Foreground-Aware Dataset Distillation via Dual-Path Patch Selection

Mingzhuo Li; Guang Li; Longzhen Li; Ren Togo; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama

cs.CVJan 6, 2026

Foreground-Aware Dataset Distillation via Dual-Path Patch Selection

Authors: Mingzhuo Li, Guang Li, Longzhen Li, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama

Organizations: Graduate School of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-Ku, Sapporo, 060-0814, Japan · Faculty of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-Ku, Sapporo, 060-0814, Japan

Abstract

In this paper, we propose a foreground-aware dataset distillation method that performs content-adaptive dual-path patch selection. With the rising computational cost of training large-scale deep models, dataset distillation has emerged as a promising approach for constructing compact synthetic datasets that retain knowledge from large original datasets. Although optimization-based methods can achieve strong performance, they often incur considerable computational and memory costs and may produce synthetic images that generalize poorly across architectures. Recent non-optimization-based methods alleviate these limitations by constructing distilled images from real-image patches. However, their inflexible patch-construction strategies may either retain redundant background content or discard important foreground structures. To address this issue, we propose a foreground-aware dataset distillation framework with content-adaptive dual-path patch selection. We first use Grounded SAM2 to estimate the foreground occupancy of each image and derive category-specific routing thresholds from the corresponding class-wise distributions. Each image is routed to one of two complementary paths: the candidate-crop selection path for images with relatively sparse foregrounds, or the direct transformation path for images with relatively dominant foregrounds. This dual-path mechanism balances foreground concentration and preservation, reducing redundant background content while retaining the structural integrity of foreground objects at the dataset level. Experiments on CIFAR-10, CIFAR-100, ImageNette, and ImageWoof with two network architectures and three distillation budgets demonstrate that the proposed method outperforms the baseline method RDED in nearly all evaluated settings.

Figures & tables

Figure 1: Comparison of images generated by RDED and the proposed method. RDED may include patches with partial foreground objects, while the proposed method shows better foreground preservation.

Figure 2: Overview of the proposed foreground-aware dual-path patch selection methodology. In the Foreground Estimation stage, Grounded SAM2 is applied to all images to obtain foreground occupancy ratios. The category-specific routing thresholds are computed from the ratio distributions of each class. In the dual-path patch Selection stage, a candidate subset is sampled uniformly from the original dataset. The images are routed to the candidate-crop selection path or the direct transformation path based on the routing threshold. In the patch aggregation stage, top- $k$ patches are selected, where $k$ depends on the distillation budget, and then sequentially aggregated into distilled images.

Figure 3: Per-class distributions of the foreground occupancy ratio

r_{i}

on ImageNette, ImageWoof, and CIFAR-10. Each bar counts the images of a class whose

r_{i}

falls within the corresponding bin. The dashed line marks the routing threshold

\mathcal{T}_{c}

. Images without valid foreground masks (No Mask) are excluded from the bars. Blue areas show remaining images that are routed to the candidate-crop selection path (Crop), and orange areas show those routed to the direct transformation path (Transform). Detailed image numbers are listed in the spare space.

Dataset		ImageNette			ImageWoof			CIFAR-10
IPC		1	10	50	1	10	50	1	10	50
ResNet-18	Random	$18.9\pm 3.2$	$40.3\pm 2.7$	$62.7\pm 1.7$	$14.6\pm 2.1$	$23.1\pm 1.3$	$38.4\pm 1.7$	$15.4\pm 1.3$	$28.9\pm 2.1$	$43.3\pm 2.2$
	CDA	$25.4\pm 0.6$	$54.6\pm 0.4$	$77.8\pm 0.3$	$14.6\pm 0.6$	$25.7\pm 0.5$	$59.7\pm 0.5$	$16.4\pm 0.6$	$30.6\pm 0.6$	$54.5\pm 0.7$
	G-VBSM	$28.9\pm 0.6$	$61.6\pm 0.4$	$81.4\pm 0.7$	$14.4\pm 0.4$	$34.5\pm 0.5$	$65.5\pm 0.5$	$17.5\pm 0.7$	$31.5\pm 0.4$	$55.6\pm 0.4$
	DWA	$29.7\pm 0.9$	$64.3\pm 0.4$	$83.2\pm 0.5$	$16.5\pm 0.5$	$36.1\pm 0.5$	$67.8\pm 0.7$	$18.3\pm 0.3$	$33.1\pm 0.4$	$59.9\pm 0.4$
	SRe 2 L	$19.1\pm 1.1$	$29.4\pm 3.0$	$40.9\pm 0.3$	$13.3\pm 0.5$	$20.2\pm 0.2$	$23.3\pm 0.3$	$16.6\pm 0.9$	$29.3\pm 0.5$	$45.0\pm 0.7$

Table 1: Comparison of test accuracy (%) on standard benchmarks with baseline methods. Results are reported as mean

\pm

standard deviation. The best results are highlighted in bold.

