Decoupling Primitive with Experts: Dynamic Feature Alignment for Compositional Zero-Shot Learning
Authors: Xiao Zhang
Haodong Jing
Yongqiang Ma
Nanning Zheng
Venue: ICLR 2026 Poster (2026)
Keywords: Compositional zero-shot learning; Multi-modal learning
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

Abstract: Compositional Zero-Shot Learning (CZSL) investigates compositional generalization capacity to recognize unknown state-object pairs based on learned primitive concepts. Existing CZSL methods typically derive primitives features through a simple composition-prototype mapping, which is suboptimal for a set of individuals that can be divided into distinct semantic subsets. 
Moreover, the one-to-all cross-modal primitives matching neglects compositional divergence within identical states or objects, limiting fine-grained image-composition alignment. In this study, we propose EVA, a Mixture-of-Experts Framework for Semantic Variant Alignment. Specifically, we introduce domain-expert adaption, leveraging multiple experts to achieve token-aware learning and model high-quality primitive representations. To enable accurate compositional generalization, we further present semantic variant alignment to select semantically relevant representation for image-primitives matching. 
Our method significantly outperforms other state-of-the-art CZSL methods on three popular benchmarks in both closed- and open-world settings, demonstrating the efficacy of the proposed insight.

Published as a conference paper at ICLR 2026
DECOUPLING PRIMITIVE WITH EXPERTS: DYNAMIC FEATURE ALIGNMENT FOR COMPOSITIONAL ZERO- SHOT LEARNING
Xiao Zhang--2; Haodong Jing'.2; Yongqiang Ma-.2 Nanning Zheng1.21
Nattioal Key Laboratory of Human-Mann Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics, Xi an Jiaotong University (xzhang, j jinghd]estu. .tape/sstusitt.... xjtu. edu. cn, (musaya, nnzheng) @ @xjtu. edu cn
ABSTRACT
Compositional Zero-Shot Learning (CZSL) investigates compositional general- ization capacity recognize unknown state-object pairs based on learned primi- tive concepts. Existing CZSL methods typically derive primitives features through simple composition-prototyyy mapping, which is suboptimal for set of indi- viduals that can be divided into distinct semantic subsets. Moreover, the one- to-all cross-modal primitives matching neglects compositional divergence within identical states or objects, limiting fine-graine tneeggrined image-compen composiom alignment. In this study, we propose EVA, a Mixture-of -ffxper- Framework for Semantic Variant Aligmment. Specifically, we introduce domain-expert adaption, lever- aging multiple experts to achieve token-aware learning and model high-quality primitive representations. To enable accurate compositional generalization, we further present semantic variant alignment to select semantically relevant rep- resentation for image- seageeprimitives matching. Our method significantly outperforms other state-of-the-aa CZSL methods on three popular benchmarks in both closed- and open- world settings, demonstrating the efficacy of the proposed insight.
INTRODUCTION
Compositional generalization (Atzmon al. 2016 Lake al. 2017) enables artificial intelli- gence systems to derive concepts from ex- isting knowledge, thereby accurately interpret- ing unseen visual instances. Inspired by this pacity for compositional generalization, Com- positional Zero-Shot Learning (CZSL) (Misra et al. 2017 Li et al. 2020; Naeem et al., 2021) aims to recognize novel compositions of states and objects by leveraging primitive knowledge acquired from seen compositions. A central challenge of CZSLL lies in primitive polysemy: the meaning and visual manifestation of the same primitive can differ substantially depend- ing on its compositional context. Addressing this semantic variability is crucial for building robust composition representations.
Prior
Old
Ours
Old Mac
Old Book
Old
ald Stree
Image Encoder
Expert
Expert
Expert N
Figure 1: Left: Prior primitive alignment neglects com- positional divergence within primitive, thus disrupting the topological structure of multimodal space. Right: Our method selects the most semantically relevant fea- ture from demain-experts for improved fine- tine-grained cross-modal alignment.
Recent CZSL approaches (Huang et al.) 2024
Xu et al. 2024 [u et al. [2023] Jing et al. 2024)
have advanced performance by incorporating composition-level supervision, typically through con- trastive objectives that align visual compositions with their textual embeddings. Primitive-level
"qqual contributions.
Corresponding authors.

