Consistency-Driven Calibration and Matching for Few-Shot Class Incremental Learning
Authors: Qinzhe Wang
Zixuan Chen
Keke Huang
Xiu Su
Chunhua Yang
Chang Xu
Venue: ICLR 2026 Poster (2026)
Keywords: Continual learning
Few-shot class-incremental learning
Primary area: transfer learning, meta learning, and lifelong learning
TL;DR: To solve the knowledge conflict problem in FSCIL, we propose Consistency-Driven Calibration and Matching framework.

Abstract: Few-Shot Class Incremental Learning (FSCIL) is crucial for adapting to the complex open-world environments. Contemporary prospective learning-based space construction methods struggle to balance old and new knowledge, as prototype bias and rigid structures limit the expressive capacity of the embedding space. Different from these strategies, we rethink the optimization dilemma from the perspective of feature-structure dual consistency, and propose a Consistency-driven Calibration and Matching (ConCM) framework that systematically mitigates the knowledge conflict inherent in FSCIL. Specifically, inspired by hippocampal associative memory, we design a memory-aware prototype calibration that extracts generalized semantic attributes from base classes and reintegrates them into novel classes to enhance the conceptual center consistency of features. Further, to consolidate memory associations, we propose dynamic structure matching, which adaptively aligns the calibrated features to a session-specific optimal manifold space, ensuring cross-session structure consistency. This process requires no class-number priors and is theoretically guaranteed to achieve geometric optimality and maximum matching. On large-scale FSCIL benchmarks including mini-ImageNet, CIFAR100 and CUB200, ConCM achieves state-of-the-art performance, with harmonic accuracy gains of up to 3.41% in incremental sessions. Code is available at: https://github.com/wire-wqz/ConCM

Published as a conference paper at ICLR 2026
CONSISTENCY-DRIVEN CALIBRATION AND MATCH- ING FOR FEW-SHOT CLASS INCREMENTAL LEARNING
Qinzhe Wang's Zixuan Chen' Keke Huang's | Xiu Su², Chunhua Yang', Chang Xu
1 School of Automation, Central South University, China
2 Big Data Institute, Central South University, China
3 School of Computer Science, Faculty of Engineering, The University of Sydney, Australia
{ (waggginzhe, chenzixuan, huangkeke, xiusu, ychh)@cste sdu, on, c. xu@sydney. wewpyyddeyyeu..... au
ABSTRACT
Few-Shot Class Incremental Learning (FSCIL) is crucial for adapting to the com- plex open-world environments. Contemporary prospective learning-based space construction methods struggle to balance old and new knowledge, as prototype bias and rigid structures limit the expressive capacity of the embedding space. Different from these strategies, we rethink the optimization dilemma from the per- spective of feature-structure dual consistency, and propose a Consistency-driven Calibration and Matching (ConCM) framework that systematically mitigates the knowledge conflict inherent FSCIL Specifically, inspired by hippocam- pal associative memory, we design memory-aware prototype calibration that extracts generalized semantic attributes from base classes and reintegrates them into novel classes to enhance the conceptual center consistency of features. Fur- ther, to consolidate memory associations, we propose dynamic structure matching, which adaptively aligns the calibrated features to session-specific optimal man- ifold space, ensuring cross-session structure consistency. This process requires no Hlas-nnumer priors and is theoretically guaranteed achieve geometrie op- timality and maximum matching. On large-scale FSCIL benchmarks including mini-ImageNe CIFARI00 and CUB200, ConCM achieves sfate-of-the-art per- formance, with harmonic accuracy gains of up to 3.41% in incremental sessions.
Code is available at:
INTRODUCTION
Deep Neural Networks (DNNs) have achieved impressive success in various applications (He et al., 2016; Huang al., 2017). Predominant approaches operate under closed-world assumptions, op- timizing for static, predefined tasks. However, visual concepts in open-world environments are inherently unbounded and continuously evolving (McDonnell et al., 2023; Goodfellow al., 2013). Moreover, acquiring sufficient training samples remains critical bottleneck in DNNs training, es- pecially for emerging categories (Wang et al., 2020). Humans, by comparison, exhibit lifelong learning capabilities: adapting new concepts from limited information while minimizing forgetting of past experiences. Inspired by this, FSCIL has emerged as compelling paradigm that mimics human-like learning (Wang al., 2023; Tao et al., 2020). FSCIL divides training into base session with ample data and multiple incremental sessions with few-shot novel classes, requiring models to learn new concepts under limited supervision while retaining prior knowledge.
The scareity of novel class samples induces overfitting in the backbone (Deng & Xiang, 2024). Thus, recent methods advocate fully training the backbone in the base session and freezing thereafter, pairing it with a prototype-based classifier (Ayub & Fendley, 2022) for session-wis. adaptation. While this decoupling strategy enhances stability, novel class features often lack adaptability to the embedding structure. This leads to knowledge conflict between classes, as shown in Figure 1 (a). Recent approaches adopt prospective learning to pre-allocate embedding space for novel classes in the base session and employ projection modules during incremental updates to ensure compatibility
"quual contribution. Corresponding author.