Dataset		CIFAR-100
IPC		1	10	50
ResNet-18	SRe 2 L	$6.6\pm 0.2$	$27.0\pm 0.4$	$50.2\pm 0.4$
	RDED	$11.4\pm 0.8$	$44.6\pm 0.5$	$63.4\pm 0.2$
	Ours	$12.8\pm 0.8$	$47.9\pm 0.7$	$64.6\pm 0.2$
ConvNet	MTT	$24.3\pm 0.3$	$40.1\pm 0.4$	$47.7\pm 0.2$
	IDM	$20.1\pm 0.3$	$45.1\pm 0.1$	$50.0\pm 0.2$

Table 2: Comparison of test accuracy (%) on CIFAR-100 with other baseline methods. Results are reported as mean

\pm

standard deviation. The best results are highlighted in bold.

Panel of Figure 4 — Figure 4: Effect of the category-specific routing threshold $\mathcal{T}_{c}$ on ImageNette, ImageWoof, and CIFAR-10 using $\mathrm{IPC}=10$ with ResNet-18. The horizontal axis denotes the routing rate $\rho$ that defines $\mathcal{T}_{c}$ , and the vertical axis denotes test accuracy. The shadow denotes the minimum and maximum values. The dashed line shows the baseline RDED.

Panel of Figure 5 — Figure 5: Analysis on fixed thresholds. The experiment is conducted on ImageNette, ImageWoof, and CIFAR-10 using $\mathrm{IPC}=10$ with ResNet-18. The horizontal axis denotes the fixed threshold, and the vertical axis denotes test accuracy. The shadow denotes the minimum and maximum values. The dashed line shows the test accuracy using the routing rate $\rho=0.3$ .

Dataset	$Z=1$	$Z=4$	$Z=16$
ImageNette	$66.6\pm 1.1$	$63.5\pm 1.4$	$57.1\pm 2.0$
ImageWoof	$42.7\pm 2.1$	$47.7\pm 1.5$	$35.3\pm 1.1$
CIFAR-10	$38.6\pm 0.9$	$36.1\pm 1.9$	$25.1\pm 3.0$

Table 3: Ablation study for

Z

. Experiments are conducted on CIFAR-10, ImageNette, and ImageWoof under

\mathrm{IPC}=10

using ResNet-18. Results are reported as mean

\pm

standard deviation. The best results are highlighted in bold.

Panel of Figure 6 — Figure 6: Visualization results of distilled images for ImageNette.

Transformation	IPC = 1	IPC = 10	IPC = 50
Squash	$22.4\pm 0.5$	$47.7\pm 1.5$	$69.7\pm 0.6$
CenterCrop	$21.3\pm 1.3$	$46.5\pm 2.2$	$69.6\pm 0.9$
RandomSquare	$21.3\pm 1.6$	$47.2\pm 2.4$	$69.3\pm 1.2$
RRC	$19.8\pm 0.8$	$47.1\pm 2.5$	$70.0\pm 0.2$

Table 4: Comparison of test accuracy (%) between different transformation strategies on ImageWoof using ResNet-18. Results are reported as mean

\pm

standard deviation. The best results are highlighted in bold.

Panel of Figure 8 — Figure 8: Visualization results of distilled images for CIFAR-10.

Routing Strategy	IPC = 1	IPC = 10	IPC = 50
AllCrop	$20.9\pm 0.8$	$46.0\pm 1.0$	$68.7\pm 0.9$
AllTransform	$20.5\pm 0.6$	$44.5\pm 0.5$	$68.9\pm 0.3$
RandomRoute	$20.2\pm 0.2$	$45.4\pm 0.9$	$67.8\pm 0.4$
Ours	$22.4\pm 0.5$	$47.7\pm 1.5$	$69.7\pm 0.6$

Table 5: Comparison of test accuracy (%) between different routing strategies on ImageWoof using ResNet-18. Results are reported as mean

\pm

standard deviation. The best results are highlighted in bold.

Dataset	IPC	Foreground	RDED	Ours	Val
ImageNette	1	28.3 min	18.1 s	15.2 s	18.3 s
	10		6.8 s	4.8 s	88.6 s
	50		6.9 s	4.9 s	177.4 s
ImageWoof	1	27.6 min	17.1 s	14.0 s	15.8 s
	10		6.2 s	4.6 s	86.7 s
	50		6.4 s	4.8 s	176.1 s

Table 6: Comparison of time cost between foreground extraction (Foreground), RDED dataset synthesis (RDED), dual-path dataset synthesis (Ours), and downstream validation (Val) using ResNet-18.

Dataset Distillation

Dataset distillation compresses training data into a small synthetic or selected set that retains the information needed to train useful models.

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