Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning
Authors: Xinrui Wang
Shao-Yuan Li
Jiaqiang Zhang
Songcan Chen
Venue: ICML 2025 poster (2025)
Keywords: multi-label learning
online continual learning
Primary area: general_machine_learning->supervised_learning
TL;DR: Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning

Abstract: Multi-Label Online Continual Learning (MOCL) requires models to learn continuously from endless multi-label data streams, facing complex challenges including persistent catastrophic forgetting, potential missing labels, and uncontrollable imbalanced class distributions. While existing MOCL methods attempt to address these challenges through various techniques, \textit{they all overlook label-specific region identifying and feature learning} - a fundamental solution rooted in multi-label learning but challenging to achieve in the online setting with incremental and partial supervision. To this end, we first leverage the inherent structural information of input data to evaluate and verify the innate localization capability of different pre-trained models. Then, we propose CUTER (CUT-out-and-Experience-Replay), a simple yet versatile strategy that provides fine-grained supervision signals by further identifying, strengthening and cutting out label-specific regions for efficient experience replay. It not only enables models to simultaneously address catastrophic forgetting, missing labels, and class imbalance challenges, but also serves as an orthogonal solution that seamlessly integrates with existing approaches. Extensive experiments on multiple multi-label image benchmarks demonstrate the superiority of our proposed method. The code is available at \href{https://github.com/wxr99/Cut-Replay}{https://github.com/wxr99/Cut-Replay}

Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning
Xinrui Wang" 12
Shao- Yuan Li 123
Jiaqiang Zhang 12 Songcan Chen 12
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Multi-Label Online Continual Learning (MOCL) requires models to learn continuously from end- less multi-label data streams, facing complex chal- lenges including persistent catastrophic forget- ting, potential missing labels, and uncontrollable imbalanced class distributions. While existing MOCL methods attempt to address these chal- lenges through various techniques, they all over- look label-specific region identifying and feature learning a fundamental solution rooted in multi- label learning but challenging to achieve in the online setting with incremental and partial super- vision. To this end, we first leverage the inher- ent structural information of input data to evalu- ate and verify the innate localization capability of different pre-trained models. Then, we pro- soauttandd-pprieeee---eeee simple yet versatile strategy that provides fine- grained supervision signals by further identifying, strengthening and cutting out label-specific re- gions for efficient experience replay. It not only enables models to simultaneously address catas- trophic forgetting, missing labels, and class imbal- ance challenges, but also serves orthogonal solution that seamlessly integrates with existing approaches. Extensive experiments on multiple multi-label image benchmarks demonstrate the superiority of our proposed method. The code available at
Memory Buffer
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Misiin labds
Milniag labah
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Figure I. Two unique challenges in MOCL compared with tradi- tional OCL: (1) Massive missing past and future labels in both coming data stream and memory buffer. (2) Severe class imbalance that persists in the memory buffer even with re- re-balancing strategies like CEBS(Wei Li, 2019a; Yan al., 2021).
pose CUTER
1. Introduction
Online continual learning (OCL) enables models learn from continuous, endless data streams. Significant progress has been made in this area through various techniques to mit- igate catastrophic forgetting, such as knowledge distillation, gradient regularization, and experience replay.
However, these OCL approaches focus primarily on single- label classification, while real-world data often exhibit mul- tiple semantic concepts and objects, motivating the study of Multi-Label Online Continual Learning (MOCL) (Kim et al., 2020). Figure 1 shows an illustrative example. At each time step t, the training data D, concerning label space Ye for task & arrive in a streaming manner. For each ex- ample (x, v) E D C Y, denotes the tagged relevant label set. Given sequence of T tasks, the objective of MOCL is efficiently adapt the learning model to the current task while preventing catastrophic forgetting of pre- viously learned tasks.
Replay
'College of Computer Science and Technology, Nanjing Uni- versity Acronautics and Astronautics, Nanjing 211 106 'MITT Key Laboratory of Pattern Analysis and Machine Intelligence, Nan- jing University of Aeronautics and Astronautics, Nanjing 21 216106 The State Key Laboratory of Novel Software Technology, Nan- jing University, P.R. China. Correspondence to: Songcan Chen <s.chen @ cdu............
Compared to single-label scenarios, MOCL faces two spe- cific data challenges: (1) Pervasive missing labels: samples in task t are only annotated with labels from Y,, even when containing objects from old classes Viat1 or future classes
Proceedings of the 42nd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning
M/41:T. These unlabeled classes become false negatives, aggravating catastrophic forgetting. (2) Uncontrollable im- balanced classes: the categories often follow long- long-tailed distribution. Training on such data would lead the model to be biased towards overfitting the head classes and under- fitting the tail classes. The co-occurrence of head and tail classes within one sample further complicates this issue.
object-level regions and supervision signals stored in the buffer, it enables more effective experience replay under the same memory overhead, where class imbalance is easily addressed by controlling class distributions in the buffer.
To this end, we propose CUTER (CUTT-out-nnd- -xprannnnn-xpeenccee-- Replay), a simple yet versatile approach that efficiently localizes, strengthens, and cuts out label-specific regions for MOCL with richer fine-grained supervision signals. Mo- tivated by the recent inspiring trials vision pre-trained models' ability for zero-shot coarse segmentation (Caron et al., 2021; Siméoni et al., 2021; Wang ct al., 2023b), we make a thorough study of the pre-trained modes' localiza- tion capability. Through extensive empirical validations over several widely used pre-trained models (e.g., DINO, MoCo, MAE), and established theoretical supports from graph theory, we show that the averaged Fiedler value (sec- ond smallest eigenvalue of graph I Laplacian) of the feature patch similarity graph can serve as valid evaluation mea- sure for selecting pre-trained models good at localizing precise label-specific regions. The identified salient regions are then selectively stored in a memory buffer based on their prediction confidence and label alignment, effectively trans- forming multi-label image classification replay into multiple single-label sub-image classification tasks. To further com- bat the forgetting impact of the model's 's localization ability during the continual learning process, we draw inspiration from image segmentation principles and incorporate a low- rank constraint on the feature- similarity adjacency matrix to improve inter-patch separability for more precise region cropping, supported by graph spectral theory.
Several works have attempted to address these challenges. (Kim al., 2020) first identified the severe forgetting of minority classes in long-tailed scenarios and proposed Partitioning Reservoir Sampling (PRS) strategy to balance head and tail classes in replay-basee based approaches. To ad- dress the computational inefficiency of PRS, (Liang & Li, 2022) developed Optimizing Class Distribution in Memory (OCDM), which reformulates memory updates as sample selection optimization problem solvable through linear- time greedy algorithm. The challenge of missing labels in MOCL was first highlighted by (Du et al., 2022), which introduced an Augmented Graph Convolutional Network (AGCN). This model generates predictions for previously seen classes while modeling dynamic label relationships across sequential tasks and mitigating forgetting through distillation and relationship-prest preservnng losses. Building on this, (Dong et al., 2023) proposed the Knowledge Restore and Transfer (KRT) framework, which combines dynamic pseudo-labeling for old classes with session-specific knowl- edge transfer. More recently, (Du et al., 2024) tackled both challenges simultaneously through two key components: Asymmetric Knowledge Distillation (AKD) and Online Re- labeling (OR). AKD rebalances the learning process by emphasizing negative label learning in classification loss while reducing the impact of overconfident predictions in distillation loss. OR complements this by recovering miss- ing labels in the memory buffer through online relabeling.
Serving as orthogonal solution to exiting OCL models, it enables seamless integration with existing models to address catastrophic forgetting, missing labels, and class imbalance challenges. We conduct extensive experiments on multiple multi-label image benchmarks, showing that our method significantly outperforms ofut-offtthearr methods in MOCL. In summary, our main contributions are threefold: i) We introduce label-specific learning into MOCL and conduct the first systematic analysis of pre-trained models' potential for MOCL; ii) We propose CUTER, simple yet versatile replay strategy that simultaneously enhances model perfor- mance while addressing catastrophic forgetting, missing labels, and class imbalance; iii) Extensive experiments and ablation studies demonstrate that CUTER not only achieves state-of-the- performance but also serves comple- mentary component that can be seamlessly integrated with existing approaches.
Although these works demonstrate promising results, their feature learning mechanisms have inherent limitations with multi-label data. The conventional approach of extracting single feature vector per example, along with techniques like pseudo-labeling and resampling, suffers from co-occurrence bias between head and tail classes. This limitation is par- ticularly critical with missing labels and class imbalance, where discriminative feature learning requires minimizing interference from label co-occurrence patterns.
With the above understanding, we resort to label-specific feature learning, which was shown to be superior to uni- fied sample-wise features in offline multi-label learning (Zhang & Wu, 2014). Suppose we can successfully identify label-specific regions in images through a straightforward cut-out-and and-ellly mechanism, i.e., cutting out these re- gions and storing them in the memory buffer for replay. This mechanism would naturally avoid label co-occurrence interference and missing label issues. Furthermore, with
2. Method
In this section, we focus on better extracting and utiliz- ing label-specific regions to address MOCL problem. Our method consists of three key steps. First, we propose
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Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning
an annotation-free evaluation protocol assess the zero- shot localization potential of popular pre-trained models in MOCL. Second, leveraging the selected pre-trained model and its localization capability, we design simple yet ver- satile cut-out-and-Pn cut-out-and-replay strategy for effective and efficient region-label matching, cropping, and storage. Finally, to prevent the deterioration of model's localization capability during MOCL progression, we incorporate regulariza- tion term that continuously consolidates and strengthens the model's localization and segmentation abilities.
180
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120
100
10
35
54 15 L06 12. 15. 125 ado alo 25. persehoticcs Indde capacty JAPal
morsehstioo// bationn casaciy (APs)
Figure 2. Correlation between the averaged Fiedler Value and zero-shot detection performance (APso) on Pascal VOC07 and MSCOCO.
2.1. Zero-Shot Localization Assessment for Pre-trained Models
A key prerequisite for our cut-out-and-replay strategy is identifying suitable pre- treitriine models through their inher- ent localization capabilities. Recent studies have shown that advanced vision pre-trained models can naturally local- ize objects through feature clustering (Caron et al., 2021; Siméoni et al., 2021), making them potential candidates for MOCL. However, conventional evaluation metrics requir- ing ground truth boxes or masks impractical, most multi-label datasets lack such annotations. This motivates our development of an annotation-fire evaluation protocol.
