CARL: Camera-Agnostic Representation Learning for Spectral Image Analysis
Authors: Alexander Baumann
Leonardo Ayala
Silvia Seidlitz
Jan Sellner
Alexander Studier-Fischer
Berkin Özdemir
Lena Maier-hein
Slobodan Ilic
Venue: ICLR 2026 Poster (2026)
Keywords: Representation Learning
Self-Supervised Learning
Spectral Imaging
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

Abstract: Spectral imaging offers promising applications across diverse domains, including medicine and urban scene understanding, and is already established as a critical modality in remote sensing.
However, variability in channel dimensionality and captured wavelengths among spectral cameras impede the development of AI-driven methodologies, leading to camera-specific models with limited generalizability and inadequate cross-camera applicability.
To address this bottleneck, we introduce CARL, a model for Camera-Agnostic Representation Learning across RGB, multispectral, and hyperspectral imaging modalities.
To enable the conversion of a spectral image with any channel dimensionality to a camera-agnostic representation, we introduce a novel spectral encoder, featuring a self-attention-cross-attention mechanism, to distill salient spectral information into learned spectral representations.
Spatio-spectral pre-training is achieved with a novel feature-based self-supervision strategy tailored to CARL. 
Large-scale experiments across the domains of medical imaging, autonomous driving, and satellite imaging demonstrate our model's unique robustness to spectral heterogeneity, outperforming on datasets with simulated and real-world cross-camera spectral variations. 
The scalability and versatility of the proposed approach position our model as a backbone for future spectral foundation models. Code and model weights are publicly available at https://github.com/IMSY-DKFZ/CARL.

Published as a conference paper at ICLR 2026
CARL: CAMERA-AGNOSTIA REPRESENTATION LEARN- ING FOR SPECTRAL IMAGE ANALYSIS
Alexander Baumann" 1,2,3, Leonardo Ayala² Silvia Seidlitz2:4.5.6
Jan Sellner Alexander Maudier-Fischer2... 7,8,9,00 Berkin Özdemir
Lena Maier-Hein 2,3,4,5,6,1
Slobodan Ilic
Siemens AG, Munich
2 Division of Intelligent Medical Systems, German Cancer Research Center (DKFZ) Heidelberg
3 Medical Faculty and Faculty of Mathematics and Computer Science, Heidelberg University
5 National Center for Tumor Diseases (NCT), NCT Heidelberg
6 HIDSS4Health, Heidelberg
7 Department of Urology and Urosurgery, University Medical Center Mannheim
8 Department of General, Visceral, and Transplantation Surgery, Heidelberg University Hospital
9 Division of Intelligent Systems and Robotics in Urology, DKFZ Heidelberg DKFZ Hector Cancer Institute, University Medical Center Mannheim
ABSTRACT
Spectral imaging offers promising applications across diverse domains, including medicine and urban scene understanding, and is already established as a critical modality in remote sensing. However, variability in channel dimensionality and cap- tured wavelengths among spectral cameras impede the development of Al-driven methodologies, leading to camera-specine models with limited generalizability and inadequate cross-camera applicability. To address this bottleneck, we intro- duce CARL, a model for Camera-Agnoste Representation Learning across RGB, multispectral, and hyperspectral imaging modalities. To enable the conversion spectral image with any channel dimensionality to a camera-agnostic repre- sentation, introduce novel spectral encoder, featuring self-attention-eron attention mechanism, to distill salient spectral information into learned spectral representations. Spatio-spectral pre-training achieved with novel feature-based self-supervision strategy tailored to CARL. Large-scale experiments across the do- mains of medical imaging, autonomous driving, and satellite imaging demonstrate our model's s unique robustness to spectral heterogeneity, outperforming on datasets with simulated and real-world cross-camera spectral variations. The scalability and versatility of the proposed approach position our model as backbone for future spectral foundation models. Code and model weights are publicly available http://gjihbbb.omm///SSDXZZZZAZ
INTRODUCTION
Spectral imaging, including RGB, multispectral, and hyperspectral imaging, capture channel-wise reflectance information for camera-specific wavelengths. The enriched spectral information, contained few to hundreds of channels, enables applications in a variety of fields, including segmentation
and classification tasks in medicine (Seidlitz et al.) [022] Ayala et al.) 2023), urban scene perception (Theisen et al. 2024; Shen et al.), and remote sensing (Lu et al.] 2020 Thenkabail et al., 2018). To develop robust solutions for these tasks, data-driven models have emerged the prevailing standard, maximizing performance through the utilization of all available images, regardless of
camera characteristics. However, the evolution of spectral imaging technology has resulted in significant variability in camera devices (Qian (02T), leading to the formation of camera-specific data silos. These silos share valuable domain-specific geometric information but differ in spectral characteristics such as channel dimensionality and covered wavelengths. Conventional imaging models such as Convolutional Neural Networks (CNNs) (He al. 2016) cannot accommodate these
"Corresponding Author: baumann. eexanderr resiemens com,
1: Equal Contribution

---

Published as a conference paper at ICLR 2026
Table 1:
Compartson of state-of-the-art spectral image encoding approaches. The proposed
model is the only one that incorporates all four desirable characteristics simultaneously.
Model
Wavelength awareness
Channel-invariance
Spatio-spectral encoding Spatio-spectral SSL pre-training
SpectmlGPPT
Spectral Earth
DOFA
Coperiius-FM
SMARTIES
CARL (Ours)
variations, resulting in camera-specific models and absent knowledge transfer between these data silos. Therefore, such models ignore large amounts of data, limiting their robustness and cross-apliicability. Furthermore, supervised downstream models inherently limited by the availability of application- specific annotations. Given that manual labeling is time-intensive and often infcasible for large-scale datasets, self-super pre- pre-training has emerged as a powerful alternative (He et al.) [022] Devlin et al. 2019]] Caron et al., 2021 He et al. (020). Empirical findings in Natural Language Processing have demonstrated that the effectiveness of sulf-ssuerrisee-leenee learning (SSL) scales with the amount of training samples (Kaplan et al. (020). This motivates the use of extensive cross-silo datasets to enhance pre-training. However, existing strategies are not camera-agnostic, restricting pre-training to camera-specific data silos and limiting their effectiveness. To overcome these obstacles, we propose a novel camera-agnoste model with a tailored SSL strategy that is capable of unlocking the data treasures of different cameras that are not yet accessible (Fig. T. Our contribution is threefold:
1. First approach to spatio-spectral camera-agnostic representation learning: We propose the first method that enables spatio-spectral encoding in a camera-agnostic manner. To this end, we introduce wavelength positional encoding for establishing cross-camera channel correspondences, and learnable spectral representations for efficient representation learning.
2. First camera-agnostic spatio-spectral self-supervision framework: We propose a novel spectral feature-based SSL strategy tailored to CARL, which can be seamlessly combined with I-JEPA spatial pre-training (Assran et al. 2023) to form an end-to-end framework for camera-agnostic meatio-spectral self-supervised pre-training
3. Large-scale cross-domain validation: We validated the proposed model in three application areas, specifically medical imaging, automotive vision, and satellite imaging. Across all experiments, approach outperformed both camera-specific and baselines, demonstrating superior cross cross-modalin modality knowledge transfer and unique robustness spectral heterogeneity arising from simulated and real-worke camera variations.
2 RELATED WORK
Feature extraction strategies for spectral imaging Generating rich image representations remains fundamental challenge in computer vision, with significant implications for downstream tasks such as image segmentation. For 2D spectral images, the encoding process inherently spans three dimensions: two spatial dimensions and one spectral dimension. In datasets with uniform spectral properties, conventional models such as CNNs and Vision Transformers (ViTs) are commonly em- ployed (Dosovitskiy et al. 2 Theisen al. 2024). However, these models focus solely on spatial encoding (2D projections, VIT blocks) and assume a fixed channel dimension. Recent approaches in remote sensing have introduced models that jointly encode spatial and spectral information, for exam- plc, by forming spatio-spectral patches (Cong et al.) [022] Hong et al. (024). Yet, these methods lack invariance the channel dimension, preventing their applicability to cross-camera datasets. |Baaaa et al. (2024) addresses this by introducing a Spectral Adapter that resolves the channel dimension through ID convolutions and pooling operations. However, it overlooks channel relationships derived from camera-specii wavelength information. To overcome this limitation, recent work has proposed channel-adaptive 2D projection layers that learn wavelength-conndiionee conditiondd projection matrices (Li et al. 2025al Xiong et al. 2024 Varga et al. 2023)] Wang et al. (2025). While effective, these methods rely on spatial operations and do not explicitly encode salient spectral information, which may reduce robustness spectrally heterogeneous datasets. Alternative strategies encode pixel- or patch-wise
2

---

Published as a conference paper at ICLR 2026
Previous: camera-specifie representations
Applications
Organ segmentation
Urban scene segmentation
Satellite imaging
Uiee model
HSI
Training:
HSI MSI
HSI RGB
MSI
Validation:
HS1
MSI
Image (reconstruted)
PEDI
Spectal
Spetial Enoodey
Reference
Chonnel- invorance
V
Spohoopecttal chondig V
Prediction
CARL: camera-agnostic representation
Figure 1: CARL addresses spectral camera variations by learning camera-agnostic represen- tations. Unlike existing methods that require retraining for each channel configuration, CARL generalizes across cameras and outperforms both camera-specific and channel-invariant approaches across domains. The model processes one image time, ensuring flexibility without dependence on fusion strategies.
information only along the channel dimension (Hong et al. 2021 Hang et al. 2019 Seidlitz et al. 2022). However, these designs cannot model geometric structures, which significantly impairs downstream performance. Fusion-based methods offer another solution by aligning multi-modal data with varying channel dimensions through early, mid, or late fusion (Lin al. 2023 Audebert
et al. 2018). Typically, these architectures incorporate modality-specific projection layers or entire encoders before feeding into multi-modaa encoder (Astrue et al.) 2025] Tseng et al.) 2025 Jakubik et al., 2025 Fuller et al. (203). Crucially, such methods assume access to all modalities during both training and inference. While this assumption may hold remote sensing with standardized sensors, it is impractical for industrial medical spectral imaging applications, where sensor diversity is broader, often unknown, and sensor-specific data silos contain relatively few samples.
Self-supervised learning strategies for spectral imaging With growing compute resources and data availability, SSL strategies have gained importance in recent years. In RGB imaging, masked image modeling has emerged as central paradigm, where input patches are randomly masked and reconstructed at the pixel level using strong encoder paired with lightweight decoder (He
et al. 2022). This paradigm has been extended to spectral imaging, encompassing camera-specilic ppatio-spectral encoding (Hong et 2024 Cong al. 2022) and camera- aamera--gnostic spatial modeling
(Xiong et al. 2024 Wang et al. 2025 Sumbul et al. 2025). Building on advances in RGB vision,
feature-based SSL has proven more efficient than pixel
approaches (Caron et al. 2021 Assran
et al. 2023), benefit that is particularly important in spectral imaging, where pixel values are highly sensitive to factors such as atmospheric conditions in satellite data or illumination calibration in laboratory settings (Baumann et al.. 2024). Accordingly. feature-basel SSL has been adapted to spectral imaging models, though existing methods remain restricted to spatial encoding and spatial self-supervision (Tseng al. 2025 Astrue al. 2025: Waldmann al. 2025). Notably, SSL
framework whether camera- aggossic,, feature-baser or both-has has yet been designed to capture spatio-spectral encoding. An overview of existing approaches is provided in Tab. [
FRAMEWORK FOR CAMERA-
SPECTRAL IMAGE ANALYSIS
In this paper, we present CARL, a novel model for spectral image processing, designed to unlock the potential of camera-specific data silos. As illustrated in Fig. 2 the proposed framework transforms camera-dependent spectral information into camera-agnosia representation through novel spectral encoder Expec. followed by the extraction of geometric information through a standard spatial encoder Espat- To establish cross-camera channel correspondences, translate the concept of positional encoding, traditionally used for discrete token positions within transformers (Vaswani et al. 2017), to channel-specifie wavelengths. To facilitate efficient representation learning along
3