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Published as a conference paper at ICLR 2026
(a) Baseline
(b) Prospective learning
(c) CarCM (Ours)
Base session
Space in session 1
Space in session
Base F Features
Class
Class
Novel Features
Class
Real Prototypes
Incremental session
Class
Blas Prototypes
Reserved
Space in session
Space in session
Space in session 2
Tuned Prototypes
G Geometry Vectors
Blas Distribution
Real Distribution
Dynanic
Figure 1: (a) FSCIL setup and baseline. It trains the backbone through the base session and freezes it. (b) Prospective learning-based space construction method. pre-allocate fixed embedding spaces for novel classes, subject to the a priori assumptions of the class. (c) The ConCM framework (Ours). It leverages semantic attributes to calibrate novel class features by transferring knowledge from base classes, while incorporating dynamical mechanism of feature-structus structure mathcing.
(Zhou et al., 2022a; Yang et al., 2023; Ahmed et al., 2024). However, by enhancing novel class performance the expense of compressing old class embedding, these methods lack global structure optimization across sessions. As a result, the knowledge conflict remains unresolved.
In terms of embedding structure optimization, many methods enhance feature space generalization by designing specialized structures (Zhang et al., 2025; Ma' sum et al., 2025), as shown in Figure 1 (b). For instance, OrCo constructs orthogonal pseudo-targets to maximize inter-class distances (Ahmed et al., 2024), while NC-FSCIL pre-assigns classifier prototypes as an equiangular tight frame (ETF) guide the model toward a fixed optimal configuration (Yang et al., 2023). Although these methods enhance structural adaptability, two key issues persist in FSCIL. First, the inherent feature bias in few-shot settings (Pan et al., 2024; Goswami et al., 2024) causes inconsistency be- tween novel class prototypes and their true centers. Second, fixed structural constraints impose rigid priors on novel classes, restricting their matching flexibility and resulting in inconsistency between the actual and expected embedding structures. To this end, we explore new unified perspective in FSCIL: optimizing dual consistency between feature and structure through structured learning across incremental sessions to enable robust continual learning.
The human ability to robust continual learning is largely attributed to hippocampal associative mem- ory (Gutiérrez al., 2024). Inspired by this, propose Sonsistency-Driveer Calibration and Matching Framework (ConCM), which systematically mitigates the knowledge conflict inherent in FSCIL, shown in Figure (c). Specifically, aware prototype calibration. It extracts generalized semantic attributes from base classes to build class-related memory index, which is then queried and aggregated via meta-learning to calibrate novel class prototypes, thereby ensuring feature consistency. To consolidate memory associations, further propose dynamic structural matching that constructs an evolving geometric structure and adaptively aligns features with it, thereby ensuring structural consistency across sessions. Theo- retical analysis confirms that the proposed structure satisfies geometric optimality by equidistant prototype separation and achieves maximum matching by minimal global structural change.
eliminate feature bias, design memory-
In summary, our contributions are: (1) We alleviate the knowledge conflict in FSCIL, from uni- fied perspective of consistency-struccuree learning. The proposed ConCM framework systemati- cally resolves the dual consistency challenges prototype bias and structure fixity. (2) To ensure consistency between novel class prototypes and their true centers, we draw inspiration from the hip- pocampal associative memory and propose memory-aware prototype calibration. (3) A geometric structure is constructed to jointly satisfy geometric optimality and maximum matching, along with a rigorous theoretical analysis. (4) Extensive evaluations on large-scale FSCIL benchmarks confirm the state-of-the-ae performance, with ablation studies validating the contribution of each module.
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(a) Memory-aware Prototype Calibration
(b) Dynamic Structure Matching
Semantic Acquire
Attribute Separation
Dynamsic Structure
Bird
Canidue
White
Matching:
Feather
Attribute
class Name
Optimality
Base session
Attribute Completion
MSE
M
MPC Module
11
11,
Module
Incremental sassion
Session
Session
Session
cohesion
Backbone
Inc. Clarci
Projector
Festure-Structure
+
Feature-Structure Contrastive Loss
Matching
Dataset FSCII Setting
Memory
Feature-Stracture Matching
Traizable Network
Test Embrdding
Protutope
Frove Netwark
Visio Embeddlig
Callbrated
Figure 2: The proposed ConCM framework has two main modules: (a) MPC module. Generalized semantic attributes extracted from base classes, i.e., attribute separation, followed meta learn- ing-based retrieval and aggregation for prototype calibration, i.e., attribute completion. (b) DSM module. Each incremental step dynamically updates embedding structure ensure geometric opti- mality and maximum matching, while adaptively aligning features through loss-driven optimization.
2 RELATED WORKS
FSCIL aims to enable continual learning under limited supervision. We focus optimization- bbased approaches, which mainly include: replay (Zhang et al., 2021; Hu et al., 2025) or distillation (Zhao et al., 2023; Kukleva et al., 2021), meta learning (Hersche et al., 2022; Zhou et al., 2022b; Chen et al., 2025), feature fusion-based (Goswami et al., 2024; Wang et al., 2023; Akyürek et al., 2022), and feature space-based methods (Zhou al., 2022a; Yang et al., 2023; Ahmed al., 2024). The latter two methods aim to obtain more efficient feature representations and are highly relevant to our method. Therefore, we focus on these methods below, with further details provided in Appendix B.
Feature bassd FSCIL methods. They aim to construct more accurate feature embeddings by integrating representations derived from diverse information sources or feature extraction meth- ods. The semantic subspace regularization method (Akyürek et al., 2022) and TEEN (Wang et al., 2023) leverage semantic relations fuse base and novel prototypes. ples common features within family and transfers them to new species. In contrast, we construct mearry-aware generalized semantic attributes enhance semantic guidance.
(Liu et al., 2025) decou-
Feature space-based FSCIL methods. Aiming to reserve embedding space for novel classes in prospective learning to mitigate knowledge conflict. FACT (Zhou et al., 2022a) creates virtual proto- types to reserve embedding space for novel classes. NC-FSCIL (Yang et al., 2023) pre-assigns fixed classifier as an equiangular tight frame and uses dot-regression loss to maintain feature-classifier alignment. OrCo (Ahmed et al., 2024) constructs globally orthogonal embedding space, combin- ing feature perturbation and contrastive learning to reduce inter-class interference. In contrast, we construct evolving geometric structure ensure consistency across sessions.
3 THE PROPOSED FRAMEWORK: ConCM
Overview of the framework. In Section 3.1, we detail the FSCIL task and analyze its core chal- lenges by preliminary experiments: the feature-structure dual consistency problem. We then propose ConCM framework to mitigate the conflict between learning and forgetting, depicted in Figure 2. For feature inconsistency, we propose memory-aware prototype calibration (MPC Section 3.2). For structure inconsistency, propose dynamic structure matching (DSM in Section 3.3).
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(a) Issue
(b) Reason 1
(c) Issue 2
(d) Reason2
Jest Seople
Rea Paretpe
25
Noud
25
Proditoo Labds
Preliminary results. We identify two issues and causes: (a) Issue 1: The accuracy on novel classes consistently declines as prototype deviation increases, caused by (b) Reason 1: Novel class prototypes deviate from the true centers, L..., feature inconsistency. (c) Issue 2: false positive classification, caused by (d) Reason 2: feature embedding confusion, i.e., structure inconsistency.
Figure 3:
3.1 TASK REDEFINITION AND PROBLEM ANALYSIS
FSCIL task redefinition. As shown in Figure 1, FSCIL consists of a base session DD and multiple incremental sessions D ,DT Each session D' is represented as a triplet of training images,
one-hot encoded labels, and textual class names, i.e., D = {(T; Vi. {:) !Pa! In the base session, we a feature extractor. A projector g(:; 0g) E R& is further trained to map features into a geometric space, enabling initial 8N.]. In the following T incremmentll sessions
train the backbone on dataset with sufficient base class samples and freeze
alignment with the target structure A [8,, 62. (where 7' is unknown). the model has access only to Dr. Using D we fine-tune the projector g(: 0g) to expand the geometric space and adaptively align features to novel classes C while preserving base classes C performance. Each incremental session is formulated as typical N-way K-sho few- shot learning task, i.e., ID'| NK. Label spaces do not contain any overlap C n C² = 0, Vt * t'. Notably, before projection we add a prototype calibration network h(:; 0%) E IR that leverages textual class name &; extract latent semantic attributes and calibrate novel class prototypes.
After training, session is evaluated using a nearest class mean (NCM) classifier, which assigns class labels based on the distance between projected feature vectors and corresponding geometric vectors. The test set De spans all seen classes, implying C = - with N, = | total classes.
Consistency problems in FSCIL. Preliminary experiments on mini-ImageNet reveal two critical issues: (1) Feature Inconsistency. The accuracy of novel classes declines as prototype bias (it is quantified as 1 - cos(p, )) increases (Figure 3 (a)) caused by the misalignment between few- shot prototypes and actual class centers (Figure 3 (b)). This motivates to calibrate prototypes to ensure consistency with the true centers 3.2). (2) Structure Inconsistency. Despite ensuring prototype consistency by calibration, novel samples are often misclassified old classes (Figure (c)). The root cause lies in the implicit constraints imposed by the fixed space on novel classes, which hinders effective optimization and causes structure inconsistency, ultimately leading inter- class confusion of projector output features (Figure 3 (d)). Accordingly. we propose to dynamically refine the geometric structure maintain cross-session consistency (Section 3.3).
3.2 CONSISTENCY-DDRIEER MEMORY- PROTOTYPE CALIBRATION
Hippocampal associative memory, essential for human continual learning, operates in two stages (Gutiérrez et al., 2024; Luo et al., 2022): first, perceptual information is encoded and separated into high-level representations, constructing a memory index by neural routing; second, when partial perceptual signals are received, the associated memory in the index is retrieved and integrated to reconstruct complete representation. Thus, we mimic human associative memory with attribute separation and attribute completion to calibrate novel class prototypes, defining attributes as latent semantic attributes that are class-discriminate and anvironment-invarian (Zhang et al., 2023).
Attribute separation. Base classes, with abundant and diverse samples, provide text labels that embed rich semantic information. Therefore, we first leverage WordNet (Ge et al., 2022) to parse
base class text labels [c] and obtain semantic extensions such as synonyms and hypernyms, from which latent semantic attributes extracted. The words extended from class labels are further
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integrated into candidate attribute pool A {ai)-= and encoded as word embeddings S, (sa.)=i In addition, we construct a visual embedding prototype fa, for each attribute, defined as the mean feature of all samples possessing that attribute.
Ja
Σ
(xx00)) Fa Fa
Da
(E.y)eDa,
The set of attribute visual prototypes is denoted as Fa. Extending to any session t, we similarly encode the current class text labels {ct)ee into word embeddings St {s for attribute completion. A binary association matrix R' 12,, -Ticy] E R Nxxx is further introduced match current classes with attributes in pool, where Ja, 1 if class has attribute a, and Tack = otherwise. Therefore, through attribute separation, the semantic knowledge set of session t represented as II, {Sa. Fa. R.
Attribute completion. As the core of attribute completion, the MPC network adopts an en- code-aggregate-deood architecture. The encoder estimates the low-dimensional representation of both attribute visual prototype Fa and class prototype Pk- The aggregator performs attribute retrieval based on relevance weight w/ The decoder outputs the calibrated prototype Pt- w is derived by cross-attention (Vaswani et al., 2017). Specifically, Semantic association are evaluated by the sim- ilarity between word embeddings of the base attribute pool S, and those of the current class labels St. while visual association measured by the distance between visual prototypes Fa and ps- Therefore, by jointly considering these two associations, we have
Na
Eu = Tee (pu; (ip2;0f, 0%)
Σ
Softmax (wa,) x he Me(Sai:8h) Pu = halEni (%)
(2)
1=1
w (Kopp (Sa,) 0%
hgg (ok, 0%5)
(hog (Jays 0%; hag (pxs 02:))
Fack
(3)
2/d.
d
where w represent the relevance weight of attribute a, to class and EN represents the aggregate output. 0% and 0g are the encoder and decoder parameters. 0g = 00t 0g 00f 0% denotes the parameters of the attention layer. d, and dy the dimensions of semantie and visual embeddings. To,k serves to mask according to the binary class-attribute associations. (.,-) represents the vector inner product. In summary, all parameters of the network are represented by A, = - 0% 8%,.
The network parameters trained via meta-learning (Snell al.. 2017), where series of K-shor episodic tasks are constructed in the base session to obtain biased prototypes piante, and the actual base class prototypes pk plase are used as supervision. The network is trained with an MSE loss to learn completing associated attributes.
L m (SF (a Tl; 0z), pk hase), k No
(4)
After training, similarly extend to session 6, where the completed prototypes Pr: lp I1, 0x) are obtained and combined by weighting to form the final class prototypes:
p = aaaa x Pk + (1 - a) x fu. D Aug(E)), N < k K
(5)
where a controls the calibration strength. In addition, since old class samples are inaccessible and novel class samples are insufficient, we further construct an augmented dataset Dj via Gaussian sampling for subsequent projector training. More details are provided in the Appendix D.
3.3 CONSISTENCY-DRIVER DYNAMIC STRUCTURE MATCHING
Dynamic structure. Structure inconsistency in feature space is key source of knowledge conflict. Our goal is to optimize the projector to construct a dynamic structure that satisfies both geometric optimality and maximum matching. These two properties are defined as follows.
Definitionl (Geometric Optimality): The neural collapse theory reveals an optimal feature structure formed (Papyan et al., 2020). A structural matrix A. (81, 82, ,N.]] TRdxXN, (dg No)
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satisfies geometric optimality if the following condition holds.
No
Vi 6, 5,
N₂
N
where Az,j when i j. and 0 otherwise. It implies that prototypes are equidistantly separated. Deftnition2 (Maximum Matching): Fixed structures constrain matching for novel classes. There- fore, we aim embed new classes with minimal structural change, which can be expressed as:
A. arg max
(8)
6:E2:
We maximize the similarity between the optimal target structure A, and the initial structure A ...... ,8y.] E x (The historical structure that incorporates the embedding of novel classes) to distill structural knowledge and ensure minimal structural change.
Base class prototypes and covariance diagonal are stored. Each session generates training data Aug(S,, through Gaussian sampling-basee -baed prototype augmentation from the prototype repository )
Ω (pbase bb=1 f ) 1 The projected mean serves as the initial structure A, Theorem describes the structure optimization.
Theoreml: For session r, the dynamic structure that satisfies geometric optimality and maximum matching can be updated based on the following formulation.
WAV A/
IN. N₂ IN. 1..
U. WVT
No U. - A V N₂
N₂ 1N. IN,1N
(9)
where Ug = [u1, u2 ... uN.] E R is the column-wise orthogonal matrix, IN. E N. is the identity matrix, and IN, RRNxx1 is all-ones column vector. W Rd,xN, A R.N... and VT E RRNN.N, represents the compact SVD result.
Proof: for any pair of vectors and 8j. have:
N₂
Vi.j. 5, T6 = N₂ - (u, Ne
(10)
According to the properties of singular value decomposition, W a column-wist orthogonal matrix, ie., W W IN, and V is unitary matrix, i.e., V VVT IN.- It can be deduced that U, is also a column-wise orthogonal matrix, i.e., U, Ue = IN,- Therefore, we satisfy geometric optimality, i.e., Eq. (6). Furthermore, Eq. (7) can be expressed
N
1 N
U. arg max tr U. U,U,TIAN
U.(TN,
IN. M))
(11)
N₂
Let /
N. M IN. N.
N. 1N, 1 N where M is symmetric matrix. Hence, we obtain:
U. arg max k (-tr((A(M))T)) TU,) U. U.-
(12)
This is classic orthogonally constrained trace maximization problem. Let WAVT A/M denote the SVD matrix. The optimal solution under the optimal matching relationship is given by U, WVT. Please refer to Appendix E for more details.
Feature-structure matching. Aug(S)) is taken input, and the projector g(: 0g) is optimized to match the target structure A, by the joint feature-structure matching loss and contrast loss. The matching loss between projected vector and its corresponding structure vector òn is defined as:
exp ((zz, 0x))
Maaech (2,) log
(13)
Σ No exp ((2, 8,))
This classification loss minimizes the distance between projected class means and their correspond- ing dg. We also employ Supervised Contrastive Loss (SCl) et al., 2020) to enhance intra-class