Lemma 2.2. (Chung, 1997) For weighted undirected graph G, let X2 be its Fiedler value, h(G) be its Cheeger constant, and A = max; d(i)) be the maximum degree in the graph. Then we have:
A < SGG VAA 2
Based on this theoretical foundation, we propose assess model's potential zero-shot localization capability by com- puting the average Fiedler value of features extracted from a small subset of the downstream dataset. As shown in Figure 2 and Figure 3, they reveal a clear correlation be- tween zero-shot localization performance and the averaged Fiedler value of patch feature graphs. This aligns with graph theory principles: a lower average Fiedler value indicates weaker graph connectivity (Royle & Godsil, 2001), suggest- ing stronger feature separability and thus better suitability for partition-ban based operations like MCut and NCut.
To address this challenge, we propose a novel evaluation metric inspired by graph theory and spectral clustering. Our key insight stems from examining popular unsupervised localization methods like Normalized Cut (NCut)(Shi & Malik, 2000), Mask Cut (Maut)(Wang et al., 2023a), and Token Cut (TCut)(Wang et al., 2023b). These methods perform better when feature maps form graphs with stronger separation (weaker connectivity), which is mathematically characterized by the Cheeger constant:
Definition 2.1. (Royle & Godsil, 2001) Let G FV, F, E) be weighted graph with vertex set V. edge set E, and edge weight matrix W. The Cheeger constant h(G) is defined as:
Applying this metric to evaluate popular pre-trained mod- els, including MoCo(He et al., 2020; Chen et al., 2020; Chen et al., 2021), DINO(Caron et al., 2021; Oquab et al., 2023), MAE(He et al., 2022), and iBOT(Zhou et al., 2022), we uncover that multi-crop consistency training signifi- cantly enhances innate localization ability. In contrast, reconstruction-bast training inherently encourages feature sharing during recovery, potentially limiting the effective- ness of spectral bustering-bbsse localization methods. We leave the detailed analysis in Appendix D. In summary, when partial downstream task data is available, we can evaluate models by computing their average Fiedler value. However, when facing completely unknown future tasks, selecting a model pre-trained with multi-crop consistency is likely a more reliable choice.
C(S, S) SCV min V),, SV
((G) = min
where is a non-empty subset of V. V S represents its complement and C()) measures the similarity between two sets, e.g., C(A, B)= Ev.EA.YEB W.y.
Although computing the Cheeger constant is NP-hard, we can leverage its relationship with the graph's 's Fiedler value as shown in the following Lemma 2.2. Given a sample's feature map (xx), we can construct a weighted undirected graph G = (V, E, A) where vertices repre- sent feature vectors of image patches and edge weights ((((,,))((22)11= encode patch connectivity Ai = exp((1110 (10p()_1((..))>>)))))) 20
2.2. Selective Replay via Label-Region Matching
using a Gaussian kernel. The Fiedler value A is then com- puted as the second smallest eigenvalue of the graph Lapla- cian matrix = D - A. where D is an N x N diagonal degree matrix with elements d(i) = Ej Aij.
After selecting the initialization model based on the pro- posed average Fiedler value, the next obstacle for imple- menting our envisioned cut-and-replay strategy lies estab-
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Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning
Supervised
MoCo v3
DINO vl
DINO v2
MAE
iBOT
Figure Visual comparison detection (coarse bounding boxes) segmentation (coarse masks) capabilities across pre-trained models using VIT-S/16 backbone, obtained via two-round MaskCut (Wang et al., 2023a).
lishing precise one-to-one correspondence between image region and its associated label.
updated model, aim replay these extracted objects instead of the entire image as done in previous works.
Last
Cal
(1 p log(pe),
Ye = 1, Yc 0,
(1)
300
OCBM
PRS
Ck
pd log(1 Po),
175
CUTER
150
Number sumple
To achieve this goal, for each incoming data pair (x, y), we make the prediction = f(x) and apply the asym- metric loss (Eq.1) like most MOCL methods, where ye represents the binary label indicating the presence of class and y+, > denote the positive and negative focusing parameters respectively. Subsequently,
125
100
50-
employ MCut to generate a list of binary masks {m² J==1 that identify potential foreground objects, where N is hyper-parameter determining the number of MCut iterations (MCut's de- tailed background and procedure is provided in Appendix B). These binary masks are then used derive bounding boxes extract N potential foreground objects (#rdee ( j== from the input image æ.
Figure 4. Class distribution in the memory huffer (size=1000) for different re-halancing methods after training VOC dataset.
{(H,, wf, hig, w²)) )= enabling
This storage process of (1/ J. consists of two steps. First, to establish reliable label-region correspondences, we retain only the objects that maintain high classification confi- dence post-resizing and correspond a single label. Specif- ically, for prediction Paeg- we select extracted object 2/s into the candidate for the memory buffer if:
The extracted objects {#ed)) } are then resized and fed into classification model again obtain foreground object prediction P2 f( Our experimental results in Table 6 (CUTER vs CUTER w/ Fixed Backbone) demon- strate that for object localization in MOCL, using model trained with asymmetric loss 1F (Ridnik et al., 2021) outperforms using fixed pre-trained backbone like DINO, showing superior performance in identifying current task classes (C*) and localizing regions corresponding to given labels. After making the cut out through this continuously
Plss..1) > T Pann. 0.5
where Pobg,(2) denotes the k--th largest element in Pia, and is the confidence threshold. Meanwhile, to balance the class distribution in the memory buffer, we employ two thresholds

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Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning
repulattodiie
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Algorithm 1 CUTER w/ regularization for multi-label on- line continual learning
-eweago Medde Value
Groph Cententtrr
Graph Ciemertiiie
1: Input: Online streaming data (x; y). pre-trained en-
-2.
coder 0g. classifier & model f = Φb
200
Initialize: Memory buffer M < 0, Candidate set
174
115
C < (}. f- 80
3: for i = 1 to N do
4: Implement MCut to obtain foreground object Tobji
5: Minimize Eq.4
Number Clocons
Number Clasona
6: Calculate mask using Eq.6
7: Select objects into candidate set based on:
Figure 5. Visualization of model's zero-shot localization capability on PASCAL VOC dataset during MOCL. training.
Prediction Pobj = fZobj)
Selection threshold (Eq.2)
10: end for
Ti and 72 (71 T2) based on class frequency. For any class with frequency less than half of the most frequent class, we use the lower threshold 71, while assigning to the others.
11: Compute adjacency matrix A on features ((x)) and loss using Eq.3
12: Replay memory data (x"", ,"") according to Eq.1
13: Update model parameters f
Second, we adopt a modified rebalanced reservoir sam- pling strategy for the memory buffer. For a new candidate object, its sampling probability is 1 m/Mmazz where m denotes the number of samples with predicted label y =armaa(r''s) in the memory buffer, and mmaz rep- resents the count of the most frequent class. To maintain class balance, we randomly remove samples from the most frequent class when the buffer is full. This straightfor- ward sampling strategy achieves better class balance than OCDM(Liang & Li, 2022) and PRS(Kim al., 2020) with lower computational overhead, as shown in Figure By storing only regions with single-label correspondence, we enable direct control over class distribution while preserving more discriminative information with reduced memory cost.
14: M is not full then
15: Put candidate set C in memory buffer M
16: end if
17: if M is full then
18: Update memory buffer M by sampling from C with
class-balanced probabilities
19: end if
nature of cigenvalue sorting operations graph Laplacian matrices. Inspired by spectral perturbation theory for sym- metric matrices, we approach this challenge from a different perspective. For effective object localization, an ideal fea- ture map should exhibit high intra-object feature similarity while maintaining low inter-object correlations. This prop- erty can be mathematically represented by an adjacency matrix A that approximates a symmetric block diagonal structure, where the rank h corresponds to the number of distinct objects in the input image x. Such structured adja- cency matrices are theoretically guaranteed to yield optimal bipartitions under NCut or MCut criteria.
2.3. Localization-Aware Feature Regularization
Thus far, we have introduced our cut-and-replay strategy for preserving single-label regions and their corresponding fea- tures. While this strategy effectively maintains model's clas- sification performance on past classes through a memory- efficient replay mechanism and demonstrates strong adapt- ability new classes, we observe that the inherited localiza- tion capability from pre-trained models gradually deterio- rates during MOCL progression phenomenon analogous to the catastrophic forgetting in continual learning. As il- lustrated in Figure 5, this deterioration manifests as higher averaged Fiedler value and more failed object localizations.
Without loss of generality, we can assume that during train- ing, the adjacency matrix of each feature graph can be de- composed A = A where A' represents ideal block diagonal matrix and e denotes non-sparse noise ma- trix. Based on these assumptions, we can prove that the Fiedler value of the feature graph is directly upper bounded by the norm of this perturbation matrix e.
In previous section, our analysis reveals a strong correlation between the model's localization performance and the aver- aged Fiedler value of patch feature graphs. This naturally raises the question: can we enhance localization accuracy, and consequently improve MOCL performance, by mini- mizing the Fiedler value of feature maps' graph Laplacian?
Theorem 2.3. For any adjacency matrix A can be written as the sum of a block diagonal matrix A and noise matrix with zero diagonal entries, the second smallest eigenvalue (Fiedler value) of A's Laplacian matrix L satisfies:
>² S ||<|/2 + ||elloo
However, direct optimization of the Fiedler value through neural networks is infeasible due to the nin-differentiabee
Theorem 2.3 provides us calculable and differentiable
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Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning
Table Comparison results on PASCAL VOC datasct with memory size being 1000
224 224 x 3.
