Evolving and Regularizing Meta-Environment Learner for Fine-Grained Few-Shot Class-Incremental Learning
Authors: Li-Jun Zhao
Zhen-Duo Chen
Yongxin Wang
Xin Luo
Xin-Shun Xu
Venue: NeurIPS 2025 poster ()
Keywords: Fine-grained classification
Few-shot class-incremental learning
Primary area: deep_learning

Abstract: Recently proposed Fine-Grained Few-Shot Class-Incremental Learning (FG-FSCIL) offers a practical and efficient solution for enabling models to incrementally learn new fine-grained categories under limited data conditions. However, existing methods still settle for the fine-grained feature extraction capabilities learned from the base classes. Unlike conventional datasets, fine-grained categories exhibit subtle inter-class variations, naturally fostering latent synergy among sub-categories. Meanwhile, the incremental learning framework offers an opportunity to progressively strengthen this synergy by incorporating new sub-category data over time. Motivated by this, we theoretically formulate the FSCIL problem and derive a generalization error bound within a shared fine-grained meta-category environment. Guided by our theoretical insights, we design a novel Meta-Environment Learner (MEL) for FG-FSCIL, which evolves fine-grained feature extraction to enhance meta-environment understanding and simultaneously regularizes hypothesis space complexity. Extensive experiments demonstrate that our method consistently and significantly outperforms existing approaches.

Evolving and Regularizing Meta-Environment Learner for Fine-Grained Few-Shot Class-Incremental Learning
Li-Jun Zhao', Zhen-Duo Chen',, Yongxin Wang?, Xin Luo', Xin-Shun Xu
School of Software, Shandong University, China School of Computer and Artificial Intelligence, Shandong Jianzhu University, China 1j_zhaao12000 10280163...... com, chenzd. swudgmaiddsmaaaaa com, yxinwang@ho twaie. com luoxin. lxin@gmail com, xuxinshun@sdu, edu. cn
Abstract
Recently proposed Ien--Grinned Few-Shot Class- maas-Incrementll Learning (FG- FSCIL) offers a practical and efficient solution for enabling models to incrementally learn new fine- ame-ggaieed categories under limited data conditions. However, existing methods still settle for the fine-grained feature extraction capabilities learned from the base classes. Unlike conventional datasets, fine-grained categories exhibit sub- tle inter-class variations, naturally fostering latent synergy among sub-categories. Meanwhile, the incremental learning framework offers an opportunity to progres- sively strengthen this synergy by incorporating new sub-category data over time. Motivated by this, we theoretically formulate the FSCIL problem and derive a generalization error bound within shared fine-grained meta-category environment. Guided by our theoretical insights, we design a novel Dota-Environment Learner (MEL) for FG-FSCIL, which evolves fine- aeeegraiied feature extraction to enhance meta-environment understanding and simultaneously regularizes hypothesis space complexity. Extensive experiments demonstrate that our method consistently and significantly outperforms existing approaches.
Introduction
Fine Fine-grained classification 12 33 focuses distinguishing instances from subordinate categories within the same meta-category (c.g.. bird species, car models), and has been widely applied in domains such ecological protection and security monitoring (28 Traditionally, research has centered on improving fine-grained feature extraction using fixed and fully annotated datasets. However, in practical scenarios, the collection and annotation of fine-grained data often limited by domain expertise and time costs, resulting in data scarcity and continuously evolving category sets. To address these challenges, recent studies have explored fine-grained classification under Few-Shot Class-Incremmnntal Learning (FSCIL) 2 setting. FSCIL enables models to learn new classes with limited samples after training on base classes, while retaining the ability to recognize all seen classes. Fine-Grained FSCIL (FG-FSCIL) [18] thus offers a practical and efficient solution for updating models with new fine- ime-grained category information under limited data conditions.
Due to the high discriminative difficulty of fine ine-ggained categories, FG-FSCIL research draws on traditional ideas from finengrained classification methods and places greater emphasis extracting fine-grained discriminative features. However, due to the limited training samples available during incremental sessions, this idea still settles for the fine-graine seee-graindd feature extraction capabilities learned
"Corresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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from the base classes. Notably. fine-grained data differs from conventional data in that its sub- categories exhibit smaller inter-class variations, which naturally facilitates synergy across sub- categories. Meanwhile, the incremental learning setting provides the opportunity to progressively enhance this synergy by continuously incorporating new sub-category data over time. Motivated by data characteristics and the learning scenario, we aim to develop model that not only refines its understanding of the meta-category environment using limited incremental data, but also leverages this evolving understanding to improve classification across all fine-grained sub-categories.
In this paper, we provide a theoretical formulation of the FSCIL problem and derive generalization error bounds for the expected eπror incurred when learning sub-category samples within a shared entrrmmeni.n..tt. While traditional efforts enhance fie-grained feature extraction help reduce empirical error and thus lower the bound, our analysis reveals that further reducing the bound relies on minimizing the complexity of both per-session sub-categories and the overall meta-enviiroment. Crucially, we show that leveraging incremental sub-categories to enhance understanding of the breakthrough point. The derived bounds also offer valuable guidance for
meta-environment entrreenviromeett achieving this under limited training data. Guided by these theoretical insights, propose novel Eetta-Enviromment Learner (MEL) for FG-FSCIL, comprising two key processes: Evolving and Regularizing. Specifically, we introduce an incrementally optimized meta-category vector to enable evolving finegrained feature extraction, enhancing the feature extractor's understanding of the meta-ennvirnment while avoiding unnecessary complexity. In parallel, cubecoy relationship regularization guides feature space transformation and further constrains hypothesis space complexity, thus further regularizing two complexity terms. Our key contributions are summarized as follows:
We theoretically formulate the FSCIL problem and analyze its generalization bound within the fine-grained meta-category environment, which is consistent with the core intuitions and motivations.
Based on the derived generalization bound, we design novel Cett--nnvirommen Learner (MEL) for FG-FSCIL, which simultaneously evolves fine-grain -ee-grained feature extraction and regularizes hypothesis space complexity.
Extensive experiments show that our method significantly outperforms both recent FSCIL methods and FG-FSCIL methods, demonstrating the effectiveness of our theoretically grounded design.
2 Related Work
Fine-grained classification
36 23 4 focuses on distinguishing visually similar sub-categories
within a general meta-category, such as different species of birds, different breeds of dogs, or different models of airplanes. Compared to generic classification tasks. it demands models to capture subtle and localized discriminative features, as inter-class differences are often minimal. As a result, fine-grained place greater emphasis on learning feature representations than conventional classification approaches. Given the challenges of collecting and annotating line-grained
classification methods [39 31 [7]
data in real- world scenarios, Few-Shot
Classification (FSFG) 29 10
14 has
emerged to address the problem of recognizing novel ane-grained categories using only few labeled examples. However, existing works overlook the inherent potential of fine- mne-griined sub-categories to collaboratively and incrementally enrich shared meta-category representations, which could serve as a natural foundation for continual learning.
26 35 is a learning paradigm where the model first learns base classes with sufficient labeled data, then incrementally learns novel classes from only few labeled samples. To improve performance, most methods [32] 37 [7 [T]] lever- age pretrained parameters to establish a strong initialization for subsequent learning, or employ data augmentation strategies, including the generation of pseudo-classed to facilitate later sessions. Although (6] includes experiments on fine- fine-grained datasets, it remains a FSCIL method without specific discussion or methodological design for the characteristics of iineinede data. Compared to traditional datasets, fine-grained datasets exhibit subtler inter-class differences and offer fewer labeled samples in the base session, making it challenging for conventional FSCII. methods to perform effectively in such settings. Recent work [18] has started to explore Fine-Grained Few-Shot Class- Incrementll Learning (FG-FSCIL). However, similar to general FSCIL methods, they overlook
Few-Shot Incremencrllltll I Learning (FSCIL)) [5