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Table 1: SOTA comparison mini-ImageNNt. AHM denotes the average harmonic mean. FA denotes the Top-1 accuracy in final session. The top two rows list CIL and FSL results implemented in the FSCIL setting. Detailed results for the remaining datasets presented in the Appendix.
Session-wir Harmonic Mean (6)
Methods
Base Ace
AHM
FA
2
5
|CaRL/Rebufi et al., 2017)
61.31
8.45
13.86
14.92
13.00
14.06
12.74
12.16
11.71
12.61
17:21
IW(Qi et al., 2018)
61.78
25.32
20.45
22.62
25.48
22.54
20.66
21.27
22.27
22.58
41.26
FACTiZhou al., 2022a)
75.78
27.20
27.84
27.94
25.17
22.46
20.54
20.88
21.25
24.16
48.99
CECIZhang et al., 2021)
72.17
31.91
31.84
30.98
30.74
28.14
26.78
26.96
27.42
29.35
57.95
C-PSCILL(eeschhe al., 2023)
76.60
9.74
20.53
28.68
31.93
34.85
35.05
37.72
37.92
29.55
51.09
LIMIT al,, 2022h)
73.27
40.34
33.58
31.81
31.74
29.32
29.11
29.57
30.28
31.97
48.08
MetaNC((Ran 2024)
79.05
30.33
30.55
38.25
29.44
21.34
28.85
41.92
42.09
32.85
53.11
SAVC(Song et al., 2023)
80.02
42.40
37.80
36.56
37.20
33.53
31.72
31.98
32.94
35.52
54.34
LCwoF(Kukkeva 2021)
64.45
41.24
38.96
39.08
38.67
36.75
35:47
34.71
35.02
37.49
37.69
FACL(Nema & Kurmi, 2025)
86.23
50.21
40.35
37.45
39.59
35.61
32.32
32.66
34.22
37.80
58.63
CLOSER(Oh al, 2024)
76.97
43.58
40.36
37.76
35.72
38.86
BiDistZhhao et al., 2023)
74.67
42.42
43.86
43.87
40.34
38.97
38.01
36.85
38.47
40.35
$5.69
ADBS(Li et 2025)
79.53
44.88
45.60
41.68
40.58
43.12
42.58
TEEN et al., 2023)
74,70
52.39
47.25
44.53
43.54
41.27
39.97
39.96
40.42
43.66
51.80
Mamba-FSCII,(LL al., 2024)
84,93
58.01
53.33
44.06
41.72
47.25
et al, 2023)
84.07
62.34
61.04
55.93
53.13
49.68
47.08
46.22
45.57
52.62
57.97
OrCo(Ahmed et al., 2024)
83.30
67.14
64.12
60.71
57.16
55.34
51.52
50.96
51.44
57.30
56.04
ConCM (Ours)
83.97
70.34
66.59
63.38
59.59
57.05
53.95
53.49
53.92
59.78
59.92
Betler than secund
+3,20
+2.47
+2.67
+2.43
+1.71
+2.43
+2.53
+2.48
+2.48
+0.56
compactness. By treating the structure vector as an anchor and including it in the positive set, the model learns structural information explicitly.
Σ expl(zz, 23) /T)
Coone (2i) = P.
(14)
exp((zt, zx) /T)
E,EP:
BEEN,
P. represents the positive sample set, which includes the anchor and instances from the same class. The negative sample set N₁ includes instances from other categories. the temperature parameter. Therefore, combining Eq. (13) and Eq. (14) yields the final loss for optimizing the projector.
Crooj
Caathh Loont
(15)
4 EXPERIMENTS
In Section 4.1, the main experiment details are introduced. In Section 4.2, comparison is made with SOTA methods on mainstream benchmarks. Section 4.3 provides a comprehensive analysis to demonstrate the superiority of the proposed method and the effectiveness of each module.
4.1 MAIN EXPERIMENTAL DETAILS FSCIL
We evaluated the ConCM on three FSCIL benchmark: mini- mini-ImageNee(Deng et al., 2009), CI- FARI00 (Krizhevsky et al., 2009) and CUB200 (Wah al., 2011). The dataset split followed Zhang et al. (2021). For fair comparison with existing methods (Ahmed et al., 2024: Zhao et al., 2023: Yang et al., 2023), we replay 5 samples per incremental class, consistent with prior work. All expeiments are conducted on RTX 4090 GPU. Please refer to Appendix F for more details.
4.2 COMPARISON TO STATE-OF-THE-ARRT
In this section, we compare the proposed ConCM with the latest SOTA methods. The experimental results provided in Table 1, Table 2 and Figure 4. We adopt the Harmonic Mean (HM) (Ahmed et al., 2024) as a balanced metric. The proposed method outperforms previous SOTA methods across all datasets, with the highest performance improvement in incremental sessions of 3.20%

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Table 2: SOTA comparison on CIFARI00. AHM denotes the average harmonic mean. FA denotes the Top-1 accuracy in final session.
Session wise Harmonic Mean (%)
Methods
Base Ace
AHM
FA
3
8
FACTiZhou et al,, 2022a)
78.32
42.85
38.21
33.03
32.35
31,71
34.15
33.37
33.62
34,91
51.83
CRC(Zhang et al., 2021)
39.18
34.25
33.27
C-FSCII.(Hersche et al,, 2022)
77.35
31.47
28.33
26.68
23.11
24.85
24.29
23.04
23.49
25.66
49.32
LIMITZ/huu et al,, 2022b)
33.24
32.64
32.28
31.96
31.40
MetaNC(Ran et al., 2024)
79.17
31.13
25.75
31.60
22.87
18.34
19.26
28.40
27.04
25.55
51.99
SAVC(Song ot al., 2023)
78.07
34.95
30.42
28.90
30,30
31.59
31.68
30.79
32.16
51.93
FACL(Nema & Kurmi, 2025)
83,65
34,79
33,90
30.12
30.09
29.31
29.24
31.38
30.17
31.13
56.74
CLOSER(Oh ol., 2024)
44.98
41.99
40.87
40.02
41.64
40.23
42.48
53.55
BiDist(Zho al., 2023)
78.72
47.87
42.68
40.15
37.02
35.42
33,80
33.25
30.94
37.65
46.84
ADBS(Li et al., 2025)
34.39
32.84
32.82
33.25
29.75
29.33
32.98
TEEN et ol., 2023)
78.47
46,66
40.28
36.85 88
35.32
35.55
36,28
35.43
34.97
37.67
51.62
Mamaho-FSCII.4 (1. et al., 2024)
47.29
45.10
42.86
40.77
NC-FSCU (Yang et al., 2023)
82,52
56.66
54.41
49.70
45.27
44,36
46.65
44.22
41.87
47.89
56.11
AhCee 2024)
63.92
56.90
51.78
49.57
57.12
ConCM (Ours)
82.82
72.27
67.33
60.09
$7.08
54,93
55.21
52.95
$2.51
59.05
58.33
Better than second
+0.16
+3.41
+3.19
+1.85
+1.55
+1.18
+1.17
+2.94
+1.97
+0.82
FACT CBC
C-PSCIL
SAVC
TACL
BiDist THEN
Marba ISCIL
OrCo
LIMIT
LOWOF
CLOSER
NC-FSCII
ConCM
mini ImageNNt
(b) CIFARI00
(c) CUB200
70
70
1416
Hamoricc Momn
2/28%
Hunronic MomnoniiMoon Mo0
150%
Hermonic: Meen()))
1,206
(2)
300
30
20
20
Session
Session
Session
Figure 4: SOTA comparisons on three FSCIL benchmark datasets. Performance curve is har- monic mean accuracy. The underlined denotes the average performance improvement. The red denotes the highest performance improvement.
mini-ImageNet, 3.41% on CIFAR100, and 1.70% on CUB200. Compared to related works, NC- FSCIL(YAng et al., 2023) is static structure optimization method without considering the inherent bias of the embedded features. ConCM achieves structural dynamic matching without requiring prior knowledge of the categories number and achieves 5.04% 11.16% improvement in AHM. While PA'S (Liu et al., 2025) family-level knowledge extraction is limited to datasets with family labels, ConCM captures attribute-level knowledge, enabling finer-grained analysis that achieves 6.17% performance improvement on CUB200 and generalizes to universal datasets.
4.3 ANALYSIS
ConCM alleviates knowledge conffict. Feature confusion between old and novel classes leads to misclassification, which is a tangible manifestation of knowledge conflict. Following Wang al. (2023).
we treat base classes as "positive" and novel classes as "negative", and use the Balanced Error Rate (BER (FPR FNR)/2 to quan- tify conflict. Table reports the metrics for each session, showing that ConCM achieves the lowest misclassification rate and the high- est novel class accuracy. Compared Figure 3 (c), Figure demon- strate that our method effectively mitigates false positive classifica- tion. Notably, for all increments, the average NAcc of ConCM in- creased by 2.8%, better aligning with practical requirements. Com- pared dwelling on old memories, learning new things is essential for adapting the environment.
Tiee Labels
30
Figure 5: The confusion matrix result ImageNet.
8

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Published as a conference paper at ICLR 2026
Table 3: Analysis of knowledge conflict mini-ImageNet. The metric results for eight incremental sessions are reported. NAce denotes the novel-class accuracy, reflecting plasticity of novel elasses. BER denotes the balanced error rate, reflecting the degree of knowledge conflict. Higher NAcc is better, and lower BER better.
Session
NAcoBBER
NAcc
NAcc
BER
NAcc
NAcc
BER
NAcc
NAcc
BER
NAcc
BER
Bascline(Snell 2017)
19.80
38.20
16.90
36.53
13.53
37.09
11.10
37.22
10.80
36.06
10.00
34.99
10.03
10.57
34.78
FACT (Zhou ol., 2022a)
16.60 37.24
17.10
37.01
17.20
37.18
15.16
36.98
13:24
36.63
11,93
36.65
12.17
35.92
12.43
36.07
CEC (Zhang et al. 2021)
20.60
35.92
20.60
35.55
19.93
34.48
19.75
33.02
17.78
36.63
16.63
33.13
16.80
31.38
17.19
30:49
TEEN (Wang et al., 2023)
41.00
27:21
35.30
24.98
32.53
25:37
31.65
25.25
29.44
24.48
28.23
24.48
28.34
23.33
28.93
23.16
OcCo (Ahmed et al., 2024)
60.20
14.63
56.90
15.12
53:40
16.48
48.45
17.87
46.40
17.55
41.70
17.85
41.43
18.13
42.55
18.56
ConCM
67.20
13,00]
60.30
13.85
56.33
15:40
50.75
16.39
47.72
16.48
43.60
16.93
44,00
17.24
43.52
17.34
Ablation studies. AHM denotes the average harmonic mean. FA denotes the accuracy in final session. NAcc denotes average novel-class accuracy. PD denotes performance dropping rate. 1 denotes higher is better. I denotes lower is better. Appendix G presents more results.
Table 4:
Methods
mini-Imaggeet
CIFARIO0
CUB200
MPC DSM
AHM
FAt
NAce?
PDJ
AHM
FAT
NAcef
PDJ
AHM
FAT
NAce
PDJ
22.00
52.62
12.84
31.35
21.73
51.14
12.64
31.68
46.78
52.97
33.87
27.55
47,83
56.22
35.17
27.75
48.05
55.33
35.80
26.67
58.48
58.82
47.99
22.00
52.35
57.23
40.65
26.74
53.31
56.74
42.31
25.26
59.88
59.01
50.43
21.51
56.79
58.29
46.81
26.68
56.96
56.99
47.95
25.83
60.19
59.59
51.43
20.93
59.78
59.92
51.74
24.05
59.05
58.33
24.49
62.20
62.66
53.96
17.86
(a)
(b)
Buse Seaion
Sosian
Sasian
Sessin
Session
Sample
Real
10-
Calibnttd
E-NNE Foltie?
Bins
Boseline
(Oun)
-20
-10
10
Clan
Class 61
Class
Clas 71
Class 76
Claw
Class
Class 07
Class
Class 77
L-S Feature
Figure 6: (a) Prototype calibration visualization. Different markers indicate the sample feature, the real class center, the calibrated prototype, and the biased prototype. (b) Embedding space visualization on mini-ImageNet. Each point represents a sample, colored by class.
Ablation studies of each module. Table 4 summarizes the ablation results. Performance Dropping (PD) measures the average accuracy drop from the base to the final session, reflecting forgetting. The baseline uses a frozen backbone, and g(-) denotes fine-tuned projector. The results show that both the MPC and DSM modules improve performance. For example, on mini-ImageNer. AHM increased by 4.52% and 8.96%, respectively. The integration of both modules entails feature- structure dual consistency, achieving the best performance with 11.95% improvement.
ConCM achieves better feature consistency. We performed visualization on the features, as shown in Figure 6. Compared to Figure (b), ConCM enhances feature representations by semantic asso- ciations between different classes, effectively reducing the deviation from the true centers. Based on the calibrated features, we further visualized their embedding structure, shown in Fig. 6 (b). Compared to the baseline, its embedding is no longer scattered chaotically across the structure, but instead forms more compact spatial distribution, naturally reducing classification difficulty.
ConCM achieves better structure consistency. To validate the effect of maximum matching in structure consistency, we define the Structure Matching Rate (SMR = Σ (ō',, e, d.)) quantify the deviation between the initial and target structure. The results are shown in Table 5. Random Dy- namic Matching (RM) randomly constructs and matches an optimal structure. which disrupts con- sistency. GM refers to greedy matching (Kuhn, 1955) leading to improvement, but still constrained by the structure itself. Fixed Structure (FS) pre-allocates a structure for all classes, but leads to a trivial solution. ConCM outperforms in all incremental sessions, achieving the highest SMR and HM, while obtaining optimal performance with minimal structure adjustments.

