Weave Mamba Fusion: Global Cross-Scale Interaction for Lightweight Face Detection

Dohun Kim; Jinmyung Jung

cs.CVOct 5, 2026

Weave Mamba Fusion: Global Cross-Scale Interaction for Lightweight Face Detection

Authors: Dohun Kim, Jinmyung Jung

Organizations: School of Data Science, The University of Suwon, Korea

Abstract

Feature pyramid methods, from FPN to BiFPN, have achieved strong performance in face detection by fusing multi-scale features. However, detecting faces under unconstrained conditions, such as small scale, occlusion, and extreme pose, remains difficult, as it requires global cross-scale dependencies that local fusion cannot model. State space models such as Mamba provide global context with linear complexity by scanning features as a sequence, and therefore offer a promising direction for this problem. Nevertheless, such a scan needs the two pyramid scales combined into a single feature map, and the way they are combined determines whether cross-scale structure is preserved. Summation collapses the two scales before the scan, so the scan has no cross-scale structure to exploit, while concatenation keeps both scales but at far higher cost. To address this, we propose \textbf{Weave Mamba Fusion (WMF)}, which interleaves two adjacent pyramid scales column by column so that each step of a horizontal bidirectional SS2D scan moves from one scale to the other. With partial-channel processing and parameter-free de-weaving, WMF enables efficient cross-scale interaction while preserving feature structure. Integrating WMF into every fusion node yields \textbf{WeaveBiFPN}, the neck of our \textbf{WeaveFace} detector. On WIDER FACE, WeaveFace achieves 91.41% mean AP with only 0.34M parameters and 1.16 GFLOPs, outperforming prior detectors under 0.5M parameters. Its largest gains are on the Hard subset, where it reaches 87.14% AP. The code is publicly available at https://github.com/dohun-mat/WeaveMambaFusion.

Figures & tables

Figure 1 : Feature map visualization of different feature pyramid networks. Given the input image, we visualize the fused features from (a) FPN, (b) PANet, (c) BiFPN, and (d) our WeaveBiFPN. Our WeaveBiFPN separates faces from the background more clearly than the others.

Figure 2 : Comparison of feature pyramid architectures. (a) FPN. (b) PANet. (c) BiFPN. (d) Our WeaveBiFPN. The dashed boxes enlarge the feature fusion in each design. Our WeaveBiFPN keeps the bidirectional structure of BiFPN but replaces its weighted fusion with Weave Mamba Fusion.

Model	Backbone	Neck	Fusion	Easy	Medium	Hard	Avg. (%)	#Params (M)	GFLOPs
DSFD ‡ [ 13 ]	ResNet-152	FEM	Conv.	96.60	95.70	90.40	94.23	120.06	259.55
TinaFace ‡ [ 37 ]	ResNet-50	FPN	Conv.	97.00	96.30	93.40	95.57	37.98	172.95
TransEnc-R50 †	ResNet-50	FPN	Self-attention	93.03	92.89	88.56	91.49	33.72	32.92
RetinaFace ‡ [ 6 ]	ResNet-50	FPN	Conv.	96.70	96.10	91.40	94.73	29.50	37.59
SCRFD-10GF [ 9 ]	Basic Res	PANet	Conv.	95.93	94.95	90.81	93.90	3.86	9.98
FaceBoxes ‡ [ 34 ]	-	-	-	85.90	81.60	55.70	74.40	1.01	0.28

Table 1 : Comparison with state-of-the-art face detectors on the WIDER FACE validation set (multi-scale AP). Fusion denotes the operator family used for multi-scale feature fusion and context aggregation. Results are from original publications or the multi-scale evaluations in [ 9 ] ( ‡ ) and [ 11 ] ( § ). † : self-attention baseline (ResNet-50 backbone with a DETR Transformer encoder [ 3 ] ).

Method	AFW AP (%)	PASCAL AP (%)	#Params
MogFace [ 17 ]	99.85	99.32	85.26M
EfficientSRFace-L [ 24 ]	99.94	98.84	18.84M
SCRFD-0.5GF [ 9 ]	98.60	98.54	0.57M
SCRFD-1.0GF [ 9 ]	99.70	98.60	0.64M
SCRFD-2.5GF [ 9 ]	99.82	98.91	0.67M
FaceBoxes [ 34 ]	98.91	96.30	1.01M

Table 2 : Comparison on AFW and PASCAL Faces without fine-tuning, following the AP protocol of [ 19 ] . All models are trained solely on WIDER FACE. Bold : our model; underline : best result in each column.

Figure 4 : Qualitative comparison between EResFD (left) and WeaveFace (right) on the WIDER FACE Hard subset. Red boxes represent successful detections. Yellow boxes indicate faces missed by EResFD but correctly identified by WeaveFace, while green boxes highlight false positives generated by EResFD that are effectively mitigated by WeaveFace.

Fusion	Easy	Medium	Hard	Avg.	#Params (M)	FLOPs (G)	Lat. (ms)
Sum (w/ sep. conv.)	92.88	90.87	84.76	89.50	0.217	0.564	15.1
Cross-attention	93.47	91.55	86.46	90.49	0.577	11.228	23.7
WMF	94.33	92.76	87.14	91.41	0.344	1.159	23.3

Table 3 : Comparison of different fusion operators on the BiFPN topology. We evaluate our proposed WMF against standard summation and cross-attention baselines. Bold : our model; underline : best result in each column; Lat. : latency.

