Learning De-Biased Representations for Remote-Sensing Imagery
Authors: Zichen Tian
Zhaozheng Chen
Qianru Sun
Venue: NeurIPS 2024 poster (2024)
Keywords: Adaptation
Long-tailed learning
Remote Sensing
Primary area: machine_vision
TL;DR: We propose debLoRA to adapt foundation models to data-scarce remote sensing domains with long-tailed distributions, by efficiently and unsupervisedly augmenting minor class features using major class features to mitigate representation bias.

Abstract: Remote sensing (RS) imagery, which requires specialized satellites to collect and is difficult to annotate, suffers from data scarcity and class imbalance in certain spectrums. Due to their data scarcity, training large-scale RS models from scratch is unrealistic, and the alternative is to transfer pre-trained models by fine-tuning or a more data-efficient method LoRA. Due to class imbalance, transferred models exhibit strong bias, where features of the major class dominate over those of the minor class. In this paper, we propose debLoRA, a generic training approach that works with any LoRA variants to yield debiased features. It is an unsupervised learning approach that can diversify minor class features based on the shared attributes with major classes, where the attributes are obtained by a simple step of clustering. To evaluate it, we conduct extensive experiments in two transfer learning scenarios in the RS domain: from natural to optical RS images, and from optical RS to multi-spectrum RS images. We perform object classification and oriented object detection tasks on the optical RS dataset DOTA and the SAR dataset FUSRS. Results show that our debLoRA consistently surpasses prior arts across these RS adaptation settings, yielding up to 3.3 and 4.7 percentage points gains on the tail classes for natural $\to$ optical RS and optical RS $\to$ multi-spectrum RS adaptations, respectively, while preserving the performance on head classes, substantiating its efficacy and adaptability

Learning De-Biased Representations for Remote-Sensing Imagery
Zichen Tian Zhaozheng Chen Qianru Sun
School of Computing and Information Systems
Singapore Management University
{zichen. tian. 2023, zzchen. 2019 (a119)pppccss smu. smu.mmueu edu sg, qianrusunssmu. edu. 5g
Abstract
Remote sensing (RS) imagery, which requires specialized satellites to collect and is difficult to annotate, suffers from data scarcity and class imbalance certain spectrums. Due to their data scarcity, training large-scale RS models from scratch is unrealistic, and the alternative is to transfer pre-trained models by fine-tuning or a more data-efficiem, method LoRA 22 Due to class imbalance, transferred models exhibit strong bias, where features of the major class dominate over those the minor class. In this paper, propose debLoRA generic training approach that works with any LoRA variants to yield debiased features. an unsupervised learning approach that diversify minor class features based the shared attributes with major classes, where the attributes are obtained by a simple step of clustering. To evaluate we conduct extensive experiments
transfer learning scenarios in the RS domain: from natural to optical RS images, and from optical RS multi-spectrum RS images. We perform object classification and oriented object detection tasks on the optical RS dataset DOTA and the SAR dataset FUSRS. Results show that our debLoRA consistently surpasses prior across these RS adaptation settings, yielding up to 3.3 and 4.7 percentage points gains on the tail classes for natural -> optical RS and optical RS - multi-spectrum RS adaptations, respectively, while preserving the performance on head classes, substantiating its efficayy and adaptability
Introduction
Remote sensing (RS) is crucial in various applications such as environmental monitoring, resource management, and disaster response [7]] RS data is collected by various sensors and has multiple spectrums, including optical RS imagery (dubbed as ORS, 400-700nm) [32], multi-spectral RS imagery (MSRS, 400-2500nm) [ and synthetic aperture radar imagery (SAR, 1mm-1m) [8] [3]
These spectrums differ significantly in imaging mechanisms, leading to distinct data characteristics and processing pipelines [70]. Given this diversity, learning robust and generic representation models for such data is desirable to reduce processing costs and complexities.
Recently, in natural image domains, large-scale pre-trained visual foundation models (e.g., CLIP 45 Stable Diffusson [47]. and DINO have shown great advances robustness and generalization ability. The zero-shot features extracted from the models show impressive performance in downstream tasks such as object classification, detection and semantic segmentation [66], even outperforming the supervised models trained on the specific datasets of those tasks. However, in the RS domain, training such foundation models from scratch remains challenging. Even though some trials have been made in past years g [6], their works have clear limitations. First, they require arrge-scale RS data for effective training, which are available for only ORS but not other spectrums such as SAR
Code: http:///jithhbcccommdme/////ee
38th Conference on Neural Information Processing Systems (NcurIPS 2024)

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Collecting and annotating images in "other" spectrums is difficult due to many factors such as military restrictions, sensor availability, and high acquisition costs, so the data scarcity is unlikely to be alleviated in the near future (70] Second, their works are constrained in small- or medium-scale models, Le., they use ViT-I. (300M) [8] and S1in-I.(197M) in [16, while the foundation models in the natural image domain are much larger (e.g., Latent Diffusion has 860M, and OpenCLIP-HI 14 has 986M). Third, their training-froom femiing-from-seee morm-hcattch approaches are computationally inefficient, requiring huge amount of GPU memory (VRAM). For instance, [16] reported the need of 80 * A10 GPU with 80GB VRAM each, totaling 6.4TB.
and MSRS 43 10 17
Instead of learning a foundation model from scratch, we propose to transfer existing foundation models to RS domains. This upproach is both data-effiiient and computation-effit two questions: 1) Which foundation models to transfer? 2) Which transfer learning methods to use?
We answer
For the first question, consider foundation models pre-trained on natural images (e.g.. CLIP [4). Stable Diffusion (47) as well as the models from remote sensing (RS) images (e.g.. SkySense [6].. A positíve aspect of these models is that they contain the seman- tic knowledge necessary for learning a new RS domain. However, a great challenge is the large domain gap between natural images and RS domains, or between different RS spec- trums. In our preliminary study, we conduct validation experiments. Fortunately, we ob- serve successful transfer results both from nat- ural to ORS in Figure 1 and between different
Train Samples (K)
Macro F1-Sco P1-Score
30
Middle
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LoRA
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RS spectrums in Table 3 when compared to the method of TRSSResTOO [65] which does not perform any transfer learning. The success of natural-+ORS is due to the shared underly- ing visual elements like edges, textures, and contours, which are intrinsic both natural and RS images. The success of ORS oother RS is due to the shared spatial structures, e.g., urban areas, buildings, and object outlines, in different RS spectrums.
0.75
Tail
small- mall-vebill
ship
larg- lagp-wwiek whice
plane
harbor
sturape kands
teani--oe conn--cour court
bridge
poaa poa!
delicopioc
toakettall backetball-cogg court
kaseball- cout
round sorcer fied
graadd mond---rek
trakk
swinningg
Figure 1: Long-tailed Problems. This figure shows 1) ORS datasets (take DOTA [59] as an example) have the long-tailed distribution issue. 2) Model adaptation methods suffer from weak performance in tail classes
For the second question, we found that data-efficient transfer learning methods on foundation models exhibit strong bias towards major classes. As shown in Fig. [ both Fine-Tune and LoRA have significantly lower Fl scores for tail classes. This is because their learned feature space is biased towards the discriminative features of head classes while neglecting the tail [62]. Taking the head class ship (which takes 28.35%) and tail class helicopter (0.64%) as examples on the DOTA dataset [59]. Fig. 2 a) shows biased LoRA features of "oval tail" in the ship sample and "rotor tail" in the helicopter sample m. We say biased because the LoRA fails to understand the "oval tail with a rotor" in another helicpperr sample m' and embeds m' wrongly as ship sample in the feature space. Please note that the real feature distribution is shown in Figure 3 to support the This long-tail issue is particularly severe for transfer learning in the RS
illustration of Figure 2 domain due to two reasons. First, RS datasets suffer from more severe data imbalance than natural image datasets. For instance, the imbalance ratioy ? of RS datasets DOTA and SmapRSlma reach 86 and 112, respectively, while CIFARI00-LE 22. a natural image dataset with a similar data scale, has ratio of only 50. This because annotating under-represented tail class samples in RS, e.g., identifying rare naval vessel, such as the "Nimitz", from SAR image, requires a high level of domain expertise. Second, the data scarcity in RS domains determines that RS adaptation methods must be data-efficien such as LoRA. However, as shown in Table using fewer parameters in LoRA (being more data-efficient) exacerbates long-tail issues. The reason is that this restricts the model capacity and forces the model to prioritize limited number of ussully from head classes
2 The imbalance ratio is measured by nh /ne,, where 1 and k are the largest and smallest categories. It reflects
the severity of data imbalance (69)
2