---

Published as a conference paper at ICLR 2026
H W P
H W P
XxXxO
P XCXD
X D
X
P
P
2D Potching
Projection
E spec
E Apat
Cureroo Specifi
Extraction of camera-agnostic represention
Es,
Lorae excoral
S
SK
Cross- Attention
Self-Artentio
PPE())
Figure 2: Conversion camera-specific spectral image into a camera-agnostic representation. To address the heterogeneity in denendet spectral properties, dedicated spectral encoder extracts a camera-agnostic representation by leveraging spectral tokens encoding wavelength infor- mation. A spectral image of dimension H x W X C' is divided into patches of size P and projected band-wise into a D-dimensional feature space. The spectral encoder Éspec processes each patch individually, and hereby resolves the spectral dimension. In particular, Espec encodes the wavelength X of channel i as positional encoding (E(A,) and adds it to the embedded patch A,. Self-Attention across spectral tokens (A,)isc and Cross-Attention with K learned spectral representations yield en- riched representations (Sj)jsk- After aggregation into camera-agnostic representation, a standard image encoder, Espat: captures spatial relationships.
the spectral dimension, we propose novel encoder that distills channel information into sparse of spectral representations. Ultimately, the encoder produces camera-agnostic feature map enriched with spectral attributes, which can be seamlessly forwarded to established transformer- based spatial encoders (e.g., ViT). The spatial encoder Espar operates subsequent to Espee and enhances feature representation by capturing spatial relationships. Two learning paradigms are proposed for optimizing the spectral representations derived in Espex- Specifically, they are either learned implicitly by minimizing downstream task-specifie loss explicitly by minimizing self-supervised loss (CARL-SSL, as described in Sec. 3.2 and Fig.
3.1 ARCHITECTURE OF CARL
Given spectral image 1 H with arbitrary channel dimension, C. the objective is derive a camera-agnostic representation that contains salient spectral information. To project / to feature space, each channel is processed by shared 2D convolution with kernel size and stride equal to the patch size P and output channels D, yielding tensor of dimensionality # W x C' x D. A patch, denoted as A (Ai,..., Ac) E RCDD, is then independently processed by the spectral encoder, Espee. to generate a camera-agnostic representation. We first construct a positional encoding PE(A) E CCDD where = (Ai,..., Ac) and X; corresponds to the wavelength of channel i such that the model is capable of establishing channel correspondences across cameras with different wavelength specifications. this work, we employ the sinusoidal Fourier Features TTancik et al. 2020) to encode positional information within the spectral dimension, defined as:
P
PE(A.) = [cos (2TrA;;), sin (2)aX, E D
(1)
where a E IR is a scaling factor, B N (0, 02) E R/2 and o E R. Here, both a and or are hyperparameters. Subsequently, PE(A) is added to the patch A, thereby encoding the position of each A, along the wavelength axis. As illustrated in Fig. 2 selffaatention-crros-ttttiiiii module is introduced to process the spectral tokens, (At)sc. and derive spectral representations. Specifically, K learnalle D D-dimensional spectral representations, denoted as (5)))<K, are initialized from a truncated normal distribution. Following self aelf-attention block applied to the spectral tokens, the spectral representations, (S)j attend to the spectral tokens, (Ai)is,, via cross-attention,
4

---

Published as a conference paper at ICLR 2026
effectively distilling the most salient information. This self-attention-cret pross-attentioo module is iterated times to learn enriched spectral representations, (Sy)jsK. Subsequently, a readout function, in this instance summation, is applied to (Sz)işk to aggregate the information into camera-agnostic representation for the patch A. As the spectral encoder generates such a representation for each patch independently, the incorporation of spatial relationships necessitates the utilization of subsequent spatial encoder. It is noteworthy that since Espe has encoded device-dependent spectral properties within the feature space, most common transformer-based spatial encoders, such as ViT, may be employed for spatial encoding. To ensure dimensional compatibility between the spectral and spatial encoder, layer normalization and a linear transformation are applied prior to spatial encoding. After capturing inter-patch relationships, task-specific head can be added for the intended downstream application.
3.2 SELF-SUPERVISED TRAINING STRATEGY
Tailored CARL, we propose self-supervised pre-training strategy, CARL-SSL, to leverage large-scale unlabeled datasets. As illustrated in Fig. 3 the procedure is disentangled into spectral and spatial self-supervised pre-training within an end-to-end framework. While I-JEPA (Assran et al. 2023) is adapted for spatial self-supervision, we introduce novel feature-based spectral SSL strategy. Given student encoders Expes and Espat from Sec. 3.1 along with their teacher counterparts, Expec and Expar, which are updated via exponential moving average, we apply masking strategy specific regions of the students' input. The remaining tokens are then encoded by the student networks. The SSL objective is to predict the masked features generated by the teacher encoders, using dedicated predictors Aspee and Sppat. Specifically, for an image I #xxxxx the initial convolution is applied as described in Sec. [.1] A spectral mask, denoted by M S (1,..., ......... C), containing the masked channel indices, sampled for a patch A, and the unmasked tokens, denoted by (As) are forwarded to the student spectral encoder, Espec: to generate spectral representations, (S,)şeK (see Fig. 3). Conversely, the teacher spectral encoder receives all spectral tokens as input, producing learned spectral tokens, (A.)ss, via self-attention, and spectral representations, (Š))ssk- The objective of spectral pre-training is then to predict the masked spectral tokens, (A,)ieM. based on the student spectral representations, (Sy)agK, and the positional encoding of the masked wavelengths. To this end, transformer-based predictor, denoted by éspec is employed, receiving as input a sequence with the spectral representations and dedicated mask tokens (Devlin al. 2019). The mask tokens are |M| copies of shared, learnable embedding and are summed with wavelength positional encoding (PE(A)) corresponding to the masked wavelengths. Subsequently, Pspec processes this sequence through multiple self-attention blocks, resulting in learned mask tokens predictions for (Ar) Network optimization uses the VICReg loss (Bardes al.. 2021) on the spectral predictions, denoted Lapec. which comprises invariance, variance, and covari- ance terms. The invariance term minimizes the mean- squared error between predicted and target spectral tokens, while the variance and covariance terms con- tribute to training stability and the prevention of fea- ture collapse. For joint spatial training, the spectral representations from both the student and teacher encoders are aggregated into 2D camera-agnostee representations. Subsequently, 2D region of the student's is feature representation is masked, and the remaining spatial tokens are processed by E Expur Anal- ogous spectral pre-training, the spatial predictor, Sspat receives the student features and the positional encoding of the masked tokens as input, and predicts the masked features generated by the teacher spatial encoder, Esgur The spatial loss function, Cspat. is likewise defined using the VICReg loss on the spatial predictions. Finally, the overall training objective is
Masking
Agsr-qation agnostie nupresenaation
JJDAA
Proposed specmal self-ruporviston Spetial reporsspontsion
Figure 3: CARL-SSL enables joint learning of camera-agnostic representations and spatial relations. Spectral superviien involves recon- struction of masked spectral channels in feature space. The student Fages extracts spectral repre- sentations (S)) from a spectrally masked in- put, while the predictor cipp predicts the masked spectral tokens using masked wavelengths, and (S))iık- Target tokens are generated by the teacher Expe from the complete input. The ag- gregated camera-agnostic representations are sub- sequently processed by I-JEPA.
5

---

Published as a conference paper at ICLR 2026
Table CARL-SSL demonstrates superior performance compared with both camera-specifie and camera-agnostic models. The mloU scores with the 95 % confidence intervals on the HSICity test set. While the camera-specific model was pre-trained on Cityscapes and fine-tuned exclusively on HSICity, the other models are channel-invariant adaptations which were concurrently trained on both datasets.
Cumerr-specifie model
Spectral Adapter
HyperFree
Hyve
DOFA
CARL
CARL-SSL
mloU
44.6 [10.9: 17.3]
43.4
44.6
48.0
49.6
48.6
50.1 [17.2: 52.41
[41.0: 65.2]
112.2: 46.51
[15/4: 50.0]
[16.8: 51.6]
[15.6; $1.0]
Organ seginentation
Urban segmentation
RGB
Reforence
Hyve
CARL
Image
Referenee
Camen-speciffe model
CARL
Figure 4: Our model enables cross-modality knowledge transfer. (Left) With increasing spectral heterogeneity in the training set, both the camera-specifie model and Hyve exhibit notable rise in prediction noise. In contrast, CARL consistently provides accurate predictions. (Right) In two HSICity test set examples, the HSI-specific model fails segment "poles" (gray labels) due their absence in the HSICity training set. CARL, however, jointly trained on RGB and HSI data, effectively leverages "pole" annotations from Cityscapes to inform its predictions on HSICity.
given by L= Capat + Saeee jointly optimize the student encoders and predictors. Further details are provided in appendix A.2
3.3 IMPLEMENTATION DETAILS
Following ablation studies (see Sec. we set a 3, as defined in Eq. I for the wavelength positional encoding, and the number of spectral representations within the spectral encoder to K = 8. Importantly, these hyperparameters are held constant across all three application domains, underscoring their generality. Unless otherwise stated in the experiments, we employed EVA-02 Fang et al., 2024) spatial encoder, which is modern version of ViT (Dosovitskiy et al. 2021). Further implementation details of model are outlined appendix
4 EXPERIMENTS AND RESULTS
The experiments aimed to address the following research questions pertaining to the proposed model:
(RQ1) In silico proof of concept: Does the spectral representation learning approach enable effective knowledge transfer across cameras?
(RQ2) Real-world cross-domann generalization: To what extent do the proposed model and the spectral self-supervision approach help in handling real-world cross-camera variations?
Classification performance reported as overall accuracy (OA), whereas segmentation performance measured by intersection-over-mniie (IoU).
4.1 IN SILICO PROOF OF CONCEPT: MEDICAL IMAGING
Synthetic multispectral images generated from real hyperspectral dataset, enabling isolated control over spectral variations while preserving spatial context. We employed a private collection of porcine organ images, semantically annotated into 19 classes (Seidlitz al. 2022).
Datasets.