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Published as a conference paper at ICLR 2026
alignment, when used, is often introduced as an auxiliary objective to marginally improve perfor- mance. Hence, primitive learning has not been sufficiently appreciated and explored; These meth- ods still rely on single-prototype representations for each primitive, which are shared across all compositional contexts. This one-to-all constraint disrupts the topological structure of fine-grained compiive-comoosition relations, leading semantic entanglement. As result, the quality of com- position embeddings is inherently limited by the underpowered modeling of their primitive con- stituents. For instance, single prototype for young cannot simultaneously capture the distinct semantics of young dog and young city, even when composition- losses are applied. This gap motivates the need for how to learn primitive features that can dynamically adapt to diverse seman- tic variants.
Our work builds on the observation that primitives are not only shared across compositions but are also semantically heterogeneous, exhibiting context-dependent variants that cannot be faithfully represented by a single embedding. While prior works (Purushwalkam et al., 2019 Nayak et al.) 2022 Lu et al., 2023 Xu et al., 2024 have addressed composition-level alignment, they implic- illy assume that primitive embeddings are static and context-invariant, serving as a fixed anchor across all compositions. In Fig. primitive visual features are extracted by simple expert-module and aligned with single text primitive feature, which fails to extract specific and expressive prim- itive semantic (e.g., old man and old street) and achieve une-grained cross-modal matching (i.e., different semantic alignment from old). Thus, we argue that such simplification restricts the compo- sitional generalization ability of CZSL models, especially in open- open-world scenarios where primitives frequently combine in unseen ways.
To tackle this issue, draw inspiration from the Mixtureoof-Experts (MoE) paradigm Zhou al. 2022 Jacobs et al. 1991] [Shazeer et al. 2017 Liu et al., 2024a), which is inherently suited to model the heterogeneous nature of primitive concepts. The core design of MoE MoE-which routes different inputs to specialized experts -aligns perfectly with the fundamental challenge in CZSL: the meaning of primitive varies dramatically based on its compositional context. Unlike conventional architectures that simply increases model capacity uniformly, MoE enables dynamic specialization, allowing the model to capture diverse, context-aware primitive representations, where each expert can specialize in a different semantic facet of primitive. Importantly, our use of MoE is not merely a transplant of a more powerful architecture; it is a principled, task-driven design that leverages expert specialization to explicitly address the semantic variability problem intrinsic to compositional learning. To the best of our knowledge, this work is the first to introduce and motivate the use of MoE for this specific purpose in CZSL.
Based on this insight, we propose EVA, a -fxture-of-Experts Framework for Semantic Variant Alignment. EVA introduces two effective strategies: domain-expert adaption for learning high- quality primitives representations, and semantic variant alignment for establishing robust fine- grained image-composition alignment:
1. Domain-experl adaption introduces the MoE adapter to process tokens in each layer of the im- age and text encoders. Through dynamic token allocation, each expert handles semantically similar tokens, facilitating the mastery of in-domain knowledge (common knowledge associated with spe- cific domain, e-g., color) and improving performance in prototype semantic modeling. The MoF adapter learns prototypical concepts at token level and integrates composition information through the self-attention layer, ensuring the effective transmission of semantic information. Moreover, con- sidering knowledge redundancy in multi-expert collaboration, we designate a shared expert to capture general knowledge, and other activated experts to focus on specialized knowledge. As a
2024h. EVA is result, compared to suffix modules in (Bao et al., 2024 Huang et al. 2024 Xu al. an efficient and flexible end-to-end model.
2. Semantic variant alignment is the global-to-local cross-modal solution from image and text views. In the text domain, since compositions belong to their respective state and object sets, prim- itive features can be regarded as the centroids of composition features. Thus, without explicitly maintaining a cluster of feature variants, EVA captures fine-grained primitive features. In the image domain, CLS tokens from various experts are regarded as image feature variants, which are seman- tic representations of visual content from different perspectives. With the similarity between these variants and their corresponding primitive text features, we select the highest- highest-scoring variants as the primitive visual features. Since explicitly modeling semantic variants of primitives, our method
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Published as a conference paper at ICLR 2026
complements composition-level objectives in prior work and provides new perspective how primitives can be structured for robust CZSL.
To evaluate the proposed method, conduct comparative experiments three well-known datasets: MIT-States (Isola et al., 2015), UT-Zappos (Yu & Grauman 2014), and C-GQA (Nacem al. 2021) in both closed- and open-world settings. Our method significantly outperforms other state-of-the-art CZSL approaches, achieving +0.7% % and +2.6% AUC gains on UTT-Zappos and C- GQA in open-world setting. Furthermore, extensive ablation studies robustly demonstrate the effect of the EVA components.