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Ineremental -1,,,
Inference
S train
D
T
D
Strain
Siest
X
2, (x))))) 1=1, m.. y1
E
= (x,,)). M. E
Po learnrr +
Qu
FSCIL learner
Vi
P. Q:11
Figure 1: The FSCIL learner with environment T, sample distributions Dr, training set S Strinn prior distribution Pt, posterior distribution Qt, and a hypothesis h.
the inherent synergy among sub-categories in fine- data. As result, the full potential of incremental learning is not fully exploited.
3 Methodology
In this section, we begin by providing a theoretical formulation the FSCIL problem. Based on this, we derive a generalization bound, which forms the foundation for designing our FG-FSCIL method.
3.1 Problem Formulation
In FSCIL, the model is trained on sequence of training sets (Stran J from session t. Each St train comprises set of pairs 2 (I,, yı) i 1, ..., my, where is sample from class y C For fine- grained dataset, all classes C ... U belong to the same superclass. Vi, and * j, n C = Ø. The training set Strain in the base session contains sufficient samples, i.e., mg is much larger than others. In each subsequent incremental session (t 2 1), the training set Strain contains limited samples and follows
N- N-way K-shot format. This means there are only K samples for each of the N classes, i.e., my = N x K for 2 1. During the inference stage in session f, the test set St is generated from all sample distributions D UD! U.. D' over all seen classes Co UC! UC.
To model this setup, following 20 21, all classes from different sessions are assumed to share the same sample space 2, hypothesis space H. and loss function é H x 2 - [0, 1]. As shown in Figure 1. the FSCIL learner can access the dataset Strain that is sampled i.idd from an unknown sample distribution D, over the class set C' in session t. All sample distributions D7 are drawn i..d from an unknown environment distribution T 3. To solve the FSCIL problem, the learner takes as input training set St. Syrain and prior distribution Pt, and output a posterior distribution Q2 ( Pt) over H. that is, a mapping Z M + .M, where M denotes the set of distributions over H. The initial prior Po can be modeled as Gaussian distribution over neural network weights [19]. For subsequent sessions, the prior Pt is set the posterior from the previous session, i.c. P. & Qi-1- Then, the expected error and empirical error for each sample distribution D, in session are given by averaging over the posterior distribution Qe E M, namely er (Qv, D2) & SINQ,,EEWD, (hh,2) and ēr (Qu, Strain) Rn-QU (1/m²) Dm (h, 2x) for a single hypothesis h H.
3.2 Generalization Bounds for FSCIL and Meta-Category Environment
Based the setting introduced in Section 3.1 we derive generalization bounds for FSCIL in session t'. Given the openness the learning scenario, our goal to enable the FSCIL learner to extract knowledge from the available categories that can benefit all classes within the same environment.
Therefore, the effectiveness of the posterior distribution Qe is evaluated by the expected er- ror incurred when learning classes drawn from the same environment T. i.c., er (Qv, T) & ED,-TEA,O0, Bz-D, fh, 2). Specifically, we analyze the generalization error for both the newly introduced classes each session and the overall environment. Formal proofs are provided in Section A. of the supplementary material.
3

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Theorem 3.1. 6, - 2(t'+1ja For any å E (), the following bound holds uniformly for posterior
distribution Qe, with probability at least 1 - å over a sequence of training set St ttain
S
train'
er(Qu. T) aurrQuv Strain)
t=
( Eit log 2(1' + 1)
+
(2)
2(m - 1)
5
t=
(KLL(Qr + log 2(1' +1)
V
2t'
where (-I(- denotes the Kullback-Libble (KL) divergence and a = |C* /
The overall bound comprises the empirical error (Equation] D and two complexity terms: the session complexity from each sample distribution D, (Equation 2 and the environment complexity across all sessions (Equation
3) which together offer potent insights for tackling the FSCIL challenge.
Insight O. Reducing empirical error er, (Equation 1 can tighten the bound. Since ag 0z (t 2 1). the error Strain plays dominant role in this reduction. In practice, focusing on fine-grained feature extraction on Sirain can effectively lower ér Er(Qu, ( Strain).
Previous FG-FSCIL methods focus on training the feature extractor during session and then freeze it, aligning with Insight 0 to achieve smaller overall empirical error. Other common techniques in FSCIL, such as initializing with pretrained models and applying data augmentation, essentially reduce the complexity terms-either by making the initial prior Po closer the final posterior Qe' or by effectively increasing the number of training samples m.
A deeper understanding of Theorem 3.1 in the context of fine- rreeggraiedd data enables more systematic analysis of the inherent synergistic learning potential that is often overlooked by existing FSCIL and FG-FSCIL methods. Based widely recognized characteristies of fine-gre one-grained datasets, begin by providing the following definition,
Definition 3.2 (Meta-Fine Environment). In fine-grained settings, where all sub-categories typically belong to the same meta-category. the shared environment T-From which all sample distributions D; are drawn-is is referred to as the Meta- Environment. This structure implies smaller inter-class variation and facilitates positive transfer across sub-categories.
Consequently, the FG-FSCII. learner is in a favorable position to leverage the information from seen sub-categories to extract transferable knowledge that benefits all sub-categories within the same meta-category. This makes the environment complexity term a breakthrough point for further reducing the overall generalization error in fine-grained settings:
Insight 1. As t' increases, the environment complexity (Equation 3) tends to decrease. resulting in a tighter bound. This suggests that leveraging sub-categories from incremental sessions to train the feature extractor the intuition and motivation discussed above.
enhance its understanding of the overall meta-environment consistent with
Insight 2. Since mn >> mt 2 1), controlling (Q+ 1) can effectively reduce the
overall session complexity (Equation [). This indicates that while enhancing the feature extractor's understanding of the meta-environment, it is critical limit significant changes in large parameter subsets during incremental learning.
Insight 3. Both complexity terms in Equations 2 and characterize detandepnnd hypothesis space complexity, which is often positively correlated with the number of learnable parameters. From a generalization perspective of deep neural networks (38), hypothesis space complexity is primarily governed by classifier parameters, while feature space transformation may potentially simplify the classification.
3.3 Meta-Environment Learner for FSCIL
Building upon the theoretical insights above, we propose a novel Envirrmmvi Learner (MEL.) for FG-FSCIL. The overall architecture is illustrated in Figure 2 Unlike prior FG-FSCIL methods that

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Evolving
Regularizing
Slow
[ow
flow
Flow
Meta-Category Guided Fine- Grained Fenture Extraction
m
->
Sub-Categories Guided
Incremental Sub-
Lmutual
F
Feature Space Transformution
Category Relationship Optimization
Flow
Incremental Meta- Cutegory Optimization
Fnigh
i night
nigh
fnight
Shigh
Base Incremental Session Information Flow
Inference Information Flow
Base Session Optimization
Incremental Session Information Flow
Optimized during Incremental Session
Incremental Session Optimization
Figure 2: Overall method framework. During the incremental sessions, only Meta- Category Op- timization and Sub-category Relationship Optimization are performed. Inference relies solely on high-level features.
freeze the feature extractor during incremental sessions, MEL continuously learns and leverages both the meta- cetea-y and its sub-categories. This allows the meta-environment to evolve progressively (Section 3.3.1) while remaining properly regularized (Section 3.3.2).
3.3.1 Evolving: Incremental Meta-Cater Neta-Category Guided Fine-Grained Feature Extraction
Compared conventional datasets, FG-FSCIL aims minimize empirical risk (Equation by identifying subtle differences among similar sub-categories through an evolving process (cf. Insight 0). To continuously optimize the fine-grained feature extraction process without significantly increas- ing the complexity terms (Equations 2] 3). we avoid introducing additional heavy modules and instead primarily leverage the existing backbone structures (cf. Insight 3). Accordingly, for any backbone, we denote deeper-layer outputs as high-level semantic features Fnigh E R H and shallower-layer outputs low-level fine-grained features Flw E H Wi Their average pooled representations are denoted as high E g and flo E RC: respectively.
Meta-Category Guided Fine-Grained Feature Extraction. With a lightweight design, our ap- proach not only utilizes high-level semantic information but also integrates fine-grained details from low-level features. Specifically, to capture instance-specific cues, the high-level semantic features Fnigh of each instance can be utilized to spatially localize critical fine-grained features in Frow. To further refine these cues across samples by leveraging the inherent correlations among fine- one-grained categories, we introduce learnable meta-category vector RC, which encodes meta-category knowledge a channel- -wine manner with minimal parameter overhead (cf. Insight 2).
Frigh @ (m)) E IR CixHixW
where a denotes the leaky ReLU activation function and @ indicates element-wise multiplication. To align with the spatial resolution of the low-level feature map Flow, we upsample the cues F, and then reduce the channel dimension to generate N attention masks:
M (I NxH.wW
where I denotes bilinear interpolation, Pe a convolutional block, and Sigmoid denotes the sigmoid activation function. Then, the generated masks are applied to the low-level feature maps to highlight the key regions, forming the final reñned fine-grained representation,
low Concat Pool (F low M)) N-C.
(6)
where Pool denotes masked average pooling under each of N masks, and Concal represents the concatenation of resulting vectors.
5