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Published as a conference paper at ICLR 2026
Table 5: Analysis of structure consistency on mini-ImageNNt.. HM denotes harmonic accuracy. SMR denotes structural matching rate. ConCM achieves the hest performance by maximal matching relation.
Session
HM/SMR
SMR
SMR
SMR
SMR
SMR
SMR
HM
SMR
RM
66.03
0.01
60.90
0.01
58.62
0.02
53.04
0.02
52.77
0.01
49.45
0.01
48.89
0.01
49.88
0.02
GM
66.62
0.19
61.02
0.20
57.78
0.20
54,20
0.18
52.77
0.19
49.31
0.20
49.56
0.20
50.65
0.20
FS( Yang et ol., 2023)
70.10
0.85
63.44
0.65
61.95
0.69
56.01
0.73
55.83
0.74
52.74
0.78
52.66
0.81
52.76
0.80
ConCM
70.34
0.93
66.59
0.83
63.58
0.84
59.59
0.84
57.05
0.85
53.95
0.85
53,49
0.85
S3.92
0.85
Feature-structure dual consistency fosters more robust continual learning. Features are matched to the optimal structure via loss-driven optimization. To analyze the impact of differ- ent losses, we use cross-entropy loss Lee (i.e., without structure), matching loss Latatch (i.e., structure), general SCL loss LSCl and con- trastive loss with anchor LCont. We also introduce the intra-class (Simass) and inter-class (Sima)) cosine similarities in the final session to quantify the degree of matching. Table 6 shows that Ci fosters intra-class cohesion, whereas LCont encourages inter-class separation. Structure anchors help the model better capture the structure, resulting optimal Teature-structure matching. The ab- lation results of the MPC module show that feature inconsistency weakens matching performance.
Table 6: Ablation Loss.w/o denotes without MPC.
Train Strategy
Simar. 4
Sim.. ?
NAce ?
AHM f
Loe
0.0366
0.3140
35.17
47,83
Lafuthh
0.0111
0.3187
43,93
55,28
Lasatch + Lacz
0.1233
0.4182
51.35
59.42
Lasarch + Losst wlo
0.0406
0.3174
46.81
56.79
Lasuten + Loost
0.3355
51.74
59.78
ConCM achieves SOTA with less memory, shorter time, and comparable complexity. For fair comparison, we adopt a uniformly pre-trained backbone. As shown in Table 7, ConCM improves performance with compara- ble complexity and achieves the lowest time
Table 7: Comparison of Computational Efficiency.
Method
Parameter
FLOPS
Time
Memory
AIIM
NC-FSCIL
13.62M
4.7G
18.
24.78M
52.78
OrCo
12.49M
4.7GG
18.34min
24.78M
57.30
CocCM
13.22M
4.77G
16.37min
9.57M
59.37
cost, reducing total time by 11% by avoiding repeated backbone propagation. In terms mem- ory, while OrCo and NC-FSCII. store five samples per class, ConCM reduces overhead by storing only the base-class feature mean and covariance diagonal.
ConCM remains competitive when knowl- edge base coverage is insufficient. ConCM is feature-structure joint calibration and match- ing framework. If the class names are not pre- sented in WordNet's knowledge base, ConCM can solely utilize the DSM module to construct an effective embedding structure. In this scenario, compared to the baseline method NC-FSCII. as shown in Table ConCM still achieved a perfor- mance improvement of 3.05% 9.07% in AHM.
Table 8: Comparison Limited Knowledge Base.
Method
mini- ImageNet
CIFAR100
CUB200
NC-FSCIL
52.62
47.89
57.14
ConCM w/o MPC
56.79
56,96
60.19
5 CONCLUSION
FSCIL involves the conflicting objectives of learning novel knowledge from limited data while pre- venting forgetting. In this study, we further explore two potential causes of knowledge conflict: feature inconsistency and structure inconsistency. To this end, we propose consistency-driven calibration and matching framework (ConCM). Memory-wware prototype calibration ensures con- ceptual consistency of features by semantic associations, while dynamic structure matching unifies cross-session structure consistency via structure distillation. Experimental results show that the method achieves more robust continual learning by consistent feature-structure optimization.
The FSCIL methods in this paper typically use most classes (e.g., 60%) as base classes, requiring diversity samples for reliable semantic attributes. Despite this, experiments show that ConCM still alleviates knowledge conflict. Moreover, Future work will explore more realistic incremental tasks, g., active learning on streaming data, to overcome dataset constraints.
Limitations:
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Published as a conference paper at ICLR 2026
ACKNOWLEDGEMENT
This work was supported in part by the National Natural Science Foundation of China (92467107 62573440), in part by the Scientific Research Innovation Capability Support Project for Young Fac- ulty (XXGXOXXKYYCXNLXXXXXEX))
STATEMENTS
Ethics Statement. Our study does NOT involve any of the potential issues such as human subject, public health, privacy, fairesss, security, etc. All authors of this paper confirm that they adhere to the ICLR Code of Ethics.
Reproducibility Statement. For our theoretical result Theorem 1, we offer the proof Appendix E. All datasets used in this paper public and have been cited. Please refer to Appendix F for the dataset descriptions and the implementation details of our experiments. Our source code is released at
REFERENCES
Noor Ahmed, Anna Kukleva, and Bernt Schiele. Orco: Towards better generalization via orthog- onality and contrast for few-shot caassinncrmmentl learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, pp. 28762-28771, 2024.
Afra Feyza Akyürek, Ekin Akyürek, Derry Tanti Wijaya, and Jacob Andreas. Subspace regularizers for few-shot class incremental learning. In International Conference on Learning Representa- lions, 2022.
Ali Ayub and Carter Fendley. Few-shot continual active learning by a robot. Advances in Neural Information Processing Systems, 35:30612 35:30612-30624, 2022.
Shawn Beaulicu, Lapo Frati, Thomas Miconi, Joel Lehman, Kenneth Stanley, Jeff Clune, and Nick Cheney. Learning to continually learn. In European Conference Artificial Intelligence, pp. 992 1001. 2020.
Chaofan Chen, Xiaoshan Yang, and Changsheng Xu. Pseudo informative episode construction for few-shot class maas-inncremetall learning. In Proceedings of the AAAI Conference Artificial Intelli- gence, volume 39, pp. 15749-15757, 2025.
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. International Conference on Machine Learning, pp. 1597-1607, 2020.
Ali Cheraghian, Shafin Rahman, Pengfei Fang, Soumava Kumar Roy, Lars Petersson, and Mchrtash Harandi. Semantic-aware knowledge distillation for few-shot class-incremental learning. Pro- ceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 2534 2543, 2021.
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hier- archical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.
2009.
Yao Deng and Xiang Xiang. Expanding hyperspherical space for few-shot class-incremental learn- ing. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp- 1967-1976, 2024.
Chelsea Finn. Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In International Conference on Machine Learning, pp. 1 1126-1 1135, 2017.
Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. Sharpness-awar minimiza- tion for efficiently improving generalization. In International Conference on Learning Represen- fations, 2021.
11

---

Published as a conference paper at ICLR 2026
Mengshi Ge, Rui Mao, and Erik Cambria. Explainable metaphor identification inspired by concep- tual metaphor theory. In Proceedings of the AAAI conference on artificial intelligence, volume 36, pp. 10681-10689, 2022.
lan Goodfellow, Mehdi Mirza, Da Xiao, Aaron Courville, and Yoshua Bengio. An empiri- cal investigation of catastrophic forgetting in gradient- based neural networks. arXiv preprint arXiv: 2013.
Dipam Goswami, Bartlomiej Twardowski, and Joost Van De Weijer. Calibrating higher-order statis- tics for few-shot ilnsemnnclllental learning with pre-trained vision transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1075-4084, 2024.
Bernal Jiménez Gutiérrez, Yiheng Shu, Yu Gu, Michihiro Yasunaga, and Yu Su. Hipporag: Neuro- biologically inspired long-term memory for large language models. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp- 770-778, 2016.
Michael Hersche, Geethan Karunaratne, Giovanni Cherubini, Luca Benini, Abu Sebastian, and Ab- bas Rahimi. Constrained few-shot class-ine caass-nncremenal learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9057-9067, 2022.
Yijie Hu, Guanyu Yang, Zhaorui Tan, Xiaowei Huang, Kaizhu Huang, and Qiu-Feng Wang- Covariance- space regularization for few-shot class incremental learning. In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 9566-9576, 2025. doi: 10.1109/WACV110411.20 20w.004....02.09922222
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700-4708, 2017.
Khurram Javed and Martha White. Meta-learning representations for continual learning, Advances in Neural Information Processing Systems, 32, 2019.
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinske Barwinska, al. Overcom- ing catastrophic forgetting in neural networks. Proceedings of the National Academy of Sciences, 114(L3):35213352, 2017.
Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009.
Harold W Kuhn. The hungarian method for the assignment problem. Naval research logistics quarterly, 2(1-2):83-97, 1955.
Anna Kukleva, Hilde Kuehne, and Bernt Schiele. Generalized and incremental few-shot learning by explicit learning and calibration without forgetting. In IEEE/CVF International Conference on Computer Vision, pp. 9000-9009, 2021.
Linhao Li, Yongzhang Tan, Siyuan Yang. Hao Cheng, Yongfeng Dong, and Liang Yang. Adaptive decision boundary for few-shot class-incremental learning. In Proceedings of the AAAI Confer- ence on Artificial Intelligence, volume 39, pp. 18359-18367, 2025.
Xiaojie Li. Yibo Yang, Jianlong Wu, Yue Yu, Ming-Hsuan Yang, Liqiang Nie, and Min Zhang. Mamba-fseil: Dynamic adaptation with selective state space model for few-shot class-incremental learning. arXiv preprini arXiv:2407.06130 2024.
Zhizhong Li and Derek Hoiem. Learning without forgetting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(12):2935-2947 2018.
12