Average Performance
Last Performance
Method
Source Task
Avg mAP
Avg CF1
Avg OF1
mAP
CF1
OF1
RS
75.05=1.228
60.32=1.35
61.29=0.74
19.71=1.75
39,88=1.04
46.09=1.24
GSS
76.01=1.24
60.84=0.82
62.37=1.14
18.64+1.29
40.10=0.82
45.13=#0.90
OCL
iCarl
72.67
56.24.12.31
58.46_1.78
50.77.12.18
36.5111.770
41.02.11.91
NsCE
75.24=1.41
64,42=1.055
65.30=0.97
16.87+1.14
40.78+1.22
42.39=11.50
KRT
59.45-1.17
46.49=00.88
57.12-1.38
38,90
33.47=11144
36.82=00,95
MLCII
APPLE
76.24-1.25
67 67.01 12.12
66,74_1.23
58,27:10.82
43.77-11003
50,21
PRS
75887=0.82
18.1511.46
61.62-1.69
54.67:10.64
42.15
43.87
OCDM
76.14-1.11
52.84+0.799
65.08
45.35+1112
36,41+0.25
40.45+1001
AGCN
MOCL
75.06=1.01
62.37:10.89
61.87=2.04
57.21
42.0621.47
44.7911.91
AGCN++
74,14-00 0.78
65.04-+: 1.24
63.55 1.45
55.34+00.7
40.06+0075
44.21++1114
CUTER w/ Rd
82.07 22507-0.53
72.19 0.70
75.27 0.57
67.89 1.28
11.35-00.9
alternative to enhance the model's localization capacity. Al- though during MOCL training we cannot obtain the ideal adjacency matrix A for each sample's 's features to directly constrain the corresponding noise matrix e, considering that we hope to guide the adjacency matrix A toward an ideal block diagonal structure, directly imposing constraints on A is also a viable choice, as shown in 3q
Experiments Setup. Following prior studies (Mai et al., 2021; Liang & Li, 2022; Dong al., 2023), we partition the datasets into several tasks with disjoint given label sets but overlapping ground truth support sets, simulating a realistic multi-label data stream. Unlike some multi-label class incre- mental studies that rely on a base task, we argue that with pre-trained models, this online learner should be capable of rapid adaptation to dynamic data streams from any starting point. Thus, we divide PASCAL VOC 2007 into 5 tasks with 4 classes each, MS-COCO into tasks with 10 classes each, and NUS- into 8 tasks where the first task has 11 classes and the remaining tasks have 10 classes each. For all datasets, the order of class assignments to tasks follows the lexicographical order of class names, as described in (Dong et al., 2023; Du et al., 2024; 2025).
L = = (f x, + R(A)
(3)
This matrix decomposition perspective relates to robust graph structure learning through noise suppression. Draw- ing from common regularization terms in graph learning (low-rank, sparsity, and smoothness), we find the nuclear norm R(AA) = ||Al|| particularly effective for reducing the noise matrix c in MOCL (Figure 5). Detailed comparisons and more analysis are presented in Table and Appendix D.
Baseline Methods. In our experiments, we compare CUTER with 10 advanced continual learning methods span- ning OCL, multi-label class incremental learning (MLCIL), and MOCL. For comprehensive evaluation, we adapt four OCL methods to our MOCL setting by modifying their clas- sifier architectures and classification loss functions, includ- ing representative replay-based methods RS(Chaudhry et al., 2019b). GSS(Aljundi et al., 2019b). iCarl(Rebuffi al., 2017), and the recent comprehensive approach NsCE(Xinrui et al., 2024). We also evaluate six multi-label continual learning methods across MLCII and MOCL categories. Among these, <RT(Dong et al., 2023), APPLE(Song et al., 2024), AGCN and AGCN++(Du et al., 2022; 2023) pri- marily address missing labels through knowledge distilla- tion or label correlation, while PRS(Kim al., 2020) and OCDM(Liang & Li, 2022) focus on class imbalance via carefully designed sampling strategies. Additional details on each method are provided in the Appendix E
Up till now, we have fully presented our proposed cut- aut-and-experience-rpph strategy (CUTER) for MOCL with a detailed algorithm procedure in Algorithm 1. For proofs(Appende) C.2), detailed explanations (Appendix D.2), please refer to Appendix.
3. Experiments
3.1. Datasets and Experimental Setting
Datasets. Following the experimental frameworks of pre- vious works(Dong et al., 2023; Liang& & Li, 2022), we eval- uate our method on three benchmark datasets: MS-COCO 2014(Lin et al., 2014), PASCAL VOC 2007(Evenn et al., 2015), and NUS- WIDE(Chua et al., 2009). Specifi- cally, PASCAL VOC 2007 consists of 5,011 training and 4,952 test images spanning 20 classes, averaging 2.4 labels per image. MS-COCO contains 82,081 training and 40.504 validation images across 80 classes, with an average of 2.9 labels per image. As available, we reconstructed the dataset by re-scraping from Flickr, obtaining 126,034 training and 84,226 test images across 81 classes, with an average of 2.4 labels per image.
Evaluation Metrics. Following (Du et al., 2023; 2024),
we use several standard metrics: mean average precision (mAP), per-class F1 score (CF1), and overall F1 score (OF1). Beyond conventional multi-label classification evaluation, we report both cross-task average performance (on seen classes) and model's ''s final performance on all classes Cl:K
is no longer publicly
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Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning
Table 2. Comparison results on MSCOCO dataset with memory size being 1000 x 224 x 224 x 3.
Average Performance
Last Performance
Method
Source Task
Avg mAP
Avg CF1
Avg OF1
mAP
CF1
OF1
RS
48.12=1.77
32.21=1.044
37.36=0.92
18.97=2.01
6,44+11.56
12.84=1.05
GSS
49.41=0.84
32.85=0.70
38.26=0.55
2..67=1.34
11.82=#0.6
17.40=1118
OCL
iCarl
1..3
33.09.10.86
37.51_1.099
19.24
7.62.11.38
12.5110.89
NsCE
51.24=0.95
37.10=1.110
44.73=0.96
26.19+1.24
17.07+1.04
22.30:20.94
KRT
44.34=0.95
19.0
43.51=1.17
36.51+00.87
27.41=0.92
30.16=11.15
MLCII
APPLE
48.67-1.28
42.34.10.85
46.86-1.31
38,47:10.94
30.12.1.08
33,51
PRS
51.79-1.08
3..64.11.32
38.06-0.94
27.95:11.98
15.3312.47
18.22.11.29
OCDM
55.45-0.87
46.78+1.00 1.02
50.59-0.92 0.92
40.56+00880
28.45+0.71
31,29+0.55
AGCN
MOCL
56.45=0.92
18.03=11.35
51.27=0.91
37.48:40.7
27.82-0.99
12.38:1129
AGCN++
54.31 0.82
52.27 0.85
36,45-+0.74
29.64+00.8
33.05
CUTER w/ Rd
60.14 0.60
51,53- 0.61
54.92 0.62
4..62
35.94+0.77
39.18
Implementation Details. After evaluating the localiza- tion capacity of different pre-trained models, we adopt ImageNet-21k pre-trained (DINO v1) VIT-S/16 our back- bone. All methods compared in Tab 1, Tab 2 and Tab 4 use the same backbone and pre-trained initialization, except KRT which uses TresnetM, as our attempts to reproduce KRT with ViT-S/16 did not yield superior performance to the original TresnetM implementation. We follow the data augmentation strategy from (Dong et al., 2023). All exper- iments in the main text were repeated five times, and we report the mean and standard deviation. Additional imple- mentation details are provided in the Appendix E.
ization Rum = - -((0())))((;;)))) where (ar;) represents the feature corresponding to the i-th patch of the input image. As demonstrated in Table 3, compared with R2 which can consistently boost model's performance, Ram and Rep often lead to performance degradation. We hy- pothesize that this inconsistency might stem from potential confficts between these so-called ideal graph structures and the model's classification objectives, which we discuss in detail in Appendix D.
Thirdly, we evaluate CUTER as a plug-in component for other popular technique in methods including PRS, OCDM, KRT, and AGCN. For fair comparison, we use a basic ver- sion Rept without re-balanced sampling and low-rank regularization. As shown in Table 5, our strategy effec- tively complements different balanneee sampling methods, and can be further enhanced by techniques like knowledge distillation and graph-based label mining.
3.2. Overall Performance
At first, we conduct comprehensive evaluation of our pro- posed CUTER by comparing its performance with several existing state-of-the-art MOCL methods and various con- tinual learning variants. Our comparison includes online continual methods, multi-label class incremental learning methods (MLCIL), and MOCL methods. While there are more recent MI.IIL methods like CSC and RebL.L(Du et al., 2024; 2025), we excluded them from our comparison due to their unavailable implementation details (no public code links) and different experimental settings. Tables 1, 2, and 4 display both average and overall performance across three synthetic benchmark datasets. The results demonstrate that our proposed method, CUTER, consistently outperforms other approaches. Notably, CUTER shows particularly sig- nificant performance improvements in the later tasks of continual learning, with more pronounced advantages in terms of last performance on all classes.
Additional visualization and experimental results, including mAp curves throughout the training, performance across different backbone architectures (pre-trained initializations), and throughput evaluations of different methods, can be found in Appendix E.2.
3.3. Ablation Studies
In this section, we conduct a series of ablation studies to investigate the specific effects of different proposed compo- nents. From Table 5 and Table 6, we can draw several con- clusions: (1) Each component (cut and replay, re- re-balanced sampling, and low rank regularization) provides consistent performance improvements, with cut and replay (Cut.Rep) showing the most significant effect. (2) While our pro- posed re-balanced sampling method may not consistently outperform other carefully designed sampling methods like PRS and OCDM across all datasets and settings, it achieves comparable results with lower computational cost (O(B) compared with O B3 Ml) in OCDM, where B is the batch size). (3) Compared with fixed backbone, updating the model continuously during training improves performance,
Secondly, we compare three commonly used regularization in robust graph structure learning as additional loss terms for localization capacity consolidation. Specifically, we implement low rank regularization R₁ = ||A||.. sparse reg- [Alla/|lA| l12, where ||Al|ı and ||Al]12 represent the 11 and 12 norms calculated after flattening ma- trix A into a vector, following the definition and constraints of sparsity from (Xinrui et al., 2024), and smooth regular-
ularization Rep
7

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Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning
Table 3. Comparison of different regularization terms for preserving localization ability.
VOC
COCO
NUSWIDE
Model
Rep
Ri
Rop
Ram
Avg mAP
Last mAP
Avg mAP
Last mAP
Avg mAP
Last mAP
CUTER w/ Rap
78.34=0.76
66,27=0085
38.68+1.08
46.74=0.62
49.73=0.50
37.06=1.16
CUTER w/ Rom
79.01 0.84
65.63.10.92
58.61100.8
44.90.1.85
50.14.10.76 51.14:0.72
18.0911.34
CUTER w/
V
82.07=0.53
67.89++1.28
60.14:0.60
47.82=0.60
37.17=1.46
Sensitivity analysis on
Seasitivity analysis 13
Sensitivity analysis
85-
85-
85-
80-
80-
80-
75-
75-
75-
70
20
70
VDC
VDC
VOC MSCOEDO NUSWID2
65-
NS CDCO
65
MSCOCD
655
NUSWIDE
NUSWIDE
66-
65-
65-
56-
50-
50
0.0
0.1
022
0.3
0-1
0.5
0.500 0.525 0.550 0575 0.000 0,625 0.ebi 0,575 5.100
0,200 0,725 0.750 0.775 0,500 austss 2.000 5.675 D.000
Figure 6. Sensitivity analysis on coefficient and confidence thresholds and T2.
efficient shouldn't be too large since in our method this low rank regularization is implemented without common constraint to regulate the magnitude of changes in adjacency matrix A. Therefore, an excessively big a would cause A to approach a zero matrix, which is obviously undesirable. Mcanwhile, an excessively large Tι would make diffi- cult for the model to select a sufficient number of tail-class samples, thereby affecting the overall performance.