---

Published as a conference paper at ICLR 2026
Table 3: CARL excels at cross-sensor learning across different application domains. (a) Hyperspectral images per subject (S) transformed to multispectral images using real-world filters (F). CARL achieves stable performance in contrast to existing methods. (b) CARL exhibits the small drop in IoU when traffic light and sign classes were removed from HSI training set. (c) Ablation study on the loss components of CARL-SSL. Overall accuracy (OA) is reported on the m-forestnet validation set using a feature-based k-NN classifier.
(a) Medical Data
(b) Automotive Data
(c) Ablation on CARL-SSL
Training Data
Hyve
DOFA
CARL.
Class
Hyve
DOFA
CARI.
SSL Strategy
OA
Synthetic MST (4S, IF) Real (8S)
Trafic Light
15.5
8.4
29.2
Sparial SSL Eag
22.
52.1
58.1
64.6
-37.6
-60.4
-4.5
Spoctral SSL Lxpex
32.6
Trafic Sign (A)
10.9
14.9
31.7
Synthetic MSI (48, 2H) Real HSI (8S)
47.7
49.2
60.3
-43.8
-45.0
-20.7
= CARL-SSLL
mloU (A)
427
42.7
46.2
Synthetic MSI (6S. IF)
-5.3
-6.9
2.44
35.4
$2.0
62.1
Real HSI
Acquisition was performed with a Tivita@ Tissue HSI camera (Diaspective Vision GmbH, Am Salzhaff, Germany), capturing 100 spectral channels spanning 500 nm to 1,000 nm. The training set comprises 254 images from 12 subjects. To emulate realistic multispectral acquisition with optical filters, we modeled each filter response as a Gaussian function. Specifically, the number of channels C' in a virtual multispectral camera was first sampled uniformly from {10,... 25). The corresponding C center wavelengths, serving as the Gaussian means, were selected within 550 nm to 950 nm using farthest point sampling. To obtain a realistic range of variances, we fitted Gaussian functions to the filter responses of commercial multispectral camera and sampled C' variance values from this range. Given the sampled means and variances of C' channels, the filter functions were constructed and applied to an hyperspectral image to generate the corresponding multispectral image via matrix multiplication. In this way, we simulated six distinct camera configurations and progressively replaced hyperspectral images the training set with their multispectral counterparts on per-subject basis, while keeping the hyperspectral validation and test sets unchanged. This protocol isolates spectral variability and allows for a rigorous assessment of model robustness to spectral heterogeneity. Additional details on the data generation can be found in appendix [F] Baseline methods. The model's s performance was benchmarked against a camera-specific baseline and six channel-invarian methods, which can be grouped into three categories: spatio-spectral encod- ing (Spectral Adapter (Braham et al. 22044)) channel-adaptive embedding layers (DOFA (Xiong et al., 2024), Hyve (Varga et al. (2023), and HyperFree (Li et al. 2025a)); ); and camera-speeific embedding layers (Early Fusion (Astruc et al. 2025)). The camera-specte specific model employs a standard U-Net, representing the state of the art on the original hyperspectral dataset (Seidlitz et al., (022), and was trained exclusively on the hyperspectral subset of each training set variant. The other methods were in- tegrated with either a U-Net or a ViT-based architecture, depending on which proved most compatible. For ViT-based methods, including CARL, we adopted the ViT-Adapter (Chen et al. (023) for hicrarchical fea- tures and UperNet (Xiao et al.] (018)) for segmentation. All models followed the same training protocol.
Dymeriiez SpetiallAdttterr
y Fuaire
0.1
Results. The mIoU scores a function of the fraction of multispectral subjects within the training set is presented in Fig. 5 The proposed method uniquely maintained high mloU across all training set variants. This is qualita- tively confirmed in Fig. 4 where prediction noise of the baseline methods increases with spectral heterogeneity. while our model remains stable and accurate. While sim- ulated filter functions enable scalable evaluation under spectral heterogeneity, realistic modeling remains cru- cial. We therefore conducted additional experiment in which multispectral images were synthesized using real-world filters instead of Gaussian approximations, As shown in Tab, Baj CARL consistently outperforms baseline methods across all training data configurations.
0.7
0.
255
Fractonn Thinitg Sultecs Replaced MSI
Figure 5: Our model shows unique robust- ness spectral heterogeneity the organ ex- periments. As spectral heterogeneity increases with the multispectral replacements in the train- ing set, CARL. uniquely maintains high mloU score on the hyperspectral test set. Shaded area: 95 % confidence interval.
7

---

Published as a conference paper at ICLR 2026
Table CARL learns strong in-distribution features. Linear-probing results on four Sentinel-2 benchmarks. CARL attains the highest accuracy on three of the four datasets and the best average rank across all eleven benchmark datasets.
m m-bigearthnet
m-eurosat
m-cashew
m-SA-croo-type
Rank over II datasets
SpectralGPT
45.0
69.9
14.5
13.7
5.5
Galilco
49.8
84.2
10.5
19.3
5.5
Croma
59.5
86.6
12.1
25.2
5.0
DOFA
61.0
89.9
18.2
21.7
3.2
Copemicus-FN FM
62.1
87.2
14.5
26.5
2.6
SMARTIES
62.0
92.6
12.7
24.3
2.6
CARL
69.0
84.4
18.9
26.5
1.6
Table 5: CARL produces robust features on unseen sensors. Linear-probing results on four out-of- distribution sensors. Despite large spectral heterogeneity, CARL achieves the best performance on three of the four datasets, demonstrating strong cross-sensor generalization.
LoveDA Urban
m-foreettnt
WHU-OHS
Wuhan
Sensor
RGB (3 bands)
LandSat-8 (6 bands)
Orbita (32 bands)
Gaofen-5 (116 bands)
DOFA
12.6
43.8
1.5
20.3
Copernicus-NM
15.4
44.8
1.5
18.1
SMARTIES
13.5
49.8
1.5
18.8
CARL
29.0
47.0
21.5
4.2 REAL-WORLD EVALUATION: AUTOMOTIVE
The purpose of this experiment was to test the capabilities our approach under real-world conditions in the context of autonomous driving. Specifically, we investigated whether model can effectively leverage RGB and hyperspectral images for urban scene segmentation.
Datasets. The Cityscapes RGB dataset (Cordts al. (016), comprising semantic annotations for 19 classes, was employed alongside its hyperspectral counterpart, HSICity (Shen et al.)) HSICity contains images with 128 channels (450 nm 950 nm) and shares the same labels. However, its training set suffers from coarse annotations and class imbalance; for instance, the "pole" class is present in the test but absent from the training set. We therefore added finely annotated Cityscapes images containing "pole" labels, resulting in 4,029 training images (1,054 from HSICity). To assess cross-modality learning, models were trained on this combined dataset and evaluated on the HSICity test set. For self-supervised pre-training, we leveraged collection of heterogeneous urban datasets, including Cityscapes, HSICity, and the multispectral datascts HYKo VIS ct al. 2017) and HSIDrive (Basterretxea et al. 2021).
[Winkens
Baseline methods. Swin Transformer (Liu et al. 2021) with a Mask2Former head (Cheng et al. the Cityscapes dataset, adapted through the integration of a channel-invarinn module. Our spectral encoder, serving as this module, was compared with channel-adaptive layers of HyperFree, DOFA, and Hyve, as well with the Spectral Adapter. Additionally, as camera-specifee baseline, the RGB-pre-trainee SwinMask2Formee was trained exclusively on HSICity. All models adhered to an identical training protocol.
2022). referred to as SwinMask2Former, pre-trained
Results. The mloU scores on the HSICity test set are presented in Tab. 2 CARL-SSL demonstrated superior performance compared the baseline methods. Due to the absence of the "pole" class the HSICity training set, the camera-specific model failed to segment any poles in the test set, despite RGB-pre-trinning (see Fig. 4). In contrast, our model effectively transferred knowledge from the "pole" labels in Cityscapes to improve its predictions on HSICity, achieving the highest IoU for this class. To further assess cross modality learning, we removed the "traffic light" and "traffic sign" classes from the HSICity training set, and re- re-trained the models. As shown in Tab, 33 CARL most effectively leveraged RGB supervision achieve superior HSI predictions for the excluded classes on the test set compared to the baselines.
8

---

Published as a conference paper at ICLR 2026
Table 6: Ablation studies on CARL. (a) Wavelength positional encoding (PE) essential, with 3 yielding the best performance. (b) Summation proves to be the most effective strategy for aggregating spectral representations, and (c) using K spectral representations suffices to distill the channels. (d) Moreover, CARL benefits from larger embedding dimensions.
(a) Positional Encoding
(b) Aggregation
(c) # Spectral Rep.
(d) Feature Dim.
Method
mloU
Method
mloU
Spectral Rep.
mloU
Size
mloU
No PE
18.3
Summation
62.7
K
57.8
D 384
64.4
55.1
Concatenation
61.8
66.2
PE (a 3)
61.5
Maximum
61.8
K
8
63.9
PE (or 10)
57.2
Attention Pooling
60.0
K
16
62.2
4.3 REAL-WORLD EVALUATION: SATELLITE IMAGING
The third experiment aimed to evaluate the capabilities of CARL-SSL in satellite imaging. Dataset & Baseline methods. To facilitate benchmarking against strong pre-trained baselines such as SpectralGPT+ and DOFA, we scaled up pre-training to a corpus of approximately 800,000 images. comprising Sentinel-2 multispectral data (Sumbul ct al. 2019) and EnMAP hyperspectral data (Fuchs) and Demir 2023 Braham et al. 2024). Feature quality was assessed via linear probing on eleven datasets (Lacoste et al. 2023 Li et al. 20222 Wang et al. [021 Hong et al. (023). These include five in-distribution datasets (Sentinel-2) and six datasets captured by sensors not present in the pre-training
set. Further dataset details are given in Tabs. 7 7 We compared against six remote-sensing foundation
models, three of which are camera-agnostic and therefore can be evaluated on every dataset.
Results. Results are summarized in Tabs. 5 CARL delivers consistently strong performance across the benchmarks and achieves the best average rank across all eleven datasets. In particular, CARL exhibits robust generalization OOD sensors, outperforming the second-best method by a substantial margin on several datasets. We attribute this advantage to CARL's camera-agnostic spatio-spectral encoding scheme, which unique in comparison the baseline methods.
ABLATION STUDY
The ablation study in Tab. 6 examines the contribution of CARL's key architectural components. Training was conducted on dataset variant from Sec. [.1 while evaluation was performed on an HSI validation split. Removing the wavelength positional encoding severely impairs the model's ability to align channels across different cameras. For aggregating the spectral representations, simple summation achieves the highest accuracy while remaining computationally efficient. With respect to the number of spectral representations, we find that K = 8 tokens are sufficient to capture the essential spectral information. Notably, performance gains beyond this point are primarily achieved by increasing the embedding dimension rather than K. We also conducted an analysis of CARL- SSL's two self-supervision tasks- spectral and spatial- as summarized in Tab. 3c To isolate their individual contributions, we performed two pre- pre-training variants under a reduced training budget: one employing only spatial self-supervision, and the other utilizing our proposed spatio-spectral strategy. The resulting image embeddings were evaluated using a k-NN classifier on the m-forestnet validation set (Lacoste et al.) 2023). The model trained with spatial self-supervision alone exhibited collapse in the spectral representations, leading to significantly reduced accuracy. In contrast, incorporating spectral self-supervision effectively mitigated this collapse and yielded substantially stronger representations, resulting in a +0.5 OA improvement.
6 DISCUSSION
We introduced CARL, to our knowledge the first camera-agnostic framework that unifies spatio- spectral encoding with spatio-spectral SSL pre-training. Our approach tackles critical gap in spectral image processing: the lack of a representation learning framework that generalizes across spectrally heterogeneous datasets. We demonstrated its effectiveness in both traditional satellite imaging and in domains such medical imaging, where sensor variability is particularly pronounced due to the diversity of commercial camera manufacturers. Adaptive embedding approaches, including HyperFree, Hyve, and DOFA, rely solely on spatial operations and neglect crucial inter-channel