RELATED WORK
Compositional Zero-shot Learning (CZSL). CZSL (Misra et al. 2017 Lu et al. 2023 Nayak
ct al. 2022 Atzmon et al. 2020) learns the entanglement between states and objects from seen com- positions in training to recognize unseen compositions during test. Recent methods (Nayak et al., 2022 Xu et al. 2022 Huang et al., 2024 Jing et al. 2024) leverage high-qualioe features from pre-trame traitr VLM (Radford et al. 202 achieving impressive zero-shot performance. CSP (Nayak) et al. 2022) designs a single learnable prompt for each primitive, without considering the primi- tive polysemy and the impact of prompt learning the features is limited. Troika (Huang al.) 2024) proposes a cross-modal traction module to adaptively learn text representations relevant to visual content. However, for various tokens with different semantic contents, single expert module inadequately achieves deep primitives learning. GIPCOL (Xu et al.) 2024) introduces a graph-based prompt learning method, but only focuses on the relationships between the primitives at the compo- sitional level. In contrast, we propose domain-expe expert adaption to introduce MoE adapters at each layer of both image and text encoders, enabling deep and efficient intra-primitive feature learning.
Concept Representation Learning. Learning transferable and generalizable concept representa- tions is central challenge deep learning. In NLP filed, several methods (Vaswani et al.] 2017] Devlin et al. 2019, Radford et al., 2019; Brown et al.) 2020) attempt to pretrain models large datasets, learning general and accurate token representations. Recent works (Jiang et al. 2024
Liu et al. 2024a) further utilize the MoE layer to achieve dynamic token routing for in-domain knowledge learning. However, identifying fundamental visual concepts for description remains challenging. visual concept learning typically requires natural language supervision (Li et al. 2021).
CLIP (Radford et al.) 202T) 1) learns transferable visual representations through contrastive learning on large-scale image-pair datasets. BLIP (Li al. 2022) is pre-trained with both understanding- based and generation- based tasks to learn generalizable representations. LLaVA (Liu et al.) (024b) introduces visual instruction tuning to align LLM with the multimodal space. These methods fo- cus on learning general visual concept features with the supervision from text, while we propose multi-expert representation method with the supervision from semantic variants, enhancing primi- tive features expressiveness for moosl alignment.
METHODOLOGY
3.1 PRELIMINARY
The goal of CZSL is to develop a model with compositional generalization capacity, enabling accurately recognize unseen compositions based on learned primitive knowledge (i.e., state and object). Given primitive concept set H: state label set S = {s), $2,..., s and object label set O (01, .2..... on): the state-object composition set C defined as the Cartesian product of these two primitive concepts sets (i.c., C S x O). For zero-shot evaluation, we denote seen and unseen composition label sets as C* and C respectively, which are disjoint subsets C. The training dataset is defined as T [(x;, c2) E X; C*}. where X' denotes the image space, and only the seen set C* accessible during training. In closed-world setting (Naeem et al. 2021), the test composition set C includes both seen and unseen sets, defined C = -C. In the more challenging open-ve open-world world setting (Mancini et al. 202T), )) the state-object composition set C is utilized the test composition space.
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Semantic variant alignment
FoneFod Natork
Rostal
Adperr
Layehoon
fo
+
Expert
fy
to
Multi Head
OOO
LayerVorm
Domain-cxpert adaption
Lx
*
MoE Adapters
MoE Adapters
(b) Domain-expert adaption
Imaage Eacoder
Text Encoder
Text-lu-image alignment
Image-to-fest alignment
old
dog
"A photo of stuie"
young
clace
"A ahjorr
Variznt
P photo objecs"
young
youngg
(a)
(c) Semantic variant alignment
Figure 2: (a). The framework EVA consists of domain domain-expert adaption for token-aware representation learning and semantic variant alignment for fine- ane-grained caage-primitive matching. (b). Domain expert adaption utilizes MoE adapter to dynamically process semantically relevant tokens with in-domain knowledge. Semantic variant alignment introduces text-to-image and image-to-text alignment select relevant features for cross-modal matching from the text image views, respectively.