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x
Head Samples
A
Tail Samples
Refined Samples
Biased Center
De- De-biased Center
Cluster Streamilieed Tail
Bake
Ditp-happ
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4
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m
m
(a) Biased Space
(b) Clustered Space
(c) De-Biased Space (d) Real Samples
Figure 2: Two key steps of debLoRA: feature clustering and calibration. (a) The baseline LoRA
feature space is biased towards head classes. Red crosses represent head class samples, and blue triangles A represent tail class samples. The blue star * indicates the center of tail class samples. Dashed blue triangles A show the validation samples of the tail class wrongly embedded in the head class region, indicating the model bias towards head classes. (b) We cluster all features (clusters denoted by gray dotted boundaries) regardless of class labels. A, B and C' are cluster centers used to generate a de-biased center D, as in Eq. 2. (c) We calibrate the tail class features by "moving" them closer to D, as in Eq. 3. After these steps, we train the debLoRA module the calibrated features of tail classes (together with the original head class features).
To mitigate this bias without needing more data or labels in tail classes, we propose an unsupervised learning approach, debiased LoRA, dubbed debLoRA. debLoRA is based on the features extracted from LoRA (or LoRA variant) and is generic to LoRA variants. To be concise, we use LoRA in the following to represent itself and its variants. Given the LoRA features, debLoRA has three steps: clustering, calibration, and training. First, it clusters all the features regardless of class labels by K-means. Each obtained cluster center represents an attribute from one or shared by multiple classes. Second, these cluster centers are used to calibrate the LoRA features of tail classes and enhance the territory of tail classes in the feature space. We illustrate these two steps in Figure 2 Last, the calibrated features are used as the learning objectives to train debLoRA module with similar network architecture to LoRA. The learned debLoRA is thus a di-bia feature extractor.
We observe that after K-means clustering, each cluster center captures a general visual attribute shared across different classes. For instance, in Figure bb) cluster corresponds the general vehicle attribute "streamlined tail", which includes both head class sample and tail class sample m. Such clusters can thus yield balanced representation base, making the tail more robust by integrating common attributes with the head.
One may ask "what if some attributes are dominated by the attribute features of head classes?" We address this question by proposing a weighting scheme, in the step of calibration. In specific, for each tail class sample (e.g., in Fig. (c)) we calibrate it by forcing its feature closer to the diabed center (D)-the weighted average of all cluster centers. The weights are determined by the number of samples in each cluster, ensuring that this center is not dominated by clusters with mostly head class samples. This calibration process results in de-biased representations that capture a more comprehensive range of visual attributes shared across classes, leading to improved features of tail classes (e.g., m'). Lastly, we re-train a LoRA module to map biased representations towards these debiased centers. Please find more details of justifications in Sec. 4.4 Our method significantly improves the features of tail classes. Moreover, it is efficient as it learns only a lightweight low-rank module while keeping the original foundation model frozen.
Our contributions can be concluded three-fold: 1) We demonstrate the effectiveness of adapting foundation models for data-scarce RS domains. 2) We propose Incremental LoRA, a novel method that diabia category-specific representations for long-tailed RS adaptation. 3) We conduct extensive experiments to validate our approach on multiple RS adaptation settings and downstream tasks.
2 Related Works
Representation Learning for RS Images. Self self-supervised representation learning in RS image domains mainly includes contrastive- and generative- based methods. Contrastive-based methods, such as Tile2vec [27], Seasonal contrast [37 and SauMoCo [30], heavily rely on rich temporal data or high-resolution samples, which are often unavailable for data-scarce RS spectrums (56]. Generative-
methods, such as RR-SSL (67 and SGSAGANs [15] reconstruct inputs to capture the global data distribution and learn fine-grained patterns. However, they require large-scale
data to form robust latent space [1]. Recently, foundation models in the RS domain, such as
3

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SatMAE [8 SpectralGPT 20) and SkySense [[6], have shown superior performance for ORS tasks. SpectralGPT (20] tackles spectrum diversity by pre-training separate tokenizers for each spectrum. which still needs large amounts of data. Another problem is that existing RS foundation models are much smaller than those in the natural image domain (e.g., SatMAF-L. [ has 300M parameters v.s. 986M of OpenCIJI-H/IL [5)) Instead of self-supervised training RS models from scratch, we propose to adapt existing foundation models to RS tasks. Our approach: 1) reduces computational cost significantly, 2) can be easily adapted to various data-scarce RS spectrums, and 3) benefits from powerful representations from natural vision foundation models.
Long- Long-tailed data distribution, where a few head classes cover most of the samples, is prevalent in both natural and RS image domains [55] 69].
Long-tailed Data Distribution and its Bias Problem.
This imbalance leads to biased feature representations, where the model focuses on discriminative
features for head classes while neglecting subtle but crucial features for tail classes 69
28 Zhang
et al. [69] observed that such feature space is usually broader for head classes than tail classes, and the decision boundary tends to be biased towards head classes, i.e., many false positive predictions
for head classes. Existing solutions include sample-level, meta-learning, and representation-level approaches [69]: Sample-level methods, such as re-sampling [49] and data augmentation [7], aim to directly balance the sample distribution. However, they require sample annotations 12 49] or
rely on data diversity 77], both of which are unrealistic in the data-scarce RS spectrums such as SAR [13) and MSRS [8]. Meta-learning methods [26] (57] formulate the problem "learning to learn" " and adapt the model to a balanced meta-test set. They depend on the data diversity of the training sets and the availability of balanced validation sets, and therefore, are less applicable for data-scarce RS domains. The representation-leve methods enhance the learned representation space, including metric learning losses (23), margin-based losses 2]. and feature transfer from head to tail classes [33 63). However, they are designed for supervised single-domain settings and do not address the challenges of model adaptation to RS: 1) handling multiple downstream tasks (e.g., small object detection, scene segmentation, change detection), and 2) multiple spectrums (such as ORS and SAR). In contrast, we propose an unsupervised adaptation method to tackle these challenges in this paper.
Transfer Learning in Remote Sensing. Transfer learning in remote sensing primarily focuses on adaptation within the optical imagery domain. They can be categorized into supervised and unsupervised methods. Supervised methods [2] 46 39] align distributions using target labels. However, they require task-specific annotations, which are scarce in SAR and multispectral domains and limit the applicability of the obtained models to multiple downstream tasks. Unsupervised DA (UDA) methods aim learn domain-invariant features without requiring labeled data in the target domain, including transfer component analysis [42 40) manifold alignment [53] 60 61]. and adversarial learning 51 However. they are designed for single-source, single-target adaptation within the same spectrum 38 Besides, the manifold alignment and adversarial methods require sigmificant computational resources, often involving the training of several copies of the source model, while component analysis methods involve complex pipelines. These factors make them unsuitable for foundation models, which are already computationally intensive. In contrast, our method tackles multi-spectrum adaptation without requiring extra labels. It is also computationally efficient.
3
LoRA and cLoRA
Our d is based on the LoRA 22 its variants 64 but is orthogonal and generic them.
LoRA. LoRA was initially proposed to adapt a pre-trained large-scale language model to downstream tasks. It assumes adapted parameters are sparse during model training when the data is limited. It introduces a low-rank factorization of the difference between original and adapted parameters, i.e., A := B-A. Here, 0 E Ra represents the parameters of pre-trained model, and B Rdx and denote low-rank factors, with & min(d, k). The updated parameters i are thus given by
A E x â A+ = B.A. A. During inference, the obtained LoRA modules could be combined through a weighted sum, i = + Σ, wr where wy denotes combination weights.
CLoRA. To tackle the stilet issue of LoRA, we also explore its variant cLORA (64). The key idea of cLoRA is learn a separate LoRA module for each class, denoted as A0, for class c, to ensure that the learned representations of one class do not interfere with those of other classes. Formally, the adapted parameters for class are given by Ô. 0 + A0.- 0 + B. Ac,, where B, E Rdxr and A, E Rr are the low-rank factors specific to class c. During training, each cLoRA module A0, is optimized using only the data from class c, allowing it to capture class-specific features. During

