CloSeR: Unified Relational Distillation from Closed-Set Teachers for Category Discovery

Yuanpei Liu; Zhenqi He; Jialu Tang; Kai Han

cs.CVAug 26, 2026

CloSeR: Unified Relational Distillation from Closed-Set Teachers for Category Discovery

Authors: Yuanpei Liu, Zhenqi He, Jialu Tang, Kai Han

Organizations: Visual AI Lab, The University of Hong Kong, Hong Kong SAR

Abstract

Generalized Category Discovery (GCD) is an intriguing open-world problem that has garnered increasing attention: given partially labelled data, the goal is to correctly recognize known classes while discovering coherent novel categories from unlabelled samples. Recent GCD methods typically adapt foundation models by jointly optimizing supervised classification and unsupervised discovery objectives on mixed labelled and unlabelled data. While effective, this coupled training can entangle closed-set recognition and open-set discovery, leading to objective conflict and biased predictions, and may disturb the semantic geometry of pretrained representations under limited labels and noisy pseudo-labels. We propose CloSeR, a simple plug-and-play framework that injects Closed-Set Relational knowledge into GCD training. CloSeR first builds a domain-adapted closed-set teacher by tuning lightweight block-wise adapters on labelled known-class data while keeping the foundation model backbone frozen, thereby preserving pretrained priors at low training cost. It then transfers the teacher's knowledge to downstream GCD via Unified Relational Distillation (URD), which distills complementary global sample-to-prototype relations to anchor known-class semantics and local sample-to-sample relations to preserve neighborhood structure, using separate feature pathways to reduce optimization interference. CloSeR is head-agnostic and readily integrates with both parametric and non-parametric GCD methods. Extensive experiments with DINO and DINOv2 backbones on six benchmarks (CIFAR-10/100, ImageNet-100, CUB, Stanford-Cars, and FGVC-Aircraft) show consistent gains over GCD baselines, achieving state-of-the-art performance. Project page: https://visual-ai.github.io/closer/

Figures & tables

Figure 1: The Old -class vs. New -class ACC on CUB [ 58 ] , Stanford-Cars [ 28 ] , and FGVC-Aircraft [ 35 ] using DINOv2 [ 38 ] backbone. We compare two representative GCD baselines (the non-parametric GCD [ 55 ] and the parametric SimGCD [ 63 ] ) with two simple alternatives that first adapt the backbone on labelled known classes (closed-set features) and then perform GCD via either $k$ -means clustering or learning a parametric classifier. Note: “GCD” here denotes the method proposed in [ 55 ] , not the task name.

Figure 2: Overall pipeline of CloSeR (illustrated with SimGCD as an example; it is also applicable to other baselines): (a) Closed-Set Transfer Learning; (b) Unified Relational Distillation. Note : The representation loss in SimGCD is omitted for clarity.

			CIFAR-10 [ 29 ]			CIFAR-100 [ 29 ]			ImageNet-100 [ 11 ]			Average
	Method	[Venue]	All	Old	New	All	Old	New	All	Old	New	All	Old	New
DINO	GCD [ 55 ]	[CVPR 2022]	91.5	97.9	88.2	73.0	76.2	66.5	74.1	89.8	66.3	$79.533\,333\,333\,333\,33$	$87.966\,666\,666\,666\,67$	$73.666\,666\,666\,666\,67$
	PromptCAL [ 70 ]	[CVPR 2023]	97.9	96.6	98.5	81.2	84.2	75.3	83.1	92.7	78.3	$87.4$	$91.166\,666\,666\,666\,67$	$84.033\,333\,333\,333\,33$
	GPC [ 73 ]	[ICCV 2023]	90.6	97.6	87.0	75.4	84.6	60.1	75.3	93.4	66.7	$80.433\,333\,333\,333\,33$	$91.866\,666\,666\,666\,67$	$71.266\,666\,666\,666\,67$
	InfoSieve [ 44 ]	[NeurIPS 2023]	94.8	97.7	93.4	78.3	82.2	70.5	80.5	93.8	73.8	$84.533\,333\,333\,333\,33$	$91.233\,333\,333\,333\,33$	$79.233\,333\,333\,333\,33$
	SPTNet [ 59 ]	[ICLR 2024]	97.3	95.0	98.6	81.3	84.3	75.6	85.4	93.2	81.4	$88$	$90.833\,333\,333\,333\,33$	$85.2$

Table 1: Results on generic datasets (CIFAR-10/100 [ 29 ] and ImageNet-100 [ 11 ] ), reported for All / Old / New categories. Right: average over datasets.