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Incremental Meta-Category Optimization. In particular, during incremental sessions, it is es- sential utilize newly available data progressively refine the model's understanding of the meta-environment (ef. Insight 1). At the same time, it is important to avoid significant parameter scenarios with limited training data (cf. Insight 2).
shifts- especially especil-
To this end, based on Equation 4 the fine-grained feature learning process can be continuously optimized by updating the meta-category vector using a limited number of training samples. During the incremental session t', we compute the relation coefficients between the sample semantic representations fligh and the existing meta category vector m.
a m T hhgh// Ch E R 1, M4,
The final attention weights corresponding the meta-category vector obtained via softmax function,
exp(a:) 0:)
(8)
exp(a,)
Then, the meta-category vector can absorb semantic information from all training samples based on attention weights and update itself accordingly,
m - (1 - a: m + aΣ (a, hight E R a |
1/Ei
(9)
where is class proportion- based weight. This attention- based aggregation samples further constrains the update magnitude of the meta-category vector, thereby preventing a substantial increase in the KL divergence.
training
3.3.2 Regularizing: Incremental Sub-categories Guided Feature Space Transformation
Considering the hypothesis space complexity of deep neural networks, the two complexity terms (Equations and 3) can be further regularized before the final classification step (cf. Insight 3). On the one hand, with proper guidance, feature space transformation enables the simplification of fine-grained classification boundaries, making them easier to model with simpler classifier parameters. the other hand, consistency regularization between fine-grained and semantic representations provides directional guidance for the transformation, encouraging a reduction in mutual information between input and representation. This helps eliminate redundant information and constrains the hypothesis space to focus task-relevant variations.
Sub-categories Guided Feature Space Transformation. To retain complementary information from both the original and transformed spaces, the two type representations flw and fligh obtained in Section 3.3. aare first individually mapped into a new feature space, and then fused with their original counterparts,
fio (Biow ho (fow)) 1)/2, Enigh (Enigh + hes (figgh)))
(10)
where hg denotes a fully connected block. During the base training phase, the overall loss function guided by sub-categories for feature extraction and feature space transformation is defined as,
Lighh + BLoow + Cmmutall:
(11)
where 3 and are hyperparameters that control the weights each term, and Camnuaal denotes the consistency regularization term. Both Lnigh and Liow cross-entropy losses,
mg
C. mh
log @ (TS) [high, low),
(12)
where Sü denotes cosine similarity between f. and classifier w!, T is the temperature parameter, and & is defined the softmax operation.
exp (TSt
@ (TTS
(13)
|ec0||
Σ j=1 exp (TS,))

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Table 1: Performance on four fine- aineggrined benchmark datasets. Due to space limitations, only the
accuracy of the final session (Acet) and average accuracy across all sessions (AcC Ang) are presented.
CUB200
Stanford Dogs
Stanford Cars
FGVCAireraft
Method
Accr
Acg
Accr
Acc Aug
Accr
Acc Aug
Accr
Acc
CEC 32
34.50
43.71
32.65
40.56
37.09
48.24
29.57
36.94
C-FSCIL
13.86
17.62
16.68
22.78
18.90
27.76
28.28
38.60
FACT 37
32.71
42.50
33.26
42.96
43.98
56.17
31.60
40.46
TEEN [26]
30.08
39.33
39.14
47.86
45.32
57.37
30,98
43.42
CLOSER 17
31.74
43.95
34.37
44.69
34.49
53.78
36.56
48.08
PFR [8
33.99
43.70
36.01
44.25
44.56
58.12
34.42
43.05
ADBS
27.85
38.71
29.15
41.54
29.79
46.50
35,42
45.23
Ours
36.00
45.44
44.06
52.38
57.58
68.43
40.72
54.17
CBC
CPSCIL
FACT
TEEN
CLOSER
PER
ADBS
Ours
60
70
10
30
10
Sendume
Sestinns
Dogs
(4)
(4) FGVCAArentt
Figure 3: Classification accuracy for each session on four fine-grained benchmark datasets.
Incremental Sub-category Relationship Optimization. Meanwhile, Lmutual is used to continu- ously constrain the sub-category relationship distributions between the fine-grained and semantic representations,
1 mñ
Lmutual
DKL (
1,1:(000)) ||
2 @ shigh
1710
1=1
+ DKL
(T
Shigh
1,1:/0m0)
1 (
(14)
where (K = logp;//4 /qj After the base training phase, as in most FSCIL methods, the classifier is replaced with prototype-based classifiers and can be extended during the incremental session to accommodate novel classes, w = Nam, Ey. where N Num, denotes the number of training samples for class Therefore, during the incremental training phase, the mutual learning regularization term Lumtual can computed on the new training data maintain and refine the novel sub-category relationship distributions, enabling ongoing interaction and refinement between the two types of sub-category prototype representations. This regularization not only constrains the feature space used for classification but also encourages mutual complementarity between the two types of representations. thereby implicitly integrating fine- ane-grained knowledge into the semantic representations.
Thus, the classification inference in session t' can be performed using only fugh.
y = argmax high
(15)
oeuf 1,1:2' (C0))
4 Experiments
4.1 Experimental Setup
Datasets. Following the benchmark setting in [18 we evaluate our method on four fine-grained
datasets: CUB200 (5). Stanford Dogs [8. Stanford Cars [30], and FGVCAireraft [5). The data
split details are provided in Appendix A.3
7

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Table 2: Ablation studies our proposed method on the Stanford Dogs dataset.
Accuracy in each session (%)
Model
Infer
3
4
5
6
7
Mo
Shigh
62.78
55.10
51.55
48.68
45.25
42.15
40.12
38.23
36.68
M1
Shigh glow
65.58
57.32
53.82
51.08
48.07
45.93
43.45
41.45
39.82
M2
Shigh glow
66.51
58.37
54.70
52.05
49.08
46.79
44.44
42.28
40.63
M3
Shigh glow
66.51
58.40
55.12
52.59
49.73
47.70
45.37
43.22
41.50
M1
Shigh glow
66.16
58.27
54.63
52.06
49.67
47.40
45.05
43.28
41.60
Ms
Shigh Slow
65.78
58.44
56.03
53.48
50.87
48.95
46.97
44.68
43.36
Ms
Slow
65.03
57.68
55,40
52.72
49.97
48.03
45.98
43.67
42.30
Shigh
65.57
58.44
56.03
53.84
51.34
49.45
47.42
45.29
44.06
Experimental details. Following 18]. we employ ResNet-12 as the backbone. In our experiments, Frigh is the output of the final layer of the backbone, while Flow is the output of the penultimate layer. Following previous works [8]. the temperature hyperparameter T is set to 16. N is set as an integer 4 to ensure diversity in fine-grained features and kept small for efficiency. and y set to 0.5 to balance the optimization strength among different components. For comparative methods whose reported in [8]. reproduce their performance under the experimental settings using the publicly available source code. Please see Appendix A.3 for details.
results
4.2 Comparison Results
In this section, we compare our method with recent FSCIL methods and FG-FSCIL method Table T and Figure It can be observed that our method achieves the best overall performance. Specifically, our method achieves 1.21% % higher average accuracy (AccAvg) than CLOSER on the CUB200 dataset, and notably outperforms it by 4.26% in the final session (Acer). This suggests that our method, by progressively evolving fine-grained feature extraction while constraining hypothesis space complexity, effectively improves performance leading to more pronounced gain in the final session. Although ADBS considers model adaptation during the incremental sessions, it lacks mechanisms for inter-class synergy modeling and hypothesis space complexity regularization, resulting in limited performance. Detailed experimental results can be found in Appendix A.4
incremental classes with limited samples, often
4.3 Ablation Study
To demonstrate the effectiveness of each component in our method, we conduct ablation experiments in Table 2 Shigh denotes using only high-level features during inference, as described Equation 15 while Slow denotes using only low-level features. Shigh glow represents using the sum of their similarity scores. Mo represents the baseline.
(1) Firstly, Mj applies fine-graine -ne-ggraied feature extraction without the meta-category vector (i.e., under Equations 6 is directly set to Fnigh), together with the basic cross-entropy loss (Equation 12). yields initial performance improvement. (2) M2 builds Mj by introducing the meta-category vector (Fiquation 4. (3) and M3 further performs incremental optimization of this meta-category vector (Equations 9.. leading additional performance gains. Further analysis in Section 4.4. demonstrates the effectiveness of our optimization strategy. The improved fine-grained feature extraction benefits all sub-categories within the meta-category environment, yielding relatively consistent performance improvements across sessions. (4) M4 adds feature space transformation (Equation TOP to M3 does not lead to significant performance gains. (5) However, Ms. which further includes sub-category relationship regularization (Equation 14). shows clear improvement. This indicates that regularizing feature space transformation process to eliminate redundancy while preserving classification-eelvvaa information, effectively reducing hypothesis space complexity and, consequently, the two complexity terms. This reduction is especially beneficial for classes with fewer training samples, leading to more
alignment between the two relationship distributions enables

