TS-MOF: Two-Stage Multi-Objective Fine-tuning for Long-Tailed Recognition
Authors: Zhe Zhao
Zhiheng Gong
Pengkun Wang
HaiBin Wen
Cankun Guo
Bo Xue
Xi Lin
Zhenkun Wang
Qingfu Zhang
Yang Wang
Venue: NeurIPS 2025 poster ()
Keywords: Long-tailed learning
Multi-objective optimization
Primary area: general_machine_learning

Abstract: Long-Tailed Recognition (LTR) presents a significant challenge due to extreme class imbalance, where existing methods often struggle to balance performance across head and tail classes. Directly applying multi-objective optimization (MOO) to leverage multiple LTR strategies can be complex and unstable. To address this, we propose TS-MOF (Two-Stage Multi-Objective Fine-tuning), a novel framework that strategically decouples feature learning from classifier adaptation. After standard pre-training, TS-MOF freezes the feature backbone and focuses on an efficient multi-objective fine-tuning of specialized classifier heads. The core of TS-MOF's second stage lies in two innovations: Refined Performance Level Agreement for adaptive task weighting based on real-time per-class performance, and Robust Deterministic Projective Conflict Gradient for stable gradient conflict resolution and constructive fusion. This approach enables effective synergy between diverse LTR strategies, leading to significant and balanced performance improvements. Extensive experiments on CIFAR100-LT, ImageNet-LT, and iNaturalist 2018 demonstrate that TS-MOF achieves state-of-the-art results, particularly enhancing tail class accuracy (e.g., +3.3\% on CIFAR100-LT IR=100 tail) while improving head class performance, all within a remarkably short fine-tuning period of 20 epochs.

TS-MOF: Two-Stage Multi-Objective Fine-tuning for Long-Tailed Recognition
Zhe Zhao
Zhiheng Gong' Pengkun Wangl
HaiBin Wen Cankun Guo
Bo Xue?
Xi Lin
Zhenkun Wang'
Qingfu Zhang² Yang Wang
'University of Science and Technology of China CCit University of Hong Kong
Suzhou Institute for Advanced Research, USTC *Southern University of Science and Technology
5 Nanchang University AAnhui Provincial Key Laboratory High Performance Computing
Abstract
Long-Tailed Recognition (LTR) presents significant challenge due to extreme class imbalance, where existing methods often struggle to balance performance across head and tail classes. Directly applying multi-objective optimization (MOO) to leverage multiple LTR strategies be complex and unstable. To address this, we propose TS-MOF (Two-Stage Multi-Objective Fine-tuning), a novel framework that strategically decouples feature learning from classifier adaptation. After stan- dard pre- treiniiining TS-MOF freezes the feature backbone and focuses on an efficient multi-objective fine-tuning of specialized classifier heads. The core of TS-MOF's second stage lies in two innovations: Refined Performance Level Agreement for adaptive task weighting based real-time per-class performance, and Robust De- terministic Projective Conflict Gradient for stable gradient conflict resolution and constructive fusion. This approach enables effective synergy between diverse LTR strategies, leading to significant and balanced performance improvements. Exten- sive experiments on CIFARIO0-LT, ImageNet-LT, and iNaturalist 2018 demonstrate that TS-MOF achieves state-of-the-ee results, particularly enhancing tail class ac- curacy (e.g., +3.3% on CIFAR I00-LT IR=100 tail) while improving head class performance, all within remarkably short fine-tuning period of 20 epochs. We provide the detailed code in https://github. wwtpgiit...oo.mmieee com//ataLaie aatetaaL--otttS--taa.a
Introduction
The ubiquitous long-tailed distribution of real-world visual data presents formidable obstacle to training deep learning models that can generalize well across all categories [1] Models trained on such imbalanced datasets inevitably develop bias towards data-rich head classes, leading to significantly deficient performance on data-scarce tail classes. Although numerous Long-Taile
Recognition (LTR) strategies have been proposed spanning resampling [4] 24, loss re-weighting [19]
5). and advanced representation learning 11 29 they often suffer from the "seesaw dilemma": efforts enhance tail class performance frequently come at the cost of head class accuracy, exposing an inherent trade-off that is difficult to perfectly reconcile with single strategy.
Given the limitations of individual strategies and the tendency for different LTR methods to exhibit unique strengths across various class regions, "multi-strategy collaboration" has emerged as an attractive research direction. Recently, ideas from Multi-Objective Optimization (MOO) [26] 6 and strategy fusion (27) have begun to be introduced into the LTR domain, attempting to find more balanced solutions by jointly optimizing multiple conflicting objectives. However, directly applying multiple complex LTR strategies to end-to-end training of an entire network can lead to
"Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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substantial computational overhead and tuning difficulties. It may also result in suboptimal feature representations and unstable training dynamics due to potential conflicts between strategies and interference with the feature learning process. This complexity has limited the widespread application and usability of advanced MOO concepts in the LTR field.
To address these challenges, we propose TS-MOF (Two-Stage Multi-Objective Fine-tuning), a novel LTR framework. The core idea of TS-MOF lies in strategic decoupling: first, high-quality generic feature representations are obtained through standard pre-training; subsequently, with this feature backbone frozen, the focus shifts to efficiently fine-tuning a set of classifier heads specifically designed for LTR using multi-objective optimization. This two-stage design significantly lowers the barrier to combining multiple LTR strategies with MOO, as the complex MOO process acts only on the classifier heads, which have fewer parameters, and without the risk of corrupting well-learned features. This enhances overall training robustness and methodological ease of use. Notably, this targeted multi-objective adaptation process is highly efficient; we demonstrate that it can be completed within a very short fine-tuning period (e.g.. just 20 epochs) while achieving excellent performance.
The efficacy of TS-MOF hinges on its sophisticated multi-objective optimization mechanism in the second stage, driven by two core innovations that ensure effective integration and synergy of knowledge from different LTR strategies:
1. Refined Performance Level Agreement (R-PLA) Weighting: R-PLA is an adaptive dynamic task weighting mechanism that surpasses traditional fixed or simple heuristic weight assignments. By evaluating the instantaneous accuracy of each LTR strategy ("task") on various classes in real-time and combining this with its cosine similarity to a preset reference performance pattern (transformed by a cube root and non-negative clipping for enhanced robustness and differentiabilityy) R-PLA intelligently identifies and amplifies signals from strategies that contribute most improving critical class performance the current learning stage. This ensures that optimization resources are directed towards the most effective paths within the limited fine-tuning window.
2. Robust Deterministic Projective Conflict Gradient (RD-PCGrad): To resolve gradient conflicts and negative transfer issues that may arise when updating parameters with multiple LTR strategies, we developed RD-PCGrad. This technique is a significant enhancement over existing conffict-vvoidacce gradient methods (like PCGrad). By performing deterministic iterative projections and numerically stable aggregation of task gradients (weighted by R-PLA), it forms Pareto-improving direction. RD-PCGrad not only guarantees the repro- ducibility and training stability of the multi-task learning process-cruee in a short time- but more importantly, it enables the constructive fusion of heterogencous knowledge from different LTR strategies (c.g., some focusing on class balance, others on hard-sample mining). jointly propelling the model towards more balanced and superior performance state.
for convergence
During inference, we employ the Evolving Optimal Strategy Selection (EOSS) mechanism 32 to integrate predictions from the different classifier heads. The core performance improvements and unique advantages of TS-MOF, however, primarily stem from the efficient, stable, and synergistic multi-objective tne-tuning process during training, driven by R-PLA and RD-PCGrad on the frozen feature foundation.
We validate the superior performance of the TS-MOF framework through extensive experiments on several challenging LTR benchmark datasets, including CIFAR100-LTT, ImageNet-I.T, and iNaturalist 2018. The experimental results demonstrate that TS-MOF not only surpasses existing -fate-of-the-art (SOTA) methods in overall accuracy across various benchmarks but also achieves significant break- throughs in crucial tail-class recognition accuracy, while also improving or effectively maintaining head-class performance. For instance, on the most challenging CIFAR100-LE (IR=100) benchmark, TS-MOF achieves 56.8% overall accuracy and 39.9% 9% tail accuracy, a significant improvement over the previous SOTA method LOS (27] (53.9% overall, 36.6% tail), with head accuracy also increasing from 70.3% to 79.0% within an efficient 20-epoch fine-tuning budget. These results fully sub- stantiate that our strategy of two-stage decoupling combined with advanced MOO components offers a robust, user-friendly, and efficient solution for tackling complex long-tailed recognition problems. We will release our code and pre-trained models to foster further research in the community.

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2 Related Work
2.1 Long-Tailed Recognition
Long-Tailed Recognition (LTR) addresses the challenge of learning from datasets where class distributions are heavily skewed, with a few head classes having abundant samples and many tail classes having very few. Initial efforts focused on data re-sampling, either by over-sampling tail classes 4 or under-sampling head classes 2]. However, naive over-sampling can lead to overfitting on tail classes, while under-sampling discards valuable data from head classes.
Another prominent direction is loss re-weighting, which aims to assign higher importance to samples from tail classes during training. Techniques like Focal Loss [19] down-weight easy examples, indirectly helping with class imbalance, and have inspired further work such as Focal-SAM which combines focal-style weighting with sharpness-aware minimization [18]. More directly, methods like Class-Balanced Loss [3] re-weight the loss based on the effective number of samples per class. Saabel-Dstribbution-AAe Aware Margin (LDAM) loss [ enforces larger margins for minority classes, and Balanced Softmax [25] adjusts the softmax function to account for class frequencies. Other approaches modify the logits directly to achieve re-balancing, such as through Gaussian-based adjustments [15] The theoretical underpinnings of re-weighting and logit-adjustment have also been explored to provide a unified understanding of their generalization properties in imbalanced learning [30].
Representation learning and knowledge transfer techniques seek to learn more robust and gen- eralizable features, or transfer knowledge from head to tail classes. Methods like BBN 33 use a two-branch network to learn representations and perform cumulative learning. Other approaches focus on feature augmentation for tail classes, disentangling feature norms from directions [20], or fusing features from head to tail classes improve representations [7]. Some research delves into the intrinsic properties of learned features, unveiling and mitigating biases by analyzing perceptual manifolds 222. Recently, adapting large pre-trained models using visual prompt tuning has emerged as a promising direction, with methods designed to improve performance on tail classes [16] 1T. Multi-expert models, such as RIDE 229.. train diverse experts specialized in different parts of the data distribution.
2.2 Two-Stage Long-Tailed Learning
Recognizing the difficulty of simultaneously learning good features and a balanced classifier on long-tailed data, two-stage approaches have gained significant traction. The seminal work by Kang et [3] proposed decoupling representation learning from classifier learning. In their framework, features are first learned using mustance-balancee sampling, and then the classifier is re-trained or
(24]. adjusted using class-balanced sampling or other techniques like logit adjustment (24). This decoupling strategy allows the backbone network learn general and high-quality features without being overly biased by the imbalanced data distribution during the initial stage. Subsequent works have built upon this paradigm. For example, LOS [27] explores an optimal strategy within a two-stage framework by adaptively combining multiple re- ro-balancing strategies. Our proposed TS-MOF framework also adopts two-stage design, but uniquely focuses the second stage on sophisticated multi-objectivv fine-tuning of specialized classifier heads using novel components like R-PLA and RD-PCGrad.
2.3 Multi-Objective Optimization in Deep Learning
Multi-Objective Optimization (MOO) is concerned with simultaneously optimizing multiple, often conflicting, objectives. In deep learning, MOO has been increasingly applied to multi-task learning (MTL) and problems with inherent trade-offs. Sener and Koltun (26) framed MTL as MOO, proposing methods to find Pareto optimal solutions by optimizing weighted sum of losses or manipulating gradients. Gradient manipulation techniques are particularly relevant. For instance, MGDA (Multiple Gradient Descent Algorithm)
[ finds a common descent direction that improves all tasks. PCGrad (Projective Conflict Gradient) 31 mitigates conflicting gradients between tasks by projecting task's 's gradient onto the normal plane of another task's gradient if they conflict. Our RD-PCGrad builds upon such principles to ensure stable and deterministic conflict resolution. The goal of MOO is often to find a set of solutions on the Pareto front, where no objective can improved without degrading at least one other objective (6). While traditional MOO often involves complex evolutionary algorithms,
3