			CUB [ 58 ]			Stanford-Cars [ 28 ]			FGVC-Aircraft [ 35 ]			Average
	Method	[Venue]	All	Old	New	All	Old	New	All	Old	New	All	Old	New
DINO	GCD [ 55 ]	[CVPR 2022]	51.3	56.6	48.7	39.0	57.6	29.9	45.0	41.1	46.9	$45.1$	$51.766\,666\,666\,666\,67$	$41.833\,333\,333\,333\,33$
	PromptCAL [ 70 ]	[CVPR 2023]	62.9	64.4	62.1	50.2	70.1	40.6	52.2	52.2	52.3	$55.1$	$62.233\,333\,333\,333\,33$	$51.666\,666\,666\,666\,67$
	GPC [ 73 ]	[ICCV 2023]	52.0	55.5	47.5	38.2	58.9	27.4	43.3	40.7	44.8	$44.5$	$51.7$	$39.9$
	$\mu$ GCD [ 57 ]	[NeurIPS 2023]	65.7	68.0	64.6	56.5	68.1	50.9	53.8	55.4	53.0	$58.666\,666\,666\,666\,67$	$63.833\,333\,333\,333\,33$	$56.166\,666\,666\,666\,67$
	InfoSieve [ 44 ]	[NeurIPS 2023]	69.4	77.9	65.2	55.7	74.8	46.4	56.3	63.7	52.5	$60.466\,666\,666\,666\,67$	$72.133\,333\,333\,333\,33$	$54.7$

Table 2: Results on fine-grained datasets (CUB [ 58 ] , Stanford-Cars [ 28 ] , FGVC-Aircraft [ 35 ] ), reported for All / Old / New categories. Right: average over datasets.

			CUB [ 58 ]			Stanford-Cars [ 28 ]			FGVC-Aircraft [ 35 ]
Adaptation Method	GCD Method	Pretrained	All	Old	New	All	Old	New	All	Old	New
(1) Full Fine-tuning	SimGCD	DINO	68.1	76.3	64.0	60.9	77.5	52.9	54.8	53.2	55.6
(2) Fine-tuning Last Block	SimGCD	DINO	58.5	56.2	59.6	46.7	56.8	40.3	45.8	41.8	47.8
(3) Block-wise Adapter	SimGCD	DINO	68.2	76.9	63.9	63.0	80.2	54.7	56.6	55.5	57.2
(1) Full Fine-tuning	SimGCD	DINOv2	76.9	77.4	76.6	78.4	91.7	72.0	67.9	62.6	70.5
(2) Fine-tuning Last Block	SimGCD	DINOv2	77.3	80.2	75.8	77.9	90.5	71.8	67.7	64.6	69.2

Table 3: Experimental results using different transfer learning methods with DINO [ 4 ] and DINOv2 [ 38 ] pretrained weights on SimGCD. Results are reported on SSB [ 56 ] .

Global Relation	Local Relation	Feature Decoupling	All	Old	New	All	Old	New	All	Old	New
			CUB [ 58 ]			Stanford-Cars [ 28 ]			FGVC-Aircraft [ 35 ]
✗	✗	✗	73.6	75.3	72.8	58.5	75.6	50.3	57.1	64.7	53.3
✓	✗	✗	80.5	78.9	81.2	61.9	81.0	52.6	59.0	65.1	56.0
✗	✓	✗	80.2	78.5	81.1	64.3	82.3	55.7	63.0	66.7	61.1
✓	✓	✗	81.0	78.5	82.2	65.8	82.8	57.6	64.9	68.4	63.2
✓	✓	✓	82.8	80.3	84.1	66.5	83.4	58.3	66.6	68.1	65.8

Table 4: Experimental results using different distillation components with DINO [ 4 ] pretrained weights on SelEx. Results are reported on the SSB [ 56 ] benchmark.