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Ours
0.75
BP
60
Accuncy(3)
60
Avg+ Weight
Souunee 55
55
58
Avg+Sim
50
50
45
45
47
47.5
48.5
49
49.5
40
40
Average necurncy (%6
1
2
Sessions
3
5
7
2
Sessions
5
(4) Offect of (f 0.5)
(b) Efflect of (y 0.5)
Figure 4:
Analysis of meta-
category optimization strategies.
Figure 5: The influence of hyperparameters y and B.
low-hiih
65
63
Accuracy
58
bidirectional
highelow
Accunscy
60
Accuracy
60
55
53
50
2
8
Sessions
Sessions
Sessions
(s) Ellici ofN
(b) Effiet of'
Figure Analysis of Lemuual
Figure 7: The influence of hyperparameters N and T.
noticeable performance improvements in the later incremental sessions. (6) Besides, regularization allows inference using only high-level features (Shigh to achieve performance comparable to or better than using both (Shigh glow). Further analysis in Section 4.4.2 demonstrates the impact of fine-grained feature extraction and regularization on class representations.
4.4
Further Analysis
4.4.1
Different Meta-Category Optimization Strategies
To demonstrate the effectiveness of our meta-category optimization strategy (Equations 7 9 iin Section
3. we compare the average accuracy across incremental sessions on the Stanford Dogs dataset under different optimization schemes, shown in Figure 4
be observed that directly updating the meta-category vector m by averaging it with the feature vectors high of all training samples (Avg) leads to poor performance. Introducing cosine similarity as soft weight between and the mean of high (Avg+Sim) slightly limits the update magnitude but still performs poorly. This suggests that even with few parameters, optimization under limited samples can casily cause posterior drift - a key reason why existing FG-FSCIL methods do not incorporate new class knowledge into the feature extractor. Replacing cosine similarity with class proportion-based weight a (Avg+ Weight) helps reduce the update magnitude in later sessions, leading to moderate improvements. Our proposed method (Ours, Equation 9 first computes of all Fiihh to mitigate the influence of outliers, then applies a to further constrain the impact of new classes-yine yiilding the best performance. While backpropagation based optimization (BP) also perform well, they require careful tuning of learning rate and training epochs. In contrast, strategy achieves effective updates with no additional hyperparameters.
cosine-weighte sum
4.4.2 Efffet of Terms Lmutual and Liow
To analyze the effects of the regularization term Luuntal and the fine-grained feature extraction term Lew (Section B.3.2p. we evaluate the influence of their corresponding hyperparameters and B (Equation [1] the Stanford Dogs dataset, as shown in Figure
Figure a) shows that in session 0, the value of y (i.e., whether the regularization term is applied) has little effect on performance. This suggests that for base classes with ample training samples (large ma), high-level semantic features already generalize well, and regularization has limited impact on the complexity term (Equation 2). However, subsequent sessions introduce more new

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TEEN
CLOSER
PER
ADBS
Ours
35
ADDS
C-RaiPAC
75
Acounryt
-5
Acounig
Ours
Ours
65
8
35
82
45
81
2
2
(a) miniInageNet
(b) CIFARIO0
ResNes-18
ResNet-50
VIT-R/16
VIT-R/16 with adaptor
Figure 8: Classification accuracy on conven- tional FSCIL benchmarks.
Figure 9:
Accuracy after the last session on CUB200 with different backbones.
classes with limited training samples, the advantage of regularization gradually emerges, consistently outperforming the no-regularization setting across different values. Figure shows that applying fine-grained feature extraction (3 > 0) consistently improves performance across all sessions. This is particularly evident later sessions with more incremental classes, where limited training samples lead to less reliable semantic representations, making fine-grained features more critical for recognition- -i.., a larger 3 is needed. Optimal performance is generally achieved when ry and 3 are set around 0.5. Furthermore,
analyze the difference between using bidirectional KL divergence (i.e.. mmtual and unidirectional KL divergence (i.c., only high + low or only low - high). As shown in Figure 6 bidirectional KL divergence leads to better performance. Our approach adopts bidirectional KL. divergence to enable mutual regularization and refinement between the two branches, thereby achieving better alignment of their sub- categry relationship distributions.
4.4.3 Effect of Hlyeepaarameters N and
In this section, we analyze the effect of other hyperparameters, the number of attention masks N and temperature т. The experimental results are shown F
It can be observed that MEL's performance remains relatively stable across range of values for these hyperparameters. When N 2 increasing N yields no additional performance gain, with N achieving the best overall performance, particularly in the later sessions. Additionally, consistent with other FSCIL methods, our MEL demonstrates sustained performance advantages when 16. These results are consistent with the principles behind our chosen hyperparameter settings.
4.4.4 Performance on Other FSCIL Benchmarks and Backbones
Although the analysis primarily focuses on fine-grained datasets and the proposed method is tailored for FG-FSCIL, it still outperforms recent methods on conventional FSCIL benchmarks (22 9 as
shown in Figure g This is because the generalization error bound analysis for FSCIL remains valid. While fine- mne-grained feature extraction is not crucial for conventional datasets, constraining hypothesis space complexity during empirical error minimization is still essential. Moreover, our method still achieves better performance than other approaches (c.g.. PFR [8]. ADBS [.]) and
A.eCAM/CCRaanPa [6]) across different backbones, as shown in Figure
Conclusion
In conclusion, we revisit the Fine-Grained Few-Shot Class-Incremental Learning (FG-FSCIL) prob- lem from a theoretical perspective and formulate it within shared envtaeenvioommet. By deriving a generalization error bound, we demonstrate the importance of further minimizing the complexity of both per-session sub- sub-categories and the overall meta-environment Building upon these insights, we propose the Meta-Environment Learner (MEL), which progressively evolves fine-grained feature representations while regularizing hypothesis space complexity. Extensive experiments on benchmark datasets validate the effectiveness of our approach, showing consistent and significant improvements over state-of- of-te-of-the-at methods.
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Acknowledgments and Disclosure of Funding
This work was supported in part by the National Natural Science Foundation of China under Grant 62202272, 62172256, 62202278, 62302276, in part by the Natural Science Foundation of Shandong Province under Grant ZR2024LZH002 and Taishan Scholar Project of Shandong Province under Grant tstp20250704.
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A Technical Appendices and Supplementary Material
A.1 Proof of the Generalization Bound for FSCIL
In this section, we prove Theorem 3.1 Iutilizing classical PAC-Bayes bound.
Theorem A.I (PAC-Bayes Bound [16]- Let X be sample space and X distribution over X, and let F be a hypothesis class of functions on X. Let F X X 1] be a loss function. Suppose XI,..., Xx are i.i.d. samples from X and let be a prior distribution over F (independent of the samples). Then for any ó E (0, 1}, the following holds uniformly for all posterior distributions over F (even sample dependent) with probability at least 1 1:
1 K
K
1
(DRa.6llAt) + log ) (16)
Ex
Σ
Ex-olo(sX + 2(K 2(K-
where (pl(T denotes the Kullback- Leibler (KL) divergence between the posterior distribution p and the prior distribution (KI(Q1IP)
& E log
Q(h) kaQ P(A)
Proof. According to Theorem A.I we analyze the generalization error for the classes introduced
each session. The samples are Xx St,i, K - m,, and their distribution is X 2 Dr. Formally, set p Qe and Pt. We get that for any 8, > 0, with probability at least 1 6 over St. Strain ~ D
er(Qe, S (Qu,, Strain) + 2(m; - (DII(Qr + log
my 5z
(17)
where P/ Qi-1 and Qr represents the posterior distribution in session t', i.e. Qr 2 Q(Se, Pe).
Then, we consider the generalization across D. from different sessions, thereby providing a bound on the eeviromment-level generalization. Let (De, M,, Strain) Si, ) for t 0, 0,....., t' be drawn i.i.d. from the environment and Stram Di Using Theorem A. again, let the prior be ा Po and posterior Qr. Then, for any óg > 0. with probability at least 8p.
1 2r
t'+1 ++1
er(Qu: T) (ur((Qv. D.)
V
DKL + log 80
(18)
1=0
where at Finally, to bound the probability of the intersection of the events defined Equations and we make use of the union bound. Let 8 > 0, set &g 2 and 5, A 2(1'+1)a, for 0, I,.... t. By
reflecting the relationship among the class sets introduced in different sessions.
applying the union bound argument (Lemma A.2) with probability at least 6,
er(Qu, T)
Σ (Qer, St. Strain)
(19)
t=0
my (KL(Quu + log
a
(20)
2(m 1))
8,
(=0
E' t'+1 +1
+ V
(KI((Qe(PP))) + log
(21)
21'
50
where DEL. (Qr Pt) = '2
Drr.
Dk.lll))))P)) The probability is taken over sampling of (Dr, m.) ~
T
and Strain Din 0,1,..,'''
In the following, present the statement of Lemma A.2 along with its proof.
20