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inference, as there is no class label available, we use all the cLoRA modules to extract features for the input. Specifically, for an input J, we obtain the features 2. = (x) using each cLoRA A module 0-- The final feature representation is then obtained by concatenating the features from all the cLoRA:
22:....... ;c],, where C' is the total number of classes.
De-biased LoRA (debLoRA)
The algorithm of debloRA consists of two steps: generating debiased features, and then using them train debl module. the first step, we perform unsupervised clustering on biased feature space 2 (i.e., composed by original LoRA features biased to head classes) to obtain debiased features 2. In the second step, we use E the learning target train debLoRA module. The debLoRA learns the mapping between biased and de-biased features. We justify the feasibility of learning such mapping in Section 4.4
4.1 Problem Formulation
Given a pre-trained feature extractor f X - 2 and long-tailed RS dataset D (ar, (ar,y), where x E X' an RS image, y E ) is its annotation and 2 is the biased feature space our goal is to adapt f to the target dataset 'D while yielding a de-biased feature space 2, i.e., adapted encoder is f: X + 2. The de-biased feature representation should improve downstream task performance on tail classes without sacrificing the performance on head classes.
4.2 Stage 1: Representation De-biasing
Feature Clustering. Given pre-trained encoder fo X + Z that maps input images to biased
representation space, where Se is parameterized by 0, we first extract features for each sample in the dataset: 2; = fo(x;), E N. We then apply K K-means clustering on { to obtain clusters. To mitigate imbalanced clusters, we impose constraint that each cluster should contain at least K-p samples, where p a pre-defined constant. The clustering objective is:
N
min 11:4--p -
Vk, ThA K
where and denote the center and size of the k-th cluster, respectively.
De-biased Cluster Centers. For each tail class we calculate its be-bid representation center A. by weighted averaging all the cluster centers:
Pec = Σ
Wk pk. where Wy
7
ne
R
Here ny denotes the number of samples from class in the k-th cluster, and n. the total number of samples in class The weight wy proportional to the fraction of class samples in the k-th cluster. This ensures that the de-biased center is not dominated by head classes.
4.3 Stage 2: De-Biased Low Rank Adaptation (debLoRA)
Tail Class Calibration. For each tail class sample x with representation 2, we calibrate & by moving it closer to the de-biased center fi:
== + (1- a)
where E [0, 1] is a based on the imbalance ratio y of each tail class: a min(1, 10). For tail classes with larger imbalance ratio, a higher a encourages the calibrated representation center ÿ,, the original representation is less reliable due to its learning from limited samples. While for classes with smaller 7, a lower a used to retain discriminative information z. For instance, the DOTA dataset's 's tail class helicopter has high 7 45.45, so its reaches 0.22.
controlling the degree of calibration. We empirically set
be closer to the biased
We detine feature space Z as biased if (ol > Vol(Z,), and 3t, E Z: P(z, E 2x.) > P(z. E Z,). where E, and Z, denotes the feature spaces of head and tail classes respectively, Vol(-) denotes feature space volume, and P(-) denotes the probability predicted by the model.
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Helicopter Train
Ship Train
Helicopter Val
Ship Val
Cluster Samples
Cluster Centers
(a) Helicopter Val
(c) Cluster
(d) Cluster 2
(c) Cluster
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(h) De- De-Biased Center
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10 10
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Figure 3: t-SNE visualization of validation samples and clusters. The first column shows the distribution of heli helicopter (tail) and ship (head) validation samples. Subfigures (c)-(g) are the clusters and their centers when K=5 in K K-mmans. In (h), the dotted lines and stars indicate that we compute de-biased center for the tail class (helicopter) by weighted averaging the five cluster centers, and the blue star is the original biased center of helicopter training samples.
Learning deb With the pre-trained encoder fo frozen, we learn LoRA module 9o Z parameterized by & to map the biased representations to the calibrated ones. The training objective is:
min D
(I9)(f(()))
=112
RED.
where D; is the set of tail class samples. During inference, we apply the learned LoRA module to extract the de-biased representations 2 go (f)()) for an input image æ. The complete algorithm of debLoRA is summarized in Algorithm
Algorithm debLoRA
Require: Long-tailed training set D ((x, pre-trained encoder fo X - Z, number of clusters K, balance factor p
Ensure: A LoRA module Ue that de-biases fo
1: Extract biased representations 2 fe() for each sample z E using pre-trained fo
2: Perform constrained K- K-means clustering on {2} (equation I to obtain cluster centers {1r k==1
where each cluster has at least R.,p samples
3: for each tail class do
4: Calculate its de-biased representation center i.e by weighted averaging all cluster centers
(μRR ) (equation 2
5: for each sample I E D. do
6: Extract biased representation
fo(x)
7: Calibrate z to ă by moving it closer to A. with factor a 10/7 (equation 3
8: end for
9:
end for
Learn a LoRA module gø : Z - 2 to map biased representations to calibrated ones
11: return 9s
4.4 Justification
We discuss the biased representation space of LoRA, and then justify the effectiveness of our three critical operations in debLoRAA clustering, weighting, and calibration. We show the real sample distribution in Figure 3 and an illustrative example in Figure
LoRA Biased. The feature space learned by LoRA biased towards head classes 622 evidenced by two observations. 1) The head class representations over-expand their territory into the tail class space. shown Figure most of the ship (head) validation samples are distributed within its own representation space, while many helicopter (tail) validation samples wrongly distributed

