Title: SEAL: Semantic-Aware Hierarchical Learning for Generalized Category Discovery
Venue: NeurIPS 2025
Accepted: yes
Keywords: Generalized Category Discovery; Open World Learning; Hierarchical Learning
Abstract: This paper investigates the problem of Generalized Category Discovery (GCD). Given a partially labelled dataset, GCD aims to categorize all unlabelled images, regardless of whether they belong to known or unknown classes. Existing approaches typically depend on either single-level semantics or manually designed abstract hierarchies, which limit their generalizability and scalability. To address these limitations, we introduce a SEmantic-aware hierArchical Learning framework (SEAL), guided by naturally occurring and easily accessible hierarchical structures. Within SEAL, we propose a Hierarchical Semantic-Guided Soft Contrastive Learning approach that exploits hierarchical similarity to generate informative soft negatives, addressing the limitations of conventional contrastive losses that treat all negatives equally. Furthermore, a Cross-Granularity Consistency (CGC) module is designed to align the predictions from different levels of granularity. SEAL consistently achieves state-of-the-art performance on fine-grained benchmarks, including the SSB benchmark, Oxford-Pet, and the Herbarium19 dataset, and further demonstrates generalization on coarse-grained datasets.
Project page: https://visual-ai.github.io/seal/

SEAL: Semantic-Aware Hierarchical Learning for Generalized Category Discovery

Abstract

This paper investigates the problem of Generalized Category Discovery (GCD). Given a partially labelled dataset, GCD aims to categorize all unlabelled images, regardless of whether they belong to known or unknown classes. Existing approaches typically depend on either single-level semantics or manually designed abstract hierarchies, which limit their generalizability and scalability. To address these limitations, we introduce a SE mantic-aware hier A rchical L earning framework ( SEAL ), guided by naturally occurring and easily accessible hierarchical structures. Within SEAL, we propose a Hierarchical Semantic-Guided Soft Contrastive Learning approach that exploits hierarchical similarity to generate informative soft negatives, addressing the limitations of conventional contrastive losses that treat all negatives equally. Furthermore, a Cross-Granularity Consistency (CGC) module is designed to align the predictions from different levels of granularity. SEAL consistently achieves state-of-the-art performance on finegrained benchmarks, including the SSB benchmark, Oxford-Pet, and the Herbarium19 dataset, and further demonstrates generalization on coarse-grained datasets. Project page: https://visual-ai.github.io/seal/

1 Introduction

The field of computer vision has undergone substantial progress in various tasks, including classification [53, 28], object detection [21, 52], and segmentation [27, 30, 63]. Such advancements have largely been driven by access to large-scale, human-annotated datasets [13, 37]. However, models trained on these datasets are constrained to a closed-world paradigm, limiting their predictions to the predefined labels within the training set. In contrast, there exists a wealth of unlabelled data in the open world. To capitalize on the unlabelled data, a variety of Semi-Supervised Learning (SSL) techniques [10] have been proposed, yielding notable improvements over traditional supervised learning methods. Despite substantial success in various tasks [3, 68, 11], most existing SSL methods are designed under the closed-set assumption, wherein the training and test datasets share an identical set of classes. Category discovery, initially introduced as Novel Category Discovery (NCD) [24, 29] and later extended to Generalized Category Discovery (GCD) [57, 29], has recently emerged as a compelling open-world problem, attracting significant attention. Unlike SSL, GCD tackles the challenges where the unlabelled subset may include instances from both known and unknown classes. Its primary objective is to utilise knowledge gained from labelled data to effectively categorize all samples within the unlabelled data. Concurrently, an equivalent task named Open-world Semi-Supervised Learning (OSSL) [5] has also been introduced.

Beagle

Siamese

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(b) GCD With Semantic-Aware Hierarchies

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(a) GCD With Abstract Hierarchies

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The effectiveness of GCD is rooted in the efficient transfer of knowledge from known categories to cluster samples of both known and novel categories. As a transfer clustering task [24], hierarchical information has been demonstrated to be effective in GCD [50, 26] and similarly in the parallel task of OSSL [64], particularly with fine-grained datasets [58]. In [50], the hierarchical structure is composed of abstract concepts as an implicit binary tree, where each node represents an increasingly abstract concept derived from shared binary code prefixes, and in [51], the hierarchical structure is implicitly formed by incrementally halving the category count as the hierarchy level increases with hierarchical pseudo-labeling to provide soft supervision for the training. Similarly, CiPR [26] constructs abstract hierarchies by iteratively merging data partitions through semi-supervised clustering. The hierarchical tree in [64] consists of manually defined upper and lower levels that represent different granularities, with the number of categories per level controlled by hyperparameters. More recently, HypCD [39] implicitly models hierarchies via hyperbolic embeddings, achieving strong performance and underscoring the importance of hierarchical information for GCD. These methods build hierarchical levels from abstract, weakly supervised structures that may introduce noise and errors, ultimately affecting GCD performance. As illustrated in Fig. 1 (a), the 'Siberian Tiger' , 'Bengal' and 'Egyptian Mau' can be merged into a single category while the 'Red Fox' can be divided into multiple categories. Additionally, the high similarity among categories may result in some images of the 'Basset Hound' being incorrectly merged with those of the 'Beagle' . This observation naturally prompts us to consider: whether the intrinsic, semantically grounded taxonomies present in the real world can serve as more reliable guides . In botanical research, taxonomists commonly use labelled specimens of known species to classify newly collected, unlabelled samples into existing taxonomic hierarchies or to identify unseen species [40, 33]. Similarly, studies in closed-world visual classification [9, 65, 15] have shown that hierarchical structures enhance classification. From an information-theoretic perspective, we further deduce that such semantic-aware hierarchies yield a tighter mutual information bound, providing a principled foundation for our design.