---

Lemma A.2. Let {Ei be set of events such that P(E,) 2 1 og for some 6; 2 0. = -,,. .,..., Then,
P
Ei
2 1
Σ
62.
F=1
1=1
Proof.
We begin by observing that
IP
n
Pi
P
U
F
(22)
1=
where EY denotes the complement of event E;
By applying the union bound, obtain
P
E
P(E))
Σ
(1
P(E)))
(23)
i=1
Using the assumption P(E,) 2 1 8; we have
(1 - P(E)) - Σ 6
(24)
1=1
Combining the above results gives
P
E
2 1
Σ 8
(25)
A.2 Limitations and Future Work
Following the conventional FG-FSCHL setting, our method assumes base and novel classes are sampled from the same domain. This is standard and practical choice in prior studies, ensuring consistency in semantic granularity. Nevertheless, sal-worldd scenarios may involve more diverse data sources, such as photographs and sketches. Extending the framework to cross-domain FG-FSCIL a natural and worthwhile extension, which we plan to explore future work.
A.3 Detailed Experimental Setups
Datasets. For CUB200, 100 classes are used for base training, and the remaining 100 classes are split into 10 incremental sessions. For Stanford Dogs, 80 classes are used in the base session, and the remaining 40 classes are evenly divided into 8 incremental sessions. Each incremental class contains samples, forming a 5-way 5-shot FSCIL setting. For Stanford Cars, 106 classes are used for base training, and the remaining 90 classes are split across 9 sessions, following 10-way 5-shot setting. For FGVCAircraft, 60 classes are used for base training and 40 for incremental learning. 5-way 5-shot setting is applied, resulting in 9 sessions in total.
Experimental details. Our implementation is based on the code released by PFR under the MIT license. Our method is conducted with Pororch library on single NVIDIA 3090, and SGD with momentum is used for optimization. All method-agnostic hyperparameter settings are kept consistent with those of PFR.
A.4 Detailed Experimental Results and Analysis
21

---

Table 3: Classification accuracy the CUB200 dataset.
Accuracy in each session (%)
Method
0
3
4
5
8
9
10
CEC
59.94
54.31
49.88
45.81
44.44
41.72
39.95
37.74
36.79
35.74
34.50
C-FSCIL
23.53
21.73
19.83
18.62
18.45
17.34
16.36
15.27
14.48
14.34
13.86
FACT
58.45
53.14
48.77
45.05
43.97
40.57
38.77
36.62
35.60
33.86
32.71
TEEN
53.98
49.86
45.69
42.19
40.32
37.84
35.65
33.92
32.10
31.02
30.08
CLOSER
67.35
55.90
51.37
47.37
44.22
41.22
38.86
36.94
35.04
33.46
31.74
PFR
59.32
55.19
50.03
46.26
45.24
41.59
39.64
37.72
36.75
35.01
33.99
ADBS
56.21
50.47
46.14
42.50
39.39
36.71
34.36
32.30
30.47
29.36
27.85
Ours
60.40
56.43
52.00
47.88
46.69
43.34
41.65
39.96
38.07
37.37
36.00
Table 4: Classification accuracy on the Stanford Dog dataset.
Accuracy in each session (%)
Method
0
8
CEC
55.01
46.05
43.83
41.57
39.28
37.26
35.53
33.87
32.65
C-FSCIL
32.22
28.34
25.68
23.57
21.81
20.00
19.00
17.74
16.68
FACT
58.43
50.92
47.48
44.39
41.48
39.04
36.78
34.87
33.26
TEEN
61.09
54.70
51.71
49.56
47.28
44.41
42.30
40.56
39.14
CLOSER
60.22
53.22
49.40
46.64
43.24
40.65
38.33
36.10
34.37
PFR
59.78
51.65
48.14
45.91
41.13
40.13
38.20
37.28
36.01
ADBS
61.63
53.15
47.55
43.02
39.28
35.78
33.16
31.16
29.15
Ours
65.57
58.44
56.03
53.84
51.34
49.45
47.42
45.29
44.06
Table 5: Classification accuracy on the Stanford Cars dataset.
Accuracy in each session (%)
Method
2
3
4
5
6
7
8
CEC
65.23
59.22
54.43
50.75
47.57
45.38
42.41
41.16
39.17
37.09
C-FSCIL
42.80
37.50
32.83
29.63
26.76
24.64
22.82
21.57
20.18
18.90
FACT
76.76
68.57
63.29
57.93
54.59
52.69
49.77
48.02
46.12
43.98
TEEN
75.42
69.39
64.30
60.24
56.44
54.32
50.96
49.78
47.56
45.32
CLOSER
88.56
73.75
64.85
57.85
51.58
47.01
42.98
39.78
36.92
34.49
PFR
82.02
72.22
66.19
60.73
56.19
53.04
50.00
49.22
47.02
44.56
ADBS
75.91
64.64
56.16
49.65
44.49
40.79
37.24
34.26
32.02
29.79
Ours
85.63
79.62
74.46
70.24
67.17
65.65
63.15
61.41
59.36
57.58
Table 6: Classification accuracy the FGVCAireraft dataset.
Accuracy in each session (%)
Method
0
1
2
3
4
6
7
8
CEC
50.61
43.50
40.32
37.26
34.93
33.95
32.15
30.17
29.57
C-FSCIL
56.45
48.84
43.40
39.71
36.52
33.62
31.33
29.28
28.28
FACT
55.95
49.19
45.25
41.53
38.06
35.58
34.31
32.64
31.60
TEEN
59.17
55.08
50.06
46.05
42.10
38.73
35.60
33.01
30.98
CLOSER
68.07
57.45
52.86
49.28
46.05
43.20
40.73
38.51
36.56
PFR
59.94
52.80
47.66
43.32
39.61
38.16
36.36
35.22
34.42
ADBS
59.03
53.67
50.60
46.61
44.28
41.19
39.36
36.90
35.42
Ours
71.43
66.30
61.66
56.34
52.85
49.09
46.18
42.96
40.72
22


