SAFE: Scene-Aware Feature Modulation for Color Constancy with Learned Color Space in Pure-Color Scenes

Yuan-Kang Lee; Kuan-Lin Chen; Chih-Heng Chang; Jian-Jiun Ding

cs.CVAug 14, 2026

SAFE: Scene-Aware Feature Modulation for Color Constancy with Learned Color Space in Pure-Color Scenes

Authors: Yuan-Kang Lee, Kuan-Lin Chen, Chih-Heng Chang, Jian-Jiun Ding

Organizations: MediaTek Inc., Hsinchu, Taiwan · National Taiwan University, Taipei, Taiwan

Abstract

Color constancy on pure-color scenes is challenging: when most pixels share a narrow band of hues, every chromaticity-based cue collapses to a single point and standard estimators become ambiguous. We propose a compact framework that couples two innovations: (i) SAFE, a Scene-Aware FeaturE modulation network that organizes illumination cues into a structured four-token representation, which is then selectively reweighted based on scene complexity features; (ii) the Learned Color Space (LCS), a scene-dependent chromaticity normalization that directly addresses the chromaticity collapse problem for pure-color scenes. Experiment results show that SAFE consistently improves performance in pure-color scenes. Compared to the best-performing baseline in each metric, it reduces the mean angular error by 10%, the best-25% error by 20%, and the worst-25% error by 5.8%.

Figures & tables

Figure 2: Overview of the proposed color constancy framework. Training proceeds in four phases: (1) pre-train the SAFE backbone $\phi$ alongside a coarse global $w$ -module under the angular loss; (2) freeze $\phi^{(1)}$ and search for a scene-dependent color axis that minimizes the teacher’s angular error; (3) supervise the LCS predictor $\psi$ on the oracle in logit space; (4) jointly fine-tune $\phi$ and $\psi$ end-to-end. At inference, the LCS predictor $\psi$ outputs a per-image color axis $\mathbf{w}(\mathbf{I})$ that re-parameterizes the chromaticity normalization for calculating the four-token illumination feature $\mathbf{f}_{w}$ ; the SAFE backbone then modulates $\mathbf{f}_{w}$ via a scene descriptor to predict the illuminant estimate $\hat{\mathbf{y}}$ .

Method	Mean	Med.	Tri.	B-25%	W-25%
WP [ 30 ]	5.99	4.37	4.72	0.89	14.08
GW [ 7 ]	6.78	5.53	5.69	1.73	14.08
GE [ 55 ]	4.73	3.38	3.80	1.11	10.52
SoG [ 15 ]	5.55	4.50	4.74	1.05	11.84
LSRS [ 16 ]	6.00	5.02	5.10	1.46	12.52
GP [ 59 ]	4.56	3.51	3.84	0.82	10.01

Table 1: Evaluation results on the PolyUv2 pure color dataset. We report the mean angular error (in degrees) based on 3-fold cross-validation. SAFE outperforms existing methods, yielding the lowest mean, median, tri-mean, best-25%, and worst-25% angular errors, underscoring its superior stability.

Method	Mean	Med.	Tri.	B-25%	W-25%
WP [ 30 ]	7.55	5.68	6.35	1.45	16.12
GW [ 7 ]	6.36	6.28	6.28	2.33	10.58
SoG [ 15 ]	4.93	4.01	4.23	1.14	10.20
PCA-CC [ 10 ]	3.52	2.14	2.47	0.50	8.74
Woo et al. [ 58 ]	4.30	2.86	3.31	0.71	10.14
GI [ 42 ]	3.07	1.87	2.16	0.43	7.62

Table 2: Evaluation results on the Gehler-Shi dataset. We report the mean angular error (in degrees) based on 3-fold cross-validation. The results prove that SAFE is not limited to pure-color scenes, but achieves comparable performance on general scenes.