To this end, we propose the SE mantic-aware hier A rchical L earning ( SEAL ) framework for GCD. Unlike previous approaches that either focus exclusively on single-granularity information [57, 59, 61, 66, 38] or rely on abstract hierarchical cues [50, 26, 64, 51, 39], SEAL effectively leverages the naturally occurring semantic hierarchies without manual design (shown in Fig. 1 (b)) and incorporates several innovative techniques tailored specifically for this task. Firstly , we implement a multi-task training paradigm that facilitates the simultaneous discovery of categories across several different semantic levels. Secondly , we introduce a Cross-Granularity Consistency (CGC)

module to align the class predictions from different granularities. Thirdly , we propose the Hierarchical Semantic-guided Soft Contrastive Learning to capture uncertainty in contrastive learning, ensuring that not all negative samples are treated equally. By effectively integrating these components into a cohesive framework, SEAL can be trained end-to-end in a single stage.

In summary, we make the following contributions in this paper: (i) We propose SEAL , a novel framework specifically designed to tackle the challenging GCD task by leveraging the inherent semantic hierarchies, marking the first exploration of this aspect. (ii) Within the SEAL framework, we develop two novel components: the Cross-Granularity Consistency (CGC) module and Hierarchical Semantic-guided Soft Contrastive Learning. These components function synergistically to significantly enhance the model's category discovery capabilities. (iii) Through extensive experimentation on public GCD benchmarks, SEAL consistently demonstrates its effectiveness and achieves superior performance, especially on fine-grained datasets.

2 Related Work

Category Discovery. Novel Category Discovery (NCD) was first articulated in [24], establishing a pragmatic framework for transferring knowledge from known categories to clusters of unseen categories, framed as a transfer clustering problem. Subsequently, a variety of methods have emerged to advance the research domain [22, 23, 31, 72, 74, 18]. Generalized Category Discovery (GCD) extends the NCD framework by relaxing its assumptions, incorporating unlabelled data that features samples from both known and unknown classes [57]. Recent studies [5, 26, 48, 32, 8, 62, 38] have explored a range of strategies to tackle the challenges introduced by GCD. Notably, InfoSieve [50] and CiPR [26] guide category discovery using abstract hierarchies that are automatically inferred from the data. A similar approach is employed in the OSSL task by TIDA [64], which employs handcrafted abstract hierarchies by constructing prototypes at manually defined levels. Conversely, SimGCD [66] introduces an entropy-regularized classifier that provides a robust baseline. SPTNet [61] builds upon SimGCD by incorporating spatial prompt tuning to emphasize salient object parts, while DebGCD [38] proposes a distribution-guided debiased learning framework to address the inherent label bias and semantic shifts in GCD.

Hierarchical Learning. In the realm of hierarchical learning, numerous studies [9, 49, 65, 15] have explored the use of hierarchical label information to enhance classification performance, particularly in closed-world settings. For instance, [9] employs a multi-task framework that utilises coarse-to-fine labels to improve fine-grained recognition, whereas [71] introduces hierarchical contrastive learning to enrich representations with multi-level semantic cues. More recently, hierarchical learning has been adapted to open-set recognition, as demonstrated in [36, 67], where semantic hierarchies contribute to improved generalization to unseen classes. To the best of our knowledge, our work is the first to apply semantic-guided hierarchies to the GCD task, facilitating the effective discovery and classification of both known and novel categories.

3 Preliminary

Problem Statement: GCD aims to develop models capable of classifying unlabelled samples from known categories while simultaneously clustering those from unknown categories. Formally, we are given a labelled dataset D l = ( x l i , y l i ) ⊂ X ×Y l and an unlabelled dataset D u = ( x u i , y u i ) ⊂ X ×Y u , where Y l ⊂ Y u . The unlabelled data includes samples from both known and novel categories. The number of known categories is denoted by M = |Y l | , and the total number of categories is K = |Y l ∪ Y u | . Following prior works [23, 66, 59], we assume K is known during training. When K is unknown, it can be estimated using techniques such as [24, 57].

Revisiting Baseline: SimGCD [66] is a representative end-to-end baseline for GCD that unifies contrastive representation learning and parametric classification. The model employs a Vision Transformer [14] backbone pretrained using DINO [7], where the input image x i is first passed through an embedding layer φ and the feature extractor F , followed by a projection head H to produce a normalized representation z i = H ( F ( φ ( x i ))) / |H ( F ( φ ( x i ))) | . The representation learning objective

L rep is based on the InfoNCE loss [44]:

where P ( x i ) denotes the set of positive features ( e.g. , different views of the same image), and σ ( · ; τ ) is the softmax with temperature τ . For labelled samples, additional positives from the same class are used to enable supervised contrastive learning.

For the parametric classification, SimGCD adopts a cosine-based classifier [20] with a learnable prototype set C = { c 1 , ..., c K } where each prototype c k is l 2 -normalized and the output probability for the k -th category is given by p i ( k ) = σ ( z i · c k ; τ ) . Given the pseudo-label q i obtained from a sharpened prediction of a different view, the classification loss is:

where B is current image batch and H ( p ) denotes the entropy of the mean prediction p . Specifically, for each x i in the labelled batch B l , an additional y i as the one-hot ground-truth vector is also used for supervised classification loss written as L s cls = 1 |B l | ∑ i ∈B l l ce ( p i , y i ) . Then, the overall classification loss is formulated as L cls = (1 -λ b ) L u cls + λ b L s cls where λ b is a balance factor. The final training objective combines both representation and classification terms: L bs = L cls + L rep .

4 Method

Before delving into methodological details, we begin with an intuitive hypothesis that underlies our framework: Leveraging structured semantic hierarchies across multiple levels can facilitate more informative and robust feature learning for GCD setting. To support this intuition, we first present a theoretical justification grounded in information theory, demonstrating that the incorporation of hierarchical labels yields a tighter bound on mutual information. This theoretical insight lays the foundation for the design of our approach, which we detail in the subsequent sections.

4.1 Theoretical Motivation

From the perspective of information theory, with denoting model parameter as θ , data as X , and label as Y , we write Z = f θ ( X ) as the deterministic representation of X once model θ is fixed. The optimisation objective is then to maximize the mutual information between Z and Y [4], which can be re-formulated as min θ { -I θ ( Z l ; Y l ) + β [ H θ ( ˆ Y u | X u ) -H θ ( ˆ Y u ) ]} with detailed proof provided in the Appendix, where ˆ Y u is the model prediction for unlabelled data, and β is the weight factor. Assuming the coarse-grained semantic hierarchical labels Y (1) l , ..., Y ( H -1) l are accessible for all labelled samples, the objective naturally extends to:

By applying the chain rule of mutual information, the supervised part satisfies:

Analogously, for the unsupervised component, we obtain:

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Combining the supervised and unsupervised parts, we have:

Therefore, from the perspective of information theory, incorporating semantic hierarchical labels provides a strictly tighter upper bound on the mutual information, which motivates us to introduce the semantic-guided hierarchical learning framework for GCD.