Table Comparison results on NUSWIDE datasct with memory
size being 1000 x 224 x 224 x 3.
Method
Avg mAP
Avg CF1
Last mAP
RS 13.90=:1.08
36.79:0.76
26.78:10.59
GSS
44.28:10.76
37.01:1.114
28.22:10.90
iCarl
43.50.0.83
35.7410.95
27.60:10.85
NsCE
45.72-0.52
34.9510.75
27.14:10.60
KRT
47.30+:1.07
39.70+:1.24
31.25++1.19
APPLE
47.53:10.76
40.89:10.7
32.40-11.34
PRS
42.7410.84
35.01=0.92
22.78:10.72
AGCN
49.16:20.9
38.4111.05
33.49:10.88
AGCN++
49.03+11.35
1..17+1.28
32.09++1.41
4. Conclusion
OCDM
40.05=0042
33.66+00.87
29.41-10.65
CUTER
51.1420.72
42.92+1.03
37.57::1446
In this study, we highlight the fundamental question behind two key challenges (pervasive missing labels and uncontrol- lable class imbalance) that existing MOCL methods strive to tackle. By exploring and leveraging the innate localization capacity of widely used pre-trained models, we propose method called CUTER to comprehensively address the existing limitations of MOCI. methods through identifica- tion and replay of regions corresponding to different given labels. Additionally, we further explore how to enhance model localization (unsupervised detection and segmenta- tion) performance in such an online incremental learning framework from the perspective of graph spectral theory, Our method not only achieves remarkable performance boost but also differs significantly from existing MOCL ap- proaches. We hope this study can bring fresh perspectives to the area of multi-label learning in this continual setting.
Table 5. Cut and Replay (CutRep) works as plug-in component for several MOCL methods (Averaged mAP reported).
Method
VOC
MSCOCO
NUSWIDE
Ret
77.92+0.78
53.40+00.2
46.30++1022
Rut w/ PRS
1.02
58.72:10.94
50.69-10.45
Cut.Rop w/ OCDM
81.09=0.67
57.90=10.73
11.21+1.388
CUTER
79.45=0.92
59.23:+0.72
50.51::0.64
CUTER w/ KRT
1.23
57.09++1.32
50.56++0.95
CUTER w/ AGCN
1..95=1.14
59.31:10.94
48.95110.89
as it naturally leads to better localization and cut-out quality on the current task.
3.4. Sensitivity Analysis
In this section, analyze the impact of coefficient a of the low rank regularization term and the thresholds πi and on foreground object selection for memory buffer in expe- rience replay. As shown in Figure 6, our proposed method has demonstrated relatively robust outcomes when a, Tı, 72 remain within certain ranges. It should be noted that co-
Impact Statement
This research aims advance the field of Machine Learn ing. While work has potential societal implications, we believe discussing specific scenarios is beyond the scope of
8

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Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning
Table Ablation studies. We denote low rank regularization R₁ sparse regularization as Rep. and smooth regularization Ram-
CUTER refers to CUTER using a fixed backbone for object localization and the later cut out operation.
VOC
COCO
NUSWIDE
Model
Rep
Cut.Rep
Re-balance
R2
Avg mAP
Last mAP
Avg mAP
Last mAP
Avg mAP
Last mAP
Baseline(RS)
75.05+1.228
59.71=1.75
18.12
18.97=2.01
43.90:1..88
26.78=0.59
Cut
77.92+0..8
61.52=1.07
53.40=0..22
31.17=0.69
16.30=1.022
30.41=0.73
Cut.Rop w/ R.
78.24++0.93
63.95=0076
56.37:00.4
33.84=1001
48.12++0.85
31.46=0.71
CUTER CUTER
78.62.10.77
64.95:10883
59.0111.445
45.19=0.80
49.67:10.22
36,42-10991
79.45=00.92
66.0921.03
59.23310.72
45.79=00855
50.5110.64
37.35=0.42
CUTER w/ R
82.07:00.53
67.89
60.14:10600
47.82=0060
51.14-0.722
37.17=1.46
this technical paper.
Chaudhry, A., Rohrbach, M., Elhoseiny, M., Ajanthan, T., Dokania, P. K., Torr, P. H., and Ranzato, M. On tiny episodic memories in continual learning. arXiv preprint 1rX1v:1902.10486, 2019b.
Acknowledgements
This work is supported by National Science and Technology Major Project (2022ZD01 14801), National Natural Science Foundation of China (No. 62376126), Funding for Out- standing Doctoral Dissertation in NUAA (BCXJ25-21) and Major Special Basic Research Project for Aero-engines and Gas Turbines: Research on Fault Diagnosis and Prediction Technology for Typical Transmission Components (J2019- IV-0018-0086), Additionally, we thank the anonymous re- viewers for their valuable comments and suggestions.
Chaudhry, A., Khan, N., Dokania, P., and Torr, P. Continual learning in low-rank orthogonal subspaces. Advances in Neural Information Processing Systems, 33:9900-9911, 2020.
Chen, T., Xu, M., Hui, X., Wu, H., and Lin, L. Learn- ing semantic-speciffe graph representation for multi-label image recognition. In Proceedings of the IEEE/CVF in- ternational conference on computer vision, pp. 522-531, 2019.
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Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning
A. Detailed Related Works
A.1. Online Continual Learning
OCL serves as a more realistic extension of continual learning, addressing scenarios where data distributions dynamically change over time, in contrast to traditional batch learning where complete task datasets are available upfront(Gunasekaraa et al., 2023). To mitigate catastrophic forgetting, most OCL methods maintain memory buffer for experience replay, employing various strategies for memory management and sample selection(Chaudhry et al., 2019a;b; Aljundi et al., 2019a;b; Chaudhry et al., 2020; Shim et al., 2021; Wang et al., 2022; Caccia et al., 2022; Ghunaim et al., 2023; Xinrui et al., 2024), Additionally, researchers have explored approaches to enhance feature learning and classifier adaptation in single-pan training scenarios (Reufffi et al., 2017). Recent advances incorporate techniques such as contrastive learning et al., 2021; Cha et al., 2021), mutual information maxinization(Gu et al., 2022; Guo et al., 2022), and prototype learning(Zhu et al., 2021; Wei et al., 2023) to improve model discriminative power and overall performance.
A.2. Multi-label Classification
Multi-label learning has long been a fundamental paradigm in machine learning(Zhang & Zhou, 2013; Wei & Li, 2019b; Li et al., 2024), attracting substantial research attention. In this review, we focus on works that extract label-specific features or regions, which are closely related to our approach. These studies can be broadly categorized into two groups.
The first category decomposes multi-label classification into binary (one-vs-rest) subproblems, where each label is mapped to a distinct feature subset. This category encompasses three main approaches: (1) Clustering-based methods (Zhang & Wu, 2014; Ren et al., 2019) partition the feature space into label-specific clusters by exploiting both independent and co-occurrence patterns between features and labels; (2) Label correlation mining approaches (Huang et al., 2015; 2016; Hang & Zhang, 2022) discover and utilize the interdependencies among labels by constructing label correlation matrix through a specially designed module like graph encoder(Hang & Zhang, 2021) to guide feature selection, typically through matrix factorization or graph-based techniques; (3) Regularized feature learning methods (Wei et al., 2018; Li al.. 2022; 2025) impose structural constraints the feature selection process, often using & or group sparsity regularizers to encourage label-specific feature sparsity. However, since these methods operate on abstract feature vectors rather than spatial representations, they cannot support direct sample-level operations like cut-out augmentation.
The second category adopts a more direct approach by leveraging visual attention mechanisms to localize label-specific regions (Chen et al., 2019; Narayan et al., 2021; Li et al., 2023). Specifically, these methods either utilize the inherent attention mechanisms in deep networks, employ visualization techniques like Grad-CAM (Selvaraju et al., 2017), or incorporate dedicated attention modules (Wang et al., 2017: Gao & Zhou, 2021). While these approaches achieve coarse- grained localization of label-relevant regions, their attention maps often focus on local discriminative features rather than complete object regions. This limitation makes them less suitable for MOCL, which requires precise object boundaries and comprehensive object representations.
Noting the extensive approaches proposed for label-specific feature learning in traditional multi-label learning(Yi Yu & Zhang, 2021; Hang & Zhang, 2021; Hang et al., 2022; Hang & Zhang, 2022: Chen et al., 2019; Narayan et al., 2021; Li et al., 2023), we emphasize that they rely on complete label set with heavy computation overload, or fail to identify the label specific regions, which are inherently defected by the high-efficiency requirement of MOCL task encountering the missing label factor.
A.3. Multi-label Online Continual Learning
Multi-label online continual learning or multi-label class incremental learning emerge as a novel research field that integrates the advantages of continual learning and multi-label learning(Zhang & Zhou, 2007: 2013: Zhang & Wu, 2014; Zhang et al., 2020; Wei et al., 2021). Beyond the widely discussed catastrophic forgetting, existing MOCL studies primarily address two key challenges: (1) the missing past and future labels inherent to the intersection of continual learning and multi-label classification; (2) the intrinsic and uncontrollable class imbalance prevalent in conventional multi-label data streams. Researchers have addressed missing label challenges by leveraging stored historical models through techniques proven effective in multi-label learning and continual learning, such as GCN(Du et al., 2022; 2023; 2025), knowledge distillation(Dong et al., 2023) and vision language model(Zhao et al., 2025). The class imbalance problem has been tackled through carefully designed sampling strategies for replay methods. Notable examples include PRS(Kim et al., 2020), which employs heuristic sampling, and OCDM(Liang & Li, 2022), which utilizes optimization bbassd sampling, both aiming to
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Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning
mitigate the impact of long-tailed class distributions in continual multi-label scenarios. However, these existing methods overlook the root cause of these challenges and crucial issue in multi-label learning(Zhao al., 2016; Wang al., 2017; Gao & Zhou, 2021): how to identify and discriminate label-specific regions and their corresponding features in an online manner? Our work directly addresses this fundamental question.
B. Preliminaries
In this section, we first review two unsupervised segmentation techniques: Normalized Cut(Shi & Malik, 2000) and Mask Cut(Wang al., 2023a), which motivate investigation into label-specifie region identification in MOCL.
