evaluation experiments are thorough and in-depth, the results show consistent gains over strong baselines on VOC/COCO across stepwise/multi-step protocols. The authors analyze show low sensitivity to γ and that computed k beats extreme settings, and report runtime/memory costs.", "weaknesses": "1. The core of IKI-EWC is to accurately estimate interference regions, a procedure that relies entirely on pseudo-labels generated by the previous model ($M_{t-1}$ ) on the new data. If $M_{t-1}$ has degraded in performance or exhibits prediction bias, the interference estimation may be inaccurate, thereby affecting the overall performance of the framework. Although the paper notes this in the limitations section, it does not experimentally analyze how sensitive the method is to pseudo-label quality as a function of  $M_{t-1}$ performance.\n\n2. The statistical robustness of the relative mass $k$ is not demonstrated. Since $k$  is computed as a ratio of proposal counts, it is highly sensitive to the IoU threshold, the number of proposals, class long-tail effects, and scale distributions. When sample size or the positive/negative balance fluctuates across stages, the variance of  $k$  can become large, causing the importance III to oscillate excessively. The paper lacks a systematic report of confidence intervals and sensitivity analysis for $k$ .\n\n3. Evidence for the “approximate independence/orthogonality” assumption is limited. The derivations assume that “clean historical data” and “current data” are approximately independent, and cite an early-training gradient angle of ≈90° as support. However, orthogonality is not independence, and early mini-batches do not characterize the entire training trajectory. If representations become increasingly coupled later on—especially in multi-object/multi-scale settings—the premise underlying the posterior reconstruction weakens, potentially biasing the direction of the regularization. This point requires more extensive discussion.\n\n4. PKC’s “prototype + linear projection” design is simple; however, compared with EFC’s anisotropic constraints with Gaussian-prototype updates, and LDC’s label-free learnable drift compensation, it might show clear shortcomings in the granularity of drift modeling, robustness and statistical sufficiency.\n\n5. Several implementation details are insufficiently specified—for example, the confidence threshold for pseudo-labels, the method and computational cost of Hessian/Fisher estimation, and the sampling procedure used in PKC. In addition, it is not fully transparent whether data augmentation and preprocessing are exactly matched to the strong baselines in both the rehearsal-free and rehearsal-based regimes.\n\n[1] Elastic Feature Consolidation for Cold Start Exemplar-Free Incremental Learning, ICLR 2024.\n[2] Exemplar-free Continual Representation Learning via Learnable Drift Compensation, ECCV 2024.", "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": "tzJx12UEaT", "reviewer": "Reviewer_Zo9q", "summary": "Authors introduce IIKC, a novel framework for Incremental Object Detection (IOD) that combines interference-aware Bayesian regularization (IKI-EWC) and Prototype-based Knowledge Calibration (PKC) to tackle catastrophic forgetting and knowledge conflict during continual learning of new object classes. IIKC identifies and isolates regions in new task data that create interference, using pseudo labels from the old detector, and recalculates parameter regularization based on both retained and conflicting knowledge. The PKC module corrects semantic drift by realigning previous class features with current ones using a learnable projection, retraining the classifier on calibrated prototypes. Experiments on PASCAL VOC and MS-COCO benchmarks show IIKC consistently outperforms state-of-the-art regularization and rehearsal methods on incremental settings, with higher mAP and reduced forgetting across more challenging task splits.", "questions": "1) Novelty & Conceptual Soundness: The paper introduces IKI-EWC to isolate interference knowledge in incremental object detection, but it remains somewhat unclear how this approach fundamentally differs, both theoretically and algorithmically, from prior interference-aware frameworks such as BPF (Mo et al., 2024) and GMDP (Wang et al., 2025), which also attempt to mitigate background conflicts and feature drift; could the authors provide a deeper explanation of what new insight of the reformulation and posterior correction (Eq. 10) contribute beyond existing EWC-based or distillation-based methods, and whether this formulation yields measurable theoretical guarantees or only empirical improvements?\n\n2) Methodology & Implementation: The proposed interference isolation depends heavily on pseudo-labels generated by the previous model to identify conflicting regions; since pseudo-label quality can vary widely and introduce noise, could the authors analyze how the accuracy of these pseudo-labels impacts interference estimation, describe the computational overhead of running the old detector on all new data, and clarify how the approach scales to large datasets (e.g., COCO or LVIS) where the number of proposals and IoU computations may become prohibitively expensive?\n\n3) Equation 2 and 3 mostly focus on Bayesian posterior? Is your framework Bayesian or leverage variational inference to build the network?\n\n4) Overall optimization is not clear.  Equation 2 is similar to EWC. The only difference is you replaced diagonal Fisher matrix with equation 12?. And, section 3.4: Equation 14 is just knowledge distillation on features? How this norm is helping for knowledge calibration? The total loss is combination of these two loss?