4.2 SEAL: Semantic-Aware Hierarchical Learning for GCD

Building on the advantages of semantic hierarchies, we propose the SE mantic-aware hier A rchical L earning (SEAL) framework for GCD. The overall framework is outlined in Fig. 2. In contrast to prior GCD approaches that either rely solely on single-granularity information [57, 59, 61, 66] or depend on abstract hierarchies [50, 64], we embed explicit semantic structure via three key elements: (1) a semantic-aware multi-task framework; (2) a cross-granularity consistency module to align predictions across levels; and (3) a hierarchical soft contrastive learning strategy to mitigate the 'equivalent negative' assumption by weighting dissimilarity according to semantic proximity.

4.2.1 Semantic-aware Hierarchical Learning

We first introduce the semantic-aware multi-task framework. Inspired by [9], we advocate for a joint learning framework across multiple semantic levels, allowing information across the hierarchies to guide and strengthen representation learning at the target granularity. We define H as the number of semantic levels, with corresponding ground-truth labels y 1 , . . . , y H ordered from coarse to fine. Our multi-task architecture couples a shared image encoder F followed by a projection layer ϕ to disentangle features for various granularities, which can be formulated as z = ϕ ( F ( x ) ) = [ z 1 ; z 2 ; . . . ; z H ] , where ' ; ' denotes concatenation. Following the observation in [9] that fine-grained features can benefit coarse-grained predictions but not vice versa, we reuse lower-level features when computing coarse-level outputs. To avoid training bias towards coarse branches, we adopt a gradient controller Γ to include fine-level features without allowing gradient backpropagation. Formally, the aggregated feature of sample x i at the h -th level is ˆ z i = [ z 1 ; · · · ; z h ; Γ( z h +1 ); · · · ; Γ( z H )] , where Γ( · ) stops gradient propagation during training. We train a GCD classifier at each level. For h -th level, the classification loss is denoted as L h cls .

4.2.2 Cross-Granularity Consistency Self Distillation

Although multi-level classification has been widely studied in closed-world settings [9, 65], prior methods often treat each semantic level in isolation, leading to inconsistencies such as assigning labels like 'Shiba' and 'Cat' at different granularities for the same instance. This lack of cross-level interaction weakens the benefits of hierarchical learning. We address this with a Cross-Granularity Consistency (CGC) module that distills information between granularities to keep predictions mutually coherent. Concretely, we add a self-distillation term that minimises the KL divergence between the coarse-level posterior p ( x i | θ h ) and a pseudo-coarse distribution obtained by mapping the target

posterior p ( x i | θ H ) where θ h denotes the model parameters at granularity h . Specifically, we define a dynamic transition matrix M h ∈ R n H × n h at granularity h where n h denotes the number of categories at that level. Each row of M h encodes how a fine-grained class distributes over coarse classes. For known fine-grained categories, this is a fixed one-hot vector; for novel classes, we initialize with a uniform distribution and iteratively refine it during training (See Algo. 1 Dynamic Update of M h ). The pseudo-coarse probability thus can be computed as p ( x i | θ H ) × M h and the hierarchical consistency loss at level h is defined as D KL ( p ( x i | θ h ) | p ( x i | θ H ) × M h ) . Summing across levels, the overall CGC loss becomes:

where p ( x i | θ h ) = σ ( f θ h ( x i ) , τ c ) with σ ( · ) denoting the softmax operation and τ c be the consistency temperature and f θ h ( x i ) being logits computed for granularity h .

Algorithm 1: Dynamic Update of M h

Input: Model f , number of class at level h , n h , known fine-grained classes C base Dynamic Update:

Compute logits l h , l H = f ( D )

Compute prediction probability p h , p H = σ ( l h , τ c ) , σ ( l H , τ c )

for each fine class index k not in C base do

Compute fine-grained predictions y H = argmax ( p H )

Compute average probability distribution for samples predicted as fine class k :

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4.2.3 Hierarchical Semantic-guided Soft Contrastive Learning

To strengthen the discriminative capacity of representations in GCD, we propose a Hierarchical Semantic-guided Soft Contrastive Learning approach, addressing key limitations of existing contrastive learning approaches. Prior GCD methods [23, 66, 59, 38] treat each non-positive in a minibatch as an equally hard negative, ignoring semantic relatedness. We instead leverage the hierarchy in our multi-level framework to compute similarity-aware targets, assigning softer negative weights to semantically closer samples and preserving full penalties for unrelated ones. We compute pairwise similarities within each mini-batch at every semantic level, yielding similarity matrices S h at the h -th granularity, where S h = Z h · ( Z h ) ⊤ ∥ Z h ∥×∥ Z ⊤ h ∥ ∈ R B × B with Z h being the features of the mini-batch at granularity h and B being the mini-batch size. Each fine-level matrix is then fused with its coarser counterpart, yielding a hierarchical similarity matrix ˜ S h . We then generate semantic-aware soft labels as a matrix: ˜ Y soft h = (1 -λ s ) · I + λ s · ˜ S h , where I is the identity matrix and λ s controls the smoothness of the semantic-aware soft labels. The resulting semantic-guided hierarchical soft contrastive loss is defined as:

̸

where sim ( · ) represents the similarity metric between feature z i from x i and feature z ′ j from the augmented view of x j , and ˜ Y soft h ( i, j ) refers to the ( i, j ) element of the soft label matrix. Unlike prior works [66, 61] that rely solely on angle or distance-based measure, we adopt a hybrid metric defined as sim ( z i , z ′ k ) = λ c z i · z ′⊤ k -(1 -λ c ) ∥ ∥ z i ∥ z i ∥ -z ′ k ∥ z ′ k ∥ ∥ ∥ 2 where λ c is the weighting coefficient that linearly gradually decays during training. This design implements a curriculum learning strategy: it begins with easier angle-based cues and gradually adds distance terms to refine representations. More ablation studies about the decay schedule are in the Appendix.