Normalized cut (NCut) formulates image segmentation as graph partitioning problem. Let G (V, E) be weighted undirected graph, where V denotes the vertex set with each vertex e corresponding to an image patch (feature patch), E represents the edge set, and W is the edge weight matrix with entries Wij encoding the similarity between vertices i and j. NCut aims to partition the graph into disjoint sets (background) and B (foreground object 6) by minimizing the NCut energy (AA, B, V):
E(A, B, C(A, C(A, + C(B.V)'
(4)
C(A.V)
where C measures the similarity between two sets, e.g., -) Em Wy.
Meanwhile, mask cut (MCut) extends NCut to identify multiple objects per image by iteratively applying NCut masked similarity matrix. At the t-th iteration, after obtaining the bipartition from NCut, the algorithm divides patches into two disjoint groups (background a' and object bn) and constructs a binary mask M where
1, if My > mean(b")
(5)
0, otherwise
Upon determining the foreground group bb, MCut updates the node similarity W by masking out nodes corresponding to the foreground from previous stages t:
(0: II
M IT. MC)
W
(6)
where Mij =1 My. With the updated W MCutt iteratively minimizes Eq.4 and calculates Eq, to generate multiple segmentation masks for distinct objects.
C. Theoretical Foundations of CUTER
C.1. Relationship Between Normalized Cut and Graph Spectral Theory
As discussed in Section B, the Normalized Cut (NCut) can be formulated as a graph partitioning problem. Given graph
G (V, F)) with vertex set V, edge set E., and edge weights Wij representing the similarity between nodes and j, the objective is to partition the graph into disjoint subsets A and B that: (1) minimize the inter-subset similarity, and (2) maximize the intra-subset similarity. The NCut objective function is defined as:
C(A, B) + C(A, B) C(A.V)
C(B.V)
where C measures the similarity between two sets: C(A, B)= EmMAANEER Wi.
The NCut problem is NP-hard due to its discrete nature. To make it computationally tractable, it is relaxed into continuous optimization problem using spectral graph theory. This involves the graph Laplacian L D W. To prevent nodes with extremely large degrees from dominating the graph structure, the symmetric normalized Laplacian Liym is employed:
Lymm II D-12D-1/22- 1 1/2
The problem can then reformulated as minimizing y Layml// subject to constraints ensuring y is balanced and orthogonal to the trivial solution (i.e.,) y" D1 = 0, where 1 is the all-ones vector). After relaxing y to take continuous values, the
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Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning
problem becomes:
min y Tuymm// y
subject to:
y D1=0, y Dy= =1
The solution is the eigenvector corresponding to the second smallest eigenvalue of Lsym. This continuous solution can be discretized through thresholding to obtain the final partition. For a complete proof, refer to (Shi & Malik, 2000).
C.2. Proof of Theorem 2.3
In this section, first give detailed description of the adjacency matrix of each feature graph. The features of input data can be divided into different patches, by using functions like Gaussian kernel to construct an undirected graph, with N x N adjacency matrix A. Then, without loss of generality, we can make the following assumption:
Assumption C.1. The adjacency matrix A of each feature graph can be decomposed as an ideal block diagonal matrix A and non-sparse and potentially high rank noise matrix E:
== +c
With Assumption C.I, we can rewrite the graph Laplacian I, as:
L = D A= D AD (A* +e)
where AD D D" with D is N x N diagonal degree matrix with elements d(t) E, Azy. Thus, write the elements of AD as:
AD Σ (Au
AT) Σ
Eij
j=1
j=1
Since it's clear see that AD also a diagonal matrix, we have:
|| 2| = mar; ADol S mara
[eyl = ||]|bo-
We conclude that || ||AD||2 is directly upper bounded by Based this, we can turn to analyze the upper bound of the disturbation of graph Laplacian. It easy to conclude that:
AL = L DD' - -A)) AD
Thus, have:
||//||2 I ||AD||2 + ||e|12 S |lelloo + ||e|l2- It means that if we can bound X2(L) by || 12, we can finish this proof. Before doing this part, we first introduce an important Lemma:
Lemma C.2. (Courant-Fisches Formula (Zhang, 1997)) For a Hermitian A E CNNN
Ar x-
min Ar 0f
A
min
max dFFES
max dim(S)=k
==nnn
dim
x"
From Lemma C.2, the Courant-Fischer theorem yields the following upper and lower bounds:
(A + (B) & ds(A + B) s (A) (B).
from which it follows that
max (A + B) Ax(A)) S max (B)( = |B]]2-
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Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning
This inequality shows that the eigenvalues of Hermitian matrix are well conditioned under perturbation. We rewrite the inequality in the symmetric form
max (A) (B)) - || B 2--
By substituting for A and L' = D A for B and taking &o = 2, have:
(2 A2( |
Then, according to Assumption C.I, we can deduce that I," also an ideal block diagonal matrix, therefore its second smallest eigenvalue A²(L") = 0. Combined with our previous results, we obtain:
(L) S || S |cel ||e|22.
This completes the proof.
D. Analysis on Models' Localization Ability
D.1. Pre-trained Model
In this section, discuss pre-trained models' localization capabilities and their underlying mechanisms. Based on the experimental results shown in Figure 2 and Table 7, we observe clear hierarchy in terms of zero-shot object localization ability and potential for MOCL: contrastive learning models with multi-crop augmentation (e.g., DINO vl, DINO v2) demonstrate superior localization capabilities compared to standard contrastive learning approaches (c.g.. MoCo, SimCLR), which in turn outperform conventional supervised training methods. Reconstruction based methods like MAE and iBOT exhibit relatively weaker performance in this aspect. We exclude CUTLearn from this comparison it specialized model designed specifically for zero-shot detection that requires additional fine-tuning, making it fundamentally different from the aforementioned general-purpont pre-traine mre-trained models.
When examined through the lens of spectral graph theory, these results become more intuitive. The consistency between augmented views employed in contrastive learning naturally guides the model to focus on the most salient objects in images. This effect is particularly pronounced because pre-training datasets like ImageNet are typically single-labelad and object-centered, making multi-crop augmentation consistency especially effective. This training dynamic implicitly encourages feature similarity among patches belonging the same object, thereby enhancing the separability of the feature- constructed graph (metric like cheeger constant). In contrast, supervised learning demonstrates weaker performance in this aspect, though it still retains some localization capability since images from the same class typically contain similar objects, leading to comparable feature representations even without explicit intra-image contrasting. However, mask reconstruction pre-training methods, despite their proven effectiveness in feature learning for various downstream tasks, show the poorest performance in this aspect. From feature graph perspective, the indiscriminate random masking and reconstruction likely diminishes the feature coherence between different patches of the same object, which adversely affects direct feature-based zero-shot localization or segmentation capabilities.
D.2. Further Analysis on Low-rank Regularization
Constraining the nuclear norm of a matrix (i.e., minimizing ||A the sum of singular values A) has a significant impact on reducing both the spectral norm ||A/||2 and the infinity norm || where AA c = A A". Below, we present detailed explanation.
Reduction in Spectral Norm ||ll|2 Minimizing ||A], applies soff-threshollingg to the singular values of A, reducing the rank of A and suppressing the singular values of AA:
|A ||A||
Reduction in Infinity Norm |AAl| Low-rank matrices have limited row variability, which constrains ||A/|| bo Since:
||Al I Vnl AA
reducing |A//2 indirectly reduces | /|oc.
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Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning
Similarly, sparse regularization shows comparable effects in de-noising However, unlike low-rank regularization, it disrupts the inherent structural properties of ViT parameters (Darcet al., 2023), potentially compromising classification capacity. This may explain its inferior performance in MOCL, as shown in Table 3. As for smooth regularization, we argue it may not benefit localization ability since overly similar node features could hinder spectral clustering- operations.
E. Additional Experiments and Implementation Details
E.1. Implementation Details
In this part, we provide implementation details and configurations of the methods compared in Table 1, Table 2, and Table 4:
For RS(Chaudhry et al., 2019b), GSS(Aljundi et al., 2019b), iCarl, and NsCri(inrrui et al., 2024), adopted their original codebases with minor modifications to their classifier architectures and classification loss functions. For methods employing prototypical classifiers (iCarl and NsCE), we computed cosine similarities and applied a threshold of 0.5 to determine positive labels. Regarding the classification loss, we replaced the conventional cross-entropy loss designed for single-label scenarios with the asymmetric loss widely adopted in multi-label classification settings.
For PRS(Kim et al., 2020), we directly used their provided code' with their reported optimal hyper-parameter p = 0.5. As for OCDM(Liang & Li. 2022), since their code was not publicly available but shares similar principles with PRS, we implemented it using the same
p 0.5 and report reproduction results.
For KRT and APPLE, both designed for Multi-label Class Incremental Learning, we ensured fair comparison by implement- ing vanilla experience replay based on the official KRT codebase?, with each method trained for one epoch.
For AGCN and AGCN++ we utilized the official implementation of AGCN2 with their reported optimal aaper-parameters. Since AGCN++(Lian & Li, 2022) code was not publicly available but follows similar principles to AGCN, we implemented it using the same typer-parameters and report our reproduction results.
For other common hyperparameters such as learning rate, weight decay, and batch size, we conducted grid search and report the best results for all compared methods. Unless otherwise specified above, we consistently used the W optimizer with a learning rate of le-4, and a weight decay of le-4. For PASCAL VOC, we set the data stream batch size as 12, memory buffer sampling batch size of 6. For COCO and NUSWIDE, we set the data stream batch size as 20, memory buffer sampling batch size 5.
E.2. Additional Experiments
We first provide a detailed visualization of different methods' performance (mAP%) across various datasets. As shown in Figure 7, our proposed CUTER consistently demonstrates superior performance throughout the entire training process. We observe that MI.CII. methods (e.g., APPLE and AGCN++) exhibit significantly degraded performance compared to their offline implementations, especially in the early training stages. This performance gap highlights key distinction between MOCL and MLCIL: methods relying on iterative pseudo-labeling struggle to adapt high-velocity data streams.
Secondly, we investigate how backbone architectures and pre-training strategies affect the downstream performance of MOCL, as shown in Table 7. Our method relies more heavily on backbones with strong localization capabilities and their corresponding pre-training approaches. As discussed in Appendix D, DINO pre-trained ViT models demonstrate superior performance. We also reproduced OCDM, a current SOTA MOCL method, for comparative analysis. The results show that our method maintains its advantages when using ViT as the backbone, while performance with ResNet backbones is relatively weaker. This discrepancy may be attributed to our treatment of ResNet feature maps although we divided them into patches similar VIT's approach and applied MCut for foreground object detection and cropping, this adaptation may not be optimal for CNN-based architectures.