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in the ship's space. 2) The center of the entire space is biased towards head class, as the ship training samples significantly overlap with the helicopter training samples. This bias occurs because, during training, the encoder is exposed to significantly more diverse samples of head class.
Clustering. By feature clustering. we obtain a set of cluster centers that benefit the tail classes in two ways. 1) Improved robustness. The obtained cluster centers, shown as red stars in Figure cg)((), represent visual prototypes [3, i.e., general visual attributes common to both head and tail classes, such as "streamlined tail" or "with wooden deck". These cluster centers are more robust than the original tail class representations because they leverage the diversity of head class samples. 2) Reduced imbalance. Certain clusters exhibit reduced long-tail issues. The clusters in Figure ( d))(((( contain more samples from helicopter than ship. This is because the clusters are formed based on intrinsic visual similarities among images, regardless of their imbalanced class labels. Using these cluster centers avoids the risk of tail class attributes (e.g., "rotor tail" and its variants in helicopter) being overwhelmed by head class attributes (e.g., "oval tail" and its variants in ship).
Weighting and Calibration. One might ask, "Are the data imbalances within each cluster or among different clusters still issues?" E.g., the 5-th cluster in Figure&Jjconaing only ship samples and seems irrelevant to helicopter. To answer this, we perform the weighted averaging over cluster centers, and the calibration over tail class samples: 1) Weighted averaging. When calculating the de-biased representation center for each tail class (equation 2 we assign higher weights to clusters containing larger fraction of that particular tail class. The de-biased center (red star in Figure 3 h)) better captures the true distribution of the validation samples of helicopter, compared to the original biased center (blue star in Figure h)). 2) Calibration. We calibrate the representation of each tail class sample by moving it closer to the class's ' de-biased center (equation (3). The calibration
factor a inversely proportional to the imbalance ratio of the tail class. This design ensures severely underrepresented tail classes like heli copter receive stronger calibration.
5 Experiments and Analyses
We evaluate our debLoRA on two settings: 1) adapting natural image foundation models to RS, and 2) adapting ORS foundation models to SAR. For the first setting, we conduct experiments on two representative RS tasks: object classification and oriented object detection. For the second setting, we conduct experiments on representative SAR task-fine- f-ne--innnaee fine-grained ship classification.
Natural - RS adaptation. 1) Foundation model. We use two state-of-the-ar foundation models: Stable Diffusion v1.5 (SD) (47 and OpenCLIP 225. Both models have shown impressive general- ization ability on various tasks when adapted to domains like medical images [58]. However, their transferability from natural images to the RS domain remains under-explored. 2) RS dataset. We use the DOTA dataset [10], a large-scale benchmark for RS object recognition. DOTA contains 188,282 instances from 15 categories, covering various scales, orientations, and shapes. We define the long-tail split as follows: classes with <1 % instances as tail, classes with 1%-5% instances as middle, and the remaining 6 classes (cach with >5% instances) as head. This split exhibits clear long-tail distribution, evidenced by the performance gap between head and tail classes for the baseline methods (see Table Tro 1). 3) Tasks. For the classification task, we obtain features from the adapted foundation models and train a linear classifier. We report the macro Fi-score that fairly evaluate the performance across all classes. For detection, train FCOS detector head [52 on obtained representations and evaluate using the mAP.
ORS + SAR adaptation. 1) Foundation model. We use SatMAE-L g the state-of-the-art open-sourced foundation model for RS. SatMAE-L is pre-trained on large ORS datasets using self- supervised learning. It has 307M parameters and requires 6, 144 GPU hours to train from scratch. 2) SAR dataset. We evaluate our method on the fine-graine lnn-grained ship classification task of SAR. Existing SAR ship datasets have insufficient samples to evaluate the model performance reliably, e.g., only 2 samples in test set for tail class "WingInGrnd" on the AUSAR-Ship dataset. We thus create a new dataset by combining two high-resolution (< 10m/pixel) SAR datasets: FUSAR-Ship [21] and SRSDD B1. Details of this combined dataset are provided in the Appendix. 3) Ship classification task. We follow the same setup in the natural - RS setting for this SAR task.
Implementation Details. 1) Fine-tuning baseline. We fine-tune the foundation models until the training loss stabilizes. During inference, we use null prompts as no ground truth is available. For SD, we extract features from the U-Net after applying one denoising step [50]. For OpenCIII,, we extract features from its visual encoder's 's final layer before the projection head. 2) LoRA and variants.
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We apply LoRA modules to all linear layers in the foundation models. We use a rank of 8 for LoRA, as it suffers from the most severe long-tail issues. We also validate our method with higher ranks (e.g., 64) in T During inference, we extract features from the U-Net encoder output followed by global average pooling (GAP). For cLoRA, we concatenate the category-speciff features after GAP. 3) debLoRA. The debLoRA involves two hyperparameters: the calibration factor and the number of clusters K. We set a as inversely proportional to the imbalance ratio of the tail class, as described in Section 4.4 We empirically set K=32 (ablation study on K are provided in Appendix).
Evaluation Metrics, 1) Classification. We use linear probing (i.e., train a linear classifier on the top of frozen features) to evaluate the learned representations [9 18]. is simple and avoids introducing additional learning operations. We apply GAP and ReLU on the extracted features before linear probing. We report the macro Fl-score, which assigns equal importance to all classes- toases-more suitable for evaluating imbalanced datasets. We report scores for head, middle, and tail classes separately, as well as the overall score averaged across all categories. 2) Detection. We use the lightweight FCOS [52] an anchor-free detector head, to avoid potential interference from pre-defined anchors. We extract high-resolution feature maps from the SD U-Net output. During feature clustering and re-training, we use per-instance features for each category. During inference, we extract features from the entire image and feed them to the detector head. We report the mAp metric.
Ablation study. In Table I rows 1 and 2 show the results of using zero-shot features of SD or fine-tuned SD features on DOTA to train RS ob- ject recognizers. Recognizers' performances are strongly biased to head around 12 per- centage points drop for tail classes. From rows 3 and 5, we can see such issues get resolved a bit when using LoRA methods. Rows 4 and 6 show that debLoRA significantly outperforms LoRA methods on tail classes-by by 4.2 points and 2.7 points, respectively. Specifically, com- pared to cLoRA, debLoRA does not even sacri- fice the performance for head classes. To quan- titatively validate its working mechanism, analyzed feature discrimination. Results show that debLoRA enlarges inter-class distances and reduces intra-class distances for tail classes (see Appendix). In addition, debLoRA needs just the same amount of parameters as LoRA (0.08M), which is appealing for computation.
Table 1: Ablation study of debL. We apply debLoRA based on LoRA and A. Results are reported for the adaptation from SD + DOTA recognizer. Params (M) refers to the number of updated parameters during the adaptation. Our
results are marked in gray.
Macro F1 Score (%)
Method
Params (M)
Head
Middle
Tail
Overall
Zem-Shot Fine-Tune
99.2
97.3
87.8
94.3
99.1
96.7
86.8
93.7
860
cLoRA
99.1
94.3
89.3
94.2
0.08
w/ dabLaRA
99.3
97.5
93.5
96.6
0.08
LORA
99.4
97.2
91.8
95.9
0.08
w/ debLoRA
99.1
98.7
94.5
97.1
0.08
LoRA Ranks. We investigate the impact of different LoRA ranks on the long-tailed classifi- cation performance in Table 2 We have two key observations. 1) As the LoRA rank decreases, the performance on tail classes drops more sig- nificantly than head classes. For example, when the rank is reduced from 64 to 8, the F1- score of tail classes decreases by 2.2 percentage points, while that of head classes even increases by 0.3 percentage. This supports our hypothesis that the limited parameter capacity of low-rank LoRA forces it to prioritize learning the head classes, exacerbating the long-tail problem. 2) debLoRA consistently improves the performance on
Table 2: Compare LoRA ranks. The table com- pares different ranks of the LoRA module. Our results are marked in gray.
Method
Macro F1 Score (%)
Params (M)
Heud
Middle
Tail
Overall
Rank
99.4
97.2
91.8
96.1
0.08
w/ dabLoRA
99.1
98.7
94.5
97.
0.08
Rank 16
99.0
95.9
92.4
95.8
0.16
Rank 32
99.4
96.9
93.0
96.4
0.32
Rank 64
99.1
96.9
94.0
96.7
0.64
w/ debLoRA
99.1
98.7
96.2
98.0
0.64
middle and tail classes across different LoRA ranks. Notably, with rank 64, debLoRA achieves a 2.2 percentage points improvement on tail classes while maintaining the performance on head classes
Compare with SOTA. 1) Object Classification. Table compares our debLoRA with state-of-ther -o-th---rtttttt methods under three adaptation tasks, We draw four key observations from the table. 1) debLoRA consistently outperforms LoRA on tail classes across all adaptation tasks, with the largest gain of 4.7 percentage points for ORS - SAR (i.e.. SatMAE - FUSRS). This shows the consistent efficiency of our approach in tackling the long-tail problem of RS domains. 2) Compared SD - DOTA setting, cLoRA performs exceptionally well under OpenCLIP - DOTA setting, slightly surpassing LoRA. We hypothesize that OpenCLIP's feature space aligns particularly well with eLoRA's class-specific
8