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recent adaptations for deep learning focus gradient-based approaches suitable for network training. Applying MOO LTR is nascent but promising direction. For example, Mahapatra and Rajan [23 proposed a framework unifying MTI. and LTR,, implicitly leveraging MOO principles to balance learning across diverse class groups and tasks. Our TS-MOF explicitly formulates the second-stage fine-tuning as an MOO problem, leveraging dynamic weighting (R-PLA) and conflict-aware gradient modulation (RD-PCGrad) to achieve balanced performance improvement across head, medium, and tail classes.
3 Methodology
To address the challenges of long-tailed image recognition, we propose a novel two-stage training framework. This framework first learns generic features through standard training and then, in the second fine-tuning stage, focuses on introducing advanced Multi-Objective Optimization (MOO) strategies to effectively leverage the complementary strengths of various long-tailed learning tech- niques. Compared to traditional single-stage or simpler two-stage allows for more flexible integration and balancing of multiple strategies, thereby achieving more balanced and comprchensive performance improvements across all parts of the long-tailed distribution. In contrast applying MOO throughout all stages, our method simplifies the optimization process by separating feature learning and classifier adaptation. This ensures the acquisition of high-quality base features and enables the MOO in the second stage to focus more effectively and efficiently resolving imbalance issues at the classifier level.
methods, this design
3.1 Stage 1: Generie Feature Pre-training
Let Dirain = {(rr, y Muni be the long-tailed training dataset, where x; E X' represents the input image, E E {0...., K }} is its class label, Mraain is the total number of training samples, and K is the total number classes. We employ a Convolutional Neural Network (CNN) as an encoder E(:00p (p) X + RDr, which maps the input image to Dr-dimensional feature space, with parameters 0g. This encoder is connected to a classifier head Hsi (:: 0HH)) IR Pr + R with parameters 8 HH,, The objective of this stage is to learn high-quality generic feature representation 0 by minimizing the standard cross-entropy loss LCE:
min (aa. [Eer (Hsi (F(x;0z))..) 0g.8H5)
Optionally, Label Smoothing (LS) can be applied:
LCE = ())
Σ Ler(z.j)
(2)
where z RKK are the logits output by the model, and a is the smoothing factor. This stage is trained for a predetermined number of epochs EsI, and the encoder parameters 0% that achieve the best performance on a validation set are saved.
3.2 Stage 2: Multi-Ohjective Long-Taite Taig-d Fine-tuning
This stage constitutes the core innovation of our method. It focuses fine-tuning set of classifier heads specifically designed for long-tailed distributions using advanced MOO strategies, based on the high-quality and frozen feature encoder E(: ( obtained in the first stage. This "encoder- frozen, multi-head fine-tuning" strategy decouples the complex long-tailed problem into two phases: generic feature learning and specialized classifier adaptation, significantly reducing optimization difficulty. More importantly, it provides flexible framework that conveniently allows various existing single-objective long-tailed learning techniques (often manifested as specific loss functions) to be transformed into independent "tasks" within our MOO framework, thereby exploring their synergistic potential.
3.2.1 Multi-Head Architecture and Task Definition
We equip the model with a multi-head output layer. Let (Ti,... T the set of N selected learning tasks. Each task Ti T corresponds to specific long- loig-tailed learning strategy and is

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associated with a unique loss function Cs and an independent classifier head Hz(=:HH,) : PR 7
RRK These loss functions can include standard Cross-Entropy (LCr), Balanced Softmax (LBs), Label- Distribution-Awar Margin loss (CLDAM), etc. In this fine-tuning stage, only the set of parameters for all classifier heads 0g = (OH.....,0H, is optimized.
3.2.2 Multi-Ohjective Optimization Framework
When employing multiple tasks (N 1), we face multi-objective optimization problem, the goal of which is to simultaneously minimize the expected losses of all selected tasks:
min [(Hh) min (E[C] E[LN (0x))) 8N
(3)
To effectively address this problem, we design and integrate the following key components:
Dynamic Task Weighting (Refined PLA) To adaptively adjust the relative importance of each task loss C2 in the overall optimization, we employ an improved dynamic task weighting mechanism. In each training batch of every training epoch for each task Т₃ E T ( = 1, ....,, N), compute (batch)
its performance vector j.e E T on that batch's 's data (e.g., per-class accuracy, where C' is the
(butch) j. We {a],e (butch) aN,e (batch) designate the last performance vector in the list, aN.e" as the reference performance aef,e for the current batch. For each performance vector j, with the reference performance vector:
number of classes). These performance vectors form a list A (bbtch))
(hatch) in the list, we compute its cosine similarity $j
(batch)
(batch)
a j.e
a yef.c
$1
(4)
max
||a (batch) j.c
||2
||
(batch)
2|,,
Gsim
where 6sim is a small positive constant ensuring numerical stability. Subsequently, the weight $3.e for task T₁ is obtained by applying cube root transformation the similarity and performing non-negative clipping:
B max(0, (/jj,))
(5)
The cube root transformation here adjusts the sensitivity of the weights to changes in similarity. Cosine similarity, due its insensitivity to vector magnitude, can more robustly capture similarities in performance distribution patterns among tasks in long-tailed context, while non-negative clipping ensures the rational interpretability of weights. The weighted loss is C% BL.
Conflict-Aware Gradient Modulation (Robust Deterministic PCGrad) Even after task weight- ing, the gradients gx: Von C4 E P (flattened vectors with respect to all trainable parameters 0H)) corresponding to different weighted losses L/. may still conflict. To mitigate negative transfer, we employ robust and deterministic PCGrad strategy. Let ())) {gi...., gN} } be the initial set of gra- dients, and h& E {0, 1) Pr be the corresponding gradient existence masks. PCGrad progressively mitigates conflicts between gradients through a series of deterministic, pairwise projection operations. For any pair of gradients g (current state of gradient 1) and gj. if they conflict (g) g < 0) and both contribute the parameters (i.e., [p[p] 1 h [p] for some parameter p). then g modified:
g g'
g g
g
g morm
where norm 10 ensures numerical stability of the projection process. This procedure is applied iteratively to all gradient pairs in fixed order, ultimately yielding set of conflict-mitigater gradients Gproj {g".... .... }. ). These projected gradients are then aggregated. For example, when using mean aggregation, the final gradient ginal IR Pponss used for parameter updates is:
. ビk=1 (g% A ha) Σn
Stinal
hR + Ediv
k=
where @ denotes the Hadamard product (element-wise product), division is element- and <div = 10-8 ensures numerical stability of the aggregation process. Our PCGrad implementation guarantees experimental reproducibility and enhances numerical stability.
5

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During validation and testing phases, we implement dynamic expert opinion integration. This module learns class-specific expert weights wxee based on the historical accuracy (Ta c) of each task Tą (expert) on each class e in the validation set Dval:
exp( Avp(Avai(Ta (Tk:c))
Wkec
(8)
exp(Aual (T c)) + Cootmaax
(x;00)):0 be the where Coottmax is a small constant to prevent division by zero. Let (E) 0n,) logits output by task T3 for input x. The final predicted probability PEOsS c/2:) is obtained by weighted sum the softmax probabilities from each expert head:
Hv
PEuss c/2) Wk,c
This integration method allows the model dynamically focus on the expert that performs best on a particular class, according to its characteristics.
3.2.3 Optimization
All trainable classifier head parameters 0µr are optimized using Stochastic Gradient Descent (SGD) with momentum. The learning rate 77 follows a Cosine Annealing schedule, smoothly decaying from an initial value 7/u to a smaller value Mmmin over a preset number of fine-tuning epochs Esz (e.g., 20 epochs in this paper).
Through this meticulously designed two-stage framework, particularly the enhanced multi-objective optimization strategy in the second stage, our method more effectively integrate the advantages of multiple long-tailed learning techniques. The newly introduced Refined PLA mechanism, by utilizing the independent instantancous performance of each task combined with cube root transformation. aims to fine-tune task weights more delicately. Mcanwhile, the robust and deterministic PCGrad ensures that gradient conflicts are mitigated in stable and controllable manner. These compo- nents collectively strive to improve the model's 's overall performance under long-tailed distributions, especially achieving more reliable and significant improvements on challenging tail classes.
3.3 Theoretical Advantages in Robustness
Our framework, through its specific architectural choices and algorithmic components in Stage exhibits notable theoretical advantages in terms of robustness. These are formalized in the following propositions.
Proposition (Gradient Stability and Pareto Improvement via RD-PCGrad), Let Li (0sr) be the R-PLA weighted loss for task Ta and ga = Vag [2((0) its corresponding gradient. The Robust Deterministic Projective Conflict Gradient (RD-PCGrad) mechanism, through deterministic iterative projections (Eq. 13) and numerically stabilized aggregation (Eq, 24), ensures that the final update direction giiaa a Pareto-improving or non-worsed oor-worsnning direction with respect to the set of weighted objectives {Cr, (O)))N This mitigates negative transfer between conflicting LTR strategies and enhances training stability.
Proposition 2 (Adaptive Weighting Robustness via R-PLA), The Refined Performance Level Agree- ment (R-PLA) weighting scheme, defined by 3i.e max(0, 4/5 where 8j.e is the cosine similarity (Eq. 12), yields task weights that are robust to (i) the absolute scale of per-class performance vectors (batch) and (ii) noisy instantaneous performance estimations. The transformations ensure smoother ie
weight dynamics, leading to a more robust adaptation of objective priorities.
Proposition 3 (Representational Robustness via Two-Stage Decoupling). The decoupling of feature learning (Stage from multi-objectio classifier fine-tuning (Stage 2, with frozen 8%g imbues TS-MOF with representational robusiness. By fixing the feature extractor E(:; 0z)), the MOO process is shielded from corrupting the general-pune parepse feature manifold, ensuring classifier balance improvements do not unduly degrade feature quality.
Formal derivations and further theoretical justifications supporting Propositions will be provided in the Appendix.

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Experiment
4.1 Experimental Settings
Datasets. We conduct experiments on long-tailed image classification benchmarks: CIFAR- 100- IT (3, ImageNet-LTT [21], and iNaturalist 2018 28) CIFAR-100-LJ and ImageNetI.T are manually truncated long-tail versions of the original balanced dataset, while iNaturalist 2018 a real-world dataset with natural long tail distribution. CIFAR-I00-LA has three imbalance ratio settings 10, 50, 100, and the imbalance ratio (IR) of the long tail dataset is IR=7maz/nmiin where Amaz and Amin the number training samples in the maximum and minimum classes, respectively. For each dataset, we use the official version provided.
Evaluation Metries. Our model performance is mainly evaluated based the general accuracy of Top-1 1 (All). According to the method proposed by []. statistical evaluations were conducted on the head, medium, and tail classes of long-tail datasets. These three classifications as follows: many (more than 100 images), medium (20-100 images), and few (less than 20 images). All accuracy metrics expressed percentages.
Comparison Baselines. Multiple baseline methods were compared in the experiment, covering the main technical directions of long-tailed recognition. These loss adjustment methods include cross-entropy loss (CE) [10, category re- balanlincigg methods such as CE-DRW [3, LDAM-DRW [3), KPS [14, Balanced Softmax (BS) [25] well module improvement methods such RIDE by three experts [29], SHIKE 12) and BCL [34]. In addition, there is currently the best two-stage training strategy method, LOS (27] We not only compare method with these baselines, but also integrate them into our model.
Implementation. The training process is divided into two stages: feature learning and classifier fine-tuning. For each model, we use an SGD optimizer with momentum 0.9 and weight decay of 0.005. In the feature learning stage, CIFAR-100-B a batch size of 64 and an initial learning rate of 0.01. The learning rate was decayed by a factor of 0.1 at the 60th and 80th epochs, with total training of 200 epochs. Freeze the backbone network parameters during the classifier fine-tuning stage, and fine tune multiple classification heads for 20 epochs using appropriate learning rates and weights. The computing resources based on 8 NVIDIA Tesla V100 GPUs and implemented through the Py Torch framework.
uses ResNet34 as the backbone network, with
Table 1:
Comparison for CIFAR100-LT Benchmarks. Top-1 accuracy (%) reported and CIFAR100- LT consists of three imbalanced ratio (IR) 10/50/100. Our model outperforms all the baselines, showing satisfactory accuracy in the few-class categories.
IR=10
IR=50
IR=100
Method
Head
Medium
Tail
All
Head
Medium
Tail
All
Head
Medium
Tail
All
CE
40.3
56.5
63.9
36.2
15.2
43.8
65.6
36.2
8.2
38.1
CE DRW
62.5
60.6
22.9
45.0
63.4
15.7
41.4
LDAM DRW
3
41.2
47.2
42.6
21.1
43.2
60.3
41.3
34.3
47.9
59.6
23.7
RIDE experts)
29
66.4
49.4
61.1
65.7
47.7
31.8
52.2
65.7
48.6
25.0
BCL
61.6
43.1
34.3
49.1
63.1
42.9
23.9
44.2
KPS 14
61.7
58.7
59.5
51.6
49.5
52.4
50.5
41.9
39.5
48.7
42.2
SHIKE 12
66.0
45.0
59.0
67.0
43.0
23.0
49.5
66.0
39.0
12.0
46.9
LOS 27
71.9
62.3
69.0
72.4
51.4
40.2
58.0
70.3
52.3
36.6
53.9
TS-MOF
74.7
62.0
70.8
75.2
50.7
47.5
60.2
79.0
39.9
56.8
4.2 Benchmark Results
CIFAR-100-LT. Table presennss the overall classification accuracy results on the CIFAR-100- 100-LT dataset, comparing the performance of single strategy and TS-MOF based strategy fusion methods. Compared to the existing SOTA model LOS [27], our method achieved performance improvements of 2.9%, 2.2%, and 1.8% respectively in the scenarios of IR=100, IR=50, and IR=10 on the CIFAR- 100-LT dataset. Compared with the independent long tail learning baseline method, TS-MOF exhibits significant performance enhancement and achieves sustained and substantial improvement.