Thirdly, we analyze the model throughput of our approach compared to state-of-the-art methods. As shown in Figure 8, while vanilla experience replay (RS) achieves the highest model throughput, it demonstrates relatively weaker performance. Our method achieves superior results at the cost of increased computational overhead, primarily due to the multi-round MCut operations that cannot be parallelized on GPUs. Additionally, computing the rank approximation (nuclear norm) for
httpss//gitub.com///j/iin// "batps//gituub.oo//aaiitDD////
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Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning
PASCAL
MSCOOU
NUSWIDE
CUTTR APPCE OCDM
EUTER APPLE OCDM ACCN 10ler
CUTTER
AZPLE
90-
ACON'1
80
OCDM
ACCN
IIS
RI5
70-
60
mAP(80
mAP(50)
MAP
70
30
50
60
40
30
40
30
20
10
30
500
Number of Classes
Number Classes
Number of Classes
Figure 7. Overall training results PASCAL VOC, MSCOCO and NUSWIDE. We include 5 different methods across the OCL, MICIL and MOCL methods.
Table 7. Performance comparison across different backbone architectures and pre-training methods.
VOC
COCO
NUSWIDE
Model
Bacibome
Pre-training Method
Avg MAP
Last MAP
Avg MAP
Last MAP
Avg mAP
Last MAP
CUTER w/
Vit-S
Supervised
79.56
63.82
$7.92
43.04
48.36
35.42
OCDM
Vit-S
Supervised
75.35
44.21
53.26
39.84
41.05
26.87
CUTER w/ R
Vit-S
MoCo v3
80.47
64.92
58.10
45.72
$0.29
34.70
OCDM
Vie-S
McCo v3
75.94
46.32
56.14
42.75
40.85
30.01
CUTER w/ R
VieT
DINO vl
81.84
67.50
$9.38
47.95
$2.08
36.68
w/ R
Vit-S
82.07
CUTER w/
Vi-B
DINO vl
83.25
68.14
61.08
45.70
50.91
37.52
OCDM
Vit-S
DINO vl
76.14
45.35
55.45
40.56
40.37
29.41
CUTER w/ R
Vit-S
77.31
56.42
47.24
OCDM
Vit-S
MAE
75,42
45.18
56.03
41.53
41,26
29.68
CUTER w/ R
ResNet50
Supervised
74.35
46.80
46.34
37.15
38.30
22.53
OCDM
ResNe150
Supervised
72.32
36.71
41.89
27.94
17.80
CUTER w/
ResNe150
McCo v3
75.04
48.31
47,20
37.48
40.06
23.41
OCDM
ResNet50
McCo v3
75.31
47.05
42.45
30.30
33.45
21.08
CUTER w/ R:
ResNo150
DINO vI
76.43
50.42
48.52
40.35
40.42
23.79
OCDM
ResNet50
DINO vl
75.09
48.42
46.31
33.17
39.28
21.55
CUTER w/ R
ResNe150
MAE
73.62
45.14
43.52
36.68
37.45
22.15
ResNe150
42.13
38.07
the graph adjacency matrix requires maintaining and calculating gradients for multiple foreground object predictions. These factors create a trade-off between model throughput and performance, Future work will focus on developing accelcration techniques and adaptive processing strategies based on data stream velocities.
Finally, visualize the localization capabilities of our CUTER model during multi-object continual learning PASCAL VOC. We evaluate this from two perspectives: first, by calculating the average Fiedler value of the constructed feature graphs; second, by measuring the zero-shot localization performance (APso) between the generated bounding boxes and their corresponding ground truth for each incremental task. As shown in Figure 8, as training progresses, the connectivity of constructed graphs from streaming data gradually decreases while their separability improves. This enhancement in graph separability correlates with increased accuracy of the generated pseudo bounding boxes.
F. Limitation
In the main text, our discussions primarily focus on scenarios using the ViT backbone need the structure like image patch to form feature patch which help us construct the graph. While CNN-based backbones like ResNet can also construct similar image patches, as shown in Appendix E.2, CUTER's performance somewhat degrades in these cases. Additionally, we acknowledge that performing cut-out operations introduces additional computational overhead as shown in Figure which may sometimes affect the model's generalizability in online settings. However, as a pioneering work investigating
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Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning
122-
Omopl Connenction
13T
Number of Classss
Figure 8. Left: Average model throughput comparison across 9 methods evaluated on 6 datasets. Right Evolution localization performance: tracking the localization capabilities of proposed CUTER model PASCAL VOC from two perspectives.
object localization in MOCL,, these limitations also point to promising directions for our future research.
19


Reviews:
[{"review_id": "g9YA4nlWm9", "reviewer": "Reviewer_eZ9b", "summary": "In this work, authors concentrate on Multi-Label Online Continual Learning (MOCL), mainly focusing on three main challenges: catastrophic forgetting, missing labels, and imbalanced class distributions. They introduce CUTER (CUT-out-and-Experience-Replay), a method that identifies and utilizes label-specific regions in images. By first evaluating pre-trained models' localization abilities and then implementing a region-based experience replay strategy, CUTER provides targeted supervision signals. Experimental results across multiple image benchmarks validate its effectiveness.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "_Respected Reviewer eZ9b,_ we first thank you for your valuable and insightful feedback, and for recognizing our motivation and theoretical analysis. Below, we address your concerns in a point-by-point manner.\n\nQ: **How the the accuracy and reliability of proposed localization process is ensured?**  \nA: Our approach ensures the accuracy and reliability of the proposed localization process through three key mechanisms:\n\n1. **Principled Model Selection**: We conducted detailed analysis of the localization potentials across different pre-trained models, establishing general selection principles that identify models with optimal localization capabilities for MOCL tasks.\n\n2. **Selective Label-Region Matching Strategy**: We developed a targeted matching approach that accurately aligns semantic regions with corresponding labels, minimizing false correlations and enhancing localization precision.\n\n3. **Spectral-based Regularization**: To prevent degradation of localization capacity during MOCL progression, we implemented a specialized regularization term that preserves the model's ability to accurately identify regions of interest over time.\n\nThe effectiveness of these mechanisms is substantiated by both qualitative evidence (visualizations in Figures 3 and 5 demonstrating accurate region identification) and quantitative validation (ablation studies in Table 6 showing the benefits of our Cut-out Replay strategy in capturing fine-grained spatial information). Together, these elements form a comprehensive framework that consistently delivers accurate and reliable localization performance throughout the MOCL process.\n\nQ: **Performing cut-out operations and regularization terms introduce additional computational overhead.**  \nA: We acknowledge that our approach introduces some additional training time. In fact, as shown in Figure 8 (left) in the Appendix, when not using regularization terms, CUTER achieves relatively comparable model throughput to other popular MOCL methods. Additionally, we recognize that the proposed regularization terms do cause a considerable decrease in model throughput. Finding ways to accelerate this process and achieve a better balance between performance and computational efficiency will be a focus of our future work.\n\nQ: **Nuclear norm regularization details spread across appendix and main text, affecting clarity.**  \nA: We appreciate the reviewer's valid point about the nuclear norm regularization presentation. The current separation was primarily due to page constraints. In the revised version, we will restructure Section 2.3 to present a more cohesive narrative by:   \n(1) Introducing the theoretical motivation behind nuclear norm regularization;  \n(2) Connecting it directly to the derived graph Laplacians with concise mathematical formulations;   \n(3) Providing intuitive explanations of how this regularization enhances representation learning;   \n(4) Briefly highlighting comparative advantages over alternative approaches like smooth regularization or sparse regularization on the graph laplacian before the experimental section; \n\nQ: **Typos.**  \nA: We feel sorry for any confusion or inconvenience may have caused. In the revised version, we will carefully proof-read our text to ensure that no grammar mistakes or typos still exist.", "claims_and_evidence": "**Claims**  \n1. CUTER effectively addresses multiple MOCL challenges - catastrophic forgetting, missing labels, and class imbalance - while improving model performance.\n2. The method achieves competitive results and can be readily integrated with existing approaches as a complementary component.\n3. The model's performance benefits from the proposed localization-based regularization strategy.\n\n**Evidence**  \nRegarding the paper's claims:\n\nWhile CUTER's approach to addressing multiple MOCL challenges through region localization before learning and replay follows logically from its design, the paper would benefit from a more thorough discussion of how the accuracy and reliability of this localization process is ensured.\nThe paper effectively supports its second and third claims through both visual evidence (Figure 5) and quantitative results (Tables 3 and 5), demonstrating CUTER's performance advantages and successful integration with existing methods.", "methods_and_evaluation_criteria": "This paper addresses MOCL through label-specific feature learning, introducing a novel label-attentional mechanism to identify and replay label-specific image regions. Leveraging state-of-the-art pre-trained models, the approach incorporates a regularization technique based on spectral graph theory principles commonly used in unsupervised segmentation methods. The method represents an innovative departure from existing MOCL approaches (which typically focus on memory buffer sampling or pseudo labeling techniques) and demonstrates both novelty and comprehensive design.\n\nThe evaluation methodology aligns with established practices, utilizing multi-label benchmarks and standard metrics (mAP, CF1, OF1). The authors provide thorough empirical validation through ablation studies demonstrating the effectiveness of individual components, as well as results showing successful integration with existing methods. The experimental results effectively support the paper's claims.", "theoretical_claims": "The paper's key theoretical contribution centers on analyzing the model's localization capability through its relationship to the adjacency matrix constructed from learned features. After reviewing the proof of Theorem 2.3, I find the mathematical reasoning to be logically sound.", "experimental_designs_or_analyses": "The evaluation methodology aligns with established practices in Multi-label learning and MOCL, utilizing multi-label benchmarks and standard metrics (mAP, CF1, OF1).\n\nAdditionally, the authors present an interesting connection between pre-trained models' MOCL capabilities and their derived feature characteristics. The experimental results, particularly the analysis of different regularization methods and subsequent investigations, provide empirical support for their findings.", "relation_to_broader_scientific_literature": "As authors stated in the concluding paragraph, this research contributes to advancing machine learning methodology. While acknowledging that the work may have broader societal implications, an in-depth discussion of specific impacts falls outside this paper's technical scope.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "Strengths\n1. The proposed method offers a novel approach to the problem. The authors present a clear methodology that directly addresses label-region correspondence - a challenging yet fundamental issue in the field. This approach stands out from existing MOCL methods and recent works on multi-label classification with missing labels.\n2. The proposed regularization term is well-motivated and theoretically validated. The analysis raises an interesting question about whether similar metrics, such as the averaged Fiedler value, could be applied to other domains like pre-training task design or dataset evaluation.