---

Published as a conference paper at ICLR 2026
relationships, resulting in limited performance. Altemative models, such SpectralGPT*, intro- duce spatio-spectral encoding, but depend on fixed channel dimensions, preventing generalization across different spectral cameras. In contrast, CARL employs wavelength-awee spatio-spectral
encoding that is independent of channel dimensionality, enabling robust generalization under spectral heterogeneity and scalability across modalities, demonstrated in both pre-training (Tabs.
9) and downstream tasks (Figs, S Tab. 33. Limitations of our work include higher computational cost compared channel-adaptive embedding approaches (see appendix D) well challenges
from sensor heterogeneity beyond spectral properties, such as differences in spatial resolution. The latter may be mitigated by incorporating additional sensor metadata. Despite these limitations, our approach successfully integrates real-world cross-camera datasets and outperforms existing methods. By unlocking the untapped potential of cross-sensor spectral datasets, CARL paves the way toward a more universal and accessible future in spectral imaging.
7 REPRODUCIBILITY STATEMENT
To ensure reproducibility, have provided detailed description of our method in Sec. S with additional implementation details in appendix A Pseudo-code illustrating the forward passes of Furthermore, all datasets used in Secs. 4.2
CARL and CARL-SSL can be found in algorithms [ are publicly available. Finally, full per-class loU scores are reported in Tabs, [0] 12
8 ACKNOWLEDGMENTS
This project was supported by the European Research Council (ERC) under the European Union's s Horizon 2020 research and innovation program (NEURAL SPICING, 101002198), the National Center for Tumor Diseases (NCT), Heidelberg's Surgical Oncology Program, the German Cancer Re- search Center (DKFZ), and the Helmholtz Association under the joint research school HIDSS4Health (Helmholtz. Information and Data Science School for Health). We also acknowledge the support through state funds for the Innovation Campus Health + Life Science Alliance Heidelberg Mannheim from the structured postdoc program for Alexander StudierFischhe Artificial Intelligence in Health (AIH).
REFERENCES
Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, and Nicolas Ballas. Self-supervised leamnngg from images with joint-emhedding predictive architecture. In CVPR, pages 15619-15629, 2023.
Guillaume Astruc, Nicolas Gonthier, Clement Mallet, and Loic Landricu. Omnisat: Self-supervised modality fusion for carth observation. In European Conference on Computer Vision, pages 409-40-44 427. Springer, 2024.
Guillaume Astruc, Nicolas Gonthier, Clement Mallet, and Loic Landrieu. Anysat: One earth observation model for many resolutions. scales, and modalities. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 19530-1954,, 2025.
Nicolas Audebert, Bertrand Le Saux, and Sébastien Lefevre. Beyond rgb: Very high resolution urban remote sensing with multimodal deep networks, ISPRS journal of photogrammetry and remote sensing, 140:20 32, 2018.
Leonardo Ayala, Tim J Adler, Silvia Seidlitz, Sebastian Wirkert, Christina Engels. Alexander Seitel, Jan Sellner, Alexey Aksenov, Matthias Bodenbach. Pia Bader, al. Spectral imaging enables contrast agent-free real-time ischemia monitoring in laparoscopic surgery. Science advances, 910))ead66778 2023.
Adrien Bardes, Jean Ponce, and Yann LeCun. Vicreg: Variane-iivariinc-ccovain regularization for self-supervised learning. arXiv preprint 1rXiv:2105.04900 2021.
Koldo Basterretxea, Victoria Martínez, Javier Echanobe, Jon Gutiérre- Zaballa, and Inés Del Campo. Hsi-drive: A dataset for the research of hyperspectral image processing applied to autonomous driving systems. In 2021 IEEE Intelligent Vehicles Symposiun (/V), pages 166-873. IEEE, 2021.
Alexander Baumann, Leonardo Ayala, Alexander Sludier-Fischer, Jan Sellner, Berkin Özdemir, Karl-Friedrich Kowalewski, Slobodan Ilie, Silvia Seidlitz, and lenna Maier-Hein. Deep intra-operative illumination calibration
10

---

Published as a conference paper at ICLR 2026
of hyperspectral cameras. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 120-131. Springer, 2024,
Nassim Ait Ali Braham, Courad M Albrecht. Julien Mairal. Jocelyn Chanussot, Yi Wang. and Xiao Xiang Zhu. Spectralearth: Training hyperspectral foundation models at scale. arXiv preprint (xXi:2240800844) 2024.
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin.
Emerging properties in self-supervised vision transformers. In ICCV, pages 1650-9660, 2021.
Zhe Chen, Yuchen Duan, Wenhai Wang. Junjun He, Tong Lu, Jifeng Dai, and Yu Qiao. Vision transformer
adapter for dense predictions. In The Eleventh International Conference on Learning Representations, 2023.
URL htttp:://openreee
net/ / Torum? www.pppppkuByyCPPPP
Bowen Cheng, Ishan Misra, Alexander G Schwing, Alexander Kirillov, and Rohit Girdhar. Masked-attention mask transformer for universal image segmentation. In CVPR, pages 1290-1299, 2022.
Yezhen Cong, Samar Khanna, Chenlin Meng, Patrick Liu, Erik Rozi. Yutong He. Marshall Burke, David Lobell. and Stefano Ermon. Satmae: Pre- training transformers for temporal and multi spectral satellite imagery. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, editors, NewrlPS, volume 35, pages 197- 211. Curran Associates, Inc., 2022. URL bttps: http:::::/pprcoeeeeeeee ngs neur cc/ pappr_ppp____ 1les/
pdf
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference computer vision and pattern recognition, pages 2213-2223, 2016.
Jia Deng, Wei Dong. Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 JEEE conference on computer vision and pattern recognition, pages 248-255. Icce, 2009.
Jacob Devlin, Ming- Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre- tre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume I (long and short papers), pages 4171-4186, 4186, 2019.
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenbom, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghami, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2021. URL.
htttss://ppnrest ew, net wttt//orrmiidd//crrraao
Yuxin Fang. Quan Sun, Xinggang Wang, Tiejun Huang, Xinlong Wang. and Yue Cao. Eva-02: A visual representation for neon genesis. Image and Vision Computing, 149:105171, 2024.
Martin Hermann Paul Fuchs and Begüm Demir. Hyspecnet-1 lk: A large-scale hyperspectral dataset for benchmarking learning-based hyperspectral image compression methods. In IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium, pages 1779-1782. IEEE, 2023.
Anthony Fuller, Koreen Millard, and James Green, Croma: Remote sensing representations with contrastive radar-optical masked autoencoders. Advunces in Neurul Information Processing Systems, 36:5506-5538, 2023.
Yuval Garini, Ian T Young, and George McNamara. Spectral imaging: principles and applications. Cytometry part the journal of the international society for analytical cytology, 69(8):735-777, 2006.
Renlong Hang, Qingshan Liu, Danfeng Hong, and Pedram Ghamisi. Cascaded recurrent neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 57()):5384-5394, 2019.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770-778, 2016.
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In CVPR, pages 9729-9738, 2020.
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders scalable vision learners. In CVPR, pages 16000-16009, 2022.
Danfeng Hong, Zhu Han, Jing Yao, Lianru Gao, Bing Zhang, Antonio Plaza, and Jocelyn Chanussot, Spectral- former: Rethinking hyperspectral image classification with transformers. IEEE Transactions on Geoscience and Remote Sensing, 60:1-15, 2021.

---

Published as a conference paper at ICLR 2026
Danfeng Hong. Bing Zhang, Hao Li, Yuxuan Li, Jing Yao. Chenyu Li, Martin Werner, Jocelyn Chanussot, Alexander Zipf, and Xiao Xiang Zhu. Cross-city matters: multimodal remote sensing benchmark dataset for cross-city semantic segmentation using high-resolution domain adaptation networks. Remote Sensing of Environment, 299:1 13856, 2023.
Danfeng Hong. Bing Zhang, Xuyang Li, Yuxuan Li, Chenyu Li, Jing Yao, Naoto Yokoya, Hao Li, Pedram Ghamisi, Xiuping Jia, et al. Spectralgpt: Spectral remote sensing foundation model. IEEE TPAMI, 2024.
Johannes Jakubik, Felix Yang, Benedikt Blumenstiel, Erik Scheurer, Racco Sedona, Stefano Maurogiovanni, Jente Bosmans, Nikolaos Dionelis, Valerio Marsocci, Niklas Kopp. et al. Terramind: Large-scale generative multimodality for earth observation. arXiv preprint 2rXiv:2504.1171,, 2025.
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. Scaling laws for neural language models. arXiv preprint arXiv:2001.08 083611, 2020.
Alexandre Lacoste, Nils Lehmann, Pau Rodriguez, Evan Sherwin, Hannah Kerner, Bjöm Lütjens, Jeremy Irvin, David Dao, Hamed Alemohammad, Alexandre Drouin, et al. Geo-bench: Toward foundation models for earth monitoring. Advances in Neural Information Processing Systems, 36:51080-51093, 2023.
Jiayi Li, Xin Huang, and Lilin Tu. Whu-ohs: benchmark dataset for large-scale hersepctral image classification. International Journal of Applied Earth Observation and Geoinformation, 113: 103022, 2022.
Jingtao Li. Yingyi Liu, Xinyu Wang, Yunning Peng, Chen Sun, Shaoyu Wang. Zhendong Sun, Tian Ke, Xiao Jiang. Tangwei Lu, al. Hyperfree: channel-adaptio and tuning-freee foundation model for hyperspectral remote sensing imagery. arXiv preprint 250:3 504,2 2025a.
Xuyang Li, Chenyu Li, Pedram Ghamisi, and Danfeng Hong. Fleximo: A flexible remote sensing foundation model. arXiv preprint arXiv:2503.23844. 2025b.
Junyan Lin, Feng Gao, Xiaochen Shi, Junyu Dong, and Qian Du. Ss-mae: Spatial-spectral masked autoencoder for multisource remote sensing image classification. IEEE Transactions Geoscience and Remote Sensing, 61:1-14, 2023.
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, pages 10012-10022, 2021.
Bing Lu, Phuong D Dao, Jiangui Liu, Yuhong He, and Jiali Shang. Recent advances of hyperspectral imaging technology and applications in agriculture. Remote Sensing, 12(16)2269, 2020.
Stefan Niewiadomski. Filter handbook: a practical design guide. Newnes, 2013.
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Mare Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.077 1991399....99..... 2023.
Shen-En Qian. Hyperspectral satellites, evolution, and development history. IEEE Journal of Selected Topics Applied Earth Observations and Remote Sensing, 447002-7056, 2021.
Silvia Seidlitz, Jan Sellner, Jan Odenthal, Berkin Özdemir. Alexander Fischer Samuel Knödler, Leonardo Ayala, Tim J Adler, Hannes G Kenngott, Minu Tizabi, al. Robust deep learning-basee semantic organ segmentation in hyperspectral images. Medical Image Analysis, 80:102488, 2022.
Qiu Shen, Yuxing Huang, Tianqi Ren, Ying Fu, and Shaodi You. Urban scene understanding via hyperspectral images: Dataset and benchmark. Available at SSRN 4560035.
Gencer Sumbul, Marcela Charfuelan, Begüm Demir, and Volker Markl. Bigearthnet: A large-scale benchmark archive for remote sensing image understanding. In IGARSS 2019-2019 IEEE international geoscience and remote sensing symposium, pages 5901-5904 JEEE, 2019.
Gencer Sumbul, Chang Xu, Emanuele Dalsasso, and Devis Tuia. Smarties: Spectrum-aware multi-sensor auto-encoder for remote sensing images. arXiv preprint arXiv:2506. 19585, 2025.
Matthew Tancik, Pratul Srinivasan, Ben Mildenhall, Sara Fridovich-Keit Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan Barron, and Ren Ng. Fourier features let networks learn high frequency functions in low dimensional domains. NewrIPS, 33/7537-7547, 2020.
Nick Theisen, Robin Bartsch, Dietrich Paulus, and Peer Neubert. Hs3-bench: A benchmark and strong baseline for hyperspectral semantic segmentation in driving scenarios. In 2024 JEFE/RS International Conference on Intelligent Robots and Systems (IROS), pages 5895-5901. IEFF, 2024.
12