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Table 2: Accuracy (%) on ImageNet-LT and iNaturalist 2018 datasets.
ImageNet-LI
iNaturalist 2018
Method
Head
Medium
Tail
All
Head
Medium
Tail
All
CE
64.0
33.8
5.8
41.6
73.9
63.5
55.5
61.0
CE DRW
61.7
47.3
28.8
50.1
68.2
67.3
66.4
67.0
cRT
58.8
44.0
26.1
47.3
69.0
66.0
63.2
65.2
LDAM DRW
60.4
46.9
30.7
49.8
66.1
BS
60.9
48.8
32.1
51.0
65.7
67.4
67.5
67.3
KPS
59.7
49.2
35.9
52.3
68.1
69.5
70.2
69.6
RIDE (3 experts)
64.9
50.4
34.4
53.6
69.5
71.0
70.4
70.6
BCL
65.3
53.5
36.3
55.6
69.5
70.9
71.3
70.9
LOS
63.2
50.7
42.3
54.4
69.2
70.7
71.3
70.8
TS-MOF
65.9
52.5
42.6
56.3
72.8
73.6
70.5
72.3
Strategy Performance Comparison (Epoch 20)
KPS
0.29
Average Accuracy
0
0.6
0.33
0.44
0.2
87
0.02
0.09
0.00
Frequency Group (Sorted High
Figure I: Performance comparison before and after policy fusion, the class frequency becomes lower from left to right.
ImageNet-LT and iNaturalist 2018. We further compared our proposed method with advanced long tail recognition methods on large-scale datasets such as ImageNet LT and iNaturalist 2018, 8, and the results are shown Table Consistent with the conclusion Table the strategy fusion technique significantly improved the performance of the model on complex datasets.
4.3 Further Analysis
In this section, will delve into the underlying logic of the TS-MOF mechanism and explore the following key issues. All analysis experiments were conducted based on the CIFAR-100-LT dataset (IR=100).
Figure shows the performance of the TS-MOF before and after the fusion, as well as the effects of the single strategies that were merged. Clearly, after fusion, TS-MOF exhibits the advantages of fusion strategies across all classes, thus becoming more balanced.
2c respectively, reflect the weight changes of various strategies in the head, middle, and tail. can be seen that bel strategy performs better in the head, the los strategy of the middle class is better than the other two strategies, and the kps strategy performs better in the tail class. This experimental result completely consistent with Figure 2d only has good performance in the middle class but can also obtain relatively large weights in some tail classes. The fusion of three strategies can better leverage the performance in the head, middle, and tail to address the issue of long-tail imbalance.
Figures 2a
2b
worth noting that los strategy not

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Wilggn
(a)
(b)
(c)
Tbak Stratep
22M 55:27 AMC 6 esce p wi
w 20 275 3.5--t-- 91--9--------00 935 SE-SSt.RREIE dp 25: W 23/ SPR8NS2
Actual Many
Many-shot
Medium-shot
Pew-shot
Figure 2: Further Analysis. Subfigure (a)()(c) show changes in strategy weights for head class, medium class and tail class. Subfigure (d) shows heatmap of strategy weights for all classes.
Outbut Conflict Procability compa ison tAll Classes)
IS 15-MOPIRSS BUL BCL--DS T5-VCF
I' MOMIIKCLLPPS BC.-KT5 TS-MEF
0.30
TS-NOT
KPS LOS Overall Contlct Contict Pub.)
ct hobasto
07.3
0.15
0.20
0.23
10
14
#pazh
Accuracy (%)
(a)
(b)
Figure Further Analysis. Subfigure (a) compares the probability of output conflicts occurring when different groups of strategies are fused using TS-MOF. Subfigure (d) shows the performance improvements of our method compared simpled weight fusion.
As shown in Figure 3al in the initial stage of fine-tuning, the initial fusion of multiple strategies may encounter many conflicts, which can affect the fusion of strategies. However, as the epoch increases, conflict probability
both dual strategy and triple strategy methods significantly decreases, indicating that the TS-MOF can allocate correct weights to corresponding strategies well, demonstrating the effectiveness of the TS-MOF method in conflict resolution. In Figure (3b] the performance is compared without using multi-objective optimization in policy fusion. Obviously, multi-objective optimization brings substantial benefits.
5
Conclusion
In this paper, we introduced TS-MOF, a novel Two-Stage Multi Multi-Objective Fine-tuning framework designed to tackle the persistent challenges Long- Recognition. By strategically decoupling robust feature pre-training from specialized multi-objective classifier fine-tuning stage, TS-MOF significantly simplifies the integration multiple LTR strategies. The efficacy of our approach primarily driven by two core components in the second stage: R-PLA, which adaptively weights tasks based on their real-time performance patterns, and RD-PCGrad, which ensures stable and deterministic resolution of inter-task gradient conflicts while fostering constructive knowledge fusion.

---

Our extensive experiments on challenging benchmarks demonstrate that TS-MOF not only achieves state-of-the-un overall accuracy but also yields substantial improvements in tail-class performance while maintaining even enhancing head-class accuracy. all within highly efficient fine-tuning budget. TS-MOF provides a robust, effective, and user-friendly solution for advancing long-tailed recognition.
Acknowledgements
The authors gratefully acknowledge the support from the National Natural Science Foundation of China (NSFC) under Grant Nos. 62402472, and 12227901. This work was also supported by the Natural Science Foundation of Jiangsu Province (No. BK20240461),, the Key Basic Research Foundation of Shenzhen (No. JCYJ202208 100005011), the Research Grants Council of the Hong Kong Special Administrative Region (GRF Project No. CityU 11215622), the Project of Stable Support for Youth Team in Basic Research Field, CAS (No. YSBR-005), and the Academic Leaders Cultivation Program at USTC. The Al-driven experiments, simulations and model training were performed on the robotic Al-Scientist platform of Chinese Academy of Sciences.
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12

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Theoretical Analysis
This appendix provides formal proofs for the three key propositions stated in Section 3.3 the theoretical foundations that underpin TS-MOF's robustness and effectiveness.
establishing
6.1 Mathematical Foundations and Problem Setup
The core challenge in long-tailed recognition lies in simultaneously optimizing multiple conflicting objectives. Stage TS-MOF, we address the multi-objective optimization problem:
min F(On) (O))) ... ((n))
(10)
where 0H = (0
HH denotes the parameters of all classifier heads, and each La represents a specific LTR strategy (e.g., Cross-Entropy, LDAM, Balanced Softmax).
The R-PLA mechanism dynamically weights these objectives based on real-time performance pat- terns:
(0,) = (fx) 8j.- max (0, v/8,.))
(11)
where the similarity measure šie captures the alignment between task j's performance pattern and reference:
(batch)
(batch)
$j. max (||a
j.e
(batch)
j.n
||2
Tef,c
ref,e
(batch)
||2, fxim)
(12)
Our theoretical analysis addresses three fundamental questions: (1) Does RD-PCGrrdd guarantee
stable, conflict-free optimization? (2) R-PLA robust to noise and scaling variations? (3) Does two-stage decoupling preserve feature quality while enabling effective classifier adaptation?
6.2 Proof of Proposition] 1 Gradient Stability and Pareto Improvement
Motivation:
The key challenge in multi-objective LTR is that gradients from different strategies often conflict (e.g., improving tail performance may hurt head performance). RD-PCGrad must resolve these conflicts while ensuring progress toward better overall performance.
Let g: Vo, (0n) E RDD denote the gradient of weighted task k: where = |0 is the total number of classifier parameters.
Lemma (Conflict Resolution Property). For any two conflicting gradients gi, gy with g g) the RD-PCGrad projection:
gj
gi
gj
(13)
lgg
+
Grorm
eliminates the conflict, ensuring (g.)
2 O.
Proof. The key insight is that projection onto the orthogonal complement removes the conflicting component. Computing the inner product after projection:
(g.)
gi
=
gi
g
(14)
gg
Cporm
g: g)
g
(15)
| 11 parm
(g, g g
g; gg
(16)
||g]]] +
6porm
(g. [B)
(17)
6porn
(g. g
Eporm
(18)
g Enorm
Since g; g, < 0 and 6porn > 0. have (g.)
g
0, resolving the conflict.
13

---

Lemma 2 (Descent Direction Preservation). The projection operation preserves the descent property for the objective corresponding to the projected gradient.
Proof. We must show that g remains descent direction for D/p The critical quantity is:
8j gj
gg:
g
g:
g:
(19)
[
eorm
(g, gy 2
lg
(20)
|gg 11 + Cnomn
(g. )2
g
12
(21)
g
[ 2 + 6norm
cOS
2
(gi, 8,) gy
|g
1
(22)
[g
12
Cnrmm
cos
(8i.g)))
llg:l12 sin (g:
(23)
The last inequality holds because conflicting gradients not parallel (sin(g; gj) 0), ensuring descent is preserved.
Main Proof of Proposition 1 After iterative conflict resolution, RD-PCGrad produces conflict-free gradients .g......3.. that are aggregated as:
(g% h&)
grina
(24)
Ek h& Ediv
where h2 {0, 1 are gradient existence masks and @ denotes element-wist multiplication.
Since all pairwise conflicts have been resolved, we have (g.") g" 2 0 for all i + j. For any original
gradient ga, the descent property with respect to the final direction is:
k=1 (g% h&)
gänl (g:)
(25)
k hi Ediv
N
(g: Tgg h;)
(26)
hy Ediv
gg:
(g" h;)
20
(27)
Σ h& + Cdiv
where the first inequality uses the non-negativity of cross-terms after conflict resolution, and the second follows from Lemma This establishes that -gtnal provides descent non-ascent direction for all objectives, constituting Pareto-improving or non-worsening update direction.
6.3 Proof of Proposition 2 Adaptive Weighting Robustness
Motivation: R-PLA must maintain stable task weighting despite variations in performance measure- ment scales and noisy real-time estimates. The cosine similarity and cube root transformation are specifically designed to achieve this robustness.
Part (i) Scale Invariance: Consider performance vectors scaled by factor À > āj. = >aa,. and
årefe = Aagee The cosine similarity becomes:
(Aaj,)) ((aacc,,))
jj.
(28)
Da l1 Aa l12
X2 ((aye) a
(29)
Allaj,ell2 A A//aact,ell
X²(aj,)) "aeef,e
>2llaaj, f [|z] aoot.ell2 8j.
(30)
14

