FOCUS: Bridging Fine-Grained Recognition and Open-World Discovery across Domains

Vaibhav Rathore; Divyam Gupta; Moloud Abdar; Subhasis Chaudhuri; Biplab Banerjee

Figure 1: FG-DG-GCD extends DG-GCD Rathore et al. (2025a) to the fine-grained regime, where a model trained on labeled source classes must simultaneously recognize known and discover novel categories in unseen, distribution-shifted domains. Compared with coarse-grained DG-GCD Rathore et al. (2025a) ; Rathore et al. (2025b) , this setting is substantially harder due to subtle inter-class cues, high intra-class similarity, and stronger sensitivity to domain-induced appearance changes. FoCUS addresses this challenge through part-consistent representation learning and uncertainty-aware open-space regularization, outperforming existing GCD, FG-GCD, and DG-GCD baselines on the proposed benchmarks (Table 4 ).

Method	Fine-	Domain	Novel Class	Design Choice		Part-
	Grained	Shift	Discovery	Synthetic-Free	Episodic-Free	Aware
DG Zhou et al. (2021a) ; Wang et al. (2023) ; Xu et al. (2023)	✗	✓	✗	✓	✓	✗
FG-DG Yu et al. (2025) ; Wang et al. (2026)	✓	✓	✗	✓	✓	✓
(FG)GCD Vaze et al. (2022) ; Pu et al. (2023) ; Zhang et al. (2023)	✓	✗	✓	✓	✓	✗
DG-GCD Rathore et al. (2025a) ; Rathore et al. (2025b)	✗	✓	✓	✗	✗	✗
FoCUS (Ours)	✓	✓	✓	✓	✓	✓

Table 1: FoCUS is the first framework to jointly address fine-grained recognition , domain generalization , and novel class discovery within a single formulation. Unlike prior DG-GCD methods Rathore et al. (2025a) ; Rathore et al. (2025b) , it is synthetic-free and episodic-free , reducing training FLOPs by 3

\times

and 300

\times

compared with HiDISC Rathore et al. (2025b) and DG 2 CD-Net Rathore et al. (2025a) , respectively, while delivering stronger generalization and discovery performance.

Figure 3

Dataset	#Classes	Split	#Images			#Domains	FID Scores ( $\downarrow$ )
Dataset	#Classes	K:N	Real	Painting	Sketch	#Domains	R $\rightarrow$ P	R $\rightarrow$ S	S $\rightarrow$ P
CUB-200-2011-MD	200	100:100	11,788	11,788	11,788	3	55.13	160.18	106.95
Stanford Cars-MD	196	98:98	16,365	16,365	16,365	3	29.62	62.86	41.63
FGVC-Aircraft-MD	100	50:50	10,000	10,000	10,000	3	64.33	170.27	81.78

Table 2: Summary of the tri-domain fine-grained benchmarks used in this study. Each dataset comprises real photographs and their painting and sketch counterparts synthesized via SDXL with T2I-Adapter. FID Eiter et al. (1994) scores measure cross-domain shifts, reflecting the realism and diversity of the generated domains.

Dataset	Domain Pair	LPIPS (AlexNet)	LPIPS (VGG)
CUB-200-2011-MD	Real $\rightarrow$ Sketch	0.5019	0.5558
CUB-200-2011-MD	Real $\rightarrow$ Painting	0.5564	0.5801
FGVC-Aircraft-MD	Real $\rightarrow$ Sketch	0.4525	0.5441
FGVC-Aircraft-MD	Real $\rightarrow$ Painting	0.5158	0.5585
Stanford Cars-MD	Real $\rightarrow$ Sketch	0.4284	0.4718
Stanford Cars-MD	Real $\rightarrow$ Painting	0.4599	0.4784

Table 3: LPIPS distances between real and synthetic domains. Lower values indicate higher perceptual similarity. Scores are computed with AlexNet and VGG backbones.

Figure 2: Overview of FoCUS . A ViT encoder produces global [CLS] and patch-level features. The Domain-Consistent Parts Discovery (DCPD) module extracts geometry-stable part features

\mathbf{f}_{\text{part}}

, which are fused with the global representation

\mathbf{f}_{\text{cls}}

and optimized using InfoNCE (

\mathcal{L}_{\text{InfoNCE}}

) and supervised contrastive (

\mathcal{L}_{\text{SCon}}

) losses for fine-grained discrimination. In parallel, the Uncertainty-Aware Feature Augmentation (UFA) module uses EMA-based class statistics

(\bm{\mu}_{y},\bm{\Sigma}_{y})

to synthesize outlier features

\widetilde{\mathbf{\mathcal{Z}}}

from class-tail, between-class, and hypersphere regions. A shared classifier

g(\cdot)

is trained on real features using cross-entropy, and on synthetic outliers using energy-based outlier exposure (

\mathcal{L}_{\text{OE}}

) and entropy maximization (

\mathcal{L}_{\text{ENT}}

). Together, these two pathways yield discriminative, robust, and calibrated embeddings for category discovery under unseen domain shift.