Reviews:
[{"review_id": "9jeEfeZkYe", "reviewer": "Reviewer_5ht6", "summary": "The paper focus on few-shot class-incremental learning specifically for fine-grained datasets (FG-FSCIL). The authors exploit the fact that sub-categories in fine-grained datasets has smaller inter-class variations compared to conventional datasets. The paper provides a theoretical formulation of FSCIL with generalization bounds within fine-grained meta-category environment. The authors propose a Meta-Environment Learner for FG-FSCIL which evolves the feature extractor in new tasks and also performs regularization. The proposed method outperforms FSCIL baselines across several fine-grained datasets.", "questions": "See weaknesses", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths - \n1. The paper is well-written and easy to understand.\n2. The paper provides good theoretical background for FSCIL generalization bounds.\n\nWeaknesses-\n1. The impact and scope of the work is very limited since the authors propose the method explicitly only for fine-grained datasets unlike the standard practice of general FSCIL. Only a single paper [15] follows this setting while most FSCIL methods are proposed for general purpose datasets as well as fine-grained datasets.\n2. The authors should present some analysis for the claim that sub-categories in fine-grained datasets has smaller inter-class variations and improve the motivation.\n3. Other than [15], a recent work [1] also focus more on fine-grained FSCIL using pre-trained weights and propose a very simple and effective statistics calibration method. The authors should include discussions and comparison with [1] in the paper.\n4. The method involves several hyper-parameters and the authors do not discuss how these values are decided.", "was_revised": true, "final_justification": "The authors response addressed my concerns. I raised my initial rating.", "rebuttal": "We sincerely appreciate the constructive feedback. Detailed responses to the weaknesses and questions are as follows.\n\n**1.** First, while our method is primarily designed for fine-grained datasets, it also demonstrates strong performance on conventional FSCIL benchmarks, as shown in the experiments in Sec. 4.4.3, underscoring the generality of our method.\n\nSecond, Fine-grained classification has long been established as a widely studied research direction, and Fine-Grained Few-Shot Class-Incremental Learning (FG-FSCIL) further presents its own unique and significant value.\n\n**1)** From an application perspective, fine-grained data is prevalent in real-world tasks. Compared to conventional datasets, fine-grained datasets often suffer from challenges such as difficulty in data collection and annotation, as well as the continuous emergence of new classes. These challenges naturally align with the FSCIL paradigm (L17-27). Therefore, developing FSCIL methods for fine-grained data holds substantial practical value.\n\n**2)** From a research perspective, the smaller inter-class variations in fine-grained data increase the task difficulty, but also provide an opportunity to enhance synergy across sub-categories, thereby improving FSCIL performance (L32-38). However, this potential is overlooked by existing methods. Exploring how to leverage data characteristics to solve problems not only helps better address the inherent challenges of fine-grained data, but also contributes to the further development of research in the FSCIL field.\n\nAs a relatively novel task, FG-FSCIL remains underexplored, and existing works are limited. Nevertheless, given its practical and research value, we believe it will gain broad community attention. And our work, as the first to model fine-grained inter-class synergy from the meta-environment perspective, will have a profound impact. The other reviewers also recognized the importance of the problem we tackle, the important insights and potential impact of our method in promoting progress in the FSCIL field, as well as its ability to address genuine limitations in existing FG-FSCIL methods.\n\n**2.** Thanks for the suggestion. We would like to clarify that the claim *``sub-categories in fine-grained datasets have smaller inter-class variations”* is not specific to our method, but rather a widely acknowledged premise in fine-grained research (L63-66), and thus we did not provide additional analysis on it. As defined in an authoritative survey paper (Fine-Grained Image Analysis With Deep Learning: A Survey. TPAMI 2022): *``the fine-grained nature of the problem is challenging because of the small inter-class variations caused by highly similar sub-categories, and the large intra-class variations in poses, scales and rotations. It is as such the opposite of generic image analysis (i.e., the small intra-class variations and the large inter-class variations), and what makes FGIA a unique and challenging problem.”* We will include further clarification and analysis before this claim in the Introduction.\n\n**3.** Reference [1] in our paper remains a classic FSCIL method, which is relatively older and does not specifically focus on fine-grained FSCIL. Although [1] proposes a clever tree-based statistical approach, it still freezes the feature extractor during incremental sessions and therefore does not exploit the potential synergy among fine-grained sub-categories in feature representation. Under the same experimental settings, its performance on CUB200 is $\\text{Acc}\\_T$=12.15 and $\\text{Acc}_{Avg}$=17.18, which is still clearly inferior to that of our proposed method. We will include a discussion of [1] and add its results to the comparison experiments in the paper.\n\n**4.** Hyperparameter values are decided based on previous works and experiments. More details have been given in supplementary material A.3 (L462-464), and their effect has been analyzed in Sec. 4.4.2. Given the importance of this aspect, we will move the explanation of hyperparameter selection into the main paper."}, {"review_id": "Zapj8obY1i", "reviewer": "Reviewer_KNQ9", "summary": "This paper proposes an interesting method, the Meta-Environment Learner (MEL), designed for fine-grained few-shot class-incremental learning. The approach theoretically formulates the problem, deriving a generalization error bound that motivates their design. MEL incorporates two key processes: evolving fine-grained feature extraction across incremental sessions and regularizing hypothesis space complexity. The method incrementally refines a meta-category representation and sub-category relationships. Experiments on four fine-grained benchmarks (CUB200, Stanford Dogs, Stanford Cars, and FGVC Aircraft) demonstrate that MEL achieves superior performance compared to prior state-of-the-art methods. The paper also includes ablation studies and analysis supporting the effectiveness of each component.", "questions": "1. The authors only used ResNet-12 as the backbone. Have the authors evaluated MEL with other architectures (e.g., ResNet-18, ResNet-50, or transformers)? Demonstrating that the improvements by the proposed method hold across backbones would strengthen the claim of general applicability.\n\n2. Even though the authors provided the experiments for the effect of hyperparameters, I just want to see how sensitive MEL's performance to the choice of other hyperparameters is. \n\n3. The authors mentioned in the Limitations that the method assumes all classes come from the same domain. Can the authors elaborate on how MEL could be adapted to cross-domain or more heterogeneous settings?", "limitations": "The authors explicitly discussed limitations in Appendix.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n+ The paper tackles an important problem—how to incrementally learn fine-grained categories with few samples, which is highly relevant to real-world applications. And, the authors addressed the motivation and importance of the problem very clearly.\n+ The theoretical generalization bound and the concept of progressively evolving a meta-category representation offer fresh perspectives.\n+ The comparison experiments were conducted with the recent SoTA methods, and the experimental results demonstrate improvements over strong SoTA methods.\n+ The paper is clearly written, with good illustrations and ablation studies explaining design choices.\n\nWeaknesses\n- The method is only validated with a ResNet-12 backbone; it would be helpful to see whether it generalizes to other architectures.\n- While the method is theoretically motivated, the implementation introduces multiple components whose interactions may be complex to reimplement without open-source code.", "was_revised": true, "final_justification": "The author provided detailed response with the additional experiments. I keep my accept rating.", "rebuttal": "We sincerely appreciate the constructive feedback. Detailed responses to the weaknesses and questions are as follows.\n\n**Response to Weaknesses**\n\n**1.** Please refer to the first point in the Response to Questions for details.\n\n**2.** To avoid potential reimplementation difficulties, we will release the code if the paper is accepted.\n\n**Response to Questions**\n\n**1.