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4.2.4 Overall Objective

Based on the baseline SimGCD [66] classifier, our framework is designed to be trained in a multitask manner. We first replace the original InfoNCE loss [44] in the baseline representation loss L rep introduced in Sec. 3 by our proposed hierarchical soft contrastive loss L h hscl , denoting the resulting training objective at each granularity as L h soft rep . The final training objective can be formulated as

5 Experiment

5.1 Experimental Setup

Datasets. We conduct a comprehensive evaluation of our method across a variety of benchmarks. The main paper reports results on the Semantic Shift Benchmark (SSB) [58], which covers finegrained datasets-CUB [60], Stanford Cars [34], and FGVC-Aircraft [42]-plus Oxford-Pet [46] and the more challenging Herbarium19 [55]. To gauge generalization on standard recognition tasks, we also include results on the generic benchmarks CIFAR-10/100[35] and ImageNet-100 [13] in the Appendix. For all datasets, we follow the class split protocol of [57], where a subset of classes is selected as the known ('Old') label set Y l . From these known classes, 50% of the samples are used to construct the labelled set D l , and the remaining images with instances from novel classes form the unlabelled set D u . Dataset statistics are summarized in Tab. 1.

Evaluation metrics. We evaluate GCD performance using clustering accuracy ( ACC ), following standard practice [57]. Specifically, given groundtruth labels y i and predicted labels ˆ y i for the unlabelled set D u , the ACC is computed as:

where h denotes the optimal one-to-one mapping between predicted clusters and true class labels. For a comprehensive evaluation, we report ACC separately for all classes ('All'), known classes ('Old'), and novel classes ('New').

Implementation details. Following prior works [51, 66, 57], we adopt the ViT-B backbone [14], initialized with pretrained weights from either DINO [7] or DINOv2 [45]. The model is trained for 200 epochs using a batch size of 128 and a cosine learning rate schedule, starting from an initial learning rate of 10 -1 and decaying to 10 -4 . All experiments are performed on a single NVIDIA L40S GPU with 24GB of memory. More details are provided in Appendix.

5.2 Main Results

Wepresent benchmark results of our method and compare it with nineteen state-of-the-art techniques in GCD as well as three robust baselines derived from novel category discovery. All methods are based on the DINO [7] and DINOv2 [45] pre-trained backbone. This comparative evaluation encompasses performance on the fine-grained SSB benchmark [58], Oxford-Pet [46] and Herbarium19 [55], as shown in Tab. 2 and Tab. 3.

As shown in Tab. 2, our method consistently achieves state-of-the-art performance on the SSB benchmark [58] based on both DINO [7] and DINOv2 [45] pretrained backbones. Specifically, under the DINOv2 setting, our approach reaches an average 'All' accuracy of 76 . 3% , outperforming the previous best method, DebGCD [38], by 1 . 4% margin. Our framework demonstrates strong and

stable improvements on both the Stanford Cars [34] and FGVC-Aircraft [42] datasets, achieving the highest accuracy under both backbone settings. This highlights the effectiveness of our semanticguided hierarchical design and contrastive learning strategy, particularly in domains where the semantic hierarchy aligns closely with the underlying structure of man-made categories, such as vehicles and aircraft. On the CUB [60] dataset, although our method slightly lags behind DebGCD [38] and the non-parametric method InfoSieve [50], we attribute this gap to the nature of bird taxonomy based on human-annotated semantics, which may introduce inconsistencies absent in more systematically defined hierarchies like those in artificial object domains.

As shown in Tab. 3, our method achieves competitive performance on the relatively easier OxfordPet dataset [46], outperforming the baseline. More notably, on the more challenging Herbarium19

benchmark [55], it sets a new state-of-the-art by surpassing the previous best method, µ GCD [59], by 1 . 1% on the 'All' accuracy. These results highlight the robustness of our approach across both simple and complex open-world discovery scenarios.

5.3 Analysis

Component Analysis. We conduct ablation studies to analyse the contributions of each major component in our framework: Hierarchical Learning, Consistency Self-Distillation, and Hierarchical Semantic-Guided Soft Contrastive Learning (HSCL). As shown in Tab. 4, we report results on the SSB benchmark [58], including Stanford Cars [34], CUB [60], and FGVC-Aircraft [42] datasets, evaluated over 'All', 'Old', and 'New' categories. Starting from the baseline trained solely with the GCD loss, we incrementally integrate the proposed components. Incorporating hierarchical learning alone (Row (1)) yields a modest improvement, particularly on the old categories. Adding consistency-based self-distillation (Row (2)) further improves alignment and stability, while semantic-guided HSCL (Row (3)) significantly boosts performance on novel classes by leveraging cross-instance semantic similarity. When all components are combined, the full framework achieves substantial gains with 11 . 5% on Stanford Cars, 7 . 8% on FGVC-Aircraft, and 5 . 7% on CUB.

Hyperparameter Tuning. In line with the practices in [66, 57], we perform hyperparameter tuning using a held-out validation split from the labelled data. Specifically, we tune the consistency temperature τ c and the soft negative controller λ s based on their performance on the Stanford Cars [34] dataset. Detailed results across different hyperparameter values, evaluated on both the unlabelled training set and the validation split, are provided to assess their impact on model performance. As shown in Tab. 5, we conduct a detailed grid search over the consistency temperature τ c and the soft negative controller λ s on the Stanford Cars dataset. Notably, the trends across both evaluation sets are

highly consistent, with optimal performance achieved when τ c = 0 . 75 and λ s = 1 . 0 . These settings yield the best balance between old and new class performance, highlighting the importance of carefully tuning both the consistency strength and the soft negative ratio in our framework.

Semantic Dimensions. Semantic hierarchies are not restricted to a single dimension. To further demonstrate the flexibility of our framework, we additionally adopt LLM-generated labels along an alternative semantic dimension, e.g. , complementing the vehicle type hierarchy (SUV/Van/Coupe) with a brand-based hierarchy (Audi/BMW). Tab. 6 demonstrates that our approach achieves consistently strong performance under both semantic hierarchies. This highlights the robustness and flexibility of our proposed use of semantic-guided hierarchies across different semantic dimensions, and underscores their im-

portance for GCD, whether sourced from curated taxonomies, generated by LLMs, or defined along alternative semantic structures.