3.2 FRAMEWORK OVERVIEW
The human brain consists of different functional areas that work together to complete various tasks. Inspired by distributed functional system, we propose EVA, Mfxture-of-Expert Semantic Variant Alignment framework for CZSL, which employs several domain-experts for adaptive concept learn- ing and fine-grained semantic alignment, as depicted in Fig. 2 (a). In this study, we adopt the frozen CLIP Radford et al. 2021) image encoder Ev and text encoder Eg to derive image and text rep- resentations, respectively. Specifically, we introduce domain-experi adaption, illustrated in Fig. (b), to dynamically process tokens in each layer of encoders. The proposed domain-ex sophisticated mapping to deeply learn primitives the token level. To address existing all-to-one image-primitives issues, we design semantic variant alignment, shown in Fig. 2 (c), to select the most semantically relevant feature variants for fine-grained primitive concept learning.
create
3.3 DOMAIN-EXPERT ADAPTION
As depicted in Fig. 2 (b), the MoE adapter, parallel to the of a router R. for dynamic token allocation and multiple experts { worth noting that we designate a shared expert & to learn common knowledge, while others serve as routed experts ( to focus domain-specific knowledge. We utilize standard LoRA Hu et al. 2021 1) to develop the MoE adapters, where each expert &, has two trainable parameters A E Rixa, ; B E R dxr and & This design makes experts lightweight and prevents overfitting due the limited size of CZSL datasets. Given the hidden token embedding h2 E TR in layer j. the router first computes the token-to-expert affinity G R measure each expert's contribution. Then each token is assigned to shared expert and K routed experts for domain knowledge learning:
Network (FFN), consists
G (T
(1)
ε, = B,A,,
(2)
N.
h = Σ G.E(h))
where Topk(-) function selects the K most relevant experts, while setting the scores of the other experts to -C. With the implementation of the MoE adapter in both the image and text encoders, method leverages depain-expert knowledge and inter-expert collaboration model high-quality
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Published as a conference paper at ICLR 2026
image representation E IR' xd and text representation t. Rixd respectively:
fc = Ex to = E:(oo,
(4)
where [xxxxx3 denotes the input image, and Pe 0..... 0a Pn. 0, is the learnable com- position prompt, initialized with "a photo of state object". Finally, we compute the composition probability corresponding to the image follows:
exp(fe (T)
Pe(clz) Evecs exp b /T)
(5)
where E TR is the temperature coefficient from pre-trained CLIP. The training objective for com- position classification is defined as:
Ce
1
Σ (x,0)ET
log2e(c)z).
(6)
3.4 SEMANTIC VARIANT ALIGNMENT
Primitive concept learning is essential establishing robust and accurate image-composition align- ment. Existing methods (Mancini et al., 2021; Lu et al. 2023 Huang et al. 2024) typically align the primitives visual features with state and object text representations, respectively. However, this all-to-one alignment neglects composition divergence within identical primitives, hindering fine- grained composition matching. To address this limitation, as shown in Fig. 2 (c), we propose se- mantic variant alignment which constructs multiple feature variants for adaptive amage-primitiver relations. This approach performs a global-to-local cross-modal alignment from both image and text perspectives, respectively.
textaeo- alignment. Our variant-based method leverages the local composition distribution select the most semantically relevant individual, rather than original primitives feature. Specifi- cally. we select the highest matching score among all compositions within state é as the image-state matching score Ps, formulated as:
P. (8|x) max pe(c,oo/))))
where Cs E Ctarget denotes the state š-relevant composition and Ctarget is composition label space. T, > is trainable coefficient used to adjust the distribution of state probabilities. Similarly, the image-object matching probability Pa can be obtained. To recognize primitive concept h, the cross-entropy loss employed:
LA
Σ (T,C)ET
(ggpa(hlar)
(8)
I
where Ln denotes L. and L..
Image-to-text alignment. The composition-baste based approach exacerbates the gap between the seen and unseen sets. Therefore. we propose bridging this gap by utilizing constant state and object information to refine the well-structured image representation space during training.
Specifically, given training image with state label and object label feature variants {e; No E R.Ne xd from the MoE adapter in the final layer of the image encoder E,- s=
we first model the image
The state feature t, E I2 and object feature to Id are derived from the text encoder Eg based on learnable prompt P, and Po:
1, F. t, (P())).
where P. = [ 0.......... la 92 is initialized with "a photo of state", and P. = [O,...., o. 0, is ini- tialized with "a photo of object". Since multiple experts exploit semantic information from various representation subspaces, the output representations denote the feature variants that describe differ- semantic contents of the input image. Similar text-to-image alignment, we propose inter- and intra-model affinity to select the most semantically relevant variant as the primitive feature.