---

Published as a conference paper at ICLR 2026
Prasad Thenkabail, John Lyon, and Alfredo Huete. Advances in hyperspectral remote sensing of vegetation and agricultural crops. In Fundamentals, sensor systems, spectral libraries, and data mining for vegetation, pages 3-37, CRC press, 2018.
Gabriel Tseng, Anthony Fuller, Marlena Reil, Henry Herzog, Patrick Beukema, Favyen Bastani, James Green, Evan Shelhamer, Hannah Kemer, and David Rolnick, Galileo: Learning global & local features of many remote sensing modalities. arXiv preprint arXiv:2502.09356 2025.
Leon Amadeus Varga, Martin Messmer, Nuri Benbarka, and Andreas Zell. Wavelength-aware 2d convolutions for hyperspectral imaging. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 3788-3797, 2023.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkorcit, Llion Jones, Aidan N Gomez, E ukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, Vishwanathan, and Garnett, editors, NeurlPS, volume 30. Curran Associates, Inc., 2017.
Leonard Waldmann, Ando Shah, Yi Wang, Nils Lehmann, Adam Stewart, Zhitong Xiong, Xiao Xiang Zhu, Stefan Bauer, and John Chuang. Panopticon: Advancing any-sensor foundation models for earth observation. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 2204-2214, 2025.
Junjue Wang, Zhuo Zheng, Ailong Ma, Xiaoyan Lu, and Yanfei Zhong. Loveda: A remote sensing land-cover dataset for domain adaptive semantic segmentation. arXiv preprint arXiv:2 10.08733, 2021.
Yi Wang, Zhitong Xiong, Chenying Liu, Adam Stewart, Thomas Dujardin, Nikolaos Joannis Bountos, Angelos Zavras, Franziska Gerken, loannis Papoutsis, Laura Leal-Taixé, et al. Towards a unified copernicus foundation model for earth vision. arXiv preprint arXiv:2503. 11849, 2025.
Christian Winkens, Florian Sattler, Veronika Adams, and Dietrich Paulus. Hyko: A spectral dataset scene understanding. In Proceedings of the IEEE International Conference on Computer Vision Workshops, pages 254-261. 2017.
Tete Xiao, Yingcheng Liu, Bolei Zhou Yuning Jiang, and Jian Sun. Unified perceptual parsing for scene understanding. In Proceedings of the European conference computer vision (ECCV), pages 418-434, 2018.
Zhitong Xiong, Yi Wang, Fahong Zhang, Adam J Stewart, Joëlle Hanna, Damian Borth, Ioannis Papout- sis, Bertrand Le Saux, Gustau Camps-Valls, and Xiao Xiang Zhu. Neural plasticity-inspreed multimodal foundation model for earth observation. arXiv preprint 24X3 15356, 2024.
13

---

Published as a conference paper at ICLR 2026
Appendix
A IMPLEMENTATION DETAILS
We begin by outlining general implementation details of CARL. In the experiments from Secs, 4. 4.2
we employed small version of CARL, comprising overall 12 attention blocks with an embedding dimension of 384. For the satellite experiments in Sec. 4.3 we switched to the base version of CARL, keeping the same 12-block depth but increasing the embedding dimension of the spatial encoder to 768 to align with SpectralGPT* and DOFA. The blocks were structured into L = 4 self- attention-cross cross-attention modules within the spectral encoder and self-attention blocks within the spatial encoder. To incorporate wavelength information in the positional encoding, the wavelengths were given in nanometers, and scaled by factor of a = 10-3, to obtain position coordinates approximately in the range of one. In accordance with the ablation studies presented in the main manuscript, the hyperparameter a in the wavelength positional encoding was set to 3 and K 8 spectral representations were employed. The initial convolution in our model utilized a kernel size and stride of 8, resulting in patch dimensions of 8 x 8. The spectral representations within the spectral encoder were implemented as learnable embeddings, initialized using a truncated normal distribution with mean of zero and a standard deviation of 0.5. Furthermore, a 1D sinusoidal positional encoding scheme, based on discrete token positions, was implemented to represent the positions of (S,)isk Pseudo-code of a forward step of CARL can be found in algorithm I
A.1 IMPLEMENTATION DETAILS OF DOWNSTREAM TASKS
In the first and third experiment, CARL was integrated with the ViTAdapter (Chen et al. 2023) to generate hierarchical features, which were subsequently forwarded to the UperNet segmentation head (Xiao et al. 2018). The ViTAdapter applies lightweight convolutions to the input and facilitates information exchange with the spatial transformer via injector and extractor modules. To maintain invariance with respect to the channel dimensionality, a single channel of the given spectral image was used input these convolutions. The rationale behind this approach is leverage the enhanced spatial resolution provided by the convolutions, as the encoded spectral information is contained within the camera-agnostie representation of the proposed model. As segmentation loss, an equally weighted sum of the cross entropy loss and the dice loss was employed. To enhance training stability, the attention blocks within the spectral encoder were initialized using Dinov2 weights (Oquab) et al. 2023), which provide a robust checkpoint derived from self-supervised training on large- scale dataset. The spatial encoder, EVA-02 (Fang al., (024). was initialized with self-supervised weights obtained through masked image modeling (He et 2022) Furthermore, the model was optimized using the Adam optimizer with an initial learning rate of employed to reduce the learning rate throughout
ImageNet (Deng et al.. 2009).
10-4. An exponential learning rate scheduler the training process. In the second experiment, the training procedure followed that of the original Mask2Former (Cheng al. 2022 training on Cityscapes (Cordts al. 2016). In all experiments, channel sampling strategy was employed to reduce the GPU memory consumption during training. Specifically, in instances where the channel dimension of the spectral image surpassed 32, random subsampling of 32 channels was performed. Crucially, this did not affect performance, as the spectral tokens are ordered via wavelength positional encoding, As a result, all experiments were successfully executed on single GPU endowed with a memory capacity of up to 40GB.
A.2 IMPLEMENTATION DETAILS OF CARL-SSL
The model subject to pre-training consisted of the proposed spectral encoder, in conjunction with the EVA-02 spatial encoder. As outlined in the main manuscript, the pre-training strategy employs spectral and spatial masking with reconstruction objectives in the feature space through predictor networks. Pseudo-code of CARL-SSL is outlined in algorithm 2 To manage compute resources, we downsampled each hyperspectral image to 64 channels. For spectral selffsupervvsion on hyperspectral EnMAP data, we applied single mask covering 15 % to 30% % of the channels; for Sentinel-2 (multispectral) we masked two to three channels. Our spatial SSL strategy used two masks, each spanning 30 % to 50 % of the image area. The predictors transformer architectures with depth
14

---

Published as a conference paper at ICLR 2026
Algorithm 1: Pseudo-code of forward pass of CARL.
Inpuii:
xt batch of Inagea of ahape (B, C, 8, X] wt havelengths cscresponding of shape (B,
Forxard taelf, F, K, wi: Projecticn
peagrange
B.W
CI
sul seli.peo juut
shapes (BxC, 21)
Spectral Positional Encodica
sel.. le] shapes (h, 6,
(sper_pe, "a C.E. 18 C.0.)
- x+ spoc_2c
reps_regs - (seit...ppp reps,
(3
spec_rep__p rep_pe regeat (seit. spec__ep____ rep_pe, apec_ress apec_reps + spp__rep__s
Spectra: Actention
For sel f cross_atto in sel F.l.etttctt ral ral_cctall_kkss blks:
sel sall_attn(s))
appe__eas arwesaat (app x, x]
spectra--tooSpre
Transition
spec rep spec reps, sumicin-1i shape: 1B4b4%
selr. [self.
apoc_rep rearrange (sps
x) Dr)
sel. Spatisi Fositional Encoding
(b, <t shapes [1, baw,
(ppat_pe, 61 -> B.N 2:2)
sppc_rep apea_reg + spat_pe
-............................... Spatial Atcention
for sel aten in sel salattp fal blks:
sppc_rep st___ssttspppp_____ spoc_rop (spscc
chapc: (B, be%, D)
Oul.pul Bead
retuen out
of 3 and an embedding dimension of 384. Both, spectral and spatial self-supervisio leverage the VICReg loss (Bardes et al. 202T), which is decomposed into invariance, variance, and covariance terms. Following the original recommendations, weights of I were assigned to the invariance and variance terms, and a weight of 0.05 was assigned to the covariance term. Let á and y be the predicted and target tokens, both of shape (B, N, withh with B as batch size, N sequence length, and D embedding dimension. We then compute:
Capoe + Espat
(2)
= Lvierog (Nupees Wspoe) + Livereg (Uspat: Mspas)
(3)
Lviereg (Ú. v) = Liny (Ú, v) + Lovar (0) + 0.05 Lewr (0)
(4)
N
1
Lan Lun v)
(Dr,ndd ab,
(5)
B N D
b=
d=1
N
D
(v)
max v Var((nna)
(6)
N D
m=1d= 1==1
N
(o)
Cov(On))
(7)
N D
n=
iSDD (+)
B
1 B
Cov(On)
(0b.
(Db.n DT,"" where In
Ub.n
(8)
B
b=1
6=1
While Cin encourages 9 to resemble Lyar promotes diversity among generated tokens by increasing their variance across the samples, preventing feature collapse. Finally, the covariance term minimizes off- diagonall absolute values in the feature covariance matrix, encouraging independence between feature components. This reduces redundancy and increases information across the feature dimension.
A.3 CARL-SSL ON SATELLITE IMAGES
The pre-training dataset consisted of three distinct datasets, specifically HySpecNet-1 1 1k (Fuchs and Demir 2003),. SpectralEarth (Braham et al. 2024), and BigEarthNet (Sumbul et al.] 2019), whose detailed composition is summarized in Tab. The student and predictor networks optimized
15

---

Published as a conference paper at ICLR 2026
Table 7: Composition of our remote sensing SSL-data. Three datasets were used for self-supervised pre-training on satellite images. The table reports the number of images, sensor, number of spectral channels, and covered wavelength range. 'Only subset of the full SpectralEarth dataset was used in our pre-training.
Dataset
# Images
Sensor
# Channels
Wavelength Range
HySpecNet- 1 lk SpectralEarth BigEarthNet
11,483
EnMap
202
418 nm to 2,445 nm
247,030|
EnMap Sentinel-2
202
418 nm to 2,445 nm
549,488
443 nm to 2,202 nm
Algorithm/ 2: Pseudo-code of a training step of CARL-SSL.
Inpute:
batch inages of shape 18, cusresponding wavelenglhia shape
der training stepix,
Maek Sampl
spac_ons_nane spos_pres_naak campic_sgec_naee spat_on=_napkk spst_pres_nask -.......
Target Token Concration
with Lo:ch.ne_geadd)
apec _ee__teahhe__tkera,, apat_ target sped takene app y nasse tapec. teacher.toters, tokens, spea pred maak] spat. meak]
telf. (8, at
target spptt__pst__ti spat_tokenatt_ epp y paske (apat__teache wtw.__tteeeereeeee
Studenn: Token General
coacrange
(8 C1
wtw.:.ssennt..ootetteeeeoo reazrange
N shape: (B+C, D, 5, M)
Masking
v_student agply mesksas
nask)
Speci Encading
<Ludent tape wwlf.atudent. aapeeee...ppp..... aapee uncuder (spec_a. v_viudeni)
Spectial Prediction.
v.teacher = apply mw spec
pped_prred__eeeppp pred maak)
prod_spac_token swlf. att..paac__edd_e00 too._aadd.tootttaaattt..oo wwtooottttttteeeeeeeooo
N_teachez)
Transition
Suddntt_ppoc_rog stud:nt staddat_spoc__st wwpppppp__.pppppeeeppp_____eeess sum.. sunidin 1 shapos (Bxb1%, D)
=Luden Sudieni__ppecc_tt self,
stadent__peec_eep rearsange (s.ude/aaeddddd... spe
1(3
D
D*)
Spatial Wasking
spat apply maska
spea rep, apat_enc enc
spat
.ppt
(spat studens,
enc_nenk
Spatial Fredicticn
prod_spat_tokens
pot__prciictooeee
spat_prec_maak)
Loss Curgulallonn
loss_apac ppce
saxget__pec_iokena
loss_spat VICREd (pdeddddppp_p_toeeeeeeae tokon_, target_spat_zokeng
loss = loss sgec loss spst
Opt optimisation Step
loss.backwardl
Updaze
updata_neighta teachez solf.atudont)
using AdamW with an initial learning rate of 10-4, weight decay of 0.04, and a cosine annealing learning rate scheduler with final learning rate of 10-6, Teacher networks were updated each iteration using an exponential moving average (EMA) student weights, with momentum linearly increased from 0.996 to 1.0 during pre-training, following (Assran et al. 2023). Training ran for 78,000 iterations on an NVIDIA H200 GPU with a batch size 64.
Linear-probe evaluations ran for 50 epochs on GeoBench datasets and 30 epochs SegMunich. using Adam with a 0.001 starting learning rate and a cosine annealing schedule. We applied random resized cropping for augmentation. Because model operates on 8 x patches while DOFA uses 16 x 16, we doubled DOFA's input resolution to guarantee a fair comparison.
B EXPANDED RELATED WORK
In this section, will elaborate existing approaches to spectral image encoding and specific contribution. To this end, we categorize related work, and discover gaps in the literature.
Feature extraction for spectral imaging Modern RGB image encoders typically start with a 2D patch projection that maps image patches into feature space, followed by Vision Transformer blocks
16