Visualization. We present a t -SNE [56] visualization comparing the feature representations learned by the baseline and ours. For clarity, we randomly select 20 categories, including 10 from the 'Old' set and 10 from the 'New' set. As shown in Fig. 4, our method yields tighter, better-separated clusters, indicating stronger inter-class discrimination. The zoomed view further reveals that the model preserves coarse-to-fine semantics: visually diverse subcategories within the broader 'Cab' group lie close together, yet each remains distinct. This confirms that our method captures hierarchical structure while retaining fine-grained separability.

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Ford F-450 Super Duty Crew Cab

Dodge Dakota Crew Cab 2010

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6 Conclusion

In this paper, we introduce a semantic-aware hierarchical learning framework for Generalized Category Discovery, composed of three key components. Firstly , we design a multi-task architecture that leverages naturally occurring semantic hierarchies to jointly learn coarse-to-fine category structures. Secondly , we propose a Cross-Granularity Consistency (CGC) module that distils information between levels, eliminating label conflicts across the hierarchy. Thirdly , we develop a Hierarchical Soft Contrastive Learning strategy that incorporates semantic similarity into the contrastive objective, enabling fine-grained representation learning guided by structured semantic relationships. Our framework is theoretically motivated by information-theoretic principles, which highlight the benefit of incorporating hierarchical supervision to achieve tighter theoretical bounds. Evaluations on diverse fine-grained and generic benchmarks confirm consistent, state-of-the-art gains, demonstrating both theoretical soundness and strong empirical performance.

7 Discussion

Limitations. It is important to acknowledge a limitation concerning the scale of validation within our study. The dataset used for model evaluation includes fewer than 700 instances, which constrains the breadth of category coverage. This constrained sample size may not fully represent the diversity of categories encountered in real-world scenarios. Consequently, the application of our model to category discovery in more complex and varied situations could be restricted. Further research with larger, more comprehensive datasets is warranted to validate the robustness of our findings across a wider range of categories.

Broader Impacts. This work presents a feasible method for discovering novel categories in unlabelled data, potentially benefiting a variety of applications such as robotics, healthcare, and autonomous driving, etc . However, there is a potential risk of misuse. The technology could be applied in surveillance to cluster unknown individuals, raising significant privacy concerns. Therefore, it is imperative to carefully consider ethical guidelines and legal compliance to address concerns regarding individual privacy. Additionally, to mitigate potential negative social impacts, the development of robust security protocols and systems is crucial to protect sensitive information from cyberattacks and data breaches.

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Appendix

A Theoretical Perspective

In this section, we provide an information-theoretic proof motivating the design of SEAL .

A.1 Notations & Definitions

Mutual Information (MI) quantifies the reduction in uncertainty of one random variable given knowledge of another. We have the following definitions for MI:

Definition. The MI between two continuous random variables X and Y is formulated as.

Definition. The MI between two discrete random variables X and Y is formulated as.

The notation we used and the related formulas are given in Tab. A1

Table A1: Definition of the random variables and information measures used in this paper.

Information measures

̂

[

A.2 Assumptions

The following assumptions are made in our proof.

̂

]

A.1 Independent sampling between ( X l , Y l ) and ( X u , Y u ) , which is written as ( X l , Y l ) ⊥ ⊥ ( X u , Y u ) .

A.2 Same data distribution for ( X l , Y l ) and ( X u , Y u ) - the labelled and unlabelled data follow the same underlying data distribution e.g. , same domain.

A.3 The representation mapping Z = f θ ( X ) is deterministic and per-sample independent given parameters θ .

A.3 Theoretical Motivations

As shown in [4], from the view of information theory, the optimization objective of discriminative tasks is equivalent to maximising the MI between the learned latent features Z and Y , which is:

While this objective operates under the closed-world assumption, which assumes the availability of all annotations for the training data-in the GCD setting, both labelled and unlabelled data are present during training. Therefore, we further decompose the learning objective for GCD as follows. Given the chain rule for MI: I ( X ; Y 1 , . . . , Y n ) = ∑ n i =1 I ( X ; Y i | Y 1: i -1 ) , we can extend I θ ( Z ; Y ) as:

As the two arguments of the KL divergence are identical, we finally have I ( Z l ; Y u |Y l ) = 0 . Similarly, for I θ ( Z u | Z l ; Y l ) , we have:

By the independency assumption, we have I θ ( Z u | Z l ; Y u |Y l ) = I ( Z u ; Y u ) . Therefore, the optimization objective is decomposed to:

We further introduce a weight factor β [47, 25] to balance between the supervised and unsupervised part to formulate the final objective as:

where for I θ ( Z u ; Y u ) , Y u is unknown, we introduce a variational label distribution based on model prediction q θ ( Y u | X u ) ≜ p θ ( ˆ Y u | X u ) where ˆ Y is the softmaxed model prediction. By the dataprocessing inequality that information passes through a transformation, mutual information with the source cannot increase, we have I θ ( Z u ; Y u ) ≥ I θ ( X u ; ˆ Y u ) . From which, we can rewrite:

Thus, the overall optimization objective can be reformulated as:

where β is the weight factor to balance labelled and unlabelled parts.

Assuming the coarse-grained semantic hierarchical labels Y (1) l , ..., Y ( H -1) l are accessible, the objective naturally extends to:

By applying the chain rule , we first decompose the supervised part as:

where ∑ H -1 h =1 I θ ( Z l ; Y ( h ) l ∣ ∣ Y ( h +1) l , . . . , Y ( H ) l ) ≥ 0 comes from the below. For ∀ h ∈ { 1 · · · H -1 } , we have:

Similarly, for the unsupervised part, we can also obtain:

From the above, we now have:

where we can see that the semantic-guided hierarchies provide a tighter bound on the mutual information, which motivates us to introduce the semantic-guided hierarchical learning framework for GCD.