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Table 3: State-of-the-art comparison under different adaptation settings. The experiments conducted on two RS adaptation settings: 1) Natural-+ORS, where we adopt Stable Diffusion (SD) and OpenCLIP foundation models and DOTA as the target dataset. ORS-+SAR, where adopt SatMAE as the foundation model and FUSRS (SAR imagery dataset) as the target dataset.
Results are evaluated by linear probing and reported in macro F1-Score (%). The highest result in each position is highlighted by bold. Our results are marked in gray
SD
DOTA
OpenCLIP - DOTA
SatMAE
FUSRS
Mean
Method
Heaad
Middle
Tail
Ilead
Middle
Tail
Ieead
Tail
Head
Middle
Tail
Zero-Shot
99.2
97.3
87.9
93.1
92.7
91.7
78.3
67.8
90.2
95.0
82.5
Fine-Tune
99.1
96.7
86.8
93.1
91.1
89.2
88.6
73.6
93.6
93.9
83.2
cLoRA
99.1
94.3
89.3
97.3
93.3
92.2
89.9
82.0
95.5
93.8
87.9
w/ debLoRAA
99.3
97.5
93.5
97.6
95.8
95.0
92.5
86.1
96.5
96.7
91.5
LoRA
99.4
97.2
91.8
96.6
92.7
91.6
87.1
76.3
94.4
95.0
86.6
w/ ResLT 9
99.4
97.7
93.0
97.7
94.1
93.8
86.6
75.4
94.6
95.9
87.4
w/ SADE (6
99.1
97.3
92.4
97.3
92.5
89.6
78.4
95.3
95.2
87.8
w/ debLoRA
99.3
97.7
95.1
97.2
95.6
94.8
90.1
81.0
95.5
96.7
90.3
design. However, debLoRA remains robust across both foundation models. 3) The perfor- mance gains of debLoRA are most significant for SatMAE - FUSRS (+4.7 points) compared to SD - DOTA and OpenCLIP - DOTA (+3.3 and +3.2 points, respectively). This suggests that our method can leverage domain similarity more effectively when adapting between related image domains (SatMAE and FUSRS are RS datasets). We think this is because debLoRA's clustering step captures and utilizes the shared domain-speciff visual patterns (e.g., spatial structures and textures) when the source and target domains are closely related. 4) debLoRA consistently outperforms long-tailed recognition methods, ResLT (9) and SADE [68] (2.5 and 2.9 points by average). ResLT and SADE mainly introduce re-weighting strategies to balance the learning of different classes, but they do not directly rectify the bias in the feature space. In contrast, debLoRA explicitly learns de-biased representation center for tail classes. 5) We further validate the generalizability of our method by conducting experi- ments on additional long-tail datasets Places365-LT (34,, iNaturalist [54], and fMoW-S2 [6] 8). Our debLoRA consistently outperforms baselines, achieving up to 7.2% improvement on tail classes (see Appendix). 2) Oriented Object Detection. We validate our method's is generalization ability on the oriented object detection task in Table We have two key findings. 1) Our debLoRA achieves the highest mAP scores across all positions. Notably, debLoRA outperforms vanilla LoRA by an impressive 6.7 percentage points. 2) Notably, all methods performed better in the middle classes than in the head. This might be attributed to the greater intra-class variation head classes, whereas middle classes have more distinct and compact features.
Table 4: Evaluation the oriented object detec- tion task. We implement debLoRA for long-tailed detection tasks. Our results are marked in gray
Method
mAP (%)
Average
Head
Middle
Tail
(%)t
Zero-Shot
71.0
73.7
55.9
66.9
Fine-Tune
76.3
84.9
64.3
75.2
LoRA
77.5
86.3
76.7
Reweight 29
74.3
86.8
66.9
76.0
ECM 22
78.1
87.4
68.5
78.0
w/ debLoRA
79.4
88.5
73,2
80.4
Conclusion
In this paper, we propose debLoRA, a novel approach for adapting foundation models to data- scarce and long-tailed remote sensing domains while mitigating representation bias. Our method introduces unsupervised clustering to capture robust visual attributes shared across classes, and feature calibration rectify the bias in tail class representations. We validate the effectiveness of debLoRA through extensive experiments on multiple RS adaptation settings and downstream tasks, where it consistently outperforms vanilla LoRA and other long-tailed recognition methods. Notably, debLoRA achieves sigmificant performance gains on tail classes without sacrificing the performance on head classes, highlighting its ability learn debiased feature representations.
A Acknoweedgments and Disclosure of Funding
The author gratefully acknowledges the support from the DSO research grant awarded by DSO National Laboratories, Singapore, and the Lee Kong Chian Fellow grant awarded to Dr. Qianru Sun by Singapore Management University.

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A Appendix
This appendix contains the following supplementary information:
1. Section A.1 details on the customized SAR ship dataset used in the ORS SAR setting, complementing the experiments in Section
2. Section A.2 and a multi multi-spectral remote sensing dataset, to demonstrate the generalizability our method.
presents experiments on additional datasets, including natural image datasets
3. Section A.3 provides ablation studies and additional analyses, including quantitative feature analysis, sensitivity to cluster number K, statistical analysis, and the comparison with self-supervised learning from scratch.
4. Section A.4 discusses the limitations of our work.
A.1 Details of the customized SAR ship dataset
We selected the FUSAR-Ship 21 and SRSDD [3 datasets as our source datasets due to their high resolution (S 10m) and fine-grained ship subcategories,
shown in Figure A1 However,
both datasets have limitations. Figure (AT]a) shows that the FUSAR-Ship dataset has insufficient test samples (i.e., certain categories have only I 15 test samples) and unclear category definitions (e.g., "Reserved" or "Unspecified" categories). Figure AT]b) reveals that the SRSDD dataset also suffers from insufficient test samples. To address these issues and establish robust benchmark, we combined the ship categories from both datasets, merging those with fewer than 10 test samples into an "others" category.
400
Test Sample
Preserved Deprecated
325
Test Sample
400
Sufficient Insufficient
200
120
Dieefenne
*Portenddr
Wegietim
9MR
LavEnbroe
Unippelllad
7ag
Tamker
Parknng
Clarpe
"Cher
Other
Tanker
Dredger
Flshing
Cargo
Honisoeli
(e) FUSAR Dataset
(b) SRSDD Datatset
Figure Al: Constraints of the SAR datasets' test sets. This figure illustrates the per-category test
sample distribution of (a) the FUSAR dataset and (b) the SRSD dataset. The FUSAR dataset suffers from insufficient test samples and vaguely defined classes (indicated by "*"). Similarly, the SRSDD dataset also has the issue of insufficient test samples.
A.2 Experiments on Additional Datasets
To demonstrate the generalizability of our debLoRA method, we conducted experiments three additional datasets: two from the natural image domain (Places365-LLT (34) and iNaturalist 2018 (54))
and one multi-spectral remote sensing dataset (IMoW-S2 66 8). These datasets were chosen for their unique properties: 1) Places365-LT exhibits substantial domain gap from Stable Diffusion''s pre-training data, allowing iNaturalist 2018 has high imbalance ratio of 500, enabling us to assess our model's performance under severe class imbalance conditions. 3) fMoW-S2 contains multi-spectral data, including visible, near-infrared, and shortwave infrared bands, Table Al: Comparison INaturalist0018 datasets. Results reported in top-
evaluate the performance of our domain adaptation model. 2)
Places365-LT and
complementing our existing experiments on op- tical (DOTA) and SAR (FUSRS) imagery. The results are given Table AT and Table
I accuracy (%). Our results marked in gray
Placec66-117
INaturalie
Moon
Middle
Midile
Middle
1) On Places365-LT and iNaturalist 2018 (Ta- ble AI]. debLoRA consistently outperforms LoRA, especially for tail classes. We observe improvements of 4.3% and 7.2% for Places365-LT and iNaturalist 2018 tail classes, respectively.
Fine-Tune
43.2
30.0
96.5
66.2
67.5
$4.9
50.1
53.5
LORA
48.3
4210
41.
71.9
746
71.2
60.1
55.3
5%.1
Wi BENEWRA
61.2
726
798
76.4
61.8
65.6
638
21