---

Published as a conference paper at ICLR 2026
Table Details of benchmark datasets for remote sensing experiments. The first block lists five in-distribution Sentinel-2 datasets that are present in CARL's pretraining set. The second block lists datasets captured by sensors that were unseen during pretraining. suitable for evaluating cross-sensor generalization.
Dataset
Sensor / Platform
Channels
Wavelength (nm)
Classes
Task
Zero-shot
In In-distribution sensors (present in pretraining)
SegMunich
Sentinel-2 2
10
442-2202
12
Seg
X
m-bigearthnet
Sentinel-2 -2
12
442-2202
43
Cls
X
m-curosat
Sentinel-2 -2
13
442-2202
10
Cls
X
m-cashew
Sentinel-2
13
442-2202
7
Seg
M--SA-rrop-type
Sentinel-2
13
442-2202
Seg
Out-of-distribuut senors (unseen during pretraining)
LoveDA Urban
Google Earth
3
RGB
Seg
LoveDA Rural
Google Earth
3
RGB
6
Seg
m-forestnet
LandSat-8
6
482-2200
12
Cls
WHU-OHS
Orbita hyperspectral sat.
32
466 940
24
Seg
Wuhan
Gaofen-5
116
420-2400
13
Seg
Beijing
Gaofen-5
116
420- 2400
Seg
or their variants. These architectures are designed for RGB inputs and therefore do not natively handle sensors with arbitrary numbers of spectral channels. Early adaptations for spectral imaging add multiple projection layers to accommodate different channel counts (Tseng et al.) [0225 Astruc al., 22025). or replace the single projection layer with complete modelity-specific encoders (Jakubik
it al. 2025; Fuller et al. 2023] Astruc et al. 2024). Such designs- Sfsigns-often implemented as early- or mid-fusion models work well for the sensors seen during training but cannot generalize to unseen sensors with different spectral dimensions. To address this limitation, channel-invarian approaches have been proposed. These include cnojection-weighte interpolation (Sumbul et al. 2025 Varga et al. 2023). and channel-adaptive projection layers (Xiong et al., 2024] Wang et al. 2025
Li et al. 2025b). Several methods also exploit known wavelength information (for example via wavelength positional encodings) establish cross-sensor channel relationships:
refer to such methods as wavelength-aware (Xiong et al. 2024 Wang et al., 2025 Li et al. (2025)) Summbu et al.) 2025 Varga al., (003). When model is both channel-invariant and wavelength-aware, it camera-agnostic since it can in principle generalize to any spectral sensor, whether seen during training or not.
call
Most camera-agnostic designs handle spectral inputs only at the projection stage and then perform purely spatial operations in feature space learn spatial structure Xiong al. (2024), Wang al. (205)) Sumbul et al. (2025); Waldmann et al. (2025); Jakubik et al. (2025) ; Fuller et al. (2023); Tseng et al. (2025); critically, they do not learn spectral relationships within that feature space. We categorize them into spatial encoding schemes By contrast, spatio-spectral encoding schemes learn joint spatial and spectral relations the feature space. This capability can be essential in spectrally heterogeneous settings where the model must align spectral signatures across different sensors, Examples include spatio-spectral patching with self-attention (Hong et al. (0224) or dedicated spectral encoders (Braham et al. 2024). However, existing spatio-spectral encoding schemes not camera-agnostic.
To address this gap, propose CARL- the best of our knowledge, the first approach to unify explicit spatio-spectral feature encoding with a camera-agnosta design. CARL achieves this by introducing channel-invariane wavelength-aware spectral encoder that compresses variable-length channel inputs into fixed-length spectral representations, enabling the model to learn rich joint spatio-spectral features and generalize strongly out-of-distribution without per-camera retraining.
Self-superes supervieed learning strategies for spectral imaging Self-supervised learning (SSL) is in- creasingly important as unlabeled data and compute scale up. Most current apectral-imaging pipelines inherit spatial encoding schemes from RGB models and therefore apply SSL that only learns spatial relations. We call that spatial self-supervision. These methods typically adapt RGB SSL recipes (e.g., MAE, DINOv2, iJEPA) remote-sensing foundation models (He et al. 2022
Oquab et al.
17

---

Published as a conference paper at ICLR 2026
Table CARL learns robust representations during pre-training. Linear-probing mloU results on three evaluation datasets. Two of the datasets are acquired by sensors unseen during pre-training (Beijing, LoveDA). CAR shows competitive in in-distribution performance and superior generalization to out-of-distribution sensors, achieving the best average rank across all 11 benchmark datasets.
SegMunich (mloU)
Beijing (mloU)
LoveDA Rural (mloU)
Avg. Rank over all 11 datasets
Sensor
Sentinel-2 (10 bands)
Gaofen-5 (116 bands)
RGB (3 bands)
SpectralGPT
27.9
5.5
Galileo
35.3
5,5
Croma
5.0
DOFA
38.2
17.4
14.9
3.2
Copernicus-FN
38.4
14.5
17.6
2.6
SMARTIES
39,1
17.1
17.3
2.6
CARL
38.9
19.1
29.8
1.6
2023
Assran et al.
2023
Fuller et al. 2023 Jakubik et al. 2025 Xiong et al. 2024, Waldmann et al.
Tseng et al. (025). contrast, spatio-spectral self-supervision learns pratio-spectral relations during pretraining. For example, SpectralGPT performs reconstruction of 3D spatio-spectral patches (Hong al.) 2024). Scaling pretraining requires camera-agnostie pretraining, but this not always feasible: some approaches require decoder heads tied to specific channel counts, which prevents straightforward cross-sensor pretraining (Sumbul et al. 2025). Moreover, SpectralGPT as backbone is not camera-agnostic. Additionally, pixeelrreconstrucctin objectives particularly sensitive in spectral imagery because pixel noise from atmospheric effects, illumination variation, and sensor distortions stronger than in RGB data. For this reason, feature-based SSL methods (e.g.. I-JEPA (Assran et al. 2023), DINOv2 (Caron et al. (02I)) which learn robust latent representations rather than raw pixel reconstructions may be better suited spectral data
2025
To the best of our knowledge, there is currently no SSL. strategy that is both camera-agnostic and spatio-spectral, nor strategy that is both feature-based and spatio-spectral. In this work we introduce CARL-SSL: a fcature-based, camera-agnostic spatio-spectral self-supervision frame- work. CARL-SSL enables scalable pretraining across heterogeneous sensors and yields robust joint spatio-spectral representations.
C ADDITIONAL REMOTE SENSING EXPERIMENTS
In addition to Tabs. particular, we evaluated segmentation performance on the three land cover datasets. While SegMunich is acquired by the Sentinel-2 sensor, and is therefore in-distribution, Beijing and LoveDA were acquired by unseen sensors (hyperspectral and RGB), and therefore emphasizes on cross- generalizability. Further details the datasets be in Tab. g While every model except of Croma can be applied on the SegMunich images that have 10 channels, only three baseline methods can process hyperspectral images. Particularly in those scenarios, CARL's outperformance is pronounced. Furthermore, we report the average rank over all eleven datasets of all baseline methods. CARL ranks best with an average rank of 1.6.
provide further results on our linear probing evaluation in Tab. 9
As the SegMunich is sufficiently large for full fine-tuning, we performed supervised fine-tuning. Due the required compute, we limit the baseline methods to DOFA, and SpectralGPT* As can seen in Tab. TO CARL keeps its outperformance and achieves score of 50.9.
D COMPUTATIONAL COMPLEXITY
Encoding high-dimensional opatio-spectry data imposes computational and memory demands; therefore, controlling excessive floating-point operations (FLOPs) is essential. For the models considered in Sec. 4.3 report parameter counts and FLOP estimates for the input sizes used in our remote-senss sensing evaluation. All experiments use the base parameter size of each model to provide a fair comparison across architectures. Two architectures, Galileo and Croma, were excluded from this complexity study because they were specifically designed for Sentinel-2 and cannot be reasonably adapted to other sensors without substantial redesign. By contrast, SpectralGPT depends the
18

---

Published as a conference paper at ICLR 2026
number of channels only through single linear layer, making it straightforward adapt different sensor channel counts; accordingly, we include in our study. The remaining models are sensor- agnostic and can be applied for the different input size out of the box. To make comparisons consistent, we fixed a patch grid of 16 x 16 and evaluated four channel configurations corresponding to RGB, Sentinel-2, OHS, and Gaofen-5 sensors (3, 12, 32, and 116 channels, respectively, see Tab. [ We report FLOPs for multiple input sizes because the models scale differently with spectral dimensionality. Finally, we analyze how computational complexity relates to model design choices as encoding schemes- and to average model performance, to highlight trade-offs between efficiency and accuracy
In Tab. I1 we summarize the measured costs. Overall, purely spatial encoding schemes are com- putationally cheaper than apatio-spectra approaches; the differences in scaling behavior explain most of this gap. SpectralGPT uses 3D patching and full self-attention across both spatial and spectral dimensions for twelve blocks. Consequently, the complexity of each attention block grows as O(H W- )))).
CARL takes different design that yields substantially more favorable scaling by exploiting two principles: (1) disentangled spectral and spatial encoding, and (2) cross-attention with K learned spectral tokens. Concretely, CARL first extracts rich features along the channel axis, aggregates them, and then applies spatial-only transformer blocks. This produces spectral encoding stages whose attention cost scales O(C²) (four blocks), and spatial-only stages whose complexity scales as O(H 2 (eight blocks). Beyond lower FL.OPs, disentanglement also simplifies spectral- and spacial-specific design choices (e.g., wavelength positional encoding) while preserving rich spatio- spectral representations. To further reduce costs, CARL interlcaves a small number of full spectral self-attention operations with cheaper cross-attention blocks inside the spectral encoder. While spectral self-attention scales as (CC*), a cross-attention over K learned spectral representations scales O K). Since K is fixed and small in our experiments (we use K 8), these cross- attention blocks are much less expensive than full spectral oeff-atteniion when C' is large (as in hyperspectral imagery). Although CARL remains more expensive than purely spatial encoders, the additional cost. is offset by substantially richer features and markedly better out-of-distributin generalization. This trade-off is reflected in CARL's strong empirical performance (average rank 1.6 in our evaluation; see Tab. IT].
Because CARL is camera-agnostic, we can exploit this property to reduce training cost using a simple channel-subsampling strategy. For the hyperspectral organ dataset (originally 100 channels), we randomly sampled 16 channels at each training step and optimized the model on this reduced input. This approach reduced training FL.OPs by roughly 75% in our experiments while preserving validation performance: we obtained a mloU 68.8, compared to the original 69.1.
Two points explain why subsampling works well here. First, CARL's spectral encoder explicitly learns relationships across channels and compresses spectral information into a fixed set of representations. Therefore, the model can integrate information across different sampled subsets and still recover rich spectral features. Second, random channel sampling acts as a form of stochastic regularization: by seeing many different channel subsets during training, the model becomes more robust to missing or shifted spectral bands and generalizes better to out-oo-distribution sensors. Practically, channel subsampling an easy-to-implement low-overhead augmentation.
E URBAN SCENE SEGMENTATION
The detailed class-wise IoU scores on the HSICity test set are depicted in Tab. 12 CARL-SSL exhibited superior performance benchmarked against camera-specific and channel-invariant spectral imaging models. As the HSICity training set does not contain any "pole" annotation, the camera- specific model exhibits "pole" IoU score of 0. Notably, CARL and CARL-SSL achieved the best IoU scores for the "pole" class, indicating superior capability of translating RGB labels from Cityscapes hyperspectral imagery.
19