** Thanks for the suggestion. We follow the existing FG-FSCIL setting by using ResNet12 as the backbone. The evaluation results of our method on other architectures are presented below. Our method consistently outperforms the baseline and recent approaches (e.g., PFR, ADBS) across different backbone networks. \n\n|CUB200|  ResNet-18 $\\text{Acc}_T$ | ResNet-18 $\\text{Acc}_{Avg}$ | ResNet-50 $\\text{Acc}_T$ | ResNet-50 $\\text{Acc}_{Avg}$ | ViT-B/16 $\\text{Acc}_T$ | ViT-B/16 $\\text{Acc}_{Avg}$ |\n|-------|:-------:|:------:|:------:|:------:|:------:|:------:|\n| Baseline| 50.17   | 60.05  | 56.79   | 66.39   | 74.89    | 77.90   |\n| PFR   | 57.54  | 65.64   | 58.04   | 67.06   |   78.98  |  81.79  |\n| ADBS  | 60.14  | 67.75   | 61.27   | 68.59   |  79.46   |  82.41   |\n| Ours  | 62.84   | 69.20   | 63.30   | 70.97  |  81.70  |  82.47  |\n\n**2.** The experimental results on the effect of other hyperparameters, the number of attention masks $N$ and the temperature hyperparameter $\\tau$, are presented below. \n\n|$N$ |  1  |  2  |  3  |  4  |  5 |  6  |\n|-------|:-------:|:------:|:------:|:------:|:------:|:------:|\n| $\\text{Acc}_T$  |   41.77  |  42.94  |  43.15 |  44.06  |  43.55  |  43.32 |\n| $\\text{Acc}_{Avg}$|  50.64  |  51.70  |  51.59 |  52.38  |  52.04  |  52.20 |\n\n|$\\tau$   |    8    |   12   |   16  |  20  |  24  | \n|-------|:-------:|:------:|:------:|:------:|:------:|   \n| $\\text{Acc}_T$  |  41.14  |  43.70  |  44.06  |  43.64  | 43.53 |    \n| $\\text{Acc}_{Avg}$|  51.14  |  52.72  |  52.38  |  52.28  | 51.94 |\n\nMEL's performance remains relatively stable across a range of values for these hyperparameters. When $N \\ge 4$, increasing $N$ yields no additional performance gain. Additionally, similar to other FSCIL methods, our MEL achieves better performance when $\\tau=16$. These results are consistent with the principles behind our chosen hyperparameter settings (L462-464).\n\n**3.** Our method follows the standard FG-FSCIL setting, where all classes are from the same domain. However, since MEL learns feature representations at two levels, allows continual adaptation of the feature extractor to novel classes, and leverages class similarity relationships to continually regularize learning, it is in principle more adaptable to heterogeneous or cross-domain settings than existing methods. The strong performance of our method on datasets such as *mini*ImageNet and CIFAR100 (Figure 6), which already involve a certain degree of domain diversity, demonstrates its potential to cross-domain settings.\n\nRecently, cross-domain Class Incremental Learning (CIL) [a] has emerged as a novel research direction. However, to the best of our knowledge, there is no existing cross-domain FSCIL method. Given the significance of this direction, in future work, we will establish such a setting and extend MEL by introducing multiple meta-category vectors to better capture domain-specific semantics and modeling their interactions, thereby adapting to cross-domain or more heterogeneous settings.\n\n[a] *Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation. CVPR 2025*"}, {"review_id": "lQGunTDAAL", "reviewer": "Reviewer_VXZc", "summary": "This paper addresses the Fine-Grained Few-Shot Class-Incremental Learning (FG-FSCIL) problem by proposing a novel Meta-Environment Learner (MEL). The authors provide a theoretical formulation of FSCIL and derive generalization error bounds within a shared fine-grained meta-category environment. Based on these theoretical insights, MEL is designed to evolve fine-grained feature extraction capabilities while regularizing hypothesis space complexity. The method introduces an incrementally optimized meta-category vector and sub-category relationship regularization to achieve better performance on fine-grained incremental learning tasks.", "questions": "(1) How does the method perform when the base and incremental classes come from different domains or have varying granularity levels? (2) Could you provide more intuition about why the attention-based aggregation in Eq. 9 is superior to other optimization strategies? The experimental comparison in Figure 4 is helpful but lacks theoretical justification. (3) The paper claims that fine-grained categories naturally foster \"latent synergy\" - could you provide empirical evidence or visualization to support this claim? (4) In Equation 14, why use bidirectional KL divergence instead of unidirectional? What's the empirical difference?", "limitations": "Yes", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths: (1) The paper provides a rigorous theoretical analysis of the FSCIL problem, deriving generalization bounds that offer meaningful insights into the learning process. The connection between theory and method design is well-established. (2) The concept of leveraging the meta-environment and inter-class synergy in fine-grained data is innovative and well-motivated. This addresses a genuine limitation in existing FG-FSCIL methods. (3) The experimental evaluation is thorough, covering four fine-grained datasets with consistent improvements over state-of-the-art methods. The ablation studies effectively demonstrate the contribution of each component.\n\nWeaknesses: (1) While the theoretical motivation is strong, the actual implementation relies heavily on standard techniques (attention mechanisms, KL divergence regularization). The meta-category vector optimization, though effective, is relatively straightforward. (2) The theoretical section jumps quickly into complex bounds without sufficient intuitive explanation. Some key concepts like \"meta-environment\" could be introduced more gradually.", "was_revised": true, "final_justification": "I have read the feedback. I will keep the score.", "rebuttal": "We sincerely appreciate the constructive feedback. Detailed responses to the weaknesses and questions are as follows.\n\n**Response to Weaknesses**\n\n**1.** The primary goal of this paper is to uncover and explore the significance of fine-grained inter-class synergy in FSCIL from the perspective of meta-environment, supported by both theoretical analysis and empirical validation. Using standard techniques and straightforward implementations helps focus on the theoretical insights and motivation itself, and strengthens the connection between theory and method design. Experimental results demonstrate that, guided by our theoretical motivation, the thoughtful design based on these techniques has achieved strong performance. This underscores the significance of our theoretical insights and highlights their potential for further research.\n\n\n**2.** We define the concept of meta-environment in Definition 3.2 (L136–139). Its intuitive explanation is derived from the widely known notion of meta-category in fine-grained data analysis (Related Work, L63–65), so we did not elaborate further before introducing it. Thanks for the reminder. We will consider adding more intuitive explanations before presenting the formal bounds and provide additional background to better introduce key concepts in the theoretical section.\n\n**Response to Questions**\n\n**1.** As mentioned in Limitations and Future Work (Sec. A.2), our experiments follow the standard FG-FSCIL setting, where all classes come from the same domain, and thus do not yet consider cross-domain scenarios. However, datasets like *mini*ImageNet and CIFAR100 already involve varying granularity levels and some degree of domain diversity. Thanks to the two-level feature representation learning, continual adaptation of the feature extractor to novel classes, and continual regularization through class similarity relationships, our method already achieves strong performance on these datasets (Figure 6), demonstrating its potential in settings with domain diversity and varying granularity. \n\nTo the best of our knowledge, there is no FSCIL method that specifically considers different domains or varying granularity levels. Thus, it is currently not feasible to obtain established datasets, baselines, and experimental setups conducting targeted experiments in the short term. In future work, we will try to establish such settings and extend MEL by introducing multiple meta-category vectors to better capture domain-specific or differently-grained semantics, and model their interactions to support such scenarios.\n\n**2.** We present an intuitive numerical example to illustrate the effect of Eq. (9) and different optimization strategies.\n\n>Assume the meta-category vector $\\mathbf{m} \\in \\mathbb{R}^2$ is initialized as $\\mathbf{m} =$ [1.0, 1.0]. A new session provides three feature vectors $\\mathbf{f}_{\\text{high}}^i$: [1.2, 1.1], [0.9, 1.0], [4.0, -3.0], with a new class proportion of 0.1. These vectors include two samples that are semantically aligned with the meta-category, and one relatively distinct outlier. The updated meta-category vectors $\\mathbf{m}'$, computed using different optimization strategies, are as follows:\n>- Avg: [1.78, 0.03]\n>- Avg + Sim: [1.62, 0.22]\n>- Avg + Weight: [1.10, 0.87]\n>- Ours: [1.06, 0.93]\n\nCompared to other strategies, our method effectively avoids large parameter shifts that may result from few-shot samples, especially mitigating the negative impact of the outlier ([4.0, -3.0]), while incorporating new useful semantic knowledge ([1.2, 1.1], [0.9, 1.0]) from the new session. This is achieved through a *stable update step* via class proportion-based weight $\\alpha$ and a *robust direction* via attention weights $\\hat{a}_i$. Moreover, these parameters can be adaptively computed based on the data, without the need for manually preset hyperparameters or backpropagation (BP), and thereby improving both practicality and efficiency.