---

Published as a conference paper at ICLR 2026
Yan-Shuo Liang and Wu-Jun Li. Inflora: Interference- low-rank adaptation for continual learn- ing. In Proceedings of the IEEE/CVE Conference on Computer Vision and Pattern Recognition, pp. 29638-23647, 2024.
Binghao Liu, Han Yang, Fang Wan, and Fei Gu. Prototype antithesis for biological few-shot class- incremental learning. In The Thirleenth International Conference on Learning Representations, 2025.
Hao Liu and Huaping Liu. Continual learning with recursive gradient optimization. arXiv preprint arXiv:2201.12555 2022.
Wenhan Luo, Di Yun, Yi Hu, Miaomiao Tian, Jiajun Yang, Yifan Xu, Yong Tang. Yang Zhan, Hong Xie, and Ji-Song Guan. Acquiring new memories in neocortex of hippocampal- mice. Nature Communicattons, 13(1):1601, 2022.
M Anwar Ma,,um, Mahardhika Pratama, and Igor Skrjanc. Latest advancements towards catas- trophic forgetting under data scarcity: comprehensive survey on few-shot class incremental learning. arXiv preprint arXiv:2502.08111, 2025.
Mark D McDonnell, Dong Gong, Amin Parvaneh, Ehsan Abbasnejad, and Anton Van den Hengel. Ranpac: Random projections and pre- vre-trained models for continual learning. Advances in Neural Information Processing Systems, 36: 1etr1122221200
2023.
Parinita Nema and Vinod K Kurmi. Strategic base representation learning via feature augmentations for few-shot class incremental learning. In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6394-6403. IEEE, 2025.
Alex Nichol, Joshua Achiam, and John Schulman. On first-order meta-leamiing algorithms. arXiv preprint arXiv:1803.02000 029999 2018
Junghun Oh, Sungyong Baik, and Kyoung Mu Lee. Closer: Towards better representation learning for few-shot class-incremental learning. In European Conference on Computer Vision, pp. 18-35. Springer, 2024.
Mei-Hong Pan, Hong-Yr Yi Xin, and Hong-Bin Shen. Semantic-hased implicit feature transform for
few-shot classification. International Journal of Computer Vision, (2(11):501450029 2024.
Vardan Papyan, XY Han, and David L Donoho. Prevalence of neural collapse during the terminal phase of deep learning training. Proceedings of the National Academy of Sciences, 117(40): 24652-24663, 2020.
Hang Qi, Matthew Brown, and David Lowe. Low-shot learning with imprinted weights. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5822- 5830, 2018
Hang Ran, Weijun Li, Lusi Li, Songsong Tian, Xin Ning, and Prayag Tiwari. Learning optimal inter-class margin adaptively for few-shot class-incremental learning via neural collapse-based meta-learning. Information Processing & Management, 61): 1036644 2024.
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert. icarl: Incremental classifier and representation learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2001-2010, 2017.
Jake Snell, Kevin Swersky, and Richard Zemel. Prototypical networks for few-shot learning. Ad- vances Neural Information Processing Systems, 30, 2017.
Zeyin Song, Yifan Zhao, Yujun Shi, Peixi Peng, Yuan, and Yonghong Tian. Learning with fantasy: Semantic-aware virtual contrastive constraint for few-shot class-incremental learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp- 24183-24199 2023.
Xiaoyu Tao, Xiaopeng Hong, Xinyuan Chang, Songlin Dong, Xing Wei, and Yihong Gong. Few- shot class-incree incremental learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12183-12192, 2020.
13

---

Published as a conference paper at ICLR 2026
Rishabh Tiwari, Krishnateja Killamsetty, Rishabh Iyer, and Pradeep Shenoy. Ger: Gradient coreset based replay buffer selection for continual learning. In Proceedings of the IEEE/CVE Conference Computer Vision and Pattern Recognition, pp. 99-108, 2022.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in Neural Informa- fion Processing Systems, 30, 2017.
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie. The caltech-ucsd birds-200-2011 dataset. 2011.
Liyuan Wang, Xingxing Zhang, Qian Li, Jun Zhu, and Yi Zhong. Coscl: Cooperation of small continual learners is stronger than a big one. In European Conference on Computer Vision, pp. 254-271 2022.
Liyuan Wang. Xingxing Zhang, Hang Su, and Jun Zhu. A comprehensive survey of continual learning: Theory, method and application. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.
Qi-Wei Wang. Da-Wei Wei Zhou, Yi-Kai Zhang, De-Chuan Zhan, and Han-Jia Ye. Few-shot class- incremental learning via training-fire free prototype calibration. Advances in Neural Information Processing Systems, 36:15060-15076, 2023.
Shipeng Wang. Xiaorong Li, Jian Sun, and Zongben Xu. Training networks in null space of feature covariance for continual learning. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 184-193, 2021.
Yaqing Wang, Quamming Yao, James T. Kwok, and Lionel M. Ni. Generalizing from few exam- ples: A survey on few-shot learning. In Association for Computing Machinery, pp. 1-34, 2020,
Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, Yandong Guo, and Yun Fu. Large scale incremental learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 374-3822 2019.
Shipeng Yan, Jiangwei Xie, and Xuming He. Der: Dynamically expandable representation for class incremental learning. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 2014-3023, 2021.
Shunzhi Yang, Jinfeng Yang. MengChu Zhou, Zhenhua Huang, Wei-Shi Zheng, Xiong Yang, and Jin Ren. Learning from human educational wisdom: A student-centered knowledge distillation method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(6):4188-444 4205- 2024.
Shuo Yang. Songhua Wu, Tongliang Liu, and Min Xu. Bridging the gap between few-shot and many- shot learning via distribution calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 9830-983888 9843, 2021.
Yibo Yang, Haobo Yuan, Xiangtai Li, Zhouchen Lin, Philip Torr, and Dacheng Tao. Neural col- alignment for few-shot class incremental learning. arXiv preprint
lapse inspired arXiv:2302.03004 2023.
Baoquan Zhang, Xutao Li, Yunming Ye, and Shanshan Feng. Prototype completion for few-shot learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(10):11250- 2023.
Chi Zhang, Nan Song, Guosheng Lin, Yun Zheng, Pan Pan, and Yinghui Xu. Few-shot incremental learning with continually evolved classifiers. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 12455-12464, 2021.
Jinghua Zhang, Li Liu, Olli Silvén. Matti Pietikäinen, and Dewen Hu. Few-shot dlassinnrrmmntal learning for classification and object detection: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(4):2924-2945, 2025.
14

---

Published as a conference paper at ICLR 2026
Linglan Zhao, Jing Lu, Yunlu Xu, Zhanzhan Cheng. Dashan Guo, Yi Niu, and Xiangzhong Fang- Few-shot iaas-inccrmmentl learning via awase- bilateral distillation. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 11838-11847, 2023.
Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, Liang Ma, Shiliang Pu, and De-Chuan Zhan. Forward compatible few-shot class-incremental learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9046-9056, 2022a.
Da-We Da-Wei Zhou, Han-Jia Ye, Liang Ma, Di Xie, Shiliang Pu, and De-Chuan Zhan. Few-shot class- incremental learning by sampling multi-phase tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence,
2022b.
15