B Additional Details

B.1 Additional Implementation Details

We adopt the class splits of labelled ('Old') and unlabelled ('New') categories in [57] for generic object recognition datasets (including CIFAR-10 [35] and CIFAR-100 [35]) and the fine-grained Semantic Shift Benchmark [58] (comprising CUB [60], Stanford Cars [34], and FGVC-Aircraft [42]), Oxford-Pet [46] and Herbarium19 [55]. Specifically, for all these datasets except CIFAR-100, 50% of all classes are selected as 'Old' classes ( Y l ), while the remaining classes are treated as 'New' classes ( Y u \Y l ). For CIFAR-100, 80% of the classes are designated as 'Old' classes, while the remaining 20% as 'New' classes. Moreover, following [57] and [66], the model's hyperparameters are chosen based on its performance on a hold-out validation set, formed by the original test splits of labelled classes in each dataset. All experiments utilize the PyTorch framework on a workstation with Nvidia L40s GPUs. The models are trained with a batch size of 128 on a single GPU for all datasets.

For the hierarchical information required by our framework, we rely exclusively on publicly available taxonomies or well-established datasets rather than any manual annotation. For the fine-grained SSB benchmarks [58], we follow the closed-world hierarchies of [9]: CUB [60] is organised into 13 orders, 38 families, and 200 species; Stanford Cars [34] is structured into 9 car types ( e.g. , 'Cab' , 'SUV' ) and 196 specific models; FGVC-Aircraft [42] is arranged into 30 makers ( e.g. , 'Boeing' , 'Douglas' ), 70 families ( e.g. , 'Boeing 767' ), and 100 models. Oxford Pets [46] is re-cast into a twolevel hierarchy with the coarse level 'Cat' vs. 'Dog' , while Herbarium19 [55] is grouped by coarsergrained genus using the GBIF botanical database [19]. For generic benchmarks, CIFAR-10 [35] is split into the super-classes 'Vehicle' and 'Animal' , CIFAR-100 [35] adopts its built-in 20 superclasses, and ImageNet-100 [13] leverages the WordNet [43] taxonomy to form coarse categories. All hierarchies are obtained via public code, openly accessible biological and lexical databases or can be generated by LLMs, ensuring that our experiments reflect realistic usage without bespoke curation.

B.2 Additional Dataset Details

We further introduce the details of the datasets used in our paper. The statistics for the commonly used datasets are summarized in Tab. A2.

Generic Datasets. (1) ImageNet100 [13] is a widely used dataset for natural image classification in computer vision, which is constructed by randomly subsampling 100 classes from ImageNet1 K. (2) CIFAR10 &CIFAR100 [35] are both natural images sized in 32 × 32 . CIFAR10 contains 50 , 000 images spanning across 10 different classes and CIFAR100 includes 100 classes, with each class containing 500 images.

Fine-grained Datasets. The most widely used fine-grained benchmark is SSB [57], which includes three datasets: CUB [60], Stanford Cars (SCars) [34], and FGVC Aircraft [42]. (1) CUB [60] is a widely used benchmark dataset for fine-grained visual classification tasks, particularly focused on bird species recognition. (2) Stanford Cars [34] is a large-scale dataset designed for fine-grained vehicle classification tasks. It contains 196 different car models, primarily spanning various makes, models, and years. (3) FGVC-Aircraft [42] is a fine-grained visual classification dataset focused on aircraft recognition. It contains 10,000 images spanning 100 different aircraft model variants,

with each image labelled by its corresponding model. (4) Oxford-Pet [46] is a large, fine-grained dataset designed for pet image classification and segmentation tasks. (5) Herbarium19 [55] is a largescale image collection focused on plant species identification, particularly for herbarium specimen recognition.

C Experiments under Realistic Situation

Following the majority of the literature, we conduct experiments mainly using the ground-truth category numbers. In this section, we test SEAL under more realistic conditions where neither coarse-granularity labels nor the number of classes are known. We adopt the same constraints used in earlier GCD works [57, 66]: only the known fine-grained classes are revealed. We first estimate the total number of targeted-granularity categories with an off-the-shelf method [57]. Next, we automatically derive coarse-level names and the fine-to-coarse mapping using ChatGPT-4o [1] with the following prompt: '{Targeted-grained Category Names}' I provide these {Number of known category} fine-grained class names, please generate the corresponding coarse-grained labels for me. After obtaining the coarse-granularity labels, we run the estimator [57] to infer the number of coarse categories. We test under such realistic condition for one fine-grained dataset (Stanford Cars) and one generic datasets (CIFAR100) and report the estimated class number about different granularities in Tab. A3. We compare SEAL with SimGCD [66], µ GCD [59], and GCD [57] in Tab. A4. Even in this realistic scenario with an unknown number of categories and automatically generated coarse-granularity labels, our method outperforms existing approaches across both datasets. These results demonstrate that SEAL can be effectively deployed without any manual access to higher-level labels or class counts, while still achieving state-of-the-art accuracy.

D Analysis on using randomly generated coarse-level labels

To further substantiate our motivation that incorrect hierarchies may introduce misleading supervision, we conduct an experiment in which the true coarse-level labels are replaced with randomly generated hierarchies. Specifically, we evaluate under two settings: 100% random and 50% random. As shown in Tab. A5, performance drops sharply across CUB, SCars, and Aircraft under both variants of randomly assigned hierarchical labels. This observation indicates that our gains arise from the semantic alignment of the hierarchy, not from the mere presence of a hierarchical structure.

E Results on Generic Datasets

Tab. A6 shows that SEAL remains effective even when only shallow hierarchies are available. With using both DINO [7] abd DINOv2 [45] pre-trained backbones, SEAL surpasses the strong SimGCD [66] baseline on all three datasets-CIFAR-10, CIFAR-100 and ImageNet-100. For generic datasets, they provide only coarse and heterogeneous groupings ( e.g. , 'Animal' vs. 'Vehicle' in CIFAR-10), so the hierarchy does not converge to a common parent class. By contrast, fine-grained datasets like CUB [60] share a clear taxonomic root ( e.g. , the class 'Aves' for all bird species), allowing our method to exploit deeper and more coherent semantic structure. Even under this less favourable condition, SEAL still delivers competitive performance, confirming the robustness of our hierarchical design.