---

Consequently, 3ye max(O, (33,e) = max (0, (/y,e) S3.e, demonstrating perfect scale invari-
ance.
Consider noisy performance vectors aj.e + 71 where 1n,l12 S 6 for small 6 0. The perturbed similarity satisfies:
Part (ii) -Noise Robustness:
(agee + 77) (abet.e nret)
(31)
ji
aaje + 77 2|2 arof. + neell2
(aje) a + (aj.e) net + 7 a + n2 nee
(32)
||aje + my 17]122[[aeff + Thell22
Using the fact that ||a + 7/12 = ||a|2 + 0()) for small 6, and applying first-order perturbation analysis:
((aj,e)
Tee + T
77 a | 0(62)
noisy
8j,o S
(33)
||a a 222 + (6))
||a 1|
Merll2 + 1, |lt,
(34)
aaj. 12 aee,,e ((6)
25 a ee l2 |aet,ell2) + ( 0())
(35)
l22,el2l||aootelll
The cube root transformation mean value theorem:
f(x) max(0, V has derivative f'(x) _ -2/3 for 0. By the
15
(f f(3j,e))
(36)
1f((z) 18500 85,el
(37)
min(
j, s3,e) 2/3 2/33) 00)) 06))
noisy
(38)
This establishes that R-PLA weights have bounded sensitivity to noise, with the cube root transforma- tion providing smoother weight dynamics compared to linear quadratic functions.
6.4 Proof of Proposition Representational Robustness
Two-stage decoupling must ensure that sophisticated MOO operations in Stage do not degrade the high-quality features learned in Stage 1. This requires formal guarantees about gradient isolation and feature preservation.
Motivation:
With encoder parameters 0g frozen during Stage 2, the feature extraction mapping E(:: X' + Rr remains constant. For any feature quality measure R Dr + R (e.g., discriminative power, cluster separability):
Feature Quality Preservation:
Q(E(x;0t) constant
Var X, Vt Stage 2 iterations
(39)
Gradient Isolation Analysis: The Stage 2 loss functions decompose as compositions:
Le (On) (e..,) (F( On.).
(40)
(( (H2 (2; (.v.v) ==E(0:))))
(41)
where E Pr represents the fixed feature vectors. The gradients with respect to classifier parameters
are:
Von, Cx(0H) (x. Von. ((Hty (z: OHe), 2)
(42)
==B((
-E Preames Von. E(1l (z; 0HE yz))
(43)
where Pieatures is the distribution of extracted features and y(z) is the label corresponding to feature
15

---

Crucially, since & does not appear in the optimization variables, we have:
onn La
=0
(44)
08
10=00
This gradient isolation ensures that MOO operations (R-PLA weighting, RD-PCGrad projections) cannot corrupt the feature representation.
Stability Under Complex MOO: The multi-objective parameter updates in Stage 2 follow:
H 8(4) (*)
1)
Sunal
(45)
((++1 1) 0 g (architectural constraint)
(46)
E
where grial
IRl00l is computed via the sophisticated RD-PCGrad procedure but contains components corresponding to encoder parameters.
The architectural decoupling provides an invariant: regardless of the complexity of MOO operations (dynamic weighting, gradient conflicts, iterative projections), the feature space (E(x; 0%)) a E X} remains unchanged throughout Stage 2. This guarantees representational robustness while enabling effective classifier adaptation through MOO.
6.5 Convergence and Complexity Analysis
Convergence Guarantee: Under standard assumptions (Lipschitz continuous gradients, bounded
feasible region), TS-MOR converges to a locally Pareto-optimal solution. The key insight is that RD-PCGrad ensures:
BR. (OH)) günal S
- gfina
(47)
for some constant > providing monotonic improvement in the weighted multi-objective function.
Computational Efficiency: Stage requires O(N2D) operations per iteration, where is the number of tasks and D 10 is the classifier parameter count. This compares favorably to end- to-end MOO requiring O(N2(D + DE)) with encoder dimension Dr > D. yielding substantial computational savings through architectural decoupling.
7 Algorithm Description
This section provides detailed algorithmic descriptions of the TS-MOF framework, with particular em- phasis on the multi-objective fine-tuning process and the two core innovations: Refined Performance Level Agreement (R-PLA) and Robust Deterministic Projective Conflict Gradient (RD-PCGrad) Algorithm| B presents the complete two-stage training procedure TS-MOF, highlighting the strategic decoupling between feature learning and classifier adaptation.
16

---

Algorithm TS-MOF: Two-Stage Multi-Objective Fine-tuning
Require: Long-tailed dataset Dirain Den,, LTR tasks T (Ti,..., Tv) Pre-training epochs Esi.
fine-tuning epochs Esz
Ensure: Trained model with balanced performance across head, medium, and tail classes
1: // Stage 1: Generic Feature Pre-training
2: Initialize encoder E(;;0E) and classifier Hs1(+ (H..)
3: for = to Es do
4: for each batch (#t; ys) Dirain do
5: Z; - (xx 8g) (Extract features]
6:
(Z,; 8H [Classification)
7: LoE + CrossEntropp (Di, yi) (Standard loss)
8: Update Or, O1te via SGD with Lce
9: end for
10: Evaluate Vaal and save best F
11: end for
// Stage 2: Multi-Objective Classifier Fine-tuning
13: Freeze encoder parameters: 0g < 0p (fixed)
14: Initialize multi-head classifiers (H&(-; nr ) for tasks T
Initialize R-PLA weighting mechanism and RD-PCGrad optimizer
16: Call BEW.IIOBEEEEVEEEEENUNUNCCCC
02). {H Duain Es2)
// Inference with EOSS
18: Train EOSS weights {wk.e} based on validation performance
19: return Model with frozen 0f and fine-tuned (0H,, Jk=1
Algorithm detaals the core Stage process, emphasizing how R-PLA and RD-PCGrad work together to achieve effective multi-objective optimization.
17

---

Algorithm 2 Multi-Objective Fine-tuning with R-PLA and RD-PCGrad
Require:
Frozen encoder ()) classifier heads { [= Training data Drrain fine-tuning
epochs Es2, Task loss functions {Cx k== number of classes C
Ensure: Fine-tuned classifier parameters {6 l==1
1: Initialize SGD optimizer for 0H {0H,..... 0Hx)
2: Initialize R-PLA weights {Bk,e] 1.V to uniform values
3: Initialize RD-PCGrad conflict resolution mechanism
4: for c= to Es2 do
5: for each batch (X ~ Dinin do
6: // Feature Extraction (Frozen)
7: 2 0x:;0%) (Extract fixed features)
8: // Multi-Heead Forward Pass
9:
logits_ dict {}. losses dict 0 for k to N do
10:
oogits_dict]oe + H₂ 0H.) losses_dicte & L2 (logits _ict[/]]_ Y)
12:
end for
14:
R-PPL Dynamic Weighting
(. IN R-PLA--UPAATH(Ooits dict, C,
16:
I/ Apply R-PLA Weights
17:
for k 1 to do
18:
C + BE.c losses [ices_dict[A]
19:
end for I/ RD-PCGrad Conflict Resolution
20:
21:
ganal f- DOD-PGRAAD((C,).. 0H)
22:
// Parameter Update
23:
Hи 6 AH 7iginnll
24:
end for
25:
Apply cosine annealing to learning rate
26:
end for
27: return [0 1
Algorithm 3 presents the detailed R-PLA mechanism that adaptively weights tasks based on their real-time performance patterns and similarity to reference performance profile.
18

---

Algorithm 3 Refined Performance Level Agreement (R-PLA) Weighting
Require: Logits dictionary logits_dich _iclsss__tttteee (logits ) True labels Y, number of classes C, Numer-
ical stability constants sim 10
Ensure: Updated task weights {.k.e
1: // Compute Per-Class Performance for Each Task
2: performance vectors t [] (Initialize empty list]
3: for k
N do
4: preds, +- arg max(logits dim = 1) (Predictions for task k) [batch] zeros(C) (Per-class accuracy vector)
5: ag.e
6: for co 0 to C do
7: class_mask < (Y = c) (Mask for class c)
8:
count class mask.sum)
9:
if total_ coull_coutt> > 0 then
correct_count < (preds, |class_maatee = c).mmm))
(batch) [c] + correct crrrcct__cunttttot total coua__ccuut kkcc
11:
12: else
(batch) [c] + (No samples for this class} ak.c
14: end if
15:
end for
(hatch)
aefformance_vectorscoom
a
end for 18: // Set Reference Performance (Last Task)
19: ef,e performance, vectors[-1]
(batch)
cerformance__vcctors|--1
20: // Compute Cosine Similarity and Weights
21: for
N do (batch)
22:
numerator +- ag.c a
(batch)
(Dot product)
23:
denominator < ||ak, (batch)
ll2 (batch)
||2
Sk.c max (ancooomminatrrraau
(Cosine similarity)
25: I/ Cube Root Transformation with Non-negative Clipping
26: Bk.e f- max(0, 3/3k,e) (Robust weighting)
27: end for
28: return (Bx.e)
Algorithm detaails Adetils the RD-PCGrad mechanism that resolves gradient conflicts through deterministic projections while maintaining numerical stability and reproducibility.
19

---

Algorithm 4 Robust Deterministic Projective Conflict Gradient (RD-PCGrad)
Require: Weighted losses {C parameters 0
Require: Stability constants Cnemm = 10 Ediv 10-6
Ensure: Conflict-resolved final gradient glinal
1: // Compute Individual Task Gradients
2: gradients <- []. masks <- []
3: for k 1 N do
4: gk <- V C. (Gradient for task k)
5: hgg 1 [g + (Gradient existence mask)
6: gradients. catpttsppppeddd... masks maakss.append(h., (h:)
7: end for
8: // Deterministic Iterative Conflict Resolution
9: projected graedd <- copy(gradients) (Deep copy for modification)
10: for i to N do
for joriti N do
(Fixed pairwise order for determinism/ conflict <- (g,) g < 0 [Check for conflict)
both_active +- (h; @ bayy (Both gradients contribute) if conflict and _ctive then
13:
// Projecct gradient away from gradient j dot_product + (g/)
16:
17:
norm_squared < g
norm
projection Jol_ product
norm squared Bj
19: g. < projection (Remove conflicting component)
20: end if
21: end for
22: end for
23: // Numerically Stable Gradient Aggregation
weighted grantd < 25: for k == to N do
weighted lecllidddgrdddapppp apadd h&) (Apply existence mask)
27: end for
numerator
weighted [gaghtss_grads(k)] (Sum of masked gradients)
29: denominator [Element-wise division)
h& + Ediv [Normalization with stability)
giinal denominator
Bumerator
31: return giiaal
7.1 Key Algorithmic Innovations
Addressing the complexities of long-tailed recognition, TS-MOF incorporates several key algorithmic innovations. The Strategic Decoupling (Algorithm [1 tackles the challenge of simultaneously learning features and balancing classifiers by freezing the Stage encoder (0% and applying complex optimization only to the classifier heads. To navigate the confficting goals of multiple LTR strategies, R-PLA provides Adaptive Task Weighting (Algorithm 3 that dynamically highlights strategies contributing most desired performance patterns, while RD-PCGrad ensures Deterministic Conffict Resolution (Algorithm 4 stabilizing the multi-objective training process against negative transfer. This principled approach, coupled with Efficient Implementation (O(N2 D + 2C²) per iteration in Stage 2), allows TS-MOF to robustly leverage complementary strengths of diverse LTR strategies, overcoming the limitations of single methods. Furthermore, our implementation simplifies the interface for integrating different strategies, practically reducing the cost of improving long-tailed learning performance using multi-objective optimization and enhancing stability.
Related Work: Multi-Objective Optimization in Machine Learning
Multi-Objective Optimization (MOO) is field concerned with mathematical optimization problems involving more than one objective function to minimized or maximized simultaneously. In many real-world scenarios and increasingly in Machine Learning (ML), multiple objectives often conflict,
20

---

meaning improving one objective may degrade another. The goal of MOO is typically to find Pareto optimal solutions, where no objective can be improved without worsening least one other.
In Machine Learning, MOO principles are naturally applied to problems involving competing goals. A prominent area is Multi-Task Learning (MTL)) [? 351, where a single model is trained to perform multiple tasks simultancously. Optimizing a simple weighted sum of task losses can be suboptimal, especially if tasks have conflicting gradients, leading to negative transfer [31] This has spurred the development of gradient- baaed MOO methods designed to find update directions that improve all tasks or, at worst, do not worsen any, moving towards the Pareto front.
Notable gradient- MOO methods include the Multiple-Gratee Gradient Descent Algorithm (MGDA) 8 and Projective Conflict Gradient (PCGrad) [3. MGDA seeks to find descent direction that minimizes the maximum directional derivative among all objectives, aiming for a common descent direction within the convex hull of the task gradients. PCGrad, on the other hand, directly addresses conflicting gradients by projecting the gradient of one task onto the normal plane of another if their dot product negative. This iterative projection process aims remove conflicting components, resulting in a set of modified gradients whose sum (or average) constitutes a Pareto-improving or non-worsening direction. These methods have demonstrated effectiveness in stabilizing training and improving performance in MTL scenarios.
Our approach differs significantly from existing MOO applications in ML and prior MOO- related work in LTR, driven by a specific, deeper motivation for tackling the LTR problem. While standard MOO methods like PCGrad [31] and MGDA [8]
purpooe tools for finding Pareto solutions in arbitrary multi-objective problems (like standard MTL), our TS-MOF framework explicitly employs MOO not a generic optimizer for abstract tasks, but principled mechanism for synergistically fusing diverse, specialized LTR strategies during targeted fine-tuning stage. The objectives in our Stage 2 MOO are not just arbitrary task losses; they
are losses derived from methods (like LDAM [3] BS (25 KPS [14. BCL (34. etc.) specifically designed to address different aspects of the LTR imbalance.
The deep motivation behind using MOO in TS-MOF is to move beyond the limitations of any single LTR strategy and overcome the seesaw dilemma by finding optimal combination that leverages the complementary strengths of multiple strategies. R-PLA uses performance-base based adaptive weighting dynamically prioritize strategies based their real-time contribution to the desired performance pattern, while RD-PCGrad provides a robust and deterministic way to reconcile the potentially conflicting gradient signals arising from simultaneously optimizing towards these different LTR-specific goals. We apply this advanced MOO specifically to the classifier heads after decoupling feature learning, ensuring the optimization focuses effectively on classification balance without corrupting the feature representation. Thus, our MOO is not just a mathematical technique applied; it the core engine enabling the principled, robust, and effective fusion of heterogeneous LTR knowledge to achieve superior and balanced recognition performance.
More Empirical Results
This appendix provides additional empirical results and analyses further demonstrate the effective- ness and underlying mechanisms of the TS-MOF framework.
9.1 Results on CIFAR-100-LT with Various Strategy Combinations
We also evaluated the evolutionary results of various combinations of strategies in the second stage of TSS TOF. Table 3hows the performance across different Imbalance Ratios (IR=10, 50, 100) on the CIFAR- 100-LT dataset when using TS- TS-MOF with different sets of constituent LTR strategies. TS- MOF achieved excellent results in the combination of the KPS + BCL + LOS strategy. as highlighted in the table, demonstrating its ability to bring obvious general improvements across class groups and imbalance settings.
9.2 T-SNE Analysis
Figure shows the t-SNE analysis the feature representations from different models. We compare the representations learned by models using BCL or KPS independently, their simple weighted fusion
21