Figure 3: Domain-Consistent Attention across Domains. Compared to prior part-localization methods (FOCUS+APL Dai et al. (2025) ), our DCPD module maintains stable attention on the same geometric regions (e.g., bird’s head) across domain shifts from Real

\rightarrow

Painting

\rightarrow

Sketch , demonstrating strong domain-invariant part discovery. See Sup. Mat. for KL divergence between the cross-domain attention maps for quantitative validation.

Figure 4: Progressive Attention Refinement within DCPD . Left to Right,the evolution of : (1) Self-Attention Priors provide initial coarse localization; (2) Image-Conditioned Part Queries refine attention to align with instance-specific geometries; (3) Differentiable Patch-to-Part Assignment creates sharp, spatially exclusive part delineations; and finally, (4) Part-Global Fusion integrates these precise structural cues into a unified representation.

Figure 5: t-SNE Van der Maaten and Hinton (2008) of DCPD embeddings with source (colored) and UFA outliers (black) occupying low-density boundaries, modeling open-space uncertainty and improving calibration.

Methods	Venue	CUB-200-2011-MD			Stanford Cars-MD			FGVC-Aircraft-MD			Average
Methods	Venue	All	Old	New	All	Old	New	All	Old	New	All	Old	New
GCD Baselines
ViT Dosovitskiy et al. (2021)	ICLR’21	24.17	24.88	23.53	16.04	15.40	17.11	17.37	16.62	18.18	19.19	18.97	19.60
GCD Vaze et al. (2022)	CVPR’22	33.34	36.11	30.51	36.46	34.61	38.89	18.95	18.49	20.01	29.58	29.74	29.80
SimGCD Wen et al. (2023)	ICCV’23	29.61	32.40	26.63	40.90	34.61	47.12	33.69	30.50	36.88	34.73	32.50	36.88
CMS Choi et al. (2024)	CVPR’24	7.82	7.63	8.00	4.79	4.71	4.90	7.11	7.17	7.05	6.57	6.50	6.65

Table 4: Performance comparison across three fine-grained datasets averaged over all domain combinations. Baselines are grouped by category (GCD, FG-GCD, FG-DG, DG-GCD and Part Aware Baselines). Our method consistently outperforms prior works, both with and without synthetic domain augmentation.

\Delta

quantifies our method’s gain or drop relative to the best prior baseline. ( Bold : best, underline : second-best).

Figure 6: t-SNE Van der Maaten and Hinton (2008) visualizations of the target domain (“ Photo ”) clusters, as produced by GCD Vaze et al. (2022) , SelEx Rastegar et al. (2024) , DG 2 CD-Net Rathore et al. (2025a) , HiDISC Rathore et al. (2025b) , and FoCUS (Ours) on the CUB-200-2011-MD dataset, with “ Sketch ” as the source domain. FoCUS produces a clean and compact embedding space, clearly separating the target clusters. For visualization, we randomly select 15 classes and 20 samples per class.

Method	Venue	PACS			Office-Home			DomainNet			Avg.
		All	Old	New	All	Old	New	All	Old	New	All	Old	New
ViT Dosovitskiy et al. (2021)	ICLR’21	41.98	50.91	33.16	26.17	29.13	21.62	25.35	26.48	22.41	31.17	35.51	25.73
GCD Vaze et al. (2022)	CVPR’22	52.28	62.20	38.39	52.71	54.19	50.29	27.41	27.88	26.13	44.13	48.09	38.27
SimGCD Wen et al. (2023)	ICCV’23	34.55	38.64	30.51	36.32	49.48	13.55	2.84	2.16	3.75	24.57	30.09	15.94
CMS Choi et al. (2024)	CVPR’24	28.95	28.13	36.80	10.02	9.66	10.53	2.33	2.40	2.17	13.77	13.40	16.50
SelfEx Rastegar et al. (2024)	ECCV’24	71.82	73.37	71.55	50.18	48.59	52.16	24.78	24.99	24.21	48.93	48.98	49.31

Table 5: State-of-the-Art Clustering Accuracy (%) on Domain Generalization Benchmarks. This table details the clustering accuracy for known (Old), novel (New), and overall (All) categories across the PACS, Office-Home, and DomainNet datasets. Our proposed FoCUS consistently achieves leading performance, surpassing both synthetic domain augmentation and non-synthetic baselines. This is particularly significant as FoCUS uses substantially fewer synthetic domains (down to 2 from 6/9) or even no synthetic data at all, highlighting its efficiency and effectiveness. ( Bold : best, underline : second best).