F Analysis on the Depth of Semantic Hierarchies

Sec. B.1 notes that our framework uses different hierarchical depths depending on dataset availability. To quantify the effect of depth, we conduct an ablation study on the two datasets that provide three explicit levels, including CUB [60] and FGVC-Aircraft [42]. For each dataset we compare: (i) a single-level baseline that uses only the target granularity, (ii) a two-level version that adds one parent level, and (iii) the full three-level setting adopted in the main paper. Tab. A7 shows that SEAL remains effective irrespective of the number of available semantic levels. When compared to the single-granularity baseline across both datasets, the incorporation of just one additional coarsegranularity level yields improvements of approximately 2% and 4% . These results demonstrate the robustness of our design, which can leverage richer hierarchies when they are present, while still providing significant benefits regardless of the number of hierarchies utilized.

G Analysis of Computational Costs

Tab. A8 presents a comprehensive analysis of the computational cost associated with our method compared to the SimGCD baseline [66] for the training stage. Despite incorporating additional multi-level supervision, our approach introduces minimal computational overhead during training. Specifically, the number of parameters increases by less than 9% across all datasets (from approximately 630 MB to 688 MB), and the GFLOPs remain virtually identical, showing only a marginal increase from 17.59 to 17.60. Importantly, the training efficiency is largely preserved, with time per epoch on the unlabelled dataset increasing by no more than 0.7 seconds in all cases. These results clearly demonstrate that our framework achieves its performance improvements without sacrificing computational efficiency in training stage. At inference, however, we discard the coarse-granularity branches and keep only the classifier for the target granularity. The cost breakdown in Tab. A9 reveals that our model actually uses fewer parameters than the SimGCD baseline [66]. Runtime on the unlabelled test sets is reduced for all three datasets - Stanford Cars [34], CUB [60], and FGVCAircraft [42]. This economy stems from our design: a single MLP projector separates features across levels without enlarging the overall feature dimension, so the target-level head is compact at test time. Consequently, our method introduces almost no overhead during training and even lowers the computational footprint at inference, while still boosting accuracy.