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2) For the fMoW-S2 dataset ATa we adapted Stable Diffusion (SD) the scene recognition task. The dataset was manually divided into "Head" (34 classes comprising 80% of the samples) and "Tail" (28 classes comprising 20% of the samples). Results were evaluated by linear probing. debLoRA achieves the highest overall accuracy (46.8%) and tail class accuracy (41.2%) surpassing the second-bess method (ResLT) by 0.3 and 2.6 percentage points, respectively.
Table A2: Results the MMoW-S2 dataset.
Method
SD
MMow-S2
Head
Tail
Overall
Fine-Tume
46.2
34.6
44.9
LaRA
38.1
w/ ResLT
46.8
38.6
46.5
debl.oKA
46.8
41.2
46.8
These results confirm that our method effectively addresses the long-
tailed distribution problem across various domains, including natural images and multi-spectral remote sensing data. The consistent improvements, particularly for tail classes, highlight the robustness of debLoRA in handling class imbalance and domain adaptation challenges.
A.3 Ablation Studies and Additional Analyses
We conducted several experimental analyses to comprehensively evaluate our debLoRA method. These experiments aim to validate the effectiveness of our approach, investigate its sensitivity to key hyperparameters (i.e., cluster number K), and demonstrate the statistical significance.
Sensitivity to Cluster Number K. We conducted an ablation study to investigate the sensitivity of our method to the number of clusters (K) used in the de-biasing process. Table A3 shows the SD - DOTA adaptation results. We can observe that the performance generally improves as increases, with the most significant gains observed for tail classes. For instance, when K increases from 16 to 32, the Fl score for tail classes improves by 4.7%. The performance peak around K=32 suggests good default value for our method. These findings indicate that our method is sensitive to K but remains effective across different values.
Table A3: Sensitivity to clus- ter number K. Default value is marked in gray.
Macro F1 Score (%)
K
Head
Middle
Tail
16
99.1
96.9
90.4
32
99.3
97.7
95.1
64
99.3
97.4
94.8
Quantitative Feature Analysis. To further vali- date the effectiveness of our debLoRA method, we present a quantitative analysis of the learned fea- tures, focusing on inter-class and intra-class distances. Table A4 shows the results on the DOTA dataset. Our analysis reveals several key observations about debLoRA First, it enlarged the inter-class distance between tail and head classes, with the average co- sine distance increasing from 0.702 to 0.719. This
Table A4: Quantitative feature analysis. Class distance evaluated by cosine distance and reported on the DOTA dataset.
Inter-class
Intra-class
Method
Head-
Tail-Tail
Tail
Fine-tuning LoRA
0.674
0.621
0,170
0.702
0.607
0.182
w/ debLoRA
0,719
0.632
0.146
indicates improved separation between these class groups. Second, debLoRA reduced the intra-class distance for tail classes, as evidenced by the decrease in average cosine distance from 0.182 to 0.6... This suggests a tighter clustering of tail samples. Finally, we observed an increase in inter-class distance among tail classes, with the average cosine distance rising from 0.607 to 0.632. This demonstrates better separation among different tail classes. These findings support the effectiveness of debLoRA in improving feature separation for tail classes.
Statistical Analysis with Error Bars. To demonstrate the statistical significance of our results, we report the results of three runs with random initializations on the SD + DOTA ex- periment. Table A5 shows the results.
Table A5: Error Bar Analysis. Reported mean + std. Our results are marked in gray...
Macro Fl Score (%)
Method
Head
Middle
Tail
These results demonstrate that our debLoRA method consistently outperforms other ap- proaches, especially for tail classes, with sta- tistically stable improvements. The small std across all methods indicate the stability of the results. Notably, d shows the most substantial improvement for tail classes, with mean Fl score of 94.8% and a standard deviation of only 0.3%, highlighting both the effectiveness and consistency of our approach in addressing the long-tailed distribution problem.
Sero-Shot Fine-Tune
99.2 1 0.1
97.4 1 0.3
87.6 0.6
99.1 + 0.1
96.7 + 0.1
86.8 + 0.2
LoRA
99.3 0.1
97.2
91.8
w/ ResLT
99.3 0.1
0.3
92.9 0.3
w/ debLoRA
99.3 0.1
97.5 0.2
94.8 1 0.3
22

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Comparison with SSL Methods. Instead of learning representations from scratch via self- supervised learning (SSL), our work focuses on adapting pre-trained models target domains. In Table A6 we compare our method with SSL methods in terms of computational and data efficiency:
Table A6:
Comparison with SSL methods.
Method
GPU Hrs
#Params (M)
Data Size
SatMAE
6,144
307
712K
MoCo-v3
4,096
86
712K
Ours
24
0.08
7K
Computational Cost. Self-supervised pre- pre-training requires substantial computational resources, e.g- SatMAE requires 6,144 GPU hours for training from scratch. In contrast, our debLoRA only requires 24 GPU hours for adaptation, achieving more than two hundred times reduction in computation while preserving the strong performance of foundation models.
2) Data Efficiency. Self-supervised methods (e-g., SatMAE and MoCo-v3) require large-scale pre- training data (more than 700K images) to learn robust representations. In contrast, our method effectively adapts new domains with limited data, e.g. achieving strong long-tailed recognition performance on the FUSRS dataset with only 6,971 images.
A.4 Limitations
While our proposed debLoRA method has proven effective in adapting foundation models remote sensing domains with limited data and long-tailed distributions, we acknowledge three key limitations: Assumption of shared visual attributes. Our method assumes that visual attributes shared across classes, enabling robust representation learning through clustering. However, the visual attributes highly class-specific there is significant intra-class variation, the effectiveness of our approach may be reduced.
Sensitivity to hyperparameters. The performance of debLoRA depends on the selection of hyper- parameters, such as the number of clusters K. The optimal value of K may differ depending on the specific dataset and adaptation setting.
Limited evaluation on SAR datasets. Due the scarcity of sarge-scale SAR datasets with sufficient samples for reliable evaluation, we created customized dataset by combining two existing SAR datasets. Further investigation needed assess the performance of our method on broader range of SAR datasets and tasks.
By acknowledging these limitations, aim provide transparent and objective assessment of work and to encourage future research addressing these challenges to further improve long-tailed adaptation in remote sensing domains.
23