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Table Accuracy (%) on CIFAR-100-LT dataset with TS MS-MOF using various strategy combinations in Stage 2 fine-tuning Results are reported for Head, Medium, Tail classes, and overall (All) accuracy across different Imbalance Ratios (IR).
IR=100
Method
Head
Medium
Tail
All
Head
Medium
Tail
All
Head
Medium
Tail
All
DE-NOFIE-LDDAM-DWW-LOO
74.90
59.71
70.19
75.95
51.05
42.61
59,74
78.23
51.34
35.
55.91
1S-MOFFCEEKPSS-LOS)
74.22
62.81
70.68
51.63
42.06
59.99
78.46
49.14
39.57
56.53
TS-MOFICE+RCI-LLO,
75.04
58.26
69.84
76.07
50.49
42.11
59.47
78.94
49.84
34,87
55.57
13-MOFICE+CE-DRW--L09
74.32
59.71
69.79
75.88
50.54
42.33
59.45
78.77
49.94
34,63
55.44
TS-MOFiCE-BB54L05)
74.49
59.55
69.86
75.76
51.61
42.17
59.81
78.80
51.83
35.
56.25
TS- ----OOSSSSKSSSSLOSS
73.99
61.42
70.09
75.61
53.17
42.00
60.36
77.49
51.54
38.27
56.64
TS DEI-IBSSCCE-DW-LLOS
74.33
60.00
69.89
76.05
51.37
42.06
59.81
79.46
51.43
34,63
56.20
MOF(BS+LDAM DRW+LOS)
TS-MOFIKPS-BCLLA
74.75
62.03
70.81
75.24
50.76
47.56
60.22
79.00
49.29
39.
56.89
TS DODIKPS+CE-DRW+LLE
74.52
61.68
70.54
75.41
51.12
45.22
60.02
78.37
50.26
38.17
56.47
DE-MOFIKSS-.DDAM.DVV-L.0.
73.77
61.52
69.97
73.02
51.41
46.89
59.46
75.06
40.87
55.89
(5-NOOFSHIKK-BS--LOO)
74.55
59.71
69.95
76.22
51.39
42.11
59.90
79.03
51.77
34.77
56.21
1S-MOFISHIKE-BBLL-LO0)
74.67
59.45
69.95
50.73
42.28
59.63
79.17
49.66
34.73
58.51
SS-MOrSSHIKE-CE-DRWALL
74.57
59.45
69.88
76.27
49.95
42.11
59.33
78.91
49.37
35.20
55.46
JS:MOFSHIKE-LDAAMDRYW..
74.99
59.35
76.15
51.15
42.22
59.79
78:40
50.74
34.73
55.62
(BCL+KPS), and proposed fusion method TS-MOF Compared with before fusion, the TS-MOF method shows a significantly increased separability of clusters in almost all categories in the feature space, indicating that our multi-objective fusion approach learns a more discriminative representation despite freezing the backbone in Stage 2. This suggests the classifier heads adapt in a way that better separates classes in the existing feature space.
Figure T-SNE comparative analysis of feature representations. We compare models trained on CIFAR-100-LT using BCL independently, KPS independently, a simple weighted fusion of BCL+KPS, and our proposed TS-MOF fusion method. Each point represents a feature vector from the test set, colored by its true class. Better separation indicates more discriminative feature space for classification.
9.3 Confusion Matrix Analysis
Figure shows a comparison of the predictive performance between individual strategies, simple fusion, and TS-MOF method through confusion matrices. The confusion matrices normalized by row (true label count) to show per-class accuracy patterns. From the figure, it can be seen that KPS pays more attention to some tail classes (higher diagonal values for later classes), while BCL focuses more on the head classes (higher diagonal values for earlier classes). Simple fusion may not effectively resolve conflicts and can potentially damage the performance TS-MOF effectively combines the advantages of individual strategies and exhibits good performance across head, medium, and tail classes, indicated by the higher and more uniform diagonal values compared to baselines and simple fusion.
classes. Our method
22