H Analysis on the Curriculum Learning Schedule

As introduced in Sec.4.2.3, we employ a linear decay schedule for λ c . Tab. A10 reports an ablation study on the decay strategy for the curriculum weighting coefficient λ c . We compare fixed values ( λ c = 0 , 0 . 5 , 1 ), and exponential decay schedule, and our proposed SEAL with linear decay. The results consistently show that decaying schedules outperform fixed baselines, validating the effectiveness of progressively shifting focus from coarse semantic alignment to finer positional discrimination. In particular, SEAL achieves the best or comparable performance across three finegrained datasets (CUB, Stanford-Cars, and Aircraft), demonstrating that the linear decay schedule provides a more stable and effective curriculum learning design.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents SEAL, a semantic-aware hierarchical learning framework for GCD, which leverages natural semantic hierarchies to address the limitations of existing methods relying on single-level semantics or manual hierarchies. It introduces a Hierarchical Semantic-Guided Soft Contrastive Learning approach and a Cross-Granularity Consistency module to enhance representation learning and prediction alignment across different granularities. SEAL achieves good performance on fine-grained benchmarks.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n- The paper is well-organized, guiding readers smoothly from motivation to methodology and experiments. Each section builds on the previous one, and visual aids like Fig. 1 help illustrate the core ideas of the hierarchy-aware design.\n- Well-presented content. The proposed modules, such as Cross-Granularity Consistency (CGC) and hierarchical contrastive learning, are clearly explained. The combination of theoretical insights and implementation details makes the method easy to follow. \n- Good performance. SEAL demonstrates strong performance on fine-grained datasets and consistent improvements across both known and novel categories. Ablation studies and t-SNE visualizations provide further evidence of the method’s effectiveness and the contribution of each component.\n\n**Weaknesses:**\n- The authors claim state-of-the-art performance in the abstract, but the CUB accuracy in Table 1 (66.2%) is lower than that of [41] (69.4%).\n- The code has not been released.\n- Missing Relevant Works: The paper overlooks important recent works, such as LegoGCD, which was accepted at CVPR 2024.\n\nQUESTIONS:\n**Motivation:** How are these hierarchical data obtained? Does the method rely on predefined structures in the dataset?  \n**Novelty:** Multi-level semantic learning was first proposed in [41]. What is the key novelty of SEAL compared to [41]?  \n**Experiments:**  \n- Why is the improvement on CIFAR-100 and ImageNet-100 smaller than that on fine-grained datasets?  \n- In Table 4, why does the accuracy drop on CUB and Aircraft after applying hierarchical learning?  Similarly, why does Semantic-Guided HSCL hurt CUB but not other datasets?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-25"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the Generalized Category Discovery (GCD) problem, where the goal is to categorize both known and unknown classes given a partially labeled dataset. The authors propose a new method SEAL, which leverages hierarchical structures to improve semantic representation. Concretely, the method introduces a Hierarchical Semantic-Guided Soft Contrastive Learning strategy that generates informative soft negatives and a Cross-Granularity Consistency (CGC) module to align predictions across different semantic levels. The proposed approach experiments with three fine-grained datasets.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths:\n+ Incorporating hierarchical information into the GCD setting is an interesting direction, as it aligns well with how semantic relationships are structured in real-world data.\n+ The proposed method demonstrates sound experiment performance across three datasets.\n### Weannesses:\n+ The problem setting presented in this paper is not sufficiently clear, particularly in terms of the hierarchical information being leveraged. For example:\n   + In Lines 54–55,  it's unclear what \"naturally occurring semantic hierarchies\" is supposed to refer to? Furthermore, how does the proposed setting differ from prior works such as [41, 55]? \n   + The caption of Figure 1 claims that prior methods define upper and lower levels as well as abstract concepts around the ground-truth level, while the proposed method leverages semantic information at different levels. However, the figure does not clearly illustrate how the two approaches differ in their use of hierarchical information.\n\n+ In Lines 41-43, it states that \"All existing methods construct hierarchical levels based on handcrafted structures, which may introduce errors, thus affecting the GCD performance\" This is a strong claim. Are there any empirical results to support this statement? This is the motivation of this paper, so I think it is necessary to provide concrete evidence that handcrafted hierarchies significantly introduce errors and affect the GCD performance.\n\n+ In Section 4.1, the theoretical analysis assumes access to all coarse-grained hierarchical labels. However, this assumption seems unrealistic in GCD settings, where only a subset of classes is labeled.\n\n+ It is unclear how the number of hierarchical levels $H$ is determined in Lines 152–153. Is it predefined based on the ground-truth hierarchy? Similarly, how is the number of classes per level obtained in Line 162? A more detailed explanation is necessary to clarify whether this structure is assumed given or derived from data.\n\n+ In Lines 173–175, the description of updating the transition matrices $M_h$ for novel classes lacks clarity. The paper mentions initializing with a uniform distribution and refining during training, yet it is unclear how this refinement is implemented. While Algorithm 1 appears to be introduced for updating $M_h$, the method of obtaining $(p_h|\\mathbf y_H == k)$ is not clearly explained.\n\n+ In Lines 189–190, the process of fusing each fine-level similarity matrix with its coarser counterpart to obtain $\\tilde{S}_h$ is insufficiently described. What specific operation or function is used in this fusion?\n\n+ In Line 191, the variable $\\lambda_s$ is introduced without explanation. Please define its meaning in the overall objective.\n\n+ The paper states that the weighting coefficient $\\lambda_c$ decays during training as part of a curriculum learning strategy in Lines 196-197. However, no details are provided regarding how this decay is implemented. Please clarify this component for a better understanding of the training dynamics.\n\nQUESTIONS:\nPlease refer to the weaknesses.\n\nLIMITATIONS:\nThe authors haven't address the limitations and potential negative societal impact of their work.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 1, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces SEAL, a new framework for Generalized Category Discovery. The core problem is to classify images from a dataset where some classes are known (labeled) and others are novel (unlabeled). The authors argue that existing methods are limited by their reliance on single-level semantics or manually created hierarchies. To address this, SEAL leverages naturally occurring semantic hierarchies to guide the learning process. The framework has two key components: 1) a Hierarchical Semantic-Guided Soft Contrastive Learning loss, which uses the hierarchy to select more informative negative samples, and 2) a Cross-Granularity Consistency module, which ensures that predictions are consistent across different levels of the semantic hierarchy (e.g., fine-grained classes and their coarse-grained superclasses). The method is evaluated on several fine-grained and coarse-grained benchmarks, where it reportedly achieves state-of-the-art results.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\nThe paper is generally well-written and easy to follow. The motivation for using semantic hierarchies in GCD is clear and compelling, and the proposed SEAL framework is described in sufficient detail.\n\nThe authors provide a theoretical motivation for their approach in the supplementary material, grounding their method in information-theoretic principles.\n\nThe supplementary material includes a valuable analysis of the computational cost of SEAL compared to baseline methods, showing consideration for the practical aspects of the proposed framework.\n\n\n\nWeaknesses\n\nThe related work section could be strengthened. In several instances (e.g., lines 75, 77, 85), multiple prior works are cited as a group without a clear explanation of their individual contributions or how SEAL specifically improves upon them. This makes it difficult to precisely understand the originality and significance of the proposed method in the context of the literature.\n\nThe empirical results, while strong on some datasets, raise questions that are not fully addressed in the paper:\n\n1. In table 2, the improvement in DINO (average) wrt to the second best method is only 0.1 (DebGCD). Indeed, DebGCD matches or outperforms the proposed method in 2 of the 3 datasets for All classes. The results for this backbone are not discussed, and an explanation for this difference is not given.\n2. The performance on the CUB dataset is a notable concern. Across the \"All,\" \"New,\" and \"Known\" class settings, SEAL consistently lags behind methods like DebGCD and SimGCD. This underperformance on a key fine-grained benchmark is not discussed.\n3. It is not immediately clear from the main text which backbone was used for the results in Table 3 (DINO or DINOv2?). Why only one for these two datasets?\n\nQUESTIONS:\n1. Could you elaborate on the relationship between SEAL and DebGCD? Given their competitive performance in Table 2, a more direct comparison of their architectural and methodological differences would be helpful to understand the trade-offs and the specific scenarios where SEAL offers an advantage.\n2. What is the potential explanation for the lower performance of SEAL on the CUB dataset? Does this suggest a limitation of the automatically-derived semantic hierarchy for this particular data distribution, or are there other aspects of the framework that may be less effective here?\n3. Regarding the related work, could you provide a more detailed differentiation from the key methods cited in lines 75-85? Specifically, for those that also employ contrastive learning, what is the key advantage of your hierarchical semantic-guided approach?\n\nLIMITATIONS:\nThe authors provide a limitations section, but it is located in the appendix. According to the NeurIPS Call for Papers and reviewing guidelines, which emphasize the importance of transparency, a discussion of limitations is a key part of a strong submission. The most relevant limitations should be summarized in the main paper to provide readers with a more balanced and complete understanding of the work's scope and applicability.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

comments: SUMMARY:
This paper proposes the Semantic-aware Hierarchical Learning framework (SEAL), marking the first utilization of naturally biological classification system to guide Generalized Category Discovery (GCD) tasks. It addresses the limitations of existing approaches that rely on manually designed abstract hierarchies through three components: Semantic-aware Hierarchical Learning, Cross-Granularity Consistency Self Distillation, and Hierarchical Semantic-guided Soft Contrastive Learning.

STRENGTHS AND WEAKNESSES:
**Paper Strengths：**

1.	The first framework to introduce biological classification systems into GCD, replacing error-prone manually designed abstract hierarchies. 
2.	Moreover, compared with the similar work DebGCD, the effect is significantly better. 
3.	This work will have a good effect in data with a clear natural hierarchy
4.	The ablation experiment is sufficient.

**Major Weaknesses:**

1.	Static Hierarchy Dependency: 
The reliance of static biological classification on predefined semantics limits its adaptability to the open world

2.	Label Sensitivity:
I notice that: 'When the real semantic structure conflicts with the preset hierarchy, performance drops sharply (e.g., non-standard biological classification)'. This also constitutes a significant limitation.

*Minor Weaknesses:*

I would recommend beautifying the images in the article.

QUESTIONS:
See Major Weaknesses

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-07-03