Config.	Loss Components					Accuracy (%) $\uparrow$
Config.	$\mathcal{L}_{\mathrm{SCon}}$	$\mathcal{L}_{\mathrm{InfoNCE}}$	$\mathcal{L}_{\mathrm{CE}}$	$\mathcal{L}_{\mathrm{OE}}$	$\mathcal{L}_{\mathrm{ENT}}$	All	Old	New
C-0	✗	✗	✗	✗	✗	16.04	15.40	17.11
C-1	✓	✗	✗	✗	✗	28.58	26.92	30.78
C-2	✗	✓	✗	✗	✗	9.46	9.01	10.06
C-3	✓	✓	✗	✗	✗	31.64	30.32	33.39
C-4	✓	✓	✓	✗	✗	36.22	33.36	39.99

Table 6: Ablation on loss components. Evaluation on Stanford Cars-MD.

Table 7: Ablation studies on the Stanford Cars-MD dataset: (a) feature fusion, (b) optimal parts

T

in DCPD, (c) prior selection percentile.

Table 8: Ablation results on the Stanford Cars-MD dataset. (a) Impact of UFA sampler. (b) Sensitivity to Margin

m

. (c) Synthetic OOD features.

Method	Space	Mechanism	Open-Space	All	Old	New
MixUp Zhang et al. (2017)	Feature	Linear interp.	✗	36.85	34.20	39.12
CutMix Yun et al. (2019)	Image	Patch repl.	✗	37.42	34.91	40.58
Manifold Verma et al. (2019)	Feature	Manifold blend.	✗	38.05	35.44	41.32
VAT Miyato et al. (2018)	Feature	Adv. perturb.	Partial	39.88	36.70	43.21
OE Hendrycks et al. (2018)	Ext. data	OOD superv.	✓	41.02	37.91	45.15
UFA (Ours)	Feature	Uncert.-driven	Self-gen.	43.36	38.55	48.12

Table 9: UFA comparison. Uncertainty-guided feature-space sampling versus baselines.

Figure 17

Figure 8: Sample images from the Stanford Cars-MD benchmark, illustrating diversity in make, model, and viewpoint. The benchmark contains

(16{,}185\times 3)

images over 196 car categories across the real , painting , and sketch domains.

Figure 9: Example images from the FGVC-Aircraft-MD benchmark, showing variation across aircraft types, manufacturers, and liveries. The benchmark includes

(10{,}000\times 3)

images spanning 100 aircraft variants, each characterized by subtle structural and texture cues.

Figure 10: Representative samples from the CUB-200-2011-MD benchmark, containing

(11{,}788\times 3)

images from 200 bird species across three domains. Its high category count and fine visual granularity make it a particularly challenging benchmark for FG-DG-GCD.

Feature	APL Dai et al. (2025)	DCPD (Ours)
Target Task	Standard GCD	Fine-Grained Domain-Generalized GCD
Query Paradigm	Global & Static ( $\mathbf{Q}$ )	Adaptive & Instance-Conditioned ( $\mathbf{Q}_{I}$ )
Discovery Prior	Perceptual (DINO features)	Geometric Blueprint (Thresholded Masks)
Global Context	Discarded (Part-Replacement)	Preserved (Dual-Stream Fusion)
Domain Handling	Domain-Agnostic	Explicit Domain-Aware Architectural Bias

Table 11: Architectural and functional comparison between APL and the proposed DCPD.

Variant	All ( $\uparrow$ )	PUE ( $\downarrow$ )
APL Dai et al. (2025)	34.70	1.95
DCPD	43.36	1.72
+ Div. Loss	43.19	1.83

Table 12: DCPD vs. APL.

Method	R→P	R→S	P→S
FOCUS+APL Dai et al. (2025)	0.274	0.312	0.295
DCPD (Ours)	0.143	0.167	0.159

Table 13: Cross-Domain Attention Consistency (KL

\downarrow

). Lower values indicate more stable and domain-invariant attention maps. We report KL divergence between domains for Real

\rightarrow

Painting, Real

\rightarrow

Sketch, and Painting

\rightarrow

Sketch.

Method	CUB	SCARS	Aircraft
Ground Truth	200	196	100
DG 2 CD-Net	285	268	175
HiDISC	245	230	150
FoCUS	230	180	125

Table 14: Estimated number of clusters . All methods deviate from the ground-truth number of categories. Our method consistently achieves the smallest error (green), while other methods show moderate (orange) or large (red) deviations.