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XPS
BC 20. Epoch (Nomallzed)
TC PS-0FF Epoch (hormalized)
Eaoch (Noomalzee))
Epoch 20 (kormsiized
P.
Labd
Figure 5: Comparative analysis of confusion matrices on CIFAR-I00) (IR=100). We compare the predictive performance of models using BCL independently. KPS independently, simple weighted fusion of BCL+KPS, and our proposed TS-MOF fusion method. Rows represent labels, columns represent predicted labels. Diagonal elements indicate correct classifications (accuracy per class).
Limitations
Despite its advancements, TS-MOF has certain limitations. Its performance is inherently tied to the quality of the Stage pre-trained features, as the encoder is frozen during fine-tuning. The framework also requires pre-selecting the specific set of LTR strategies to be included in the multi- objective optimization, which might require empirical tuning for optimal performance on new datasets. Furthermore, while designed for robustness, the framework still involves several hyperparameters that may need careful configuration.
11 Broader Impacts
TS-MOF aims to improve balanced recognition long-tailed datasets, which are common in real- world applications. By enhancing tail class performance, our method can contribute to greater fairness and equity in ML systems by providing better representation for under-represented categories, potentially benefiting applications in healthcare, social sciences, and specialized domains. Making better use of rare data can also increase the utility of ML in resource-constrained settings. However, like any advanced recognition technology, there is potential for misuse, such as in surveillance applications, emphasizing the need for responsible development and deployment.
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Reviews:
[{"review_id": "FQBoWUOgZR", "reviewer": "Reviewer_LH8y", "summary": "This paper proposes TS-MOF, a two-stage multi-objective fine-tuning framework for long-tailed recognition. The approach integrates multiple loss functions via a multi-head architecture and resolves gradient conflicts using a modified version of PCGrad (RD-PCGrad), while dynamically reweighting tasks based on a refined performance-level agreement mechanism (R-PLA). Experiments on several long-tailed benchmarks (CIFAR10, ImageNet- iNaturalist) demonstrate consistent improvements over competitive baselines.", "questions": "Please see the weaknesses.", "limitations": "The authors have adequately addressed the limitations, and no potential negative impact is found.", "rating": 6, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "### **Strengths**\n1. The proposed two-stage framework is well-motivated and aligns with prior findings that feature decoupling improve long-tailed generalization;\n2. RD-PCGrad introduces a deterministic and task-aware projection strategy, offering a practical solution to inter-task gradient interference;\n3. R-PLA dynamically adjusts task importance in a label- and batch-aware manner, enabling adaptive optimization focus across head and tail classes;\n4. The method shows consistent gains across CIFAR100-LT, ImageNet-LT, and iNaturalist, with a relatively lightweight fine-tuning phase.\n\n### **Weaknesses**\n1. While Prop. 1 states that the final gradient $g_{final}$ is Pareto-improving or non-worsening, the paper does not analyze how far this direction can deviate from the true Pareto front in the proof. In complex objective landscapes, the projection step may inadvertently suppress beneficial gradient components, and a discussion of such trade-offs is missing;\n2. The use of the cube root in Eq. (5) is not well justified. Based on the proof of Prop. 2, any concave, increasing function could preserve the robustness properties. The choice of the cube root lacks explanation on whether it confers specific benefits (e.g., smoother gradients or empirical stability);\n3. Although the proposed method is evaluated in a two-stage training pipeline, the core techniques (RD-PCGrad and R-PLA) are not inherently limited to such a setup. Gradient conflict resolution might be even more critical in single-stage end-to-end training. The paper would benefit from a more apparent justification for the choice of a two-stage framework or a discussion of broader applicability.", "was_revised": true, "final_justification": "The concerns are well-addressed. The motivation is well-considered, balancing theoretical justification with empirical support and practical utility. Therefore, I suggest accepting this paper.", "rebuttal": "### **Response to Reviewer LH8y**\n\nThank you, Reviewer LH8y, for your recognition of our work and for your constructive feedback. We address your questions point by point below.\n\n\n\n**1. On the Deviation of RD-PCGrad from the True Pareto Front:**\n\nThank you for this insightful question. Our method ensures a **Pareto-improving direction** for stability, which involves a deliberate trade-off. We formally analyze this below.\n\n##### **Formal Analysis of the Trade-off**\n\nGiven two conflicting gradients $g_A$ and $g_B$ (i.e., $g_A^T g_B < 0$), RD-PCGrad modifies $g_A$ by removing its conflicting component $p_{A \\to B} = \\text{proj}_{g_B}(g_A)$.\n\n**a. Suppressed Benefit & Cost:**\nThe suppressed component $p_{A \\to B}$ is indeed beneficial for minimizing its own loss $L_A$. The magnitude of this benefit is proportional to:\n$$(g_A^T g_B)^2 / \\|g_B\\|^2 \\ge 0$$\nThis is the cost of our projection. However, this component is equally harmful to task $L_B$. By removing it, we prioritize the **\"Do No Harm\"** principle, which is essential for preventing the \"seesaw effect\" in LTR and ensuring stable, balanced progress.\n\n**b. Deviation Analysis:**\nThe deviation from task A's optimal direction is proportional to the relative magnitude of the suppressed component, which can be expressed as:\n$$\\rho = \\|p_{A \\to B}\\| / \\|g_A\\| = |\\cos(\\theta_{AB})|$$\nThis shows the deviation is directly tied to the conflict angle $\\theta_{AB}$ between the gradients. In LTR, where conflicts between head- and tail-focused strategies are inherent, this deviation is a necessary compromise. Our goal is a **balanced solution**, not the extreme optimization of any single strategy. Thus, this shift towards balance is a feature, not a bug, for solving the long-tail problem.\n\n**2. On the Rationale for Using a Cube Root in Equation (5):**\n\nThank you for this insightful question. We acknowledge that, in theory, any monotonically increasing concave function can ensure robustness. We chose the **cube root** based on the specific advantages it offers in terms of **weight dynamics** and **gradient smoothness**, which collectively enhance the model's **empirical stability**.\n\nFirst, compared to other concave functions like the square root, the cube root provides a **better trade-off in sensitivity**. It is more sensitive in the low-similarity region (when strategy performance differs greatly), providing a stronger \"incentive\" signal for underperforming strategies to catch up. Concurrently, it is less sensitive in the high-similarity region (when strategies perform consistently), providing greater stability and preventing unnecessary adjustments to already well-performing strategies.\n\nSecond, the cube root leads to **smoother gradient flow**. Since similarity is a function of model parameters, the weighting function introduces an additional meta-optimization gradient during backpropagation. The derivative properties of the cube root make this gradient flow smoother than that of other concave functions (like the square root), especially in the extreme case where similarity approaches zero. This effectively reduces the risk of exploding gradients.\n\nWe will also add an ablation study in the appendix comparing different weighting functions to empirically support our choice.\n\n---\n\n**3. On the Motivation for the Two-Stage Framework:**\n\nThank you for this insightful question. We chose the two-stage framework deliberately because it offers significant advantages for our MOO components when tackling the long-tail problem:\n\n1.  **Ultimate Efficiency**: Compared to end-to-end training, our method only applies multi-objective optimization to the lightweight classifier heads during a short final fine-tuning stage, **drastically reducing computational cost and time**.\n2.  **Feature Protection**: By freezing the backbone, we **protect the valuable general-purpose features** learned in the first stage from being corrupted by strong re-balancing signals, thus preserving performance on head and medium classes.\n3.  **Training Stability**: Confining the complex gradient conflict resolution to the low-parameter classifier heads **greatly reduces the risk of training instability**, making the optimization process more robust and controllable.\n4.  **Plug-and-Play Modularity**: Our framework allows researchers to **easily \"plug and play\"** any existing long-tail strategy without complex end-to-end adaptation, significantly improving the method's ease of use and extensibility.\n\nTherefore, the combination of the two-stage framework and our proposed strategies enables us to achieve an **efficient, stable, and low-cost** fusion of multiple long-tail strategies."}, {"review_id": "zQdCgiL2wm", "reviewer": "Reviewer_BSGz", "summary": "This paper presents TS-MOF (Two-Stage Multi-Objective Fine-tuning), a novel decoupled-training expert-based framework for long-tailed recognition. TS-MOF decouples training into two stages: (1) Feature Learning: standard supervised training to learn good representations ; (2) Multi-head Classification Learning: freeze the learned representation and spawn multiple specialized “expert” heads, each fine-tuned with a different long-tailed method (e.g., BCL [26], LOS [20]). Key contributions in the second stage include: (a) Refined Performance Level Agreement (R-PLA): Dynamically weights each expert’s task loss based on per-class performance on the training set ; (b) RD-PCGrad: a robust deterministic variant of PCGrad [23] that resolves gradient conflicts between task objectives via gradient modulation. At inference time, a dynamic expert aggregation module weights and combines expert predictions per class, using performance on a validation set. Experiments on standard long-tailed benchmarks show that TS-MOF achieves higher balanced accuracy than single-objective baselines, with only a brief fine-tuning stage.", "questions": "- Q1 - RD-PCGrad vs. PCGrad [23]: Why choose a fixed task ordering rather than PCGrad’s random ordering? What evidence shows that RD-PCGrad changes improve robustness?\n\n- Q2 - Validation-Set requirement: In the absense of a balanced validation set, can TS-MOF work with an imbalanced holdout set matching training distribution? If so, have you tested sensitivity to validation set imbalance?\n\n- Q3 - R-PLA computation: In Eq. 4, how do you estimate early performance vectors (index j) before computing cosine similarity with the final vector (index N) within the same epoch?\n\n- Q4 - Expert-head architectures: What is the architecture of each classification head (linear layer, MLP, etc.)? How does this compare to expert-based models such as RIDE [22]?\n\n- Q5 - Hyperparameter sensitivity: Which TS-MOF hyperparameters require careful tuning (as indicated in Section 10, L600–601)?\n\nWhile the TS-MOF framework presents a novel take into decoupled-learning and expert-based frameworks, the absence of key ablations, the impractical balanced-validation-set assumption, unclear task-selection and ordering procedures, and missing comparisons to relevant expert-based methods leave important questions unanswered. A focused rebuttal that supplies the above ablations/comparisons and clearly quantifies practical costs will significantly strengthen confidence in TS-MOF’s utility—and could increase my score. Conversely, if these gaps remain unaddressed, my score will stay at 3.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n\n- S1 - Novel decoupled-training framework: introduces a two-stage learning approach that leverages multiple existing long-tailed approaches in parallel experts, with training-time adaptive task weighting and inference-time aggregation.\n\n- S2 - Theoretical insights: Provides analysis on gradient stability, Pareto improvements, convergence behaviour, and robustness of R-PLA and RD-PCGrad.\n\n**Weaknesses**\n\n- W1 - Reliance on validation set: Requires a (probably) balanced holdout set to select Stage 1 weights (L160–161) and to compute per-class aggregation weights (Eq. 8–9). In extreme imbalance scenarios, such a validation set may be impractical/unfeasible, and it isn't a common assumption in the LTR literature.\n\n- W2 - Missing component ablations: Lacks experiments isolating the contributions of R-PLA, RD-PCGrad, and the expert aggregation module. \n\n- W3 - Comparison to expert-based SOTAs: Does not discuss or compare with recent skill-diverse multi-expert models (e.g., BalPoE [A], SADE [B], MDCS [C]) that specialize experts with multiple objectives end-to-end.\n\n- W4 - Task Selection Strategy: While Appendix shows a large task combination search, the paper picks the “best” combination based on test-set performance. A more scalable, principled selection mechanism is missing from the paper.\n\n- W5 - Deterministic task ordering: The method for defining the fixed task order in RD-PCGrad is not specified.\n\n- W6 - Computational Overhead: No empirical comparison of Stage 2’s compute or memory cost versus vanilla multi-task optimization.\n\nAdditional references:\n\n[A] Sanchez Aimar, Emanuel, et al. Balanced Product of Calibrated Experts for Long-Tailed Recognition. CVPR 2023.\n\n[B] Zhang, Yifan, et al. Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition. NeurIPS 2022.\n\n[C] Zhao, Qihao, et al. MDCS: More Diverse Experts with Consistency Self-Distillation for Long-Tailed Recognition. ICCV 2023.", "was_revised": true, "final_justification": "Despite my initial concerns, the authors have clarified several key aspects: their method does not strictly depend on a balanced holdout dataset; each contribution has a relevant role per the ablation study; it outperforms recent expert-based baselines in expanded comparisons; it shows low computational overhead; it exhibits low hyperparameter sensitivity; and they’ve explained their task selection and ordering decisions. Overall, these improvements convince me to raise my score, leaning toward acceptance.", "rebuttal": "### **Response to Reviewer BSGz**\n\nThank you for your detailed and critical feedback. We have done our best to supplement new experiments to substantially improve the paper.\n\n**W1 & Q2. On the Dependency on a Balanced Validation Set:**\n\nThank you for your recognition of our experimental section. We would like to clarify that our method does not strictly depend on a balanced validation set.  For the extreme case you are concerned about, such as when a validation set is unavailable, we can also sample from the training set. To explicitly verify this, we have added a new experiment on CIFAR100-LT where we construct an imbalanced validation set by sampling from the training set. The results (shown below) indicate that although there is a slight drop in performance compared to using the original (more balanced) validation set, **TS-MOF still significantly outperforms the SOTA baseline (LOS), demonstrating its applicability and robustness.**\n\n| **Method** | **IR=10** | | | | **IR=50** | | | | **IR=100** | | | |\n| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| | **Head** | **Medium** | **Tail** | **All** | **Head** | **Medium** | **Tail** | **All** | **Head** | **Medium** | **Tail** | **All** |\n| LOS | 71.9 | 62.3 | -- | 69.0 | 72.4 | 51.4 | 40.2 | 58.0 | 70.3 | 52.3 | 36.6 | 53.9 |\n| TS-MOF(from Train Set) | 74.1 | 61.8 | -- | 69.6 | 74.8 | 50.2 | 47.1 | 59.4 | 78.1 | 49.4 | 38.7 | 56.2 |\n| TS-MOF(from Val Set) | 74.7 | 62.0 | -- | **70.8** | 75.2 | 50.7 | 47.5 | **60.2** | 79.0 | 49.2 | 39.9 | **56.8** |\n\n*Table 1: Comparison of model performance on CIFAR100-LT benchmarks using an imbalanced validation set (from Train Set) and the original validation set.*\n\n**W2. On the Supplementation of Ablation Studies:**\n\nThank you for your suggestion to supplement the experimental section. We have conducted separate ablation experiments for R-PLA, RD-PCGrad, and the Expert Aggregation Module (EOSS). The following are the results.\n\n| R-PLA | RD-PCGrad | EOSS | IR=10 | | | | IR=50 | | | | IR=100 | | | |\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| | | | **Head** | **Medium** | **Tail** | **All** | **Head** | **Medium** | **Tail** | **All** | **Head** | **Medium** | **Tail** | **All** |\n| ✗ | ✗ | ✗ | 72.3 | 47.3 | -- | 64.6 | 74.2 | 43.5 | 22.9 | 52.4 | 76.5 | 46.9 | 18.1 | 48.6 |\n| ✗ | ✗ | ✓ | 74.7 | 60.8 | -- | 70.4 | 75.3 | 50.4 | 44.5 | 59.6 | 78.2 | 49.1 | 38.6 | 56.1 |\n| ✓ | ✓ | ✗ | 74.6 | 61.5 | -- | 70.5 | 75.2 | 50.5 | 45.8 | 59.7 | 78.8 | 49.0 | 38.9 | 56.4 |\n| ✗ | ✓ | ✓ | 74.3 | 60.3 | -- | 69.9 | 75.5 | 51.0 | 43.5 | 59.7 | 78.4 | 49.8 | 36.9 | 56.0 |\n| ✓ | ✗ | ✓ | 74.6 | 60.1 | -- | 70.1 | 75.3 | 50.7 | 43.9 | 59.6 | 78.3 | 49.2 | 37.4 | 55.9 |\n| ✓ | ✓ | ✓ | 74.7 | 62.0 | -- | **70.8** | 75.2 | 50.7 | 47.5 | **60.2** | 79.0 | 49.2 | 39.9 | **56.8** |\n\n*Table 2: Ablation experiments of TS-MOF's three modules with IR=10/50/100 on CIFAR100-LT benchmarks.*\n\n**W3. Comparison with Multi-Expert SOTAs:**\n\nThank you for your valuable suggestion. We have done our best to supplement several multi-expert baseline methods within the limited time, such as BalPoE, SADE, and MDCS. All comparisons will be cited and added to the main text. As seen from the table below, our TS-MOF method still demonstrates high performance under the same settings. \n\n| **Method** | **IR=10** | | | | **IR=50** | | | | **IR=100** | | | |\n| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| | **Head** | **Medium** | **Tail** | **All** | **Head** | **Medium** | **Tail** | **All** | **Head** | **Medium** | **Tail** | **All** |\n| BalPoE | 71.2 | 55.6 | -- | 66.4 | 74.4 | 45.7 | 30.4 | 54.7 | 77.8 | 49.0 | 22.8 | 51.2 |\n| SADE | 72.3 | 49.5 | -- | 64.6 | 75.0 | 42.2 | 10.5 | 49.9 | 78.7 | 49.0 | 13.2 | 48.7 |\n| MDCS | 71.9 | 51.5 | -- | 65.6 | 75.9 | 44.5 | 34.3 | 55.5 | 78.7 | 47.9 | 28.8 | 53.0 |\n| TS-MOF | 74.7 | 62.0 | -- | **70.8** | 75.2 | 50.7 | 47.5 | **60.2** | 79.0 | 49.2 | 39.9 | **56.8** |\n\n*Table 3: Comparison for CIFAR100-LT Benchmarks, showcasing the model performance of three multi-expert methods: BalPoE, SADE, and MDCS.*\n\n**W4. On the Task Selection Mechanism:**\n\nThank you for your question. The appendix aims to demonstrate that our framework can achieve performance improvements through rapid multi-objective fine-tuning across different strategy combinations. Our goal is not to perform a search for a single SOTA performance on the test set, but rather to showcase the general advantage of performance enhancement brought by the two-stage learning + multi-objective fine-tuning based on R-PLA and RD-PCGrad.\n\nTo demonstrate this general advantage, we avoided hyperparameter tuning as much as possible. All hyperparameters for the individual strategies themselves were kept identical to their settings in the original papers and public code. The hyperparameters specific to our method (such as those for the Stage 1 representation learning, R-PLA, and RD-PCGrad) were kept consistent across different strategy groups. We have also supplemented additional experiments on these hyperparameters, which you can refer to in Table 5.\n\n**W5 & Q1. Deterministic Task Ordering in RD-PCGrad:**\n\nIn our implementation, the task order is determined by the sequence in which the strategies are passed. This deterministic approach ensures better reproducibility of our experimental results. We have also supplemented a performance comparison between RD-PCGrad and other potential multi-objective methods to illustrate its advantages; please see W6 for details due to space constraints.\n\n**W6. On Computational Overhead:**\n\nCompared to end-to-end multi-expert learning or multi-objective optimization methods, we only fine-tune for the last 20 epochs and only update the classifier head parameters. This reduces computational costs in real-world applications. Additionally, as per your suggestion, we have compared our method with other potential multi-objective approaches, with the results shown below. Macs represents the computational cost (in millions of operations), Time is the training time (in minutes), $\\gamma_1$ is the ratio of total trained parameters, and $\\gamma_2$ is the ratio of training time. The results indicate that the computational complexity and time of TS-MOF are roughly equivalent to ordinary methods, while delivering more efficient and accurate performance.