---

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Appendix
A THE USE OF LLM
All work in this study was completed solely by the authors without the use of any large language models (LLMs) for content generation.
B ADDITIONAL RELATED WORKS
B.1 CLASS- GRASS-INCREMETAL LEARNING
Mlass-nncrmmettl Learning (CIL) aims to continually learn novel classes from dynamic data dis- tributions while retaining memory of learned classes. Its main challenge is balancing the plasticity for novel class learning and the stability be categorized into five types (Wang et al., 2024): (1) Regularization-busel methods (Wu et al., 2019; Li & Hoiem, 2018; Yang et al., 2024: Kirkpatrick et al., 2017) constrain key parameters to avoid novel class learning from disrupting old class knowledge; (2) Replay-based methods (Tiwari et al., 2022; Rebuffi et al., 2017) alleviate catastrophic forgetting by retaining a subset of old class samples; (3) Optimization based methods (Wang et al., 2021; Liu & Liu, 2022; Foret et al., 2021) explicitly design gradient projections to leverage flat minima, achieving the ideal solution for multi- task learning; (4) iiepresentation-bavee methods (Javed & White, 2019; Beaulieu al., 2020) enable continual learning by creating and leveraging representational advantages, such as self-supervised learning and pre-training; (5) Architecture
old class memories. Current mainstream methods can
methods (Liang Li, 2024; Wang et al., 2022; Yan et al., 2021) build task-specific parameters through careful design of the architecture, avoiding interference between tasks. Existing methods fail to account for the knowledge compatibility be- tween tasks, and in few-shot scenarios, they risk overfitting novel class features, hindering effective generalization of new knowledge.
B.2 FEW-SHOT LEARNING
Few-Shot Learning (FSL) relies on limited labeled data for novel class learning (Wang al., 2020). Existing methods typically achieve this goal from the perspectives of data generation, metric learn- ing, and meta learning. (1) Data generation-bar
methods enhance few-shot learning by generating samples that resemble the real distribution (Yang et al., 2021; Pan et al., 2024; Zhang et al., 2023); (2) Metric learning- caarning-based based methods (Qi et al., 2018; Snell et al., 2017) focus on learning a unified distance metric and performing feature embedding: (3) Meta learning- based methods (Nichol al., 2018; Finn et al., 2017) train models to quickly adapt to new tasks by simulating the learning pro- However, it neglects the issue that class data typically arrives asynchronously, hindering joint training and adaptation to the streaming nature of real-world data.
B.3 FEW-SHOT CLASS- INCREMENTAL LEARNING
Few-Shot Incremeaatt Learning (FSCIL) combines the two learning paradigms mentioned above, closely resembling human learning. This paradigm requires the model to continually inte- grate novel classes while preserving learned knowledge and handling the sparse labeling constraints of new classes (Zhang et al., 2025; Ma' sum et al., 2025). Similar our method, aptimization-basee methods address the complexities of optimization to overcome the few-shot overfitting of novel classes and catastrophic forgetting of old classes. This includes: (1) Replay or distillation meth- ods, which use old class data or model information to preserve knowledge (Tao et al., 2020; Zhang et al., 2021); (2) Meta learning methods (Hersche et al., 2022: Zhou al., 2022b), which draw on experiences from multiple sessions to enhance future performance; (3) Feature fusion-based meth- ods (Goswami et al., 2024; Wang et al., 2023; Akyürek et al., 2022) integrate or combine features obtained from different information sources or feature extraction methods to create more compre- hensive and effective representations; (4) Feature space-based methods (Zhou et al., 2022a; Deng & Xiang. 2024; Yang et al., 2023; Ahmed et al., 2024), highly relevant to our method, focus on optimizing the feature space for FSCIL, with the core objective of learning more robust feature representations.
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For instance, FACT (Zhou al., 2022a) creates virtual prototypes to reserve extra space in the em- bedding space, allowing novel classes to be integrated with reduced interference. NC-FSCIL (Yang et al., 2023) pre-defines classifier using neural collapse theory and constrains learning through projection layer, avoiding conflicts between learning novel classes and retaining old class memories. OrCo (Ahmed al., 2024) constructs globally orthogonal feature space during the base session and combines feature perturbation with contrastive learning to reduce inter-class interference. How- ever, on one hand, these methods do not account for the inherent bias in few-shot features, and lacks an accurate anchor point (Goswami et al., 2024; Wang et al., 2023). On the other hand, fixed pre- defined structures implicitly constrain novel classes, forcing them to align with a designated space through optimization. This can lead to trivial solutions, making it difficult to achieve the desired outcome. In contrast, we provide calibrated prototype anchors for the structure using semantic at- tributes with class separability and environmental invariance, and reduce optimization complexity through maximum matching to obtain the optimal feature embedding.
C PSEUDO-CODE FOR CONCM
The training procedure for the ConCM is described in Algorithm
Algorithm Consistency-driven Calibration and Matching framework (ConCM)
Input: Training set 'D Derain (D), D' Di...., ,'.'
Output: Backbone f(;08)= E Rd, projection g( 0g) RR
Base Session Pretraining
1: for V((zi, yi, (:) E DD do
2: Train and freeze the backbone 0y) ) by cross-entropy loss Lee
3: Get semantic knowledge of attribute Πo (Sa, So RD)
4: Train the MPC h:: 02) E TR in Eq. (4)
5: Get an optimal structure Do in Eq. (9)
6: Optimize the projection g(: 0g) in Eq. (13) and Eq. (14)
7:
end for
Incremental Session Fast Adaptation
8:
for
(((1,, Wi: ()) E D' do
9:
Get semantic knowledge of attribute II, = (Sa, St R.)
10:
Calibrate prototypes in Eq. (5)
11:
Sample augmented data Aug( in Eq. (18)
12:
Update the structure A in Eq. (8) and Eq. (9)
13:
Optimize the projection g(: 0g) in Eq. (13) and Eq. (14)
14:
end for
return Backbone f(:00)) E Rdd projection g(;0,)) Rd,
D DETAILS OF PROTOTYPE AUGMENTATION
According the FSCIL setting, previous session samples are inaccessible. To address this issue, we propose augmenting the prototype of base classes to replay class distribution. Simultancously, augmented the calibration prototype of novel classes. Therefore prototype augmentation further supports structural alignment within embedding space.
Assuming that the features follow Gaussian distribution Np. Σ) (Goswami et al., 2024; Yang et al., 2021). During the base session, the distribution can be directly estimated owing to sufficient samples, allowing close approximation to the true distribution, denoted as N N Ebuse), ). Where plse represents the base feature mean, and hhase represents the base covariance diagonal. For novel classes, the feature mean 1% provided by the calibrated prototype generated by the MPC network. However, due to the scarcity of samples for novel classes, it is not possible to accurately estimate their covariance. DC (Yang et al., 2021) found that classes with similar means exhibit similar variances. We further assessed the Pearson correlation between prototype similarity and variance similarity across classes. The results indicate a statistically significant positive correlation between them 0.94, p 0.99 10-4). Therefore, we leverage the distribution information of
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the base class to estimate the covariance of novel. Specifically, the covariance estimation process is as follows:
Σ EE +
Σ Wb, Ebuse)
(16)
where, 3 denotes the covariance scaling degree. Σg denotes novel class covariance diagonal. We incorporate the base-class information via softmax similarity weighting to obtain $2 The weighting scheme is defined by the following formula:
exp(y cos(pb (p)))
Wb,
Σ No exp(7 cos(p (paae -1%
(17)
1=1
controls the sharpness of the weight calculation (with 16 in all experiments). In the session t, the prototype S2 = {pg
base 1 No
U (Pk 1= is augmented through Gaussian sampling:
ib=
[ Aug(pbusc) ~ N(p base =bese), <.b0<b< No
(18)
Aug(p') ~ N Ed,, No <<<k> N.
E THEORETICAL PROOF
Definitionl (Geometric Optimality). The neural collapse theory reveals optimal structure formed by the last-layer features and the classifier (Papyan et al., 2020). A structural matrix A, 1 .2.... ,N.] E IRo,NN (d, > N₂) is said to satisfy geometric optimality if the following condition holds.
No N₂ ---1
Vi,j, 6,
(19)
N -
where Ai = 1 when i = j. and otherwise. It implies that prototypes are equidistantly separated.
Definition2 (Maximum Matching). We define the maximum matching relationship maximiz- ing the similarity between the optimal target structure A, and the initial incremental structure A [6,,8% 82. ... [N.]] Rd,xN. (the historical structure that incorporates the embedding of novel classes), which can be expressed as:
A arg max 6,E..
>
i=1 (6' 5z)
(20)
The relationship aims embed new classes with minimal structural changes.
Each incremental session generates augmented samples Aug(S)) by Gaussian sampling from mem- ory prototypes D4, which include both the base class prototypes and the novel class calibrated prototype. The projected mean of these samples serves as the initial structure A. The structure optimization process is formally stated in the following theorem.
Theoreml. For session f, the dynamic structure that satisfies geometric optimality A, and maximum matching can be updated based on the following formulation.
WAVT = D/ IN.
1N,
1..
U. T
(21)
No
No Up IN.. N
A V N₂ -1
1 IN.
(22)
where U, = [uq u2 uN,] E &N IN. RNX.N is the identity matrix, and 1N. R Nix1 is an all-ones column vector. W E RA,MM, A RNN, and V E N represents the compact SVD result.
Proof. According to the properties of singular value decomposition, W is column-wise orthogonal matrix, i.e., WWW IN, and is an unitary matrix, i.e., VTV VVT =N,,- It can be deduced that U, satisfying:
U. U (WVT) TWT vW WVT Tn.
(23)
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Therefore, U, is also column-wise orthogonal matrix, satisfying:
Vi
J. u Uj 0; Vi J. Wh Wj = 1;
(24)
A. [81, ,2, SN,]] is the target geometry vector matrix, and 6; corresponds to category i. Ac- cording to Eq. (22), o; can be further simplified as:
N
Σ No 1==nm
8;
(u,
(25)
N
N₂
For any geometrie vectors ó; and oj, we have:
Vi J. 5, 6,
N₂
(u
Σ n=1
No
un
T (ug
E
No
No
N
N₂
N₂
(u, Uj
Σ Un Uj
E u Un
E
No
Un
Σ No
am
Un
N₂ -1
N₂
N₂
N₂ 2
(26)
N
1
(n)) U1
N
N
No
1
N₂ A N,
where Ary = when i j. and 0 otherwise. Therefore, we satisfy geometric optimality. The proof conclusion is: when U, is column-wise orthogonal matrix (i.e., U, U, IN,), the matrix calculated by Eq. (22) satisfies the geometric optimality defined in Eq. (19).
Furthermore, the maximum matching relationship requires that Eq. (20) be achieved under geomet- ric optimality, which can be expressed as follows:
N
A = arg max S,E..
88 04)
(27)
A arg max LT AT A,)
A
Substituting the optimal geometry, as constructed in Eq. (22) (with the condition UTU, = IN.) into the above equation yields:
T Ne
No 1 In, 1y.
U, arg max A
N₂ - Ue
INN.
(28)
U. U.
We let &
N.
and M IN. IN N. where M is symmetric matrix, and it satisfies
N.
M M. According to the trace properties, have:
U, = arg max k tr(A', U,M)
UJ U.=IN
arg max k trMM', U.)
(29)
U, U,U,,,
arg max k ( U. UTU.=IN.
Substitute the SVD WAVT = A.M into the above formula, where W E Rd,, e Rd V. E N,N., Ù and V are unitary matrices, and Eq. (29) can be further simplified to:
U. arg max is tr(VÀ W WT))
U. UI=IN
(30)
= arg max A W TU
U. U.=IN
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Further define R U.V R Since R R U WWTUVV IN. it follows that R is also a volumn-wiss orthogonal matrix. Substituting into Eq. (30) yields:
U, arg max k-tr( E-tr(A R) Up U.=IN,
(31)
Note that the maximum value is only related to tr tr(A R). It can be simplified to:
N2
tr R)
Σ THO
(32)
oj is a singular value, rss is diagonal element of R. Since column-wise orthogonal matrix, its diagonal elements satisfy |ral < 1, and it is obvious that the maximum value is obtained when rsi Therefore, we can get:
IN. Odd,-No) xN)
R=
(33)
where Ora, xN,, is zero matrix of size (dg No) x N, Now, substitute R back into the equa- tion for Uz: :
IN.
] Φ U. + U. W.
IN.
VT= = WVT
R
(34)
Oid,
Oldg-Ne)xn
W and VT denote the compact SVD results.
F MORE DETAILS
DATASET DETAILS
We evaluate ConCM framework three FSCIL benchmark datasets: mini- Dini-ImageNet((Dng et al., 2009) CIFAR100 (Krizhevsky et al., 2009) and CUB200 (Wah et al., 2011). The splitting details (iee, the class order and the selection of support data in incremental sessions) follow the previous methods (Zhang et al., 2021; Ahmed et al., 2024; Wang et al., 2023).
Nett::: The dataset is a subset of ImageNet, consisting of 100 classes, with each class containing 600 images. We divided it into 60 base classes and 40 incremental classes, structured into 8 incremental sessions with 5-way, 5-shot FSCIL scenario.
This dataset consists of 60,000 color images (32x32 pixels) across 100 object classes. For the FSCIL task, is split into 60 base classes and 40 novel classes, organized into incremental sessions under 5-way. 5-shot setting.
CIFAR100:
CUB200: This dataset is a fine- fine-grained bird classification dataset, consisting of 200 classes and total of 1 1,,788 images. In the FSCIL task, it includes 100 base classes and 100 incremental classes, arranged into a 10-way, 5-shot task with total of 10 incremental sessions.
F.2 ATTRIBUTE SELECTION
For standard datasets, such mini-ImageNe((Deng al., 2009) and CIFAR Krikihevyy al., 2009), the class names (e.g.. "house finch") can be obtained. We use the "part "part_ meronymss(" pro- vided by WordNet (Ge et al., 2022) obtain phrt-whole relationships as candidate attributes, such as "house finch" - "beak" "wing", "feather", etc. For base classes, we directly select the candi- date attributes as the actual attributes. Then, the attributes of all base classes are aggregated into an attribute pool for matching with the novel classes. For novel classes, their actual attributes de- fined as the intersection between their candidate attributes and the base class attribute pool, enabling prototype calibration through the associated base class attributes.
Certain datasets, such as CUB200, offer explicitly accessible attribute labels for all samples, which directly utilize. For each class, select the 28 most frequent attributes (corresponding to 28 distinct attribute categories) as candidates. The attribute pool construction and novel class attribute selection follow the same procedure as described above.
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F.3 MODEL ARCHITECTURE
In the framework of this paper, the learnable network consists of the following components:
Backbone network. The symbolic representation is f(:0g) Rdy Prior studies widely adopt ResNet architecture for FSCIL experiments. Table 9 compares the backhone networks used dif- ferent studies. Following OrCo (Ahmed al., 2024), for mini-ImageNet, we use ResNet18. For CIFARI00 we use ResNet12. For CUB200, we use ResNet18 (pre-trained on ImageNet).
MPC network. The symbolic representation h(:; E Rd,, consists of an encoder, an aggre- gator and decoder. The encoder he(:; (f) E Rd,/2 and decoder ha(;; E R& are MLP layers. Leveraging cross-attention to derive relational weights, the aggregator combines the encoder's out- puts as imput for the decoder.
Projector network. The symbolic representation is g(:;0)) E Rd, The projector further maps the embedding space to the geometric space achieve alignment between the features and the geometric structure. The projector consists of two-layer MLP. L2 normalization is applied both before and after the projection layer to construct a hypersphere-to-lyyessppee mapping.
Table A comparison of backbone networks used in different studies.