Topology	Fusion	Easy	Medium	Hard	Avg.	#Params (M)	FLOPs (G)
FPN	Sum	91.17	89.31	83.81	88.10	0.229	0.672
FPN	WMF	91.47	89.72	84.12	88.44	0.187	0.639
PANet	Concat	92.33	90.35	84.88	89.19	0.562	1.071
PANet	WMF	92.75	91.08	85.03	89.62	0.329	0.751
BiFPN	Sum	92.88	90.87	84.76	89.50	0.217	0.564
BiFPN	WMF	94.33	92.76	87.14	91.41	0.344	1.159

Table 4 : Generality of WMF across pyramid topologies. Each topology is paired with its standard fusion method (summation for FPN/BiFPN, concatenation for PANet) and the proposed WMF. Bold : WMF (ours); underline : best result in each column within each topology.

Fusion Operator	$N$	Easy	Medium	Hard	Avg.	#Params (M)	FLOPs (G)
Sum + SS2D	1	93.27	91.54	85.40	90.07	0.409	1.152
	2	91.71	89.82	83.85	88.46	0.245	0.711
	4	90.18	88.13	81.20	86.50	0.193	0.566
Concat + SS2D	1	95.14	93.79	88.40	92.44	1.080	2.649
	2	93.49	91.57	85.81	90.29	0.510	1.350
	4	93.22	91.00	84.47	89.56	0.344	0.925

Table 5 : Accuracy–efficiency trade-offs across different scale-combination schemes (summation, concatenation, and weaving) as the partial-channel divisor

N

increases.

N

determines the extent of lightweighting in SS2D, where only

1/N

of channels are processed; thus, a larger

N

reduces model parameters and FLOPs at the expense of accuracy. Bold : our final configuration; underline : best result in each column.

$s$	Easy	Medium	Hard	Avg.	#Params (M)	FLOPs (G)
1	93.87	92.07	86.51	90.82	0.323	0.991
2	94.33	92.76	87.14	91.41	0.344	1.159
4	94.01	92.52	87.06	91.20	0.386	1.496

Table 6 : Effect of the scan count

s

in WeaveBiFPN, where

s

denotes the number of scan directions utilized by SS2D. Bold : our final configuration (

s{=}2

); underline : best result in each column.

Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

Backbone	Easy	Medium	Hard	Avg.	#Params (M)	FLOPs (G)
EResNet	94.33	92.76	87.14	91.41	0.344	1.159
MobileNetV1-0.25	95.06	93.64	88.52	92.41	0.566	1.231
ResNet-50	96.29	95.44	91.20	94.31	29.756	36.275
ResNet-152	96.33	95.44	91.15	94.31	64.392	81.871

Appendix

Table 7 : WeaveFace with different backbones on the WIDER FACE validation set. The WeaveBiFPN neck (

N{=}2

s{=}2

), CBAM, and CCPM head are fixed, and only the backbone is changed. The same fusion design generalizes from lightweight to heavy backbones. Bold : our default backbone.

Level	Stride	Feature map	Anchor sizes (px)
$P_{2}$	8	$80\times 80$	16, 32
$P_{3}$	16	$40\times 40$	64, 128
$P_{4}$	32	$20\times 20$	256, 512

Appendix

Table 8 : Anchor settings of WeaveFace. Feature-map sizes are given for the

640\times 640

training resolution.

	COCO val2017					PASCAL VOC
Variant	AP	AP 50	AP 75	Params (M)	FLOPs (G)	mAP	Params (M)	FLOPs (G)
EfficientDet (baseline)	34.5	52.9	36.6	3.88	2.57	76.90	3.84	2.35
EfficientDet + WMF	36.2	53.6	38.2	4.25	3.21	78.63	4.21	2.99

Appendix

Table 9 : Results on COCO val2017 and PASCAL VOC datasets. Only WMF is added to the BiFPN neck of EfficientDet; all other settings are unchanged.

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Lightweight Complementary-Cue Fusion for Robust Video Face Forgery Detection

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Open paper AskPDF arXiv

Current face video forgery detectors use wide or dual-stream backbones. We show that a single, lightweight fusion of two handcrafted cues can achieve higher accuracy with a much smaller model. Based on the Xception baseline model (21.9 million parameters), we build two detectors: LFWS, which adds a 1x1 convolution to combine a low-frequency Wavelet-Denoised Feature (WDF) with a phase-spectrum channel derived from Spatial-Phase Shallow Learning (SPSL), and LFWL, which merges WDF with Local Binary Patterns (LBP) in the same way. This extra module adds only 292 parameters, keeping the total at 21.9 million, smaller than F3Net (22.5 million) and less than half the size of SRM (55.3 million). Even with this minimal overhead, the fused models increase the average area under the curve (AUC) from 74.8% to 78.6% on FaceForensics++ and from 70.5% to 74.9% on DFDC-Preview, gains of 3.8% and 4.4% over the Xception baseline. They also consistently outperform F3Net, SRM, and SPSL in eight public benchmarks, without extra data or test-time augmentation. These results show that carefully paired, handcrafted features, combined through the lightweight fusion block, can provide competitive robustness at a significantly lower cost than comparable frequency-based detectors. Our findings suggest a need to reevaluate scale-driven design choices in face video forgery detection.

Abstract

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