\n3. The method demonstrates strong empirical performance across multiple datasets. Through comprehensive ablation studies and informative visualizations, the authors effectively illustrate the contribution of each component to the overall system.\n\nWeaknesses\n1. As stated by the authors, performing cut-out operations and further regularization terms to consolidate model's localization capacity introduces additional computational overhead.\n2. There are some typos exist in the main text. \n3. In Section 2.3, while the authors introduce nuclear norm regularization for the derived graph Laplacians, the underlying mechanism is relegated to the appendix, and detailed comparisons are deferred to the experimental section. A more cohesive presentation of this material within the main text would enhance the section's clarity and impact.", "other_comments_or_suggestions": "See the previous parts.", "questions_for_authors": "N/A", "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": "QlLmsafYIg", "reviewer": "Reviewer_NiGx", "summary": "The paper tackles Multi-Label Online Continual Learning (MOCL) problem through a novel two-step approach. First, it identifies object-specific regions corresponding to labeled samples within each learning phase. Then, it selectively replays these regions, effectively circumventing the challenging missing label issue. The method also mitigates potential class imbalance problems in MOCL by transforming multi-label classification into single-label tasks. Experimental results across multiple image benchmarks validate its effectiveness.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "_Respected Reviewer NiGx,_ we thank you for your valuable and insightful feedback. Below, we address your concerns in a point-by-point manner.\n\nQ: **Additional experiments with varying numbers of classes per stage should be included.**  \nA: We appreciate this suggestion. We have followed the setting in previous works like KRT and OCDM to provide class-incremental settings with varying numbers of classes per stage. Due to the limited space, please refer to our response to Reviewer Nhoa's Q1 for complete tables.\n\n_MSCOCO B40-C10:_   \nIn this setting, we first train a base model on 40 classes, then incrementally add the remaining 40 classes over 4 sessions (10 classes per session).\n\n|   COCO (mAP)   | 1-40 | 1-50 | 1-60 | 1-70 | 1-80 |\n| :----:| :----: | :----: | :----: | :----: | :----: |\n| PRS  | 69.3 | 56.5 | 52.0 | 44.7 | 39.8 | \n| KRT  | 68.4 | 57.3 | 52.1 | 46.5 | 40.0 | \n| CUTER| 69.0 | 59.6 | 57.4 | 54.7 | 50.8 | \n\n_NUSWIDE B41-C5:_   \nHere, we start with 41 base classes and incrementally learn 8 sessions with 5 new classes each.\n\n|   NUSWIDE (mAP) | 1-41 | 1-46 | 1-51 | 1-56 | 1-61 | 1-66 | 1-71 | 71-76 | 1-81 |\n| :----:| :----: | :----: | :----: | :----: | :----: |:----: |:----: |:----: |:----: |\n| PRS  | 61.2 | 50.6 |44.9 | 37.0 |36.7  | 30.2 |31.4 | 32.0 | 26.3 |\n| KRT  | 62.3 | 52.8 | 48.5 | 34.6 | 35.8 | 33.4 | 31.9 | 31.0 | 29.4 |\n| CUTER| 63.0 | 54.3 | 50.0 | 42.3 | 40.8 | 38.1 | 36.8 | 35.4 | 32.8 |\n\nThese results demonstrate that our CUTER method consistently outperforms existing approaches across different incremental settings, particularly in later stages where catastrophic forgetting is most pronounced.\n   \nQ: **Additional spectral clustering literature should be discussed and cited.**  \nA: We appreciate this suggestion. In the revised version, we will expand our related work to include seminal spectral clustering works such as Shi and Malik (2000) on normalized cuts, Von Luxburg's (2007) tutorial on spectral clustering fundamentals, and more recent advancements like Tang et al. (2018) on robust spectral clustering. We'll also discuss image segmentation applications of spectral clustering beyond MCut, including works like LSC by Li and Chen (CVPR 2015) and the analysis of limitations of these spectral-based segmentation methods by Boaz Nadler (NIPS 2006).\n\nQ: **Whether other detection methods could be integrated into the MOCL**  \nA: Yes, our CUTER framework is designed with modularity in mind, allowing for integration of various detection methods beyond MCut. However, we want to emphasize that in the online continual learning scenario, MCut's advantage of requiring no training is significant. While other detection methods like LOST, TokenCut, or attention-based approaches could theoretically be integrated, they would need to overcome these online learning constraints.\n\nAdditionally, methods like SAM could be a viable choice, but without additional prompts, SAM typically produces many more segmentation masks than required for MOCL objectives. Its segmentations tend to be relatively fragmented because SAM's results are not inherently class-oriented or semantically guided. This would introduce additional challenges in establishing the correct correspondence between regions and labels.\n\nWe greatly appreciate the reviewer's suggestions and will carefully discuss these considerations in a future version of our work.\n\nQ: **Additional explanation is needed to clarify why the proposed regularization terms work and outperform conventional approaches like smoothing or sparse regularizations.**  \nA: To clarify why nuclear norm regularization outperforms conventional approaches: While all regularization methods in Table 3 are established in graph structure learning, they differ fundamentally in their effects on representation learning for our task.\n\nNuclear norm regularization promotes low-rank structure in the graph Laplacian, better preserving the intrinsic manifold structure of ViT features while removing noise $\\epsilon$ as established in Theorem 2.3. In contrast, sparse regularization, though effective for denoising, penalizes hypernode patches with naturally high cross-node similarity, disrupting inherent structural properties of ViT parameters and compromising classification capacity (Table 3). Similarly, smooth regularization forces excessive feature similarity across nodes, impairing the spectral clustering processes essential for effective localization.\n\nQ: **The model's performance in offline setting should be included.**  \nA:Despite the offline setting not being the primary goal of our proposed method, we make a simple comparison with KRT (method designed for offline setting) on MSCOCO: \n\n|       | Avg mAP | Last mAP | Last CF1 | Last OF1 |\n| :----:| :----: | :----: | :----: | :----: |\n| KRT   | 75.7 | 69.3 | 63.9 | 64.7 |\n| CUTER | 84.1 | 76.5 | 70.3 | 73.4 |\n\nThese results suggest that CUTER's design principles are broadly applicable across different multi-label classification settings.", "claims_and_evidence": "**Important claims made in this paper:**  \n1. Multi-crop consistency pre-training enhances innate localization capabilities, whereas reconstruction-based training tends to promote feature sharing during recovery, which may hinder the effectiveness of spectral clustering-based localization approaches.\n2. The Fiedler value of the feature graph has a direct upper bound determined by the norm of the perturbation matrix.\n\nFor claim 1, authors substantiate their assertions by examining the correlation between the average Fiedler values of features on the VOC dataset and zero-shot detection performance across different pre-trained models. Additional analysis in Appendix D.1 further corroborates these findings.\n\nFor claim 2, the authors provide a rigorous theoretical analysis to substantiate their claim.", "methods_and_evaluation_criteria": "This paper addresses the Multi-Label Online Continual Learning (MOCL) problem through a novel two-step approach. First, it identifies object-specific regions corresponding to labeled samples within each learning phase. Then, it selectively replays these regions, effectively circumventing the challenging missing label issue. \n\nThe proposed method is evaluated using standard protocols on multiple visual classification datasets, achieving state-of-the-art performance compared to existing MOCL and MLCIL methods. The authors further validate the effectiveness of each component and demonstrate its plug-and-play capabilities through additional experimental studies.", "theoretical_claims": "The authors establish a theoretical relationship between a graph's Fiedler value and the norm of the noise component in its graph Laplacian. Upon examination of the proof of Theorem 2.3 in the Appendix, the mathematical derivation and reasoning appear to be rigorous.", "experimental_designs_or_analyses": "The experimental methodology appears sound, as the authors follow standard evaluation practices on visual classification benchmarks, enabling fair comparisons with MOCL and MLCIL methods. The empirical validation is comprehensive, including well-structured ablation studies examining the memory buffer's data distribution and demonstrating how the regularization effectively preserves the pre-trained model's inherent localization capabilities.\n\nOne limitation is that compared to other MOCL papers that evaluate multiple continual learning scenarios, this work primarily focuses on cases with similar number of classes across incremental stages. Including additional experiments with varying numbers of classes per stage would provide a more comprehensive evaluation of the method's robustness.", "relation_to_broader_scientific_literature": "There is nothing in particular worth mentioning.", "essential_references_not_discussed": "See the weaknesses", "other_strengths_and_weaknesses": "Strengths:\n1. The topic of learning from online continual data streams is significant and represents a more realistic extension of traditional continual learning scenarios.\n2. The proposed method demonstrates strong empirical performance, with comprehensive ablation studies effectively illustrating the function of each component.\n3. The proposed regularization term is intuitive, and analyzing MOCL model degradation during continual learning from a spectral graph theory perspective offers an interesting theoretical insight.\n\nWeaknesses:\n1. Additional spectral clustering literature citations would help better contextualize how the regularization term functions. Moreover, since this work employs a detect-then-learn approach, whether other efficient detection methods could be integrated into the proposed MOCL framework.\n2. Additional explanation is needed to clarify why the proposed regularization terms outperform conventional approaches like smoothing or sparse regularizations.", "other_comments_or_suggestions": "NA", "questions_for_authors": "What about the model's performance in an offline setting?", "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": "Y06KwPdxkq", "reviewer": "Reviewer_Nhoa", "summary": "This paper tackles Multi-Label Online Continual Learning (MOCL) from a novel perspective. Unlike existing approaches that focus on challenges like class imbalance and missing labels, this paper first analyzes the localization capabilities inherent in pre-trained models and introduces a CUT-out-and-Experience-Replay (CUTER) strategy. CUTER works by extracting, segmenting, and replaying image regions corresponding to individual labels. Through this approach, it addresses three key challenges simultaneously: catastrophic forgetting, missing labels, and class imbalance. The method's effectiveness is validated through comprehensive experimental results.\n\n## update after rebuttal\nAfter reading the authors' repsonses, I decide to keep my original score.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "_Respected Reviewer Nhoa,_ we first thank you for your valuable and insightful feedback, and for recognizing the analysis and advantages of our proposed method. Below, we address your concerns in a point-by-point manner.\n\nQ: **Experiments on other incremental settings should be included.