Methods
mini-lmageNet
CIFAR 100
CUB200
FACT (Zhou et al., 2022a)
ResNet18
ResNet12
ResNet18
CEC (Zhang et al., 2021)
ResNet18
ResNet20
ResNet18
C-FSCIL (Hersche et al., 2022)
ResNet12
ResNet12
LIMIT (Zhou et al., 2022b)
ResNet18
ResNet20
ResNet18
LCwoF (Kukleva et al., 2021)
ResNet18
BiDist (Zhao et al., 2023)
ResNet18
ResNet18
ResNet18
TEEN (Wang et al., 2023)
ResNet18
ResNet12
ResNetl
NC-FSCIL (Yang et al., 2023)
ResNet12
ResNet12
ResNet18 8
OrCo (Ahmed et al., 2024)
ResNet18
ResNet12
ResNet18
ConCM(Ours)
ResNet18
ResNet12
ResNet18
F.4 EVALUATION DETAILS
Harmonic Mean accuracy. Sandard accuracy measures, such as Top-1 accuracy, tend to be skewed in favor of the base classes. Following Ahmed al. (2024); Wang al. (2023); Kukleva et al. (2021), we introduce the Harmonic Mean accuracy (HM) to mitigate this bias:
2 x BAcc, NAcc,
HM,
(35)
BAcc; + NAcc,
This metric yields a good value only when both the base class and novel class accuracies remain at high levels. In addition to this, Average Harmonic Mean (AHM) averages the harmonic mean scores across all incremental sessions for a consolidated view.
Final Accuracy. Final Accuracy (FA) denotes the Top-1 accuracy of the final session, evaluating the overall accuracy after completing the all sessions:
FA Acc_
(36)
Performance Dropping rate. Performance Dropping rate (PD) denotes the Top-1 accuracy drop- ping between the base session and the finial session, which measures the degree of forgetting:
PD
Acco Acc-1
(37)
Balanced Error Rate. We treat base classes as the "positive", novel classes as the "negative" and transform the FSCIL problem into a two-class classification task. Balanced Error Rate (BER) is used to quantify the degree of knowledge conflict.
FN
FP
FNR
100%,
FPR
100%
(38)
TP + FN
FP + TN
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Table 10: SOTA comparison on CUB200. AHM denotes the average harmonic mean. FA denotes the Top-1 1 accuracy in final session.
Session- Hormonic Mean (
Methods
Base Ace
AHM
FA
7
10
(CaRL/Rebufi et al. 2017)
77.30
44.18
12,93
36.13
30.22
30.86
29.01
26.45
26.88
26.44
25.24
32.03
25.23
IW( et al, 2018)
67.53
40.52
39.19
36.35
37.33
37.85
37.85
35.68
35.54
37.36
38.21
37.59
46,06
FACT al., 2022a)
77.23
62.16
57.86
50:47
52:21
50.58
51.55
52.01
50.29
51.74
51.58
53.05
56.39
CRO(Zhang et 2021)
75.64
56.17
53.16
45.67
48.45
45.41
46.29
46.31
45, 16
46.28
46.01
47.89
54.21
LIMIT//ho et al., 2022h)
79.63
61:11
57.19
50.70
52.40
50.52
$1.40
52.70 72.78
51. 36
52.50
53.56
53.35
57.81
MetaNC(Ran et 2024)
78.84
68.90
58.11
47.36
49.38
46.59
47.46
46.73
44.88
45.68
45.03
50.01
50.62
SAVC(Song al., 2023)
80,00
64.29
60.89
54.56
$8.27
55.03
57,92
57.10
57.35
57.36
57.72
58.05
60.82
FACL/Nemm Kurmi, 2025)
80.88
68.33
61.21
57.25
57.38
57.26
58.54
59.76
58.11
56.99
58.22
59.30
62.37
CLOSER ol., 2024)
79.40
63.69
62.29
58.21
59
59.96
57.91
59.31
59.01
59.80
62.38
et 2023)
53.08
49.50
50.74
18.
50.87
ADBS( al., 2025)
79.45
66.40
54.46 46
59.29
55.59
57.34
58.10
57.
57.00
66.96
40
60.16
TEEN/Wang et al.,, 2023)
77.26
65.99 65.99
61.83
55.59
56.91
55,49
56.07
55.47
$3.96
55.15
54.86
57.12
$7.98
Mamba-FSCIL.0 (SCLLSC et al., 20144
80.90
76.94
65.33
58.07
57.31
56.47
57.99
58.35
57, 26
$8.36
58,
61.67
PA(Lio ct al., 2025)
78.50
60.97
58.53
54.32
57.02
$4.68
56.01
55.86
55.02
53.89
53.91
56.03
58.89
NC NC-FSCIL(Yang et al., 2023)
80.45
71.30
57,30
55.25
57.56
54.15
56.05
56.60
53.55
54.63
55.03
57.14
59.44
O:ColAhmed et al., 2024)
75.59
72.02
64.37
39.38
39.78
57.19
57.14
57.10
55.45
56.80
$6.63
59.61
57.91
ConCM (Ours)
80.52
75.24
66.04
59.78
60.10
59.48
60.24
60.78
59.35
60.57
60.39
62.20
62.66
Better than second
10.71
10.40
+0.32
+0.44
11.11
10.92
11.24
11.26
8 1.38
+1.70
10.28
Table 11: Comparison on ImageNet-1k, 1k. AHM denotes the average harmonic mean.
Session-wise Harmonic Mean (%) f
Methods
Base Ace
AHM
8
NC-FSCII. (Yang et al., 2023)
74.70
50.79
47.02
43.01
41.45
40.53
39.57
38.43
37.31
42.26
OrCo(Ahmed al., 2024)
74.47
53.26
41.65
39.99
41.22
40.23
39.32
38.91
37.66
41.55
ConCM (Ours)
75.25
66.04
58.69
54.46
51.11
49.10
46.33
43.95
42.87
51.56
FNR + FPR
BER
(39)
2
Structure Matching Rate. We define the Structure Matching Rate (SMR) to quantify the deviation between the initial and target structure:
SMR- = Σ (5'1, 0%)
(40)
[v.]] Rd,xN, includes the historical structure and incorporates the embedding of novel classes. The target structure A. = [ő1, 62. , ,N,] E Rd, XN. is the optimal structure that satisfies Eq. (6).
where, the initial structure A [6 82,
F.5 TRAINING DETAILS
Following previous methods (Kukleva et al., 2021; Cheraghian et al., 2021; Ahmed al., 2024; Yang et al., 2023; Zhao et al., 2023), we maintain some exemplars from previously seen classes: exemplars are saved for novel classes, while only prototypes are maintained for base classes. Stan- dard image augmentation strategies were applied, including random cropping, random horizontal flipping. random grayscale processing, and random color jittering. The optimizer used is SGD, with cosine scheduling and warmup period for a few epochs during training. All experiments were conducted on a single RTX 4090 GPU.
We implemented meta-training for the MPC network by constructing a series of 5-shot prototype completion tasks. Structural anchors are only applied to novel classes, as base classes typically have sufficient representation. The prototype augmentation strategy resamples augmented samples in each epoch. 100 and 50 samples were generated for base and novel classes, respectively. Each image was augmented 10 times using image augmentation to balance the dataset. Hyperparameters remain consistent: т = 0.07, 7 16, 3 = 0.6, and the MPC network's 's maximum learning rate is 1. For mini-ImageNNt,, projector's maximum learning rate is le 2 with a 0.6. For CIFAR 100,
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Table 12: Ablation study of modules mini-ImageNet. AHM denotes average harmonic mean. FA denotes the accuracy final session. The baseline frozen hackbone. g(-) denotes learnable projector. MPC denotes the Maaory-aware Prototype Calibration module. DSM denotes the Dynamic Structure Matching module.
Session- wise Harmonic Mcan (%) 1
(())
MPC
DSM
AHM
FA
4
5
31.91
28.00
23.21
19.53
19.06
17.80
17.84
18.70
22.00
52.62
62.12
53.75
51.13
46.76
44.22
41.59
41.33
41.80
47.83
56.22
/
66.11
57.05
54.77
53.35
50.06
46.49
44.86
46.14
52.35
57.23
68.77
63.06
59.09
56.26
53.76
50.45
51.32
51.63
56.79
58.29
V
/
70.34
66.59
63.38
59.59
57.05
53.95
53.49
53.92
59.78
59.92
Table 13: Ablation study modules on CIFARIO0. AHM denotes average harmonic mean. FA denotes accuracy in final session. The baseline uses frozen backbone. g()) denotes learnable projector. MPC denotes the Mwaory-aware Prototype Calibration module. DSM denotes the Dynamic Structure Matching module.
Session- Harmonic Mean (%) 1
x())
MPC
DSM
AHM
FA
5
30.55
24.75
23.47
20.27
20.01
18.81
17.54
18.44
21.73
51.14
65.87
56.43
48.62
42.83
43.89
43.64
42.92
40.18
48.05
55.33
69.64
62.56
54.88
51,25
48.31
48.97
45.75
45.12
53.31
56.74
70.67
64.51
58.61
55.44
54.03
52.82
50.63
48.95
56,96
56.99
72.27
67.33
60.09
57.08
54.93
55.21
52.95
52.51
59.05
58.33
Table 14: Ablation study of modules on CUB200. AHM denotes the average harmonic mean. FA denotes the accuracy in final session. The baseline uses a frozen backbone. 9(-) denotes learnable projector. MPC denotes the Memory-aware Prototype Calibration. DSM denotes the Dynamic Structure Matching.
Session-wis. Harmonic Mean (%)
s(-)
MPC
DSM
AHM
FA
2
3
5
6
8
10
60.14
53.00
45.56
45.64
44.08
45.75
44.64
42.13
43.36
43.46
46.78
52.97
71.89
63.62
57.33
59.20
56.23
56.18
55.76
54,14
55.60
54.89
58.48
58.82
73.78
65.98
59.75
59.67
57.28
57.41
58.00
55.91
55.61
55.43
59.88
59.01
74.06
65.56
59.03
60.34
57.49
58.25
57.35
56.37
57.10
56.33
60.19
59.59
75.24
66.04
59.78
60.10
59.48
60.24
60.78
59.35
60.57
60.39
62.20
62.66
projector's maximum learning rate learning rate is 5e with 0.75.
22-2, with a 0.6. For the Cub200, projector's maximum
G MORE RESULTS
Experimental comparison with State-of-the-Art methods. Our experiment results on mini- ImageNet, CUB200,and 100AR100 are shown in Table 1, Table 2 and Table 10 (Appendix G), respectively. Experimental results show that ConCM consistently maintains the highest performance across all incremental sessions, effectively mitigating the conflict between learning and forgetting. In addition, on ImageNet- benchmark, ConCM achieved 9.30% improvement on AHM compared to the baseline methods (e.g. OrCo, NC-FSCIL) as show in Table 11. The number of categories in the ImageNet- 1k benchmark has significantly increased (1,000 classes), which includes 600 base classes and is set up in 8 incremental sessions with 50-way 5-shot configuration.
Session-wie wise ablation study of each module. In this appendix, we further report the session-we wise harmonic mean accuracy for in Table 12, Table 13 and Table 14, respectively. The proposed method achieves consistent performance improvements each session across all datasets. These results further validate the effectiveness of each module.
Ablation within the MPC module. The MPC module includes prototype calibration and Gaussian- based prototype augmentation. To further demonstrate the effectiveness of prototype augmenta-
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Published as a conference paper at ICLR 2026
Table 15: Ablation on Prototype Augmentation and Calibration within the MPC module.
Casel: Without prototype calibration. Case2: With prototype calibration. Standard represents stan- dard Gaussian sampling. Ours represents Gaussian sampling-bas based prototype augmentation.
Session-wise Harmonic Mean (%)
Case
Method
AHM
2
3
5
8
Standard
68.77
63.06
59.09
56.26
53.76
50.45
51.32
51.63
56.79
Ours
70.12
63.68
60.22
56.87
55.10
52.23
52.57
52.78
57.95
Standard
69.83
64,40
61.42
57.36
54.24
51.17
51.45
51.80
57.71
Ours
70.34
66.59
63.38
59.59
57.05
53.95
53.49
53.92
59.78
(a) The of
The An of B
The Analysis K-shot Meta leaming
70
70
Hammniic Mean (%)
0.1
Hamoniic MenooniMMeentn (%)
0.56
Simaa
1.6
10
60
0.90
02
04
0.6
0.8
2
3
4
5
6
7
8
2
3
4
5
6
8
Session
Session
Figure 7: The hyperparameter analysis on mini-ImageNNt. (a) Prototype calibration degree a. (b) Covariance scaling degree B. (c) Meta-learning under the K-shot scenario tasks.
tion, we compare two cases on mini-ImageNNt. Casel: Without prototype calibration, we compare standard Gaussian sampling with Gaussian sampling-based prototype augmentation (ours). Case2: With prototype calibration, (comparison as in Casel). Table 15 presents the experimental results. The within-cat case comparisons show that prototype enhancement brings performance improvements of 1.16% and 2.07% under the two cases, respectively. The between-case comparisons indicate that prototype calibration leads to performance gains of 0.92% and 1.83%, respectively.
Hyperparameter analysis. We conduct an analysis of critical hyperparameters in the ConCM on mini-ImageNet, which include prototype calibration degree a, covariance scaling degree B, and meta-learning under the K-shot scenario tasks.
The impact of calibration degree a is evaluated using the average cosine similarity Simrrea between all calibrated prototypes and the real prototype (Figure 7 (a)). Prototype calibration combines the MPC output prototype with the few-shot prototype, both which exhibit biases: the former due base-novel class discrepancy, the latter due limited samples. Selecting appro- priate weighting improves the representativeness of the final prototypes.
The covariance scaling degree 3 controls the dispersion of the novel-class distribution, as shown in Figure 7 (b). Appropriate scaling helps restore the real feature distribution.
The K-shot task construction employs episodic meta-training strategy. We analyze the im- pact on the mmageem Generally, a smaller K indicates that prototypes during training exhibit stronger bias. Selecting the appropriate level of difficulty for training can help improve performance.
Across all benchmark datasets, our hyperparameters remain consistent with a 0.6, 8 0.6, and K 5, except for CUB200 where a 0.75. This because CUB200 a fine-grained dataset with smaller inter-class differences.
Analysis replay strategies. Five exemplars from seen incremental class have been replayed. However, even without replaying samples and instead storing incremental class prototypes and co- variance diagonal, our method still maintains high performance. To validate this, we tested with 0, 1, and 5 replay settings, and the results shown in Table 16. This demonstrates that even without explicit sample replay, using prototypes for augmentation can still effectively mitigate catastrophic forgetting and maintain high performance.
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Published as a conference paper at ICLR 2026
Table 16: Performance comparison under different replay exemplar numbers. Without replay- ing raw samples (#Replay=0) can still maintain high performance effectively.
Session-wise Harmonic Mean ( (%) 1
#Replay
AHM
1
3
4
5
6
7
8
0
70.34
65.83
61.35
58.28
55.94
52.40
52.19
52.77
58.64
1
70.34
65.33
61.59
58.54
56.43
52.87
52.91
53.15
58.90
5
70.34
66.59
63.38
59.59
57.05
53.95
53,49
53.82
59.78
Table 17: Performance comparison under different K-shot and Cross-Domain. K-shot indicates that the number samples in each nove class. "Cross-Domain" represents the constructed cross- domain FSCIL task. The base classes are from the base classes of mini-ImageNet, and the novel classes are from the novel classes of CIFAR
100, 100, forming an 8-session 5- way 5-shot task.
Ression-wise Harmonic Mean (%) f
Setting
Method
AHM
2
4
5
8
OrCo
37.78
29.66
30.83
30.50
30.06
28.96
29.08
29.08
30.74
1-woy,1-shot
ConCM
54.91
45.98
40.12
37.91
32.96
32.44
31.75
31.43
38.43
5-way.3-shot
OrCo
61.95
54.87
53.04
49.68
47.25
45.94
45.73
47.09
50.69
ConCM
66.52
61.31
55.74
52.25
49.26
45.96
46.56
47.56
52.97
OrCo
70.52
57.01
48.99
44.98
44.86
45.23
42.31
41.36
49,41
Cross-Domain
ConCM w/o MPC
68.37
56.56
49.22
46.10
44,90
45.69
43.33
43.23
49.68
ConCM
69.56
57.10
49.55
47.02
47.85
48.16
45.62
45.36
51.28
Table 18: Performance comparison under long sequence FSCIL mini-ImageNNt. 2-way. 5- shot setting constructed a 20-sessions task. The table reports the harmonic accuracy the first sessions and the last 4 sessions.
Session-wise Harmonic Mean (%)
Setting
Method
AHM
1
4
17
18
19
20
OrCo
78.91
63.29
65.30
63.51
51.05
51.38
51.15
50.97
57.01
2- 2-woyt5-shot
ConCM
78.98
67.44
69.71
66.71
50.92
51.98
52.59
52.11
58.74
Comparative experiments under various FSCIL settings. We evaluated multiple N-way K-shot configurations on mini- Imi-lmmaeNNet against the suboptimal method OrCo (Ahmed et al., 2024). The results for different K-shot presented in Table 17. ConCM more effectively mitigates the knowl- edge conflict and demonstrates stronger robustness in more challenging few-shot settings, achiev- ing 7.69% AHM improvement under 5-way 1-shot. We further compared methods under long- sequence (20 sessions) FSCIL with varying N-way settings. The performance of the first sessions, the last 4 sessions are reported in Table 18. The proposed method surpasses the OrCo by 4.41% % (the highest) in harmonic accuracy of incremental sessions. This verifies the scalability of our approach.
ConCM's Generalization Across Domains. ConCM evaluates correlations between novel and base classes through semantic attributes and learns an optimal geometric embedding, enabling certain cross-domain generalization. our cross-domain experiment with base classes from mini- ImageNet and novel classes from CIFAR 100, ConCM outperforms the suboptimal approach, OrCo (in Table 17). We hypothesize that despite the domain shift, latent semantic relationships persist between classes, and ConCM effectively leverages these to reduce negative bias. Moreover, the geometrically aligned embedding derived from maximum matching exhibits strong domain invariance, further enhancing generalization. Additionally, the MPC module achieved 1.60% improvement in the AHM metric, indicating that it continues to perform effectively even under conditions of weaker semantic relevance.
25