\n\n| **Modules** | **Variant Method** | **macs/M** | **Time/min** | $\\gamma_1$ | $\\gamma_2$ | $\\gamma_1/\\gamma_2$ | **Accuracy(%)** |\n| :--- | :--- | :---: | :---: | :---: | :---: | :---: | :---: |\n| **R-PLA** | entropy | 3.540 | 8.45 | 1.000 | 1.001 | 0.999 | 56.3 |\n| | confidence | 3.540 | 8.78 | 1.000 | 0.964 | 1.038 | 53.5 |\n| | uniform | 3.540 | 8.44 | 1.000 | 1.002 | 0.998 | 56.3 |\n| | pla(ours) | 3.540 | 8.46 | 1.000 | 1.000 | 1.000 | **56.8** |\n| **RD-PCGrad** | mgda | 3.540 | 8.36 | 1.000 | 0.906 | 1.104 | 56.4 |\n| | gradnorm | 3.540 | 8.76 | 1.000 | 0.864 | 1.157 | 55.2 |\n| | uncertainty | 3.540 | 7.46 | 1.000 | 1.015 | 0.985 | 56.3 |\n| | pcg(ours) | 3.540 | 7.57 | 1.000 | 1.000 | 1.000 | **56.8** |\n\n*Table 4: Comparison of computational cost and model performance between TS-MOF and conventional multi-task optimization in the second stage.*\n\n**Q3. Explanation of Eq. 4:**\n\nThank you for this sharp question. We clarify the R-PLA computation process here: for the **same training batch**, we first compute the performance vectors $\\{\\mathbf{a}_1, ..., \\mathbf{a}_N\\}$ for **all N tasks** through a single forward pass. **After all vectors are computed**, we then designate the last vector $\\mathbf{a}_N$ as the reference and compute the cosine similarity of the other vectors $\\mathbf{a}_j$ with it to determine the weights. Therefore, all performance information is available when calculating the weights, and there is no temporal conflict.\n\n**Q4. On Classifier Head Architecture:**\n\nIn the main text, our model and classifier head architectures are consistent with prior works (e.g., LOS). For broader scenarios, such as when users wish to extend to more strategy combinations for rapid fine-tuning, the choice of model architecture and hyperparameters can be guided by our response in W4. We will add more detail on this in the revised manuscript.\n\n**Q5. On Introduced Hyperparameters:**\n\nThe main discussion on hyperparameters is referenced in W4. Additionally, we have done our best to supplement discussions and experiments on other hyperparameters. We hope this answers your questions.\n\n| **Modules** | **Parameter Name** | **Parameter Value** | **Many** | **Medium** | **Few** | **Accuracy(%)** |\n| :--- | :--- | :---: | :---: | :---: | :---: | :---: |\n| **R-PLA** | pla\\_beta\\_clip\\_min | 0.1 | 78.20 | 49.74 | 37.80 | 56.12 |\n| | | 0.3 | 77.91 | 49.60 | 38.10 | 56.06 |\n| | | 0.5 | 78.26 | 49.60 | 38.43 | 56.28 |\n| | pla\\_beta\\_clip\\_max | 1.0 | 78.17 | 48.86 | 37.37 | 55.67 |\n| | | 2.0 | 78.26 | 46.00 | 13.70 | 47.60 |\n| | pla\\_similarity\\_threshold | 0.1 | 78.40 | 48.40 | 37.60 | 55.66 |\n| | | 0.3 | 78.43 | 49.34 | 37.10 | 55.85 |\n| | | 0.5 | 78.20 | 48.74 | 38.57 | 56.00 |\n| | pla\\_normalization | none | 78.20 | 49.20 | 37.83 | 55.94 |\n| | | softmax | 78.23 | 50.03 | 38.37 | 56.40 |\n| **RD-PCGrad** | pcg\\_reduction | sum | 78.17 | 49.29 | 38.53 | 56.17 |\n| | pcg\\_random\\_order | True | 78.26 | 49.57 | 35.53 | 55.40 |\n| | pcg\\_epsilon | 1e-06 | 78.43 | 48.91 | 38.63 | 56.16 |\n| | | 1e-04 | 78.14 | 50.40 | 38.73 | 56.61 |\n| | pcg\\_conflict\\_threshold | -0.1 | 78.83 | 51.60 | 35.93 | 56.43 |\n| | | 0.1 | 78.31 | 49.17 | 36.27 | 55.50 |\n| | | 0.3 | 78.14 | 49.51 | 39.03 | 56.39 |\n\n*Table 5: Hyperparameter analysis for the R-PLA and RD-PCGrad modules in the TS-MOF framework with IR=100 on CIFAR100-LT benchmarks.*"}, {"review_id": "dDA2ONbfqi", "reviewer": "Reviewer_Fe6u", "summary": "This paper proposes TS-MOF, a two-stage framework for long-tailed recognition (LTR). Stage 1 pre-trains a feature backbone using standard cross-entropy loss. Stage 2 freezes the backbone and fine-tunes multiple classifier heads via multi-objective optimization (MOO). In Stage 2, the authors proposed two interesting ideas: R-PLA and RD-PCGrad for long-tailed recognition.", "questions": "See Strengths And Weaknesses", "limitations": "See Strengths And Weaknesses", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n1. The method underwent rigorous evaluation across three benchmark datasets under varying imbalance ratios.\n2. R-PLA and RD-PCGrad are novel and interesting.\n\nWeaknesses\n1. While the two strategies (R-PLA and RD-PCGrad) in the fine-tuning stage are interesting, the two-stage framework itself appears non-novel. What specific problems do these two strategies solve? The writing logic suffers from a weak connection between the paper’s motivation and these strategies. The authors should significantly reorganize the Introduction. Since the core effectiveness stems from the two MOO strategies, the Abstract and Introduction should highlight them as primary innovations rather than overemphasizing the two-stage framework.\n\n2．The paper lacks schematic diagrams, reducing accessibility. Adding visual illustrations (e.g., workflow of R-PLA/RD-PCGrad) would significantly improve intuitive understanding for readers.\n\n3. The multi-head structure resembles multi-expert models (e.g., SHIKE). However, the reported performance of SHIKE (46.9% on CIFAR100-LT IR=100 in Table 1) contradicts its original paper (56.3%). The authors must:\n(i) Clarify this discrepancy,\n(ii) Analyze how TS-MOF fundamentally differs from multi-expert ensembles in methodology and objectives.\n\n4. Critical implementation details are missing — specifically, which re-balancing strategies (e.g., LDAM? BCL?) were integrated into the multi-head MOO framework.\n\n5. Ablations are critically underdeveloped. The authors should: (1) Isolate the impact of individual components (R-PLA, RD-PCGrad); (2)Compare variants (e.g., alternative task-weighting schemes, gradient conflict resolution methods); (3) Benchmark performance across different strategy combinations.", "was_revised": true, "final_justification": "After rebuttal and discussion, I still tend to reject this paper, so I maintain my initial decision.", "rebuttal": "### **Response to Reviewer Fe6u**\n\nThank you very much for your careful review and insightful comments, which are truly beneficial for improving the rigor of our paper.\n\n**W1. On the Novelty of the Two-Stage Framework:**\n\nThank you for your valuable suggestions regarding the writing logic. This has led us to deeply reconsider how to best present our core contributions.\n\nWe will clarify that the core advantage of our work lies in the synergy between our proposed new components and the two-stage framework:\n\n*   **R-PLA and RD-PCGrad**, as the technical foundation of our method, can stably resolve gradient conflicts among multiple strategies, ensuring the optimization process rapidly moves towards a **Pareto-improving direction**. This allows the insights from **different long-tail strategies to be efficiently integrated**.\n*   The **two-stage decoupled framework**, in turn, drastically **reduces the cost of this strategy integration**. Compared to other multi-expert or multi-objective methods that require end-to-end training, our method **only needs to fine-tune the lightweight classifier heads**, significantly reducing computational overhead and training time.\n\nIn short, **the new components ensure the \"effectiveness of fusion,\" while the two-stage framework ensures the \"efficiency of fusion.\"** We will follow this logic to clarify our innovations and motivation in the revised manuscript. Thank you again for your profound insights.\n\n**W2. Adding a Framework Schematic:**\n\nApologies, but due to rebuttal guidelines, we're unable to directly include images.​ We will add a full-process schematic diagram in the manuscript, including Stage 1 pre-training and Stage 2 multi-expert fine-tuning, as well as detailed illustrations of R-PLA weight calculation and RD-PCGrad gradient coordination. This will significantly improve the method's comprehensibility and clearly highlight our contributions.\n\n\n**W3. Regarding the SHIKE Performance and Comparison to Multi-Expert Models:**\n\nWe apologize for the misunderstanding and would like to provide a clarification. Our paper uses a unified framework to ensure compatibility, allowing different expert strategies to be incorporated into the fine-tuning process. In our experiments, we used the backbone from LOS and retained the original hyperparameters for other strategies. The specific hyperparameters for the backbone and our MOF components were kept consistent across different combinations to diligently validate the method's generality and its performance improvement over baselines. Under this unified framework, the baseline performance of different methods may vary. We will emphasize this point in the subsequent revision. Additionally, we have done our best to supplement experiments to clearly demonstrate the performance gains over the baseline.\n\n**Comparison to Multi-Expert Models:**\nOur motivation differs from that of multi-expert methods. Multi-expert models (such as SHIKE and SADE) can work well independently and are often meticulously designed by researchers. In contrast, our work is a study on a gradient-based multi-objective optimization method that is strongly relevant to long-tail learning. Its goal is to **accelerate the utilization of these expert strategies and speed up the fine-tuning process**. Therefore, it can potentially absorb benefits from different representation learning methods and expert strategies that favor balance. When combined with other strategies, it can be viewed as a new type of long-tailed multi-expert method. We will add comparisons and discussions with classic multi-expert models (such as SHIKE and SADE) in the main text.\n\n**W4 & W5. Supplementing Implementation Details and Ablation Studies:**\n\nThank you for your suggestions. We will clarify these points in the main text to enhance comprehensiveness. The strategies fused by MOF in our main experiments are **KPS, BCL, and LOS** (as hinted in Figure 2), and we will explicitly state this in the revised text. Furthermore, we have diligently supplemented necessary experiments:\n| R-PLA | RD-PCGrad | EOSS | IR=10 |       |       |       | IR=50 |       |       |        |IR=100 |       |       |       |\n| ----- | --------- | ---- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ------ | ----- | ----- | ----- | ----- |\n|       |           |      | Head  | Medium| Tail  | All   | Head  | Medium| Tail  | All   | Head   | Medium | Tail   | All    |\n| ✗     | ✗         | ✗    | 72.3  | 47.3  | --    | 64.6  | 74.2  | 43.5  | 22.9  | 52.4  | 76.5   | 46.9   | 18.1   | 48.6   |\n| ✗     | ✗         | ✓    | 74.7  | 60.8  | --    | 70.4  | 75.3  | 50.4  | 44.5  | 59.6  | 78.2   | 49.1   | 38.6   | 56.1   |\n| ✓     | ✓         | ✗    | 74.6  | 61.5  | --    |   70.5    | 75.2  | 50.5  | 45.8  | 59.7  | 78.8   | 49.0   | 38.9   | 56.4  |\n| ✗     | ✓         | ✓    | 74.3  | 60.3  | --    | 69.9  | 75.5  | 51.0  | 43.5  | 59.7  | 78.4   | 49.8   | 36.9   | 56.0   |\n| ✓     | ✗         | ✓    | 74.6  | 60.1  | --    | 70.1  | 75.3  | 50.7  | 43.9  | 59.6  | 78.3   | 49.2   | 37.4   | 55.9   |\n| ✓     | ✓         | ✓    | 74.7  | 62.0  | --    | **70.8**  | 75.2  | 50.7  | 47.5  | **60.2**  | 79.0   | 49.2   | 39.9   | **56.8**   |\n\n*Table: Ablation experiments of TS-MOF three modules with IR=10/50/100 on CIFAR100-LT Benchmarks.*\n\n**Component Variant Analysis:** We have conducted experiments comparing our components with alternative methods to demonstrate their superiority.\n\n| **Modules** | **Variant Method** | **macs/M** | **Time/min** | $\\gamma_1$ | $\\gamma_2$ | $\\gamma_1/\\gamma_2$ | **Accuracy(%)** |\n| :--- | :--- | :---: | :---: | :---: | :---: | :---: | :---: |\n| **R-PLA** | entropy | 3.540 | 8.45 | 1.000 | 1.001 | 0.999 | 56.3 |\n| | confidence | 3.540 | 8.78 | 1.000 | 0.964 | 1.038 | 53.5 |\n| | uniform | 3.540 | 8.44 | 1.000 | 1.002 | 0.998 | 56.3 |\n| | pla(ours) | 3.540 | 8.46 | 1.000 | 1.000 | 1.000 | **56.8** |\n| **RD-PCGrad** | mgda | 3.540 | 8.36 | 1.000 | 0.906 | 1.104 | 56.4 |\n| | gradnorm | 3.540 | 8.76 | 1.000 | 0.864 | 1.157 | 55.2 |\n| | uncertainty | 3.540 | 7.46 | 1.000 | 1.015 | 0.985 | 56.3 |\n| | pcg(ours) | 3.540 | 7.57 | 1.000 | 1.000 | 1.000 | **56.8** |\n\n*Table: Comparison of computational cost and model performance between TS-MOF and conventional multi-task optimization in the second stage.*\n\n**Strategy Combination Benchmark:** To showcase the general applicability of our framework, we have benchmarked the performance of various strategy combinations.\n\n| **Method**                                    | **IR=10** |        |      |          | **IR=50** |        |      |          | **IR=100** |        |      |          |\n| ------------------------------------------- | ---------- | ------ | ---- | -------- | ---------- | ------ | ---- | -------- | ---------- | ------ | ---- | -------- |\n|                                             | Head       | Medium | Tail | All      | Head       | Medium | Tail | All      | Head       | Medium | Tail | All      |\n| CE+BS                                    | 72.4       | 49.9   | -- | 40.7     | 74.2       | 41.2   | 6.9 | 40.8     | 77.1       | 43.5   | 4.0 | 41.5     |\n| CE+KPS                                   | 72.5       | 47.2   | -- | 39.9     | 73.8       | 39.9   | 12.1 | 42.0     | 72.4       | 38.1   | 18.1 | 42.9     |\n| KPS+BCL                                  | 72.3       | 42.2   | -- | 38.2     | 74.1       | 37.2   | 15.6 | 42.3     | 77.7       | 37.4   | 10.8 | 42.0     |\n| CE+BS+CE-DRW                             | 72.2       | 48.4   | -- | 40.2     | 74.4       | 40.2   | 8.2 | 40.9     | 77.5       | 41.9   | 3.8 | 41.1     |\n| CE+BS+LDAM-DRW                           | 72.2       | 50.4   | -- | 40.8     | 74.0       | 41.5   | 11.2 | 42.2     | 77.3       | 43.4   | 5.7 | 42.1     |\n| CE+BCL+CE-DRW                            | 72.1       | 39.1   | -- | 37.1     | 74.6       | 35.7   | 2.1 | 37.5     | 77.9       | 38.9   | 2.9 | 39.9     |\n| CE+BS+KPS+CE-DRW                         | 72.3       | 49.2   | -- | 40.5     | 74.3       | 41.0   | 9.5 | 41.6     | 77.3       | 43.1   | 5.5 | 42.0     |\n| CE+BS+KPS+LDAM-DRW                       | 72.0       | 50.8   | -- | 40.9     | 71.6       | 41.1   | 25.4 | 46.1     | 74.7       | 43.3   | 14.3 | 44.1     |\n| $\\text{TS-MOF}_\\text{(CE+BS)}$           | 73.0       | 54.1   | -- | $42.4_{1.7}$     | 74.8       | 46.6   | 24.1 | $48.5_{7.7}$     | 77.9       | 49.8   | 13.7 | $47.2_{5.7}$ |\n| $\\text{TS-MOF}_\\text{(CE+KPS)}$          | 73.4       | 57.6   | -- | $43.7_{3.8}$     | 74.5       | 44.7   | 35.8 | $51.7_{9.7}$     | 77.4       | 43.5   | 28.1 | $49.7_{6.8}$ |\n| $\\text{TS-MOF}_\\text{(KPS+BCL)}$         | 74.0       | 57.4   | -- | $43.8_{5.2}$     | 75.0       | 43.4   | 38.8 | $52.4_{10.1}$     | 77.7       | 42.5   | 26.8 | $49.0_{5.0}$ |\n| $\\text{TS-MOF}_\\text{(CE+BS+CE-DRW)}$    | 73.5       | 54.3   | -- | $42.6_{2.4}$     | 75.2       | 47.0   | 23.8 | $48.6_{7.7}$     | 78.8       | 49.4   | 13.3 | $47.1_{6.0}$ |\n| $\\text{TS-MOF}_\\text{(CE+BS+LDAM-DRW)}$  | 73.7       | 55.1   | -- | $42.9_{2.1}$     | 75.5       | 47.5   | 27.9 | $50.3_{8.1}$     | 78.8       | 49.5   | 22.4 | $50.2_{8.1}$ |\n| $\\text{TS-MOF}_\\text{(CE+BCL+CE-DRW)}$     | 73.5       | 47.9   | -- | $40.5_{3.4}$     | 75.7       | 41.6   | 12.0 | $43.1_{5.6}$     | 78.4       | 43.2   | 7.0  | $42.8_{2.9}$ |\n| $\\text{TS-MOF}_\\text{(CE+BS+KPS+CE-DRW)}$  | 74.0       | 57.7   | -- | $43.9_{3.4}$     | 76.2       | 48.9   | 37.6 | $54.2_{12.6}$     | 78.4       | 50.2   | 29.5 | $52.7_{10.7}$ |\n|$\\text{TS-MOF}_\\text{(CE+BS+KPS+LDAM-DRW)}$ | 74.5       | 58.0   | -- | $44.1_{3.2}$     | 76.3       | 49.2   | 38.5 | $54.7_{8.6}$     | 78.5       | 50.9   | 30.9 | $53.4_{5.7}$ |\n\n*Table: Benchmark performance across different strategy combinations with IR=10/50/100 on CIFAR100-LT Benchmarks.The subscript is the improvement of the relative linear weighted combination.*"}, {"review_id": "NkCYdljbFD", "reviewer": "Reviewer_uDJM", "summary": "This paper proposes a practical solution to address the long-tailed distribution problem in machine learning. The TS-MOF framework, through its ingenious two-stage design, enables multiple long-tailed learning strategies to work synergistically, achieving excellent performance across multiple benchmark datasets.", "questions": "Q1: Does $ε_{sim}$ have a role other than ensuring numerical stability when computing $s_{j,e}$? For example, in cases of extremely low similarity, does it affect weight allocation preferences?", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "S1: The design of decoupling feature learning from classifier adaptation greatly simplifies the optimization of complex long-tailed problems, allowing the MOO process to act more stably and efficiently on classifier heads.\n\nS2: R-PLA and RD-PCGrad are the core highlights of this work. R-PLA's adaptive task weighting mechanism can dynamically adjust task weights based on real-time performance patterns, while RD-PCGrad ensures stable resolution of gradient conflicts and constructive fusion, effectively promoting synergy among different LTR strategies.\n\nS3: The paper achieves significant results on multiple benchmark datasets, demonstrating its efficiency.\n\nW1: Although the MOO components enhance robustness, there are still some hyperparameters in R-PLA and RD-PCGrad. I want see some sensitivity analysis of how these parameters affect model performance and convergence.", "was_revised": true, "final_justification": "After reading the authors' response, I would like to maintain my score.", "rebuttal": "Of course. Here is a response to Reviewer uDJM, addressing questions W1 and Q1 using the provided information.\n\n---\n\n### **Response to Reviewer uDJM**\n\nThank you for your positive feedback and for highlighting the core strengths of our work. We are grateful for your insightful questions and provide our responses below.\n\n**W1: On Hyperparameter Sensitivity Analysis**\n\nThank you for this valuable suggestion. We agree that analyzing the sensitivity of the hyperparameters in R-PLA and RD-PCGrad is important for understanding their robustness. We have conducted a sensitivity analysis for these key parameters and present the results below.\n\nThe data indicates that while the modules' performance shows some dependence on hyperparameters, our method remains robust across a reasonable range of values, consistently outperforming baselines. This suggests that meticulous tuning is not required to achieve strong results, although optimal performance can be found with experimental combinations.\n\n| **Module** | **Parameter Name** | **Parameter Value** | **Many** | **Medium** | **Few** | **Accuracy (%)** |\n| :--- | :--- | :---: | :---: | :---: | :---: | :---: |\n| **R-PLA** | `pla_beta_clip_min` | 0.1 | 78.20 | 49.74 | 37.80 | 56.12 |\n| | | 0.3 | 77.91 | 49.60 | 38.10 | 56.06 |\n| | | 0.5 | 78.26 | 49.60 | 38.43 | 56.28 |\n| | `pla_beta_clip_max` | 1.0 | 78.17 | 48.86 | 37.37 | 55.67 |\n| | | 2.0 | 78.26 | 46.00 | 13.70 | 47.60 |\n| | `pla_similarity_threshold`| 0.1 | 78.40 | 48.40 | 37.60 | 55.66 |\n| | | 0.3 | 78.43 | 49.34 | 37.10 | 55.85 |\n| | | 0.5 | 78.20 | 48.74 | 38.57 | 56.00 |\n| **RD-PCGrad** | `pcg_epsilon` | 1e-06 | 78.43 | 48.91 | 38.63 | 56.16 |\n| | | 1e-04 | 78.14 | 50.40 | 38.73 | 56.61 |\n| | `pcg_conflict_threshold`| -0.1 | 78.83 | 51.60 | 35.93 | 56.43 |\n| | | 0.1 | 78.31 | 49.17 | 36.27 | 55.50 |\n| | | 0.3 | 78.14 | 49.51 | 39.03 | 56.39 |\n\n*Table: Hyperparameter analysis for R-PLA and RD-PCGrad modules on the CIFAR100-LT benchmark (IR=100).*\n\n**Q1: On the Role of $\\epsilon_{\\text{sim}}$**\n\nYour intuition is correct; $\\epsilon_{\\text{sim}}$ has a subtle secondary role beyond ensuring numerical stability. While its primary function is to prevent division by zero when the norms of the performance vectors are near zero, it also acts as a **stabilizer in cases of extremely low similarity**.\n\nWhen the performance vectors are nearly orthogonal, their dot product is close to zero. Without $\\epsilon_{\\text{sim}}$, the denominator would also be close to zero, making the similarity value highly sensitive to small numerical fluctuations. By ensuring the denominator has a small positive lower bound, $\\epsilon_{\\text{sim}}$ **dampens this sensitivity and prevents drastic swings in the similarity value (and thus the weight allocation)** due to minor noise. In essence, it helps to regularize the similarity computation, ensuring that the weight allocation remains stable and meaningful even when strategies are performing very differently."}]