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1) Novel Theoretical Contribution: The paper makes a meaningful theoretical contribution by reformulating Elastic Weight Consolidation (EWC) in a Bayesian framework that explicitly accounts for interference knowledge — regions where unlabeled objects from future classes are mistakenly learned as background. This provides a principled mechanism to isolate and suppress task conflicts, addressing a long-standing limitation in incremental object detection (IOD).\n\n2) Comprehensive Framework: By integrating two complementary components — IKI-EWC for parameter-level stability and PKC for feature-level calibration — the proposed IIKC framework tackles both catastrophic forgetting and semantic drift. This dual approach effectively bridges low-level model regularization with high-level feature alignment, demonstrating thoughtful architectural design.", "weaknesses": "1) Dependence on Pseudo-Labels and Sensitivity to Noise: The IKI-EWC module is heavily dependent on pseudo-labels provided by the former detector to estimate interference regions. In the incremental object detection setting, these pseudo-labels are usually noisy, particularly for classes where the previous model has low performance. Subsequently, incorrect pseudo-labels may misidentify interference regions and subsequently impact the estimation parameter importance. The manuscript does not qualify any pseudo-label accuracy or sensitivity studies to suggest feasibility under varying degrees of noise for the IKI-EWC to evaluate stability. It is difficult to assess feasibility in more realistic scenarios without assessing effect when the pseudo-labels are not accurate. \n\n2) Computational Complexity and Scalability Concerns: The proposed framework has several computation-heavy procedures, including running the old model on all data for new tasks for pseudo-labels, computing interference ratios (𝑘), and estimating Fisher-based parameter importance for large networks such as Faster R-CNN. All operations can be both cost-prohibitive on memory, and time. The paper does not indicate training overhead, runtime comparison, or scalability analysis for the number of classes or incremental steps are increased (e.g., COCO 80 classes or LVIS 1200+ classes), which raises concerns for practicality of the method\n3) Residual interference: Future-class objects remain unlabeled and are still at risk of being treated as background, which may not be fully resolved by the current method.\n\nIIKC offers promising advances for incremental detection, balancing stability and plasticity, but future work could improve robustness by incorporating memory replay or better foreground-background attention mechanisms.", "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": "Y1WP9hERJy", "reviewer": "Reviewer_a9JU", "summary": "This paper addresses a key issue in incremental object detection: future classes are unlabeled in early tasks and are therefore learned as background, but later must be detected as foreground. This background conflict causes strong interference and catastrophic forgetting. The proposed framework consists of IKI-EWC, which aims to isolate and down-weight conflicting background knowledge during consolidation, and PKC, which aligns stored old-class feature prototypes to the current feature space and recalibrates the current classification head without keeping raw past images.", "questions": "1.\tPlease add current state-of-the-art methods to the PASCAL VOC and MS-COCO comparisons. \n2.\tPlease state the explicit assumptions used in the IKI-EWC derivation in the main text.\n3.\tPlease report the prototype buffer size and memory usage.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tThe paper focuses on a real problem in incremental detection: future classes are treated as background first, then must be detected later. It proposes two modules, IKI-EWC and PKC, to keep old knowledge without storing past images.\n2.\tExperiments on PASCAL VOC and MS-COCO are broad, and ablations show both modules are useful.", "weaknesses": "1.\tCompared with current state-of-the-art methods. \nThe paper claims state-of-the-art performance relative to prior incremental detection methods, but it does not compare against recent approaches such as RGR[1] and GMDP-ABR[2], which report equal or stronger final mAP on both multi-step PASCAL VOC and MS-COCO splits. The paper should include a direct comparison to RGR and GMDP-ABR in the main tables and clearly state in which regimes the proposed method is preferable, for example, no generator cost, lower complexity, or better stability on old classes.\n2.\tIKI-EWC formulation clarity.\nIKI-EWC is presented as deriving a clean posterior by separating non-conflicting and conflicting regions and then using this to define a new importance term for an EWC-style penalty. However, the core assumptions behind this construction, for example, proposal-level independence, using the previous model to approximate past label structure on current data, and a Laplace or Gaussian approximation, are only implicit. These assumptions should be stated explicitly in the main text where the final loss is introduced.\n3.\tMemory usage. \nThe paper emphasizes that it does not store past images, but PKC does maintain a feature memory of sampled ROI features and Gaussian prototypes for old classes. The total storage cost of this memory, for example, the number of stored features per class, their dimensionality and total size, is not reported, and there is no quantitative comparison to exemplar replay or to generative replay methods, which also claim to avoid storing raw past data but still keep some form of replay budget.  Reporting the memory footprint would make the comparison stronger and more credible.\n\n[1] Revisiting Generative Replay for Class Incremental Object Detection \n\n[2] HIGH-DIMENSION PROTOTYPE IS A BETTER INCREMENTAL OBJECT DETECTION LEARNER", "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)