Reviews:
[{"review_id": "MJiaxK5go9", "reviewer": "Reviewer_r61t", "summary": "This work highlights the long-tail problem in transferring existing foundation models to RS domains, and provide a interesting pipeline consisting of clustering, calibration, and training.\n\n1. A comprehensive summary on the transferring from natural images or between RS domains are provided.\n\n2. Representation De-biasing is proposed for search debiased feature centers for tail classes.\n\n3. Impressive results on a set of downstream tasks.", "questions": "1. Fig 2(a) is a bit confusing to me. The region in blue is for tail samples, are the triangles in dash line and blue for wrongly placed tail samples? And why the center is in blue region as there is no supporting head samples there?\n\n2. Please provide a fully supervised FCOS baseline for comparison.\n\n3. May the results from [7, 15] be provided?", "limitations": "Detailed discussion on limitations are provided in Appendix.", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. This work is well-written. The highlighted issues are well presented with supports from experimental results and analysis on RS datasets, which is then attributed to the tail problem, enhancing the motivation of this work.\n\n2. A historical summary on related works on long-tail problem and transfer learning in remote sensing and the brief preliminaries on LoRA series.\n\n3. The ideas of de-biased cluster center and feature calibration seem effective from the experimental results.", "weaknesses": "1. My major concern on this work is from the weighted averaging over all cluster centers for each tail class. In such a case, the debiased cluster centers can be considered from the same linear space, will such debias be sufficient enough for distinguishing different classes? Or will the center for a tail class be near some others.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We greatly appreciate your thorough review and insightful comments! Your positive remarks on our paper's structure, historical summary, and method effectiveness are encouraging. We value your critical questions and have addressed them in detail below. Thank you for helping us improve our research.\n\n**Weakness 1: Regarding linear space**\n\nThanks for the insightful comments. We address this question from three perspectives:\n\n1. Image features extracted from large-scale pre-trained foundation models already exhibit good linear separability. Recent literature [R1,R2,43] shows that applying simple linear classifiers (*e.g.*, linear probing head and k-NN classifier head) on foundation models (DINO, CLIP and SD) achieves impressive performance for discrimination tasks.\n\n2. Our de-bias method has indeed improved the feature discrimination between classes. To validate this, we report quantitative analysis on inter-class and intra-class distances in Table R5 in the attached PDF. Results show our debLoRA:\n\n   - enlarged inter-class distance between tail and head classes. The average cosine distance increased from 0.702  to 0.719, indicating improved separation;\n   - reduced intra-class distance for tail classes. The average cosine distance decreased from 0.182 to 0.146, suggesting tighter clustering of tail samples;\n   - increased inter-class distance among tail classes. The average cosine distance rose from 0.607 to 0.632, demonstrating better separation among different tail classes.\n\n3. We acknowledge the potential benefits of exploring non-linear de-biased centers. A simple way to achieve a non-linear transformation of the class centers is to apply a few non-linear layers (*e.g.*, MLP) on them (*i.e.*, taking the centers as input into a learnable MLP). The learning of these layers can be realized by either meta-learning or training together with our debLoRA parameters, where the former requires an additional validation set to be built. Both methods cost time in model designing and training. We are still working on them, and will report the results in OpenReview if we obtain any during the discussion period. We are also happy to try other methods of introducing non-linearity to de-biased centers if we receive any suggestions from the reviewers during the discussion.\n\n**Question 1: Clarity of Figure 2(a) caption**\n\nWe apologize for any confusion in Figure 2(a). We want to highlight that:\n\n1. The blue star represents the center of tail training samples (in blue color), but not the center of all samples.\n2. The dashed blue triangles represent tail validation samples misclassified as head classes. They demonstrate the model bias towards head classes.\n\nWe will refine the caption of Figure 2 to avoid any confusion. Please refer to the caption of Figure R1 in the attached PDF for the updated caption.\n\n**Question 2: Fully supervised FCOS baseline**\n\nThank you for the suggestion! Please note that the methods (except Zero-Shot) are all fully-supervisedly trained on DOTA datasets with bounding boxes as labels. If we understand the question correctly, the reviewer wanted to check the results without any transfer learning, *i.e.*, the results of training a model from scratch as in the original paper of FCOS [50]. Table R1 in the attached PDF provides such results with two backbone networks: ResNet-101 (used in its original paper) and SD U-Net (used in our submission). SD U-Net is used for fair comparison regarding the amount of network parameters. \n\nWe can observe from this table that From-Scratch underperforms transfer learning approaches (Fine-Tune, LoRA, and debLoRA), especially on tail classes, and shows a larger performance gap between head and tail classes. For example, comparing rows 2 and 4, we see that it is lower than the basic Fine-Tune method by 0.2, 0.7, and 1.5 percentage points of mAP for head, middle, and tail classes, respectively. Moreover, the head-tail gap for From-Scratch (row 2) is as high as 13.3 percentage points, while for Fine-Tune, LoRA, and debLoRA, the gaps are 12, 11, and 6.2 percentage points, respectively (please note that the lower is better). This phenomenon occurs because transfer learning can leverage the robust representation of a pre-trained model (learned on large-scale datasets), while the From-Scratch model is trained with only the long-tailed training data of a small dataset (please note that here the \"small\" is a relative concept, compared to the large-scale pre-training datasets such as LAION-5B).\n\n**Question 3: Results from [7, 15]**\n\nFirst, for SatMAE [7], we have reported SatMAE results in Table 4 of the main paper on the \"SatMAE → FUSRS\" columns.\n\nSecond, SkySense [15] results are not directly comparable to ours because they did not benchmark their method on long-tailed settings. Besides, we cannot implement their methods in our setting because their pre-trained model is not open-sourced yet. If their model becomes available during the revision period of NeurIPS'24, we will gladly include it in our final paper.\n\nAs requested by other reviewers, we evaluated our method on more general long-tailed datasets (see Tables R2 and R4). On Places365-LT and iNaturalist 2018, debLoRA consistently outperforms LoRA, especially for tail classes (gains of 4.3% and 7.2%). On fMoW-S2, debLoRA achieves the best overall (46.8%) and tail class (41.2%) accuracy, surpassing ResLT by 0.3% and 2.6%. Kindly refer to our response to reviewer bDAb (weakness 2 & 8 & 10) for detailed analysis.\n\n**References**\n\n[R1] M. Raghu et al., \"Do vision transformers see like convolutional neural networks?,\" Advances in neural information processing systems, vol. 34, pp. 12116–12128, 2021.\n\n[R2] M. Caron et al., \"Emerging properties in self-supervised vision transformers,\" in Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 9650–9660.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "TB7u5IydBt", "reviewer": "Reviewer_bDAb", "summary": "The authors propose a framework called debLoRA for adapting remote sensing foundation models.  This approach aims to learn a de-biased feature representation which improves classification/detection performance on rare classes.  Performance is assessed on transfer to two different RS datasets.", "questions": "Line 45-  \"which\" clause is modifying the wrong noun\nLine 46- \"what\" should be \"which\".  Sentence should end with a question mark\nLine 170 is awkward\n\nThe explanation around biased features at the bottom of p2 could be clearer.", "limitations": "Limitations are not discussed.  There are no significant societal concerns.", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "Remote sensing is a unique domain that is often overlooked.  Developing methods to develop models which can be used broadly to solve a number of tasks while making use of the huge amount of data available is important for the community.", "weaknesses": "The writing and sentence structure could be substantially improved for clarity and readability.  In general, reducing complex clauses would improve readability.  The opening sentence of the abstract, for example, is cumbersome to read.  It is also advised to avoid beginning sentences (or opening clauses)  with \"it\" as that can lead to ambiguity.  Other clauses within the text are modifying the wrong nouns. \n\nThe abstract does not clearly set the scope for the work.  It's clear there is a focus around remote sensing, but parts of the abstract are also written which suggest the method would apply broadly, beyond remote sensing.  Making it clear whether this approach is truly general, or specifically geared toward remote sensing is important.  \n\nThe \"why it works\" section should be much more rigorous in nature with the mathematics to justify the approach, not just the textual description. \n\nThe authors cite several Contrastive methods, then exclude them because of \"lack of high-resolution samples\".  However, these methods make on requirement on the resolution needed.  Additionally, there are newer methods than those cited which address some of these concerns.  \n\nSection 4 does not read well.  I would suggest rewriting this so the mathematical formalism is clearer and more succinct.  \n\nResults would be stronger if they were run multiple times with different initializations to generate error bars.  The results aren't significantly better (in absolute terms) in many cases, so it's not obvious that they are statistically significantly better.  \n\nThis approach should be compared to other fundamentally different (but commonly used) approaches, such as the contrastive learning methods.  \n\nThere are a huge number of remote sensing datasets out there (both general as well as task/domain specific) so I think exploring more than just two is needed.  \n\nAdditional analysis of the features learned should be performed.  A big focus of the introduction was around biased features.  The authors have tried to show that measuring classification/detection accuracy on the head/mid/tail of the distribution captures this, but actual exploration and separation of the features also needs to be quantified.  \n\nSince this was stated as a broadly general method, not just related to remote sensing, it should be tested on non-remote sensing datasets as well.  \n\nHow performance changes as a function of dataset size should be explored.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Weakness 1 & Question 1: Lack of clarity**\n\nWe sincerely appreciate the comment! In the revision, we will perform careful proofreading, *e.g.*, 1) by simplifying complex clauses; 2) by reducing starting sentences with ambiguous \"It\"; and 3) by fixing the issues noted by reviewer: correct modifier placement, replace \"what\" with \"which\", and rewrite unclear sentences.