Reviews:
[{"review_id": "FH7UEOta65", "reviewer": "Reviewer_Hr2g", "summary": "This paper introduces ConCM, a novel two-stage framework for few-shot class-incremental learning that explicitly addresses the “dual-consistency” dilemma. By emulating hippocampal associative memory, the MPC module first calibrates few-shot prototypes with semantically related attributes extracted from base classes; the DSM module then dynamically updates the embedding geometry to satisfy both equi-distant separation and maximal matching with the previous structure.", "questions": "See weaknesses", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1.\tThe illustration is clear.\n2.\tThe method and the theoretical analysis seem solid.\n3.\tThe reported performance improvement is considerable.", "weaknesses": "1.\tThe proposed ConCM uses WordNet to extract semantic attributes from class names, to calibrate the prototypes of the new classes. It works fine in the standard benchmarks such as cifar, imagenet, but if there are no semantic label names for each class, how would such calibration work?\n2.\tLack of results on benchmarks with more classes. ConCM relies on explicitly calibrating the feature space for each class. The paper does not discuss the influence of the increased number of classes, especially when the benchmarks in this paper contain at most 200 classes.\n3.\tTypo: Caption of Table 4 “mini-imagenet”.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "iPrb71GN5q", "reviewer": "Reviewer_R41s", "summary": "This paper addresses the FSCIL problem by identifying two key challenges — feature inconsistency and structure inconsistency.\nThe proposed ConCM framework introduces (1) a Memory-aware Prototype Calibration (MPC) module that leverages semantic attributes to calibrate prototypes, and (2) a Dynamic Structure Matching (DSM) module that dynamically updates class geometry to maintain global consistency.\nExperiments on multiple benchmarks show that ConCM achieves better stability and accuracy than prior FSCIL methods.", "questions": "How sensitive is the performance to the semantic quality of the extracted attributes in MPC? Would ConCM still work well if semantic information is noisy or unavailable?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The proposed modules (MPC and DSM) are conceptually sound and complementary, leading to consistent and clear improvements across multiple benchmarks.\n\n2. The paper provides clear theoretical motivation and connects the design of DSM with neural collapse geometry, giving the framework better interpretability.", "weaknesses": "1. The Memory-aware Prototype Calibration (MPC) relies on semantic attribute extraction from WordNet or class names, which might not generalize to datasets without clear textual labels.\n\n2. Conceptually, ConCM extends previous geometry-based or orthogonality-driven FSCIL ideas (e.g., OrCo, NC-based approaches), so its originality mainly lies in how these components are unified.\n\n3. Consider including at least one recent 2025 method to strengthen the comparison with up-to-date FSCIL approaches.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "7FB5YdTOga", "reviewer": "Reviewer_CEGG", "summary": "The paper investigates how to maintain representation consistency in Few-Shot Class-Incremental Learning (FSCIL). The authors propose a framework that calibrates new class prototypes inspired by human associative memory. The calibration module separates and completes semantic attributes to refine prototype representations for new classes, ensuring better alignment. Furthermore, a geometric optimization strategy is introduced to preserve structural consistency during incremental updates. Experimental results on multiple FSCIL benchmarks demonstrate consistent improvements over existing methods.", "questions": "1. It is unclear whether the proposed method can be generalized to other tasks, such as few-shot incremental semantic segmentation.\n2. What is the ratio hyper-parameter between the loss $L_{match}$ and $L_{Cont}$? Is it set to 1? Would it make a difference in performance when setting different values?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper is well written, and the motivation is clear.\n2. The proposed consistency-driven dynamic structure matching method is theoretically grounded and achieves excellent performance.\n3. The ablation experiments are comprehensive and convincing.", "weaknesses": "1. The paper missed several important and highly related references and comparative results. \n\n    [1]  Mamba-FSCIL: Dynamic Adaptation with Selective State Space Model for Few-Shot Class Incremental Learning.\n\n    [2] Learning With Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning.\n\n    [3] Learning optimal inter-class margin adaptively for few-shot class-incremental learning via neural collapse-based meta-learning.\n\n    [4] Towards Better Representation Learning for Few-Shot Class-Incremental Learning\n\n2. The pipeline of novel class prototypes calibration is the same as paper [1] except for the design of MPC network (both adopt the encode–aggregate–decode architecture). The novelty of this component should be further clarified.\n\n    [1] Prototype completion for few-shot learning. \n\n3. More recent research on CIL mainly focuses on pre-trained ViT or CLIP models. Can the proposed method be transferred or adapted to pre-trained ViT models or CLIP models?\n \n   [1] Pre-trained Vision and Language Transformers Are Few-Shot Incremental Learners.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zlLOj0oiQt", "reviewer": "Reviewer_Tr9T", "summary": "This paper proposes a consistency-driven calibration-and-matching approach that calibrates biased new-class prototypes and stabilizes the feature-space structure, thereby reducing confusion between old and new classes. The approach demonstrates strong performance across several benchmarks.", "questions": "Please refer to the 'weaknesses'. I would consider to raise my rating if the authors could address my concerns.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The motivations are clear. Biased new-class distributions caused by limited training samples, and adjustments to the feature space to accommodate new classes, are key challenges in FSCIL, as recognized by the community.\n2. Leveraging common attributes to augment the features of new classes is valid and reasonable; WordNet provides rich semantic information beyond visual cues.\n3. The proposed approach achieves strong performance on several datasets, including mini-ImageNet, CIFAR-100, and CUB-200.", "weaknesses": "1. The idea of “Attribute Separation” is similar to PA [1], which decouples common attributes within a family and transfers them to new species. It would be helpful to discuss the commonalities and differences between your approach and PA [1].  \n[1] Prototype antithesis for biological few-shot class-incremental learning.\n2. Neural collapse theory has been introduced into the FSCIL task by NC-FSCIL. The dynamic structure matching in this paper builds on that work; for example, it replaces the hard enforcement of a computed prototype distribution with a softer approach that reduces the distance between the original and optimal distributions. While this is indeed more reasonable and effective, the core idea and methodology are not fundamentally different from NC-FSCIL; in my view, this is an incremental improvement.\n3. When performing prototype augmentation, this paper makes a strong assumption, namely, that feature distributions are Gaussian. However, many real-world class distributions do not align well with this assumption. Furthermore, based on this assumption, the authors infer the covariance of new classes by assuming that classes with similar means also have similar covariances. Is there theoretical justification for this assumption?\n4. The MPC module appears highly dependent on the base-class distribution and on WordNet. If the base classes differ substantially from the new classes, or if the names of the new classes are not present in WordNet’s knowledge base, the model’s generalization ability may be greatly affected.\n5. There are a few typos. For example, “mini-IamgeNet” at line 432 and “SOAT” at line 360 (these should be “mini-ImageNet” and “SOTA”).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)