**\nA: We appreciate this suggestion. We have conducted additional experiments on different incremental settings following protocols established in previous works like KRT and OCDM. Specifically, we evaluated our approach on class-incremental scenarios with varying numbers of classes per stage:\n\n_MSCOCO B40-C10:_  \nIn this setting, we first train a base model on 40 classes, then incrementally add the remaining 40 classes over 4 sessions (10 classes per session).\n\n|   COCO (mAP)   | 1-40 | 1-50 | 1-60 | 1-70 | 1-80 |\n| :----:| :----: | :----: | :----: | :----: | :----: |\n| ER   | 69.8 | 54.2 | 50.7 | 44.8 | 36.4 | \n| PRS  | 69.3 | 56.5 | 52.0 | 44.7 | 39.8 | \n| OCDM | 65.8 | 52.1 | 50.4 | 48.2 | 37.3 | \n| KRT  | 68.4 | 57.3 | 52.1 | 46.5 | 40.0 | \n| CUTER| 69.0 | 59.6 | 57.4 | 54.7 | 50.8 | \n\n_NUSWIDE B41-C5:_  \nHere, we start with 41 base classes and incrementally learn 8 sessions with 5 new classes each.\n\n|   NUSWIDE (mAP) | 1-41 | 1-46 | 1-51 | 1-56 | 1-61 | 1-66 | 1-71 | 71-76 | 1-81 |\n| :----:| :----: | :----: | :----: | :----: | :----: |:----: |:----: |:----: |:----: |\n| ER   | 64.4 | 51.2 | 47.3 | 40.0 | 35.8 | 31.3 | 28.8 | 29.5 | 24.6 |\n| PRS  | 61.2 | 50.6 |44.9 | 37.0 |36.7  | 30.2 |31.4 | 32.0 | 26.3 |\n| OCDM | 64.5 | 50.8 | 43.7 | 36.5 | 33.4 | 32.9 |31.8 | 30.8 |30.4 |\n| KRT  | 62.3 | 52.8 | 48.5 | 34.6 | 35.8 | 33.4 | 31.9 | 31.0 | 29.4 |\n| CUTER| 63.0 | 54.3 | 50.0 | 42.3 | 40.8 | 38.1 | 36.8 | 35.4 | 32.8 |\n\nThese results demonstrate that our CUTER method consistently outperforms existing approaches across different incremental settings, particularly in later stages where catastrophic forgetting is most pronounced.\n\nQ: **Method in continual object detection should be compared and discussed.**  \nA:Thank you for your insightful comment regarding the similarities between our proposed cut-out and replay framework and continual object detection. We have conducted a preliminary literature search on this topic and will include a thorough comparison in the related works section, especially with reference [A], which adopts an approach similar to our CUTER framework.\n\nHowever, we would like to clarify that methods from continual object detection cannot be directly applied to MOCL (Multi-Object Continual Learning). This is because these methods fundamentally rely on labeled bounding boxes which, even if partially missing, enable the training of an additional detection head. Such a strategy is clearly challenging to implement in the MOCL setting where such annotations are unavailable.\n\nTo validate this point, we conducted a comparative experiment on the VOC dataset between our method and a DETR detection head using the box replay approach from [A] (the initial box was obtained by NCut). The results, as shown in the table below, demonstrate the advantages of our approach in the MOCL context where bounding box annotations are not accessible.\n\n|       | Avg mAP | Avg CF1 | Avg OF1 |\n| :----:| :----: | :----: | :----: |\n| ABR   | 71.51 | 62.52 | 65.49 |\n| CUTER | 82.07 | 72.19 | 75.27 |\n\n[A] Augmented Box Replay: Overcoming Foreground Shift for Incremental Object Detection. ICCV 2023.\n\nQ: **The analysis of different regularization terms warrants inclusion in the main text.**  \nA: We agree this analysis provides important insights into our method. In the revised version, we'll include a concise comparison of regularization terms in the main text, explaining why nuclear norm regularization outperforms sparse and smooth approaches for preserving localization capabilities in MOCL. This addition will strengthen the theoretical justification for our design choices while maintaining the paper's focus.\n\nQ: **Ablation study could be enriched by including runtime comparisons between other methods and the proposed re-balanced sampling strategy.**  \nA: We have enriched our ablation study by including runtime comparisons between our proposed re-balanced sampling strategy and other methods. For a fair comparison, we directly omit the CUT out process and measure the throughput in terms of samples processed per second:\n\n|   Implemented on RTX4090    | ER (random sampling) | PRS | OCDM | Ours |\n| :----:| :----: | :----: | :----: | :----: |\n| # samples per second   | 138.6 | 76.3 | 51.6 | 90.9 |\n\nOur proposed method delivers a strong throughput of 90.9 samples per second, which is considerably more efficient than both OCDM and PRS.\n\nQ: **Typos.**  \nA: We feel sorry for any confusion or inconvenience may have caused. In the revised version, we will carefully proof-read our text to ensure that no grammar mistakes or typos still exist.", "claims_and_evidence": "**Three important claims made in this paper:**  \n1. Modern pretrained models possess the innate localization capacity among which the multi-crop contrastive learning methods demonstrate the best localization potential.\n\n2. The proposed regularization term on the feature-constructed graph serves to consolidate and further enhance model's localization capacity\n\n3. The proposed CUTER strategy can simultaneously addresses catastrophic forgetting, missing labels, and class imbalance.\n\n**Evidence**  \nFor Claim 1, the paper provides a comprehensive analysis with clear visualization in Section 2.1, complemented by detailed quantitative comparison results across different pre-trained models in Table 7 (Appendix E.2). This systematic evaluation offers valuable insights into model selection for the proposed approach.\n\nFor Claim 2, theoretical derivation and analysis are presented in Section 2.3, supported by mathematical proofs and derivations in Appendix D.2. Conclusively, this paper establishes a strong theoretical connection between localization ability and the spectral norm of the feature-constructed graph. Certainly, it's also worth noting that the mechanism by which the proposed regularization improves model performance relies on additional assumptions.\n\nFor Claim 3, the comparison in Figure 4, Figure 5 and experimental results in Section 3 can demonstrate the method's effectiveness. The ablation studies in Section 3.3 are also informative.", "methods_and_evaluation_criteria": "**Method**  \nThis paper introduces CUT-out-and-Experience-Replay (CUTER), which uniquely addresses MOCL challenges by extracting, segmenting, and replaying label-specific image regions. By focusing on foreground object extraction, CUTER presents a novel direction that distinctly differs from conventional MOCL approaches [1,2,3], demonstrating notable innovation within this research domain.\n\nThe incorporation of nuclear norm regularization on the feature-constructed graph is noteworthy, providing both theoretical guarantees and a reasonable solution to the problem.\n\n**Evaluations**  \nThe method is evaluated on three established multi-label benchmarks using standard metrics (mAP, CF1, OF1). The comprehensive experiments, including ablation studies and comparative analyses with recent methods, convincingly validate CUTER's effectiveness both as a standalone approach and as a plug-in component. To further strengthen the evaluation, the authors could explore additional incremental settings with larger base class sets, similar to prior MOCL works [1,2,3].\n\n[1] Knowledge restore and transfer for multi-label class-incremental learning. ICCV2023.  \n[2] Confidence self-calibration for multi-label class-incremental learning. ECCV2025.  \n[3] Optimizing class distribution in memory for multi-label online continual learning.", "theoretical_claims": "The paper's key theoretical analysis examines the relationship between the Fiedler value and noise matrix norm, grounded in assumptions about ideal feature-constructed graphs.\n\nI have checked most of their proofs. Though not overly complex, they support the claims made in the main text and are clearly demonstrated.", "experimental_designs_or_analyses": "**Experimental Designs**  \nAs I stated in **Evaluations** , the experimental settings and evaluation protocols align well with established practices in related prior works. I did not find any inappropriate experimental design choices.\n\n**Analyses**  \nThe analyses for the three claims mentioned above are well structured with either empirical or theoretical verifications. A suggestion is that the ablation study could be further enriched by including specific runtime comparisons to quantitatively showcase the computational benefits of the proposed re-balanced sampling strategy, considering that Figure 8 only presents the combined running time of all components, which cannot reflect the claimed reduced computational cost.", "relation_to_broader_scientific_literature": "The paper's main contributions lie in advancing multi-label learning and its integration with online continual learning.", "essential_references_not_discussed": "I cannot think of essential references that are missing from this paper.", "other_strengths_and_weaknesses": "**Strengths**\n1. Multi-Label Online Continual Learning is an important and compelling research area. Furthermore, object region identification represents a fundamental challenge within this domain.  \n2. The analysis of localization capabilities across pre-trained models merits deeper exploration. The evaluation of these capabilities could be expanded beyond NCut and MCut metrics, as these represent just one category among many unsupervised and weakly-supervised object detection and segmentation approaches. A broader assessment incorporating diverse evaluation metrics would provide more comprehensive insights.  \n3. This paper presents an intuitive and straightforward approach that distinctly differentiates itself from existing work focused on class imbalance or missing labels. While taking this different direction, CUTER appears to address the fundamental challenges of MOCL more directly compared with strategies like pseudo labeling or rebalanced sampling, offering a more essential solution to the core problems in this domain.   \n4. The paper's use of spectral theorem analysis to examine localization capabilities across pre-trained models offers valuable insights. This theoretical foundation naturally leads to the intuitive development of low-rank regularization on the feature adjacency matrix, creating a cohesive analytical framework.  \n\n**Weaknesses**\n1. Some related work exists in continual object detection shares quite similar intuition with this submission. Including comparisons and analysis with these works would better highlight this paper's unique challenges and innovations.\n2. The analysis of different regularization terms warrants inclusion in the main text, as it constitutes a significant component of the paper's contribution.", "other_comments_or_suggestions": "Some typos exist like \"superiority of proposed our method\" in line 39.", "questions_for_authors": "See the previous parts.", "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:

Meta-review: The paper proposes CUTER (CUT-out-and-Experience-Replay), a novel strategy for Multi-Label Online Continual Learning (MOCL) that addresses key challenges including catastrophic forgetting, missing labels, and class imbalance. The method leverages pre-trained models' localization capabilities to identify label-specific regions in images, which are then selectively replayed to provide fine-grained supervision. CUTER incorporates a spectral graph theory-based regularization to preserve localization ability during continual learning, achieving state-of-the-art performance across multiple benchmarks while serving as an orthogonal solution that can integrate with existing approaches.

The reviewers unanimously recognized the paper's strengths: (1) Novelty and Technical Soundness. (2) Empirical Validation: All reviewers acknowledged the thorough experiments across three benchmarks. (3) Practical Impact: Reviewers pointed out that CUTER addresses "fundamental challenges more directly" than prior methods like pseudo-labeling. While reviewers raised minor concerns about computational overhead (eZ9b) and presentation of regularization analysis (NiGx), the authors convincingly addressed these in rebuttals by providing additional runtime comparisons. All reviewers ultimately recommended acceptance. Given this consensus, I recommend acceptance.
Decision: Accept (poster)