---

Published as a conference paper at ICLR 2026
Table 10: Fully fine-tuned CARL yields superior downstream performance. Class-wise IoU scores with 95 % confidence intervals on the SegMunich multispectral land cover dataset were benchmarked against strong SSe-pretraieed models.
Arable land
Perm. Crops
Open spaces
Monc. dump
Urbon fabrie
Pastures
Fonetts
Water
Shruub
Wetlands
Art. segi
Baildings
Method
mloU
SaMAE
72.3
15.8
49.7
81.9
74.0
13.6
27.5
41.2
37.9
18.3
65.3
50.6
45.7
48.20
SMARTIES
71.6
19.6
49.4
85.2
72.9
17.3
44.8
39.3
37.3
22.2
65.6
53.2
48.71
SpectraGGPT
72.2
21.9
$1.0
86.3
76.5
17.2
44.0
39.0
38.2
22.9
66.8
53.3
49.
[48.6,
DOFA
72.0
21.5
50.7
86.1
75.3
18.2
45.4
40.1
39.1
23.1
67.3
54.9
49.5
[48.9 49.9)
CARL
72,9
24.9
31.9
86.6
76.5
21.0
43.9
42.0
42.0
25.6
68.6
54.8
[50.4; 51.41
Table 11: Computational complexity of compared models. For each sensor in remote sensing evaluation, we report the number parameters and estimated GFLOPs for a subset of models. "Avg. Rank" denotes the mean performance rank (lower is better). CARL offers more favorable compute-performanc balance than the spatio-spectral baseline SpectraIGPT, while spatial-only encodings are cheaper but generalize poorly unseen sensors.
Params (M)
Spatio- spectral
Avg. Rank
GFL.Pss (per sensor)
Model
RGB
Sentinel-2
OHS
Gaofen-5
SpectralGPT
85.4
23
107
339
2573
V
5.5
DOFA
111.1
23
24
26
32
3.2
Copernicus-
139.3
24
32
2.6
SMARTIES
88.4
23
24
26
55
2.6
CARL
71.5
34
53
96
286
1.6
F SYNTHETIC MULTISPECTRAL DATA GENERATION
As outlined Section 4. 1. of the main manuscript, we synthesized multispectral images from given hyperspectral images to simulate spectral heterogeneity within the training set. This section provides a more in-depth explanation of the data generation procedure.
In spectral imaging, optical filters play a crucial role in isolating specific wavelength bands, allowing the system to capture reflectance information with spectral specificity, as discussed (Garini et al., 2006). These filters are designed to selectively transmit light within defined wavelength ranges, determined by their material properties and design specifications. The transmission characteristies of optical filter typically described by its filter function, which quantifies the transmitted intensity as a function of wavelength. In practice, these filter functions often exhibit smooth, bell-shaped curves centered around a target wavelength (Niewiadomski 2013).
To simulate this behavior in a proof-of-coneept setting, we modeled the filter functions as normalized Gaussian distributions, where the mean corresponds the center wavelength and the variance controls the bandwidth of the filter. This approach enables the generation of virtual multispectral cameras with tunable spectral profiles. Using these Gaussian modeledd filters, we synthesized multispectral images from hyperspectral data, while preserving the spatial context of the scene.
Specifically, we simulated multispectral channel by first sampling the corresponding filter's center wavelength y through farthest point sampling within [550 nm, 950 nm). To define realistic range for the filter bandwidth, we analyzed a real near-infrared multispectral camera (Ximea@ MQ022HG- IM-SM5X5 NIR), which features 25 spectral channels. Particularly, we fitted Gaussian curves to the camera's 's filter functions to estimate plausible variance values. Based on this analysis, the variance of each simulated Gaussian filter was then uniformly sampled from the interval [5, 25]. As the given hyperspectral images exhibited wavelengths from 500 nm to 1,000 nm with nm steps, we discretized the wavelength axis accordingly and set À (500, 505, ..., 995)* R1000 Then, defined the
20

---

Published as a conference paper at ICLR 2026
The proposed spectral encoder demonstrates superior performance as camera- agnostic model. The class-wise IoU scores with the 95 % confidence intervals of the mloU scores on the HSICity test set. While the camera-specifie model was pre-trained on Cityscapes and fine-tuned exclusively on HSICity, the other models are channel-invarian adaptations which were concurrently trained on both datasets. Notably, our spectral encoder performs best among the presented adaptation methods, and significantly benefits from self-supervised pre-training.
Table 12:
Camera Camerospecific model
Spectral Adapter
HyperPree
Hyve
DOFA
CARL
CARL-SSL
Read
93.6
94.0
94.7
Sidewalk
33.5
43.5
Building
69.8
$4,9
65.0
69.4
71.9
71.1
71.1
Wall
55.1
43.9
$4.7
$4.4
52.4
55.4
55.2
Fence
14,1
5,3
11.6
11.5
10.4
11.3
13.9
Pole
29.6
20.8
31.0
Traficc light
50.4
Traffic sign
53.4
49.4
49.1
54,5
59.9
59.1
61.5
Vegetation
80.9
72.5
79.5
80.9
82.3
82.0
81.9
Ternin
3.2
9.0
5.7
3.6
3.9
6.6
5.1
Sky
65.8
79,9
83.7
87.2
88.5
88.4
88.7
Rider
Car
86.0
88.1
86.2
88.0
89.5
89.3
90.3
Truck
53.7
50,8
57.0
60.5
73.6
57.5
63.0
Bux
67.6
80.0
79.3
80.3
83.0
87.6
87.4
Train
Motorcycle
Bicyclo
36.0
11.3
1922
29.1
26.5
29.1
33.1
mloL'
44.6
43.4
44.6
48.0
49.6
48.6
50.1
47.39
45.20
(42.2.46.5)
[45.4,30.0]
[4688,51.8
(4.06
[47.2] 52.41
Table 13: Semantic content drives CARL's feature variance. Proportion of variance in the learned feature embeddings explained by semantic content (organ class) versus imaging sensor, as measured by mean 1e across all embedding dimensions.
Feature Variance Source
Explained Variance
Confounding Variable (Sensor) Semantic Content (Organ)
0.6 %
61.6 %
L1-normalized filter function Fy.o as following:
Fu,o (Av) = E (A-=n)²
(9)
F Furo (2x)
Fno (Ni) =
(10)
E |F (Az))
By uniformly sampling the number of channels, C, within [10, 25], we obtained channel-specific filter functions, (F (osps,.)sssc as described above. These functions can be collectively represented in matrix form as follows:
(A)""
F=
E C'1000
(11)
Finally, for a hyperspectral pixel PHSI E 1000 we simulated the corresponding multispectral pixel by:
PMSI = F. PASI RC
(12)
This matrix- multiplication can be performed for each pixel. leading to an multispectral image with channels. Notably, we only altered spectral properties of the images, while preserving geometric information.
In this way, six camera configurations were simulated, resulting in six sets of synthetic multispectral images. To systematically introduce spectral heterogeneity into the original hyperspectral training data, a progressive substitution strategy with the synthetic multispectral images was employed. In each iteration, hyperspectral data from two additional porcine subjects substituted with
21

---

Published as a conference paper at ICLR 2026
60
40
20
t-SNE 2
0
-20
40
-60
-60
-40
-20
0
20
40
60
t-SNE
heart
stomach
peritoneum
kidney
lung
spleen
skin
major vein
liver
gallbladder fat subcutancous
kidney with Gerotas fascia
colon
bladder pancreas
small bowel
omentum
muscle
Figure CARL effectively disentangles organ semanties from camera variability, t-SNE projection of mean feature embeddings for each organ region across the original hyperspectral image and two simulated multispectral variants. Points are color-coded by organ class and shaped by camera type (one hyperspectral, two unseen multispectral). Embeddings from the same region connected into triangles. The dominant clustering by color and the scarcity of modality-connectin triangles demonstrate that the learned features are strongly organ-specific while remaining largely invariant to camera variations.
the corresponding synthetic multispectral images from a different simulated camera configuration. This process produced six augmented datasets that exhibit increasing spectral heterogeneity while preserving the surgical scene content. Model generalization to hyperspectral imagery was evaluated on the original hyperspectral test set, encompassing 166 images from five different poreine subjects.
G ANALYSIS ON FEATURE REPRESENTATIONS
In Sec. 4.1 we generated spectrally heterogeneous training datasets while keeping the hyperspectral evaluation set unchanged to avoid any modifications during testing. We now extend the evaluation by adding two sets of synthetic multispectral images, generated via simulated filter responses as described
in Sec. 4.1 For each hyperspectral sample in the evaluation set, we produced two corresponding multispectral versions. After training our model on a dataset variant from Sec. 4.1 we performed inference on this enlarged, spectrally diverse evaluation from the evaluation set are unseen during training. For each labeled organ region, we computed the mean feature vector cittoor-obaiieed from the spatial encoder- over its ground-truth mask. This yielded three embeddings per region: one from the original hyperspectral image and two from the simulated multispectral variants. To visualize these embeddings, we applied t-SNE to project them into two- dimensional space (Fig. 6). Embeddings corresponding the same region but different modalities were connected to form a triangle. Marker shapes indicate camera type, while colors denote organ class. The strong volor-based grouping. rather than marker-base based grouping, demonstrates that feature representations are organ-specific and largely camera- angerti..nostic. This is further supported by the scarcity of visible triangles.
Importantly, the multispectral cameras
22

---

Published as a conference paper at ICLR 2026
To quantify this observation, performed variance decomposition analysis using linear regression. Specifically, the feature embeddings were independently regressed against one-hot encoded predictors of either organ class or imaging sensor, and the coefficient of determination R was computed for each regression. The average R² across all feature dimensions was then taken as the proportion of variance explained by the corresponding factor. As reported in Table T3 organ class explains 61.6 % of the feature variance, while the imaging sensor accounts for only 0.6 %. This indicates that the representations are strongly driven by semantic content while remaining largely invariant to the confounding sensor domain, suggesting robust disentanglement of task-relevane features from acquisition -eguiiition-speciffie artifacts.
H LLM USAGE STATEMENT
Large Language Models (LLMs) were used exclusively to assist with paper writing. Specifically, they were employed to correct grammar errors and enhance the clarity and style of existing text. Importantly, L.MS were not used to generate section drafts or even write entire sections. Their role was limited to refining and improving written material.
23