In terms of inter-model affinity, we measure the state affinity score A. E Ne++ and object affinity score A, E RNN+1 for feature variants V [r and the corresponding text primitive features,
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Published as a conference paper at ICLR 2026
MIT--tates
UT-Zappos Seen
C.GQA
Method
Unseen
Seen
AUC
HM Unseon
AUC HM
Unseen
Seen
AUC
HM
Closed- World Evalaction
ICMI::
46.0
7.5
CSP Nayak al MUZZ
KUR22
49.9
46.6
19.4
36.3
66.2
64.2
33.0
46.6
26.8
28.8
6.2
20.5
DFSP
52.0
66.7
38.2
27.1
GIPCOL (Xu 2024
WWCYV'0
49.6
48.5
19.9
36.6
68.5
65.0
36.2
48.8
28.4
31.9
7.1
22.5
CDS-CZSL ef A024
C4990224
52.9
50,3
22.4
39.2
74.8
63.9
52.7
34.2
38.3
11.1
28.1
Troika (Huang ar
UVPR724
53.0
49.0
22.1
39.3
73.8
66.8
41.7
54.6
35.7
41.0
12.4
29.4
PLID Bil B03c)
EOCY
52.4
49.7
22.1
39.0
68.8
67.3
38.7
52.4
33.0
38.8
11.0
27.9
RAPR
53.3
50.0
22.5
39.2
72.8
69.4
44.5
56.5
36.0
45.6
14.4
32.0
CLUSPRO (QU 2025
ICLR2S
$4.0
52.1
23.8
40.7
76.0
70.7
46.6
58.5
37.8
44.3
14.9
32.8
LOGICZSI. Ware 2025
53.9
50.8
23.4
40.5
74.9
69.6
45.8
57.8
39.4
44.4
15.3
33.3
Ours
35.0
51.2
24.0
41.0
79.6
71.2
50.2
60.2
44.6
47.1
18,8
36,9
Open World
CLIP Radford et al 202
(CML/21
14.3
30.1
3.0
12.8
20.6
15.7
2.2
11.2
4.6
7.5
0.3
4.0
CSP S 20ze
KLR2223
15.7
46.3
17.4
44.1
64.1
22.7
38.9
28.7
1.2
6.9
DFSP Crreeu 2025
18.5
47.5
6.8
19.3
60.0
66.8
30.3
44.0
7.2
38.3
2.4
10.4
GIPCOL (x 2024
WACV124
16.0
48.5
17.9
45.0
65.0
23.5
40.1
31.6
1.3
7.3
CDS C2SL,
A134
21.8
49.4
8.5
22.1
61.3
64.7
32.3
48.2
8.2
37.6
2.7
11.6
Troika (Huale 2ee
2024
CVP8/24
18.7
48.8
20.1
61.2
66.4
33.0
47.8
7.9
40.8
2.7
10.9
PLID [300 A024
18.7
49.1
7.3
20.0
55.5
67.6
30.8
46.6
7.5
39.1
2.5
10.6
RAPR Cl al M124
20.1
49.9
8.2
21.8
$9.4
69.4
31.3
47.9
11.2
45.5
4.4
14.6
CLUSPRO (00
2025
22.1
51.2
9.3
23,0
66.2
71.0
39.5
S4.1
8.3
41.6
3.0
11.6
LOGIC7SI. Wi
2025
CVPRESS
21.4
50.7
8.7
22.4
63.7
69.9
36.2
50.8
9.3
43.7
3.4
12.6
Ours
23.2
9,4
22.
66.5
71.6
40.2
54.2
46.9
17.9
Table 1: Quantitative results on MIT-States, UT-Zappos, and C-GQA in Closed and Open- Open-World setting,
respectively (i.e., A, = Vt and A, = Vt..). In terms of intra-model affinity, we compute the affinity scores A, RRN+1 for image features and semantic variants, which introduce supervision for the global semantic content (i.e., A, = V. ). The affinity score A, is beneficial for excluding semantic variants that differ significantly from the main semantic content. Furthermore, the overall affinity score As and Ao are derived by considering both affinity scores comprehensively:
As A, a Ao Ao aAp;
(10)
where a > balancing coefficient. Finally, we select state image feature fa from semantic variants {u; 2 based on the overall affinity scores:
fa arg max of, {aī E As|0
(11)
Similarly, object feature fo can be obtained. The tmage-to-text primitives probability is defined as:
exp(fn /T)
(h(x) Enen exp(Sr (17TT)
(12)
where pj, denotes image-to-text state probability D and object probability pe. The training objective of semantic variant alignment is defined as:
C
Σ (a.0)T
log (
(13)
T
C
Σ
(olzz()lz).