Author rebuttal:

Meta-review: This paper presents TS-MOF, a two-stage long-tailed recognition framework. Stage 1 learns features; Stage 2 freezes the backbone and fine-tunes multiple classifier heads with multi-objective optimization. Two new components, R-PLA (adaptive task weighting) and RD-PCGrad (deterministic gradient conflict resolution), coordinate several LTR strategies. The method shows strong results on CIFAR100-LT, ImageNet-LT, and iNaturalist.

Reviewers highlight consistent gains across standard benchmarks, a clear motivation for decoupling feature learning from classifier adaptation, and practical components (R-PLA and RD-PCGrad) that make multiple LTR heads work together. One review rates the paper accept with excellent quality and significance. Another notes useful theoretical analysis around gradient stability and robustness. The fine-tuning phase is lightweight relative to training from scratch, which improves usability.

The negative review centers on presentation and missing detail rather than correctness: the two-stage scaffold is not new, the Introduction underplays that the real advances are R-PLA and RD-PCGrad, and the paper would benefit from schematic diagrams for both modules. Important ablations are thin: isolate R-PLA, RD-PCGrad, and the aggregation module; compare against alternative task-weighting and gradient-projection baselines; report sensitivity of key hyperparameters. After rebuttal, the reviewer maintained a borderline-reject due to the scope of revisions (writing logic, diagrams, ablations, method details, expert comparisons). Other reviews remain positive, emphasizing strong results and practical value of R-PLA and RD-PCGrad.

On balance, the empirical evidence across three benchmarks and multiple imbalance ratios, together with two concrete, generally useful MOO techniques, makes the strengths outweigh the weaknesses. The main concerns are fixable with added analysis, clearer writing, and targeted ablations, rather than fundamental flaws. I therefore recommend acceptance, contingent on addressing the items discussed during rebuttal in the camera-ready.
Decision: Accept (poster)