\n\n\nWe further organize the experimental analysis (Sec. 4.4.1, Figure 4) into the following theoretical analysis.\n\n>From Eq. (9), our meta-category update rule is\n$$\\mathbf{m}'=(1-\\alpha)\\mathbf{m}+\\alpha{\\textstyle \\sum_{i=1}^{m_t}}(\\hat{a}_ {i}\\mathbf{f}_ {\\text{high}}^{i}), $$\nthe update magnitude can be bounded as\n$$ \\|\\|\\mathbf{m}' - \\mathbf{m} \\|\\|  = \\alpha \\left\\| \\left\\| \\sum_{i=1}^{m_t} \\hat{a}_ i \\mathbf{f}_ {\\text{high}}^i - \\mathbf{m} \\right\\|\\right\\|.$$\n**1)** The *update step* is directly controlled by $\\alpha = \\frac{|\\mathcal{C}^{t'}|}{\\sum_{t=0}^{t'} |\\mathcal{C}^t|}$. As $\\alpha$ decreases over incremental sessions, the update becomes increasingly conservative, automatically preventing large deviations in later sessions with limited training data.\n>\n>**2)** $\\hat{a}_ i = \\frac{\\exp(\\mathbf{m}^\\top \\mathbf{f}_ {\\text{high}}^i / \\sqrt{C_h})}{\\sum_{j=1}^{m_t} \\exp(\\mathbf{m}^\\top \\mathbf{f}_{\\text{high}}^j / \\sqrt{C_h})}$.\nThese weights assign higher importance to feature vectors more aligned with the current meta vector $\\mathbf{m}$, effectively reducing the influence of outliers and guiding the update in a reliable *direction*.\n\n**3.** Fine-grained categories usually belong to the same general meta-category (e.g., all being bird species) (L17–18), which inherently determines that these categories share certain common characteristics. Due to rebuttal format restrictions, we cannot include visualizations, but we offer an illustrative example here. In CUB200, species like the Black-throated Blue Warbler and the Cerulean Warbler have nearly identical wing patterns and beak shapes, so learning to recognize such traits in one class can facilitate representation learning in the other. This latent synergy can be understood as positive knowledge transfer across sub-categories.\n\n**4.** While unidirectional KL divergence enables knowledge transfer from one branch to the other, it introduces asymmetry by treating one branch as the teacher and the other as the student. In contrast, Eq. (14) adopts bidirectional KL to enable mutual regularization and refinement between the two branches, better aligning their sub-category relationship distributions. This helps eliminate redundant information unrelated to category similarity relationship, thereby simplifying classification and reducing the generalization bound by lowering hypothesis space complexity (L203-210, L227-230). \nEmpirical results also show that using bidirectional KL divergence results in better performance. \n\n|    | low $\\to$ high | high $\\to$ low |  bidirectional |\n|-------|:-------:|:------:|:------:|\n| $\\text{Acc}_T$ | 43.35 | 43.41 |  44.06 |\n| $\\text{Acc}_{Avg}$ | 51.86 | 51.85  |  52.38  |"}, {"review_id": "5R8eOJhA0j", "reviewer": "Reviewer_zZGu", "summary": "The paper proposes a novel Meta-Environment Learner (MEL) for Fine-Grained Few-Shot Class-Incremental Learning (FG-FSCIL). Key contributions include:\n1.\tTheoretical formulation of FSCIL problem with derived generalization error bounds\n2.\tMEL framework with evolving feature extraction and hypothesis space regularization\n3.\tComprehensive experiments showing state-of-the-art performance on fine-grained benchmarks", "questions": "1.\tWhy not use a dual branch approach for inference when performing dual branch calculations on both high-level and low-level features during training?\n2.\tIs the fully linked module used for high-level and low-level features in formula (10) shared? Why can we do this?\n3.\tWhy does Eq (10) input features into a fully linked module, and what space do you expect the features to be transformed into?\n4.\tMany previous FSCIL works have also been experimented on fine-grained data (CUB200), although you used different backbones (resnet12 vs resnet18), I suggest you also use the same settings for comparison, which will greatly enhance the persuasiveness of this submission. \n5.\tIn the experimental section, it is recommended to adjust the initial accuracy to the same level for fair comparison.", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n•\tThe generation bound (Theorem 3.1) provides solid theoretical foundation.\n•\tThe thinking on high and low features has important insights and potential impact on promoting progress in the FSCIL field\n•\tExtensive Validation: Experiments on 4 fine-grained datasets demonstrate consistent improvements.\nWeaknesses\n•\tComputational Cost: Additional overhead from meta-category optimization not quantified\n•\tError Analysis: Lacks case studies on failure modes or challenging categories", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate the constructive feedback. Detailed responses to the weaknesses and questions are as follows.\n\n**Response to Weaknesses**\n\n**1.** Compared to other training steps involving backpropagation, the computational cost of meta-category optimization is negligible, as it involves only forward-pass arithmetic operations and does not require any backpropagation or gradient updates. We manually estimate the total cost to be approximately 0.00013G. This computational cost is too small and is not reflected in empirical timing results.\n\n**2.** Due to rebuttal format restrictions, we are unable to include failure case pictures here, but we will add failure cases and corresponding error analysis in the supplementary material. \n\n**Response to Questions**\n\n**1.** During training, we introduce a regularization term (Eq. (14)) that encourages interaction and mutual refinement between the learned representations of the two branches to achieve alignment (L230-233). Thus, a single branch, particularly the high-level branch with its semantic focus, tends to yield comparable or better performance than dual-branch inference (Table 2, last three rows), while improving inference efficiency.\n\n**2.** They are not shared. We will revise Eq. (10) to clearly distinguish them. Since the input features differ between the two branches, separate fully linked modules are required.\n\n**3.** The fully connected module is used to individually transform high-level and low-level features guided by the loss function. \n\nClassification loss term (Eq. (12)) separately guides two types of features to be transformed into class-discriminative feature spaces. Meanwhile, mutual learning regularization term (Eq. (14)) regularizes two types of features to align in feature spaces with consistent sub-category similarity relationships. Thus, during the transformation process, Eq. (12) preserves classification-relevant information, while Eq. (14) eliminates redundant information irrelevant to modeling category similarity. This simplifies the classification and further reduces the generalization bound by lowering hypothesis space complexity (L203-210). Experimental results also show that transformation with proper guidance leads to performance improvements (Table 2). \n\n**4.** We follow the existing FG-FSCIL setting by using ResNet12 as the backbone. As suggested, we also provide comparison results using ResNet18 on CUB200, as shown below. Our method still outperforms recent methods under this setting. Thanks for the suggestion. We will include these results in the paper to enhance its persuasiveness. \n\n|       |   PFR   |  ADBS  |  Ours  |\n|-------|:-------:|:------:|:------:|\n| $\\text{Acc}_T$  |  57.54  | 60.14  | 62.84  |\n| $\\text{Acc}_{Avg}$|  65.64  | 67.75  | 69.20  |\n\n**5.** Different methods introduce distinct modules during the base session, which inevitably leads to varying initial accuracies. This is a common phenomenon in FSCIL research. Given that fair comparison is important, we have kept all method-agnostic settings as consistent as possible and achieved initial accuracy close to that of some existing methods (e.g., PFR on CUB200). Moreover, our experiments include comparison methods with both higher and lower initial accuracies than ours. In all these cases, our method consistently performs better in subsequent sessions (Figure 3), which ensures a fair comparison and demonstrates its effectiveness in the incremental learning setting."}]

Author rebuttal:

Meta-review: This paper addresses the fine-grained setting of few-shot class-incremental learning. Reviewers found it easy to read, tackled important problems, and provided a good theoretical analysis.

During the review process, the paper received some initial negative scores (Reviewer "5ht6"). As the rebuttal process progressed, all reviewers' concerns were addressed.

The paper ultimately received full consensus in the final rating, with two borderline accepts and two accepts. The meta-reviewer agreed that the paper contributes to the field of few-shot class incremental learning and agreed with the reviewers' recommendation for acceptance. The AC urged the authors to include the additional comparative experiments mentioned in the rebuttal and a discussion with recent work in the camera-ready version.
Decision: Accept (poster)