CUB-200-2011-MD
Methods	Painting $\rightarrow$ Sketch			Painting $\rightarrow$ Real			Real $\rightarrow$ Sketch			Real $\rightarrow$ Painting			Sketch $\rightarrow$ Real			Sketch $\rightarrow$ Painting
Methods	All	Old	New	All	Old	New	All	Old	New	All	Old	New	All	Old	New	All	Old	New
ViT Dosovitskiy et al. (2021)	37.44	36.27	38.62	10.48	11.23	9.82	28.59	29.73	27.32	12.27	12.74	11.86	25.29	25.32	25.27	30.96	34.01	28.28
GCD Vaze et al. (2022)	47.43	51.14	43.71	17.64	18.52	16.86	35.84	40.44	31.92	16.32	17.36	15.40	33.54	34.28	32.84	43.83	45.56	42.30
CMS Choi et al. (2024)	6.47	6.03	6.85	5.32	5.34	5.29	11.30	11.24	11.35	5.60	5.42	5.77	12.13	11.52	12.70	6.11	6.21	6.02
SimGCD Wen et al. (2023)	37.34	40.69	34.02	13.03	15.23	10.84	39.22	44.82	32.67	19.39	20.65	18.15	29.05	30.82	26.98	39.63	42.20	37.09

Table 25

Stanford-Cars-MD
Methods	Painting $\rightarrow$ Sketch			Painting $\rightarrow$ Real			Real $\rightarrow$ Sketch			Real $\rightarrow$ Painting			Sketch $\rightarrow$ Real			Sketch $\rightarrow$ Painting
Methods	All	Old	New	All	Old	New	All	Old	New	All	Old	New	All	Old	New	All	Old	New
ViT Dosovitskiy et al. (2021)	1.17	0.00	2.72	22.70	21.76	23.93	27.39	27.33	27.47	19.10	18.24	20.23	24.68	23.99	25.58	1.17	1.10	2.72
GCD Vaze et al. (2022)	44.24	41.80	47.45	32.69	30.94	35.00	36.80	35.19	38.92	26.49	24.71	28.83	36.47	35.07	38.32	41.61	40.05	43.66
CMS Choi et al. (2024)	5.11	5.13	5.09	4.53	4.46	4.62	4.85	4.87	4.84	4.79	4.34	5.37	4.85	4.89	4.81	4.62	4.58	4.68
SimGCD Wen et al. (2023)	48.16	40.53	55.73	39.18	32.49	45.80	42.23	36.32	48.08	32.51	27.34	37.62	38.90	32.97	44.77	44.41	38.03	50.73

Table 26

FGVC-Aircraft-MD
Methods	Painting $\rightarrow$ Sketch			Painting $\rightarrow$ Real			Real $\rightarrow$ Sketch			Real $\rightarrow$ Painting			Sketch $\rightarrow$ Real			Sketch $\rightarrow$ Painting
Methods	All	Old	New	All	Old	New	All	Old	New	All	Old	New	All	Old	New	All	Old	New
ViT Dosovitskiy et al. (2021)	17.45	18.22	16.60	16.10	14.07	18.29	21.13	18.97	23.47	18.41	18.40	18.41	14.75	14.88	14.61	16.37	15.17	17.67
GCD Vaze et al. (2022)	37.14	34.20	40.32	2.01	3.86	1.10	31.47	29.18	33.96	2.01	3.86	1.15	2.01	3.86	1.20	39.03	35.99	42.32
CMS Choi et al. (2024)	7.49	7.50	7.49	7.10	5.94	8.36	6.80	6.75	6.87	6.83	7.09	6.55	6.98	7.96	5.93	7.46	7.79	7.12
SimGCD Wen et al. (2023)	23.59	20.04	27.15	33.60	32.04	35.15	38.32	35.95	40.71	29.92	24.55	35.25	39.03	35.11	42.95	37.68	35.28	40.07

Table 27

Figure 11: Sample augmented images

Figure 12: Comparison of InfoNCE Loss convergence across the three source domains on the CUB-200-2011-MD dataset.

Figure 13: Comparison of Sup_Con Loss convergence across the three source domains on the CUB-200-2011-MD dataset.

Figure 14: Comparison of CLS Loss convergence across the three source domains on the CUB-200-2011-MD dataset.

Figure 15: Comparison of ENT Loss convergence across the three source domains on the CUB-200-2011-MD dataset.

Figure 16: Comparison of OE Loss convergence across the three source domains on the CUB-200-2011-MD dataset.

Figure 17: Comparison of Total Loss convergence across the three source domains on the CUB-200-2011-MD dataset.

FOCUS: Bridging Fine-Grained Recognition and Open-World Discovery across Domains

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