			CUB [ 58 ]			Stanford-Cars [ 28 ]			FGVC-Aircraft [ 35 ]			CIFAR-10 [ 29 ]			CIFAR-100 [ 29 ]			ImageNet-100 [ 11 ]
Method	Teacher	Student	All	Old	New	All	Old	New	All	Old	New	All	Old	New	All	Old	New	All	Old	New
SimGCD	-	ViT-B	71.5	78.1	68.3	71.5	81.9	66.6	63.9	69.9	60.9	98.7	96.7	99.7	88.5	89.2	87.2	89.9	95.5	87.1
+ CloSeR	ViT-B	ViT-B	78.1	79.5	77.3	78.8	90.7	73.0	71.8	68.7	73.3	98.9	97.1	99.8	93.2	93.0	93.5	92.5	96.6	90.4
+ CloSeR	ViT-L	ViT-B	81.3	81.4	81.3	79.9	92.5	73.8	75.0	77.7	73.6	99.4	98.9	99.7	93.8	94.2	93.0	93.4	95.8	92.2
SimGCD	-	ViT-L	71.0	73.7	69.6	71.3	80.8	66.7	70.6	70.1	70.8	99.5	98.7	99.8	91.8	91.8	91.8	92.4	94.0	91.7
+ CloSeR	ViT-L	ViT-L	81.4	83.2	80.5	83.8	92.9	79.4	76.2	82.4	73.0	99.5	98.7	99.9	94.5	94.9	93.6	93.7	97.2	91.9

Table 5: Experimental results using different backbone sizes with DINOv2 [ 38 ] on SimGCD and SimGCD+CloSeR. Results are reported on SSB [ 56 ] .

Figure 3: Comparison of attention maps from the backbone networks of SimGCD [ 63 ] and our SimGCD+CloSeR. The results include three datasets: Stanford-Cars [ 28 ] , CUB [ 58 ] , and FGVC-Aircraft [ 35 ] , spanning both Old and New classes.

Generalized Category Discovery

Generalized category discovery learns known classes from labeled examples while grouping unlabeled data into known and previously unseen categories.

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CoGe-GCD: Reframing Generalized Category Discovery with Compositional Generalization

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The University of Hong Kong, Hong Kong SAR, China · Xiamen University, Xiamen, China · Shenzhen University, Shenzhen, China.

Geometric Representation Learning Classification Compositional Generalization Generalized Category Discovery

Open paper AskPDF arXiv

Generalized Category Discovery (GCD) assigns unlabeled instances, mixed with labeled data, to known or novel categories, requiring human-like compositional reasoning: reusing primitives learned from known classes and deciding when new combinations imply new categories. Existing GCD methods operate on unstructured token features and struggle to extrapolate to novel compositions. We propose CoGe-GCD, which rethinks GCD through compositional generalization with two coupled stages. (i) Compositional Perception structures patch tokens by mapping them to a small vocabulary of primitives and refining token embeddings via competitive token-primitive assignment and information passing, yielding coherent groups for discovery. (ii) Generalizing Induction exploits the induced geometric structure and applies a structure-preserving calibration over spatial relations, maintaining probabilistic semantics while improving extrapolation to unseen primitive combinations. CoGe-GCD is implemented as an inductive-bias module between backbone and projection head, without modifying heads or losses, and can be plugged into diverse GCD frameworks. On standard benchmarks, it consistently improves all-class accuracy, unknown-class number estimation, and geometric quality, with marginal computational overhead. Code is available at https://github.com/lytang63/CoGe-GCD.

Abstract

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