Reviews:
[{"review_id": "IsTOg3QUoz", "reviewer": "Reviewer_YZcF", "summary": "This paper proposes CARL (Camera-Agnostic Representation Learning), a framework designed to achieve cross-camera generalization across RGB, multispectral, and hyperspectral imaging systems. CARL introduces a spectral encoder with self- and cross-attention modules to map arbitrary channel-dimensional spectral images into a camera-agnostic latent space using wavelength positional encoding. The model further incorporates a spatio-spectral self-supervised learning (CARL-SSL) scheme combining spectral masking and spatial predictive learning (based on VICReg and I-JEPA). Experiments are presented across three domains, such as medical imaging, automotive vision, and satellite imaging, demonstrating improved robustness under heterogeneous spectral conditions and outperforming several baselines such as DOFA, Hyve, and SpectralGPT.", "questions": "How does CARL differ quantitatively and conceptually from prior channel-invariant or wavelength-aware transformers?\n\nWhat is the true scale of CARL in parameter count and computational cost compared to foundation models such as SpectralGPT or SatMAE?\n\nCan the authors show clearer visualizations (e.g., spectral attention maps) to support claims of camera-agnostic learning?\n\nHow robust is CARL to non-spectral variations (spatial resolution, atmospheric noise, illumination)?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "Problem relevance: The challenge of camera-specific spectral variability is real and significant in multisensor spectral imaging and remote sensing.\n\nComprehensive evaluation: The authors conduct cross-domain tests (medical, automotive, satellite), showing a broad view of potential applications.\n\nFramework completeness: CARL integrates both wavelength-aware encoding and SSL, aligning with ongoing developments in spectral foundation models.\n\nPotential impact: If scalable and reproducible, the framework could contribute to harmonized spectral representation learning across heterogeneous sensors.", "weaknesses": "Limited novelty / Incremental contribution.\nDespite a well-written motivation, the technical innovation is incremental rather than groundbreaking. The proposed spectral encoder (self-attention + cross-attention) and wavelength positional encoding are straightforward extensions of existing works such as DOFA (Xiong et al., 2024), Hyve (Varga et al., 2023), and SpectralGPT (Hong et al., 2024). The SSL design (masked prediction + VICReg) follows existing feature-level pretraining frameworks (I-JEPA, DINOv2) without novel loss functions or theoretical insight. The “camera-agnostic” formulation is largely a rebranding of channel-invariant learning.\n\nLack of clear incremental improvement.\nReported gains (typically 1–2 mIoU or OA points) over existing baselines are marginal and fall within confidence intervals. The work does not convincingly demonstrate why the proposed cross-attention or wavelength encoding leads to generalizable representations beyond what existing channel-adaptive or wavelength-aware layers already achieve.\n\nInsufficient discussion of existing multimodal or foundation models.\nThe manuscript lacks substantial comparison or discussion of recent multimodal foundation models (e.g., OmniSat, CROMA, Galileo, SeaMo, etc.) and fails to position CARL relative to these stronger baselines. This omission weakens its claim of being a “backbone for future spectral foundation models.”\n\nPoor figure readability.\nFigures (e.g., Fig. 1–5) are small, densely packed, and use fonts unreadable at standard scale. Important architectural elements and result visualizations are nearly illegible, making it difficult to evaluate the technical contributions.\n\nExperimental design limitations.\n\nThe model is only tested on moderate-scale datasets (≤800 k samples) and not on true foundation-model-scale data.\n\nCross-domain transfer is limited to a few camera pairs (e.g., Cityscapes–HSICity); results on other heterogeneous sensors (e.g., PRISMA, ZY1-HSI) are missing.\n\nComputational efficiency and scalability analyses are superficial, despite the model’s significant complexity.\n\nOverstated claims.\nThe claim of being the “first camera-agnostic framework” is inaccurate, as previous models have already introduced similar wavelength- and channel-adaptive mechanisms. The novelty is therefore overstated.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "xrcFp4UtMJ", "reviewer": "Reviewer_D1Nu", "summary": "This paper introduces CARL, a novel framework for camera-agnostic representation learning across RGB, multispectral, and hyperspectral imaging modalities. The key innovation is a spectral encoder that uses wavelength positional encoding and learnable spectral representations with a self-attention-cross-attention mechanism to convert spectral images with arbitrary channel dimensionality into camera-agnostic representations. The authors also propose CARL-SSL, a self-supervised pre-training strategy combining spectral and spatial feature-based learning. Extensive experiments across medical imaging, autonomous driving, and satellite imaging demonstrate superior performance compared to camera-specific and channel-invariant baselines.", "questions": "1. The authors mention spatial resolution differences as a limitation. Could the wavelength positional encoding framework be extended to handle spatial resolution as metadata? Have you experimented with this?\n2. How does CARL perform when test wavelengths fall outside the training distribution? For example, if trained on visible-NIR but tested on SWIR?\n3. You mention sampling 32 channels during training when C>32. What is the impact of this sampling ratio? Could adaptive sampling based on wavelength diversity improve performance?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper addresses a critical bottleneck in spectral imaging—the inability of existing models to generalize across cameras with different spectral properties. This is particularly relevant for domains like medical imaging where sensor diversity is high.\n2. CARL is the first approach to combine wavelength-awareness, channel-invariance, spatio-spectral encoding, and spatio-spectral SSL pre-training (Table 1). The wavelength positional encoding is an elegant solution for establishing cross-camera channel correspondences.\n3. The evaluation spans three diverse application domains with both synthetic and real-world spectral variations. The progressive substitution experiments (Figure 5) provide compelling evidence of robustness to spectral heterogeneity.\n4. CARL consistently outperforms strong baselines across all experiments. The cross-modality knowledge transfer demonstrated in Figure 4 (e.g., transferring \"pole\" labels from RGB Cityscapes to HSI) is particularly impressive.\n5. The ablation studies (Table 6) validate key architectural decisions, and the variance decomposition analysis (Table 10) provides insight into what the model learns.", "weaknesses": "1.  While σ=3 and K=8 are validated through ablations, the paper doesn't discuss how sensitive these choices are across different domains or whether they need domain-specific tuning.\n2. Scalability Limitations: The experiments use relatively modest model sizes (small and base versions). It's unclear whether the approach scales to larger foundation models or whether architectural modifications would be needed.\n3.  While CARL-SSL shows improvements, the relative contribution of spectral vs. spatial self-supervision could be explored more thoroughly. The ablation in Table 3c is limited to a single dataset with reduced budget.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "513g1JI39B", "reviewer": "Reviewer_BT6d", "summary": "The paper introduces a method for camera-agnostic spatio-spectral\nrepresentation learning across RGB, multispectral, and hyperspectral images. It\nintroduces a spectral encoder with self- and cross-attention to learn\ncamera-agnostic spectral representations, combined with I-JEPA spatial\npretraining for self-supervised learning. This approach is evaluated across medical\nimaging, autonomous driving, and satellite imaging, and claims to outperform\nboth camera-specific and channel-invariant baselines in terms of generalization\nto unseen sensors.", "questions": "- How does CARL compare to Panopticon or Terramind on datasets not included in the pretraining corpus, specifically for true camera-agnostic generalization?\n - Why linear probing is used for segmentation tasks instead of standard decoders, and how this choice affects results?\n - How sensitive are results to pretraining dataset composition? For example, would adding more diverse sensors improve generalization?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper tackles a relevant problem: variability across spectral cameras that limits generalization.\n - The combination of spectral tokenization, spatio-spectral aggregation, and self-supervised pretraining is reasonable.\n - Demonstrates cross-domain evaluation, including medical, automotive, and remote sensing datasets.", "weaknesses": "- The methodological contribution is limited: the components (spectral tokenization, 2D token aggregation, spatial feature extraction, I-JEPA pretraining) are standard components. Presenting it as a new ``framework'' is a bit of an overstatement.\n - The claims ``first spatio-spectral camera-agnostic representation learning'' is overstated; I am not sure why a model such as DOFA does not fall into this category. The related work Panopticon [1] seems to provide a similar channel encoding scheme (any comment on this will be appreciated). A clear positioning with respect to other models such as Copernicus-FM [2], SMARTIES [3], and Fleximo [4] would also be useful.\n - The remote sensing evaluation is a bit weak. A comparison with Panopticon would be helpful. Regarding the claim ``our approach outperformed both camera-specific and channel-invariant baselines'', for an equivalent compute budget, it is not clear that the proposed model performs better than one trained for a single sensor or a fixed set of sensors. The experimental section does not demonstrate this. Moreover, pretraining is conducted on SpectralEarth and a Sentinel-2 dataset (only two sensors), and three out of the four datasets used for evaluation contain only Sentinel-2 data, which was already seen during pretraining.\n - The evaluation on GeoBench does not use all the available datasets. Moreover, only SpectralGPT, DOFA, and SatMAE are compared to on one of the datasets. To support the claim to outperform sensor-specific models, comparisons with Terramind [5], CROMA [6], and others would be useful. (It may be possible to reuse results reported from previous papers for some of the datasets).\n - Additionally, in Table 4, linear probing is used across three datasets. This is unusual for remote sensing segmentation tasks, where models typically use an UpperNet decoder or at least a few convolutions and upscaling layers. That could explain the relatively weak results.\n - Code is not provided at submission time.\n\n\n[1] Waldmann, Leonard, et al. \"Panopticon: Advancing any-sensor foundation models for earth observation.\" CVPR 2025.\n\n[2] Wang, Yi, et al. \"Towards a unified copernicus foundation model for earth vision.\" arXiv preprint arXiv:2503.11849 (2025).\n\n[3] Sumbul, Gencer, et al. \"SMARTIES: Spectrum-Aware Multi-Sensor Auto-Encoder for Remote Sensing Images.\" arXiv preprint arXiv:2506.19585 (2025).\n\n[4] Li, Xuyang, et al. \"Fleximo: A flexible remote sensing foundation model.\" arXiv preprint arXiv:2503.23844 (2025).\n\n[5] Jakubik, Johannes, et al. \"Terramind: Large-scale generative multimodality for earth observation.\" arXiv preprint arXiv:2504.11171 (2025).\n\n[6] Fuller, Anthony, Koreen Millard, and James Green. \"CROMA: Remote sensing representations with contrastive radar-optical masked autoencoders.\" NeurIPS 2023", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "QNYfsz48Nc", "reviewer": "Reviewer_usvR", "summary": "This paper introduces an architecture called CARL and a self-supervised learning (SSL) algorithm for it called CARL-SSL. CARL consists of a spectral encoder followed by a spatial encoder. The spectral encoder is essentially a transformer over the spectral dimension, i.e., a token is a spatial patch of a single spectral channel and attention occurs over tokens at the same spatial location (= inter-channel attention). The spectral encoder outputs K tokens per spatial location, which represent the spectral information (and some spatial information, since the patch size is not 1) at the location. The spatial encoder is a ViT. \n\nCARL-SSL consists of two learning objectives. The first adapts VICReg to pre-train the spectral encoder and the second adapts I-JEPA to pre-train the spatial encoder. \n\nThe paper demonstrates CARL's superiority on many benchmarks and domains, such as remote sensing, medical imaging, and automotive data.", "questions": "Please see weaknesses", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- The paper is dense with a lot of content, yet is well-written overall\n- Both CARL and CARL-SSL are technically novel\n- CARL is demonstrated across many domains\n- The challenge that CARL aims to solve is important. Cameras with more colour channels contain more information that can be used to improve predictions, but the field has not settled on the optimal way to process many channels.", "weaknesses": "Major concern:\n- Computational cost. It is not clear how much more or less expensive CARL is relative to baseline methods. I see a computational complexity section in the appendix, showing that for an input of 128x128x48, CARL is 15X the cost of DOFA. In my opinion, this makes the comparison unfair at this input size. The cost at other sizes is unclear. I believe that CARL is computationally expensive because it uses a separate spectral encoder and it may have more tokens, since it uses k=8 tokens per spatial location. \n\nModerate concerns:\n- Limited novelty. The spectral encoder is a transformer over the spectral dimension, in a sense it reminds me of axial transformers/attention (which attend to one axis at a time). It does include learned query tokens, differentiating it from a purely axial framework, which means it always outputs K tokens per spatial location. Have the authors thought about using a spectral encoder with only self-attention, then pooling all tokens along the spectral axis? This is a simpler means of achieving K=1 tokens per spatial location.\n- Limited ablations. The paper includes some ablations, e.g., Table 3c (a single result that removes the spectral SSL task) and Table 6 (spectral position encoding, aggregation, K, and the feature dimension). These are informative but I'd like some more fundamental ablations, e.g., removing the spectral encoder and simply tokenizing all spatio-spectral patches with a vanilla transformer.\n- Benchmarks and Baselines. Although CARL is evaluated on many domains, within a domain the experiments are not rigorous enough, in my view. For example, in remote sensing (one of the primary domains of the paper), EuroSat and BigEarthNet are not used despite being the most popular benchmarks. Regarding baselines, the paper cites Galileo (SOTA RS foundation model) but does not compare to it.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)