\n\n**Weakness 2 & 10: \"not clearly set the scope for the work\" and \"tested on non-remote sensing datasets\"**\n\nThank you! Our original experiments focus on Remote Sensing (RS) because: 1) RS images present significant domain gaps from natural images, offering a more challenging test case (as detailed in Lines 22-26). 2) The RS domain has heavier problems of data scarcity and class imbalance (as clarified in Lines 36-43).\n\nWe supplement the experiments on two natural image datasets: Places365-LT and iNaturalist2018. Both datasets are widely adopted in long-tailed learning [66]. 1) Places365-LT exhibits a substantial domain gap from SD's pre-training data. It is thus suitable to evaluate the performance of a domain adaptation model. 2) iNaturalist2018 has a high imbalance ratio of 500 and can evaluate the models under severe imbalance. Results are shown in Table R2. Our debLoRA consistently outperforms second best, and gains tail improvements of 4.3% and 7.2% for Places365-LT and iNaturalist 2018, respectively. We will clarify our research scope in the revision.\n\n**Weakness 3: \"why it works\"**\n\nWe apologize for this misleading sentence. The paragraph led by \"why it works\" describes our experimental observation instead of justifying truly for \"why it works\". We will remove this sentence to avoid confusion.\n\n**Weakness 5: Rewrite Section 4**\n\nWe'll make the following improvements to Section 4:\n\n1) Emphasize the input/output of our 5-step method:\n- Feature extraction: raw images $x$ → biased features $z$\n- Clustering: biased features $z$ → cluster centers $\\mu_k$\n- De-biased center calculation: cluster centers $\\mu_k$ → de-biased centers $\\hat{\\mu}_c$\n- Feature calibration: biased features $z$, de-biased centers $\\hat{\\mu}_c$ → calibrated features $\\tilde{z}$\n- debLoRA training: original & calibrated features → debLoRA module $g_\\phi$\n\n2) Simplify formulas:\n- Equation 1: $\\min_{\\mu_k} \\sum_{i=1}^N \\min_{k} |z_i - \\mu_k|^2, \\quad \\text{s.t.} n_k \\geq \\frac{N}{K\\rho}, \\forall k$.\n- Equation 3: Add $\\alpha = \\min(1, \\frac{10}{\\gamma})$.\n\n3) Clarify key sentences:\n- Line 208: \"We calibrate each tail class representation by moving it closer to the de-biased center.\"\n- Line 217: \"For tail classes with larger imbalance ratio, a higher $\\alpha$ moves features closer to the de-biased center.\"\n\n**Question 2: Clarification of \"biased feature space\"**\n\nWe provide a more precise definition of \"biased feature space\" here. Let $\\mathcal{Z}_h$ and $\\mathcal{Z}_t$ be the feature spaces of head and tail classes, respectively. We define feature spaces as biased if $\\text{Vol}(\\mathcal{Z}_h) \\gg \\text{Vol}(\\mathcal{Z}_t)$, and $\\exists z_t \\in \\mathcal{Z}_t : P(z_t \\in \\mathcal{Z}_h) > P(z_t \\in \\mathcal{Z}_t)$, where $\\text{Vol}(\\cdot)$ denotes feature space volume and $P(\\cdot)$ denotes the probability predicted by the model.\n\n**Weakness 4 & 7: Compare with contrastive methods**\n\nThank you! Our work focuses on adapting pre-trained foundation models to solve RS domain problems. These foundation models have consistently outperformed conventional contrastive methods. For instance, SatMAE [7] achieves 63.84% accuracy on fMoW-S2, outperforming SeCo (51.65%) and GASSL (50.69%).\n\nWhile these results demonstrate the superiority of adapting foundation models, we still agree that a direct comparison would provide a comprehensive view. However, due to private data [7,43] and GPU constraints, we cannot large-scalely train contrastive models in a fair data scale (as foundation models). However, we will try our best to conduct more experiments during the discussion period if you require.\n\n**Weakness 6: Statistical error bars**\n\nAgree! We conducted three runs of the SD $\\to$ DOTA experiment with random initializations. The results are shown in Table R3.\n\nRegarding \"the results aren't significantly better (in absolute terms)\", we gracefully disagree. Our debLoRA consistently outperforms the second-best methods by notable margins on tail classes, *e.g.*, 2.5% over SADE in Table 2 and 2.4% over ECM in Table 4. These improvements are regarded as substantial. For reference, Table 3 in [R1] considers an improvement of 1.59% on DOTA as significant.\n\n**Weakness 8: More Remote Sensing datasets**\n\nGlad to do so! We conducted experiments on the fMoW-S2 dataset [7] because: 1) it contains 13 bands of multi-spectral data, including near-infrared bands, which complement our existing experiments; 2) it exhibits severe class imbalance (imbalance ratio of 130.8); 3) it provides a substantial validation set (84,966 validation samples). We performed experiments on SD $\\to$ fMoW-S2. Results are given in Table R4. Our debLoRA achieves the highest overall accuracy (46.8%) and tail class accuracy (41.2%), surpassing the second-best method ResLT by 0.3% and 2.6%, respectively.\n\n**Weakness 9: Quantified feature analysis**\n\nWe treasure your feedback! We provided an actual feature distribution in Figure 3. To further address your concern, we present quantitative experiments on inter-class and intra-class distances in Table R5. Two key findings: 1) debLoRA achieves higher inter-class distances for both head and tail classes, indicating improved feature separability. 2) debLoRA maintains lower and more consistent intra-class distances among tail classes, suggesting more compact tail features.\n\n**Weakness 11: Ablation of dataset size**\n\nThank you! The ablation study using 5% and 50% of the DOTA dataset is ongoing. We will report the results during the discussion period.\n\n**Reference**\n\n[R1] Pu, Y. et al., Adaptive rotated convolution for rotated object detection.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "pI61StGrxO", "reviewer": "Reviewer_meTz", "summary": "In remote sensing, many datasets have a long-tail problem. As a result, models trained on these datasets often perform much worse on the tail classes than on the head classes. The authors propose a fine-tuning strategy called debiased LoRA that addresses long-tail distribution problems. Their proposed approach has 3 primary steps. First, they cluster the \"biased\" features from a pre-trained model. Each cluster represents some visual attribute. Secondly, they compute a debiased cluster center for each tail class and move the respective class embeddings closer to the debiased center. Finally, they train a LoRA module to predict the debiased features from biased features. These debiased features can be used in downstream tasks to achieve better performance for the tail classes.", "questions": "Please address the points that have been raised in the weakness section. My concerns are mostly related to the lack of experiments and ablations that would have made it easier to understand the efficacy and robustness of the proposed approach.", "limitations": "The authors have done a good job of presenting the limitations of their work.", "rating": 7, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper is very well written. \n- The visualizations are very helpful in understanding the intuition behind the proposed approach\n- Long-tail distribution problems occur in many other fields outside of RS such as species distribution modeling. While the approach is proposed for RS imagery, it seems to be a general approach that can be used for any dataset with this problem.\n- The results show that the approach does not punish the head and middle classes, and even improves their performance in some scenarios", "weaknesses": "- There were no ablation studies to show how susceptible the model is to some hyperparameters. For example: an ablation on the choice of K would be helpful to understand how robust the model is when selecting the number of clusters.\n- The experiments are conducted on limited datasets. This makes it hard to confidently believe that debLoRA will generalize to all datasets with long-tail problem", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate your positive feedback on our paper's clarity, effectiveness, and novelty! Thank you for these encouraging comments. We are also grateful for your insightful suggestions regarding ablation studies and the generalization of our method. We have addressed each point in detail below and will incorporate these additional experimental results in our revised paper. Please let us know if you believe any further investigation is necessary!\n\n**Weakness 1: Sensitivity to cluster number $K$**\n\nCan't agree more! We conducted an ablation study on the number of clusters ($K$) in Table R6 in the attached PDF. We start from $K=16$, slightly above DOTA's 15 categories, to allow for potential subclusters. We can observe:\n\n1) performance generally improves as $K$ increases, with the most significant gains observed for tail classes. For instance, when $K$ increases from 16 to 32, the F1 score for tail classes improves by 4.7%; and\n\n2) the performance peaks around $K$=32, suggesting a good default value for our method.\n\n**Weakness 2: Generalization to more datasets**\n\nWe appreciate your feedback! To address this generalization concern, we conducted experiments on two natural image domain datasets (Places365-LT and iNaturalist 2018) and one multi-spectrum RS dataset (fMoW-S2). We chose these datasets based on their unique properties:\n\n1) Places365-LT exhibits a substantial domain gap from SD's pre-training data. It is thus proper to evaluate the performance of a domain adaptation model. \n\n2) iNaturalist2018 has a high imbalance ratio of 500 and can evaluate the models under severe imbalance.\n\n3) fMoW-S2 contains multi-spectral data, including visible, near-infrared, and shortwave infrared bands, which complement our existing experiments on optical (*i.e.*, DOTA dataset) and SAR (*i.e.*, FUSRS dataset) imagery.\n\nWe conducted adaptation experiments following the same setup used for Table 2 (in the main paper). On Places365-LT and iNaturalist 2018, debLoRA consistently outperforms LoRA, especially for tail classes (gains of 4.3% and 7.2%). On fMoW-S2, debLoRA achieves the best overall (46.8%) and tail class (41.2%) accuracies, surpassing ResLT by 0.3% and 2.6%. Kindly refer to our response to reviewer bDAb (weakness 2 & 8 & 10) for more detailed analysis.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
We sincerely thank all reviewers for their valuable feedback and constructive comments. We are pleased that the reviewers have recognized our work as:

- **Well-written** (Reviewer r61t) and **very well written** (Reviewer meTz)
- Technically sound (Reviewer r61t and meTz)
- **Impressive results on multiple tasks** (Reveiwer r61t) and good performance (Reviewer meTz)
- Practical solution in remote sensing (Reviewers r61t and meTz)
- Generalizable beyond remote sensing (Reviewer meTz)

We have carefully addressed each reviewer's comments and questions in their respective response sections below. For reference, we have included additional tables and figures in the attached PDF to support our responses.

Meta-review: This paper received divergent reviews. Post rebuttal, the reviewer who voted strong reject did not respond. The AC checked the paper, the rebuttal and the reviews, and felt that all the concerns raised had been addressed, with the exception of the comparison to contrastive learning/self-supervised learning approaches. The AC recommends acceptance, and encourages authors to include these comparisons as well as the results in the rebuttal pdf in the final camera ready/appendix.
Decision: Accept (poster)