Figure 3: Scene-dependent LCS deviation versus scene entropy. (Left figure) The LOWESS smoother with $95\%$ bootstrap confidence band. (Right figure) Hexbin density of the same data with the LOWESS smoother overlaid in red. Quantitatively, the lowest-entropy quintile ( $n{=}255$ ) attains $\bar{\Delta}_{w}=0.0547$ versus $0.0462$ for the highest-entropy quintile ( $18\%$ relative increase on pure-color scenes) and the Pearson correlation $r(H,\Delta_{w})=-0.149$ is highly significant ( $p<10^{-7}$ , $N{=}1271$ ). The analysis validates that the LCS design further improves the accuracy of illumination estimation in pure-color scenes.

Figure 4: Qualitative comparison on images from the PolyU Pure Color dataset V2. Images are gamma-corrected for visualization. The angular error (in degree) is reported in the bottom-right corner of each image.

Inference cost	CCC [ 5 ]	FC 4 [ 21 ]	$C^{4}$ [ 60 ]	$C^{5}$ [ 1 ]	ePCC [ 36 ]	SAFE
Parameters	700	1.71M	5.12M	412k	514	61.5k
Median/Min Latency	0.24/0.23	0.95/0.89	2.58/2.30	5.73/4.53	0.17/0.16	0.66/0.61

Table 3: Inference cost on PolyU V2 dataset. We report the parameters and median/min latency (ms) for several learning-based baselines. All measurements use batch=1, matching the standard single-image inference setting of the benchmark. Our model achieves the best illumination estimation accuracy. It is smaller in both parameters and latency than every baseline except CCC and ePCC, over which it incurs only a moderate increase.

Figure 5: Introspection on the SAFE backbone. The modulation attention

\alpha(\mathbf{g})

quantifies how strongly the modulation network preserves each feature dimension; the input projection weight

\lVert W_{0}[:,j]\rVert_{2}

quantifies how much linear weight the backbone allocates to each dimension; and the intrinsic effective contribution quantifies how important each token is to the illumination estimation.

SRM	A (8D)	B (8D)	C (6D)	D (2D)	Mean	Med.	Tri.	B-25%	W-25%
-	✓	✓	✓	✓	2.08	1.38	1.53	0.43	4.96
✓	-	✓	✓	✓	2.11	1.43	1.56	0.43	4.97
✓	✓	-	✓	✓	2.13	1.42	1.56	0.40	5.09
✓	✓	✓	-	✓	2.25	1.52	1.68	0.47	5.30
✓	✓	✓	✓	-	2.06	1.43	1.53	0.43	4.89
✓	✓	✓	✓	✓	2.03	1.38	1.50	0.39	4.80

Table 4: Ablation studies on different impact of the tokens and the scene-routing modulation module (SRM). The best performance is reached when all the four tokens and the modulation are employed, validating the design of our framework.

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White-Balance First, Adjust Later: Cross-Camera Color Constancy via Vision-Language Evaluation

Shuwei Li, Lei Tan, Robby T. Tan

National University of Singapore · ASUS Intelligent Cloud Services

Color Constancy

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Color constancy aims to keep object colors consistent under varying illumination. Cross-camera generalization in color constancy remains challenging because learning-based models often overfit to the color response characteristics of the training camera, resulting in degraded performance on images captured by other cameras. We propose VLM-CC, a feedback-guided framework that formulates color constancy as an iterative refinement process. Instead of directly estimating the illuminant from raw input, VLM-CC performs iterative correction driven by vision-language model (VLM)-based evaluation. At each iteration, the image is white-balanced using the current estimate and converted to pseudo-sRGB. A lightweight LoRA-tuned VLM then assesses the corrected image, identifying the dominant residual color cast and providing qualitative feedback. This feedback is mapped to a residual illumination direction (red, green, or blue) and used to update the illuminant estimate until convergence. Our key idea is to reframe color constancy as an iterative perceptual feedback problem, leveraging VLM evaluation instead of direct RGB regression. By replacing direct RGB estimation with VLM-guided perceptual feedback, VLM-CC achieves state-of-the-art robustness in cross-camera color constancy across multiple datasets. Code will be available at https://github.com/NothingIknow/VLM-CC.

Abstract

Color Constancy

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