(14)
(n.o)T
3.5 TRAINING AND INFERENCE
Training objectives. The final training objective of EVA is achieved by optimizing all classification loss functions, formulated as:
C- X1 + C.) d2 A2 + E,),
(15)
where \ and X2 are two coefficients. Due to the reliance label information, image-to align- ment is only applicable during training.
Inference. The final composition prediction é' combines state Ps, object Pos and composition scores P.:
os = arg max Pe(cs, + ((pa(s)o) + po(o)))), c
(16)
where the coefficient 3 is 0.5.

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Published as a conference paper at ICLR 2026
MITT-State
C-GQA
Unseen
Seen
AUC t
HM f
Unseen t
Seen
AUC 1
HM
BASELINE Domain- Adaption
51.8
47.1
20.2
36.9
32.5
38.3
10.4
26.9
54,8
50.1
23.0
40.1
42.8
45.6
17.2
35.5
Semantic Variant Alignment
52.3
49.8
22.2
39.7
34.7
41.2
12.1
29.8
Adaption Alignment
55.0
51.2
24.0
41.0
44.6
47.1
18.8
36.9
Table 2: Ablation Studies of core components on MIT-States and C-GQA datasets.
Expert split|
Unseen
Seen
AUC 1 HM f
Variant Alignment
Unseen f Seen 1 AUC
HM
0+8
43.5
46.5
18.0
36.2
BASELINE
42.8
45.6
17.2
35.5
1+8 8
44.6
47.1
18.8
36,9
121 alignment
43.8
46.4
18.0
36.2
2 8
44.2
46.8
18.4
36.2
inter-model affinity
44.2
46.8
18.5
36.5
444
43.9
46.2
17.8
35.9
+ matra-mddal affinity
44.6
47.1
18.8
36,9
(a) Expert Split
(b) Ablation on Semantic Variant Alignment
Expert Num
Unseen
Seen f AUC f
IIM
Method
Unseen
Seen
AUC
HM
1 +
44.0
46.5
18.2
36.2
EVA
44.6
47.1
18.8
36.9
1 1
44.6
47.1
18.8
36.9
semantic Esolation
44.2
46.7
18.5
36.4
1 1+
10
44.0
46.0
18.0
36.0
+ load balance
43.9
46.3
18.0
36.0
(c) Expert Number
(d) Analysis on Domain-cxpert Adaption
Table 3: Comparison experiments C-GQA dataset.
4 EXPERIMENT
4.1 SETTING
Datasets. We evaluate the proposed EVA three widely-used CZSL datasets: MIT-States (Isola et al. 2015), UT-Zappos (Yu & Grauman 2014). and C-GQA Naeem et al. 2021 MIT-States comprises 53,753 natural images annotated with 115 states, 245 objects, and 1,962 state-object com- positions. UT-Zappos contains 50,025 shoes images with 1 116 fine-grained compositions, including 16 states and 12 objects. C-GQA is the largest C7SL dataset, featuring 39,29 99,298 images labeled with 7,767 compositions, encompassing 453 states and 870 objects. In closed-world setting, we follow previous works (Naeem et al. 2021 Lu al. 2023) partition datasets into seen and unseen sets.
Evaluation Metrics. Following the evaluation protocol established in prior works (Misra et al., Lu et 2023 Huang et al. 2024), we adjust calibration bias applied unseen scores from -0 to +00 balancing the prediction scores between seen and unseen pairs. We report the Area Under the Curve (AUC) and the best Harmonic Mean (HM) to quantify the overall performance across seen and unseen compositions. Moreover, we record the best-seen accuracy Seen and best- unseen accuracy Unseen assess performance on these two disjoint subsets.
2017
Implementation Details. EVA is built upon the pre-trained frozen CLIP (Radford et al. 2021) VIT- L/14 model, which serves as both the image and text encoder. In the domain-exper adaption stage, the MoE adapter with a LoRA-based (Hu et al., 2021) intra-layer structure comprises router R, shared expert Eo and routed experts {E] The router R is single-layer layer fully connected network, while the shared and routed experts are two-layer MLPs with identical structures, where the hidden dim r is set to 64. The number of activated experts K is set to 2. Moreover, the coefficient is set to 0.5 in intra-model affinity. In the final loss function, the weighting parameters a and A2 are set to 0.5 and 0.1, respectively.
Training and Test. We train EVA, implemented PyTorch (Paske et al.) 2019). using Adam optimizer (Kingma & Bal 2014) for 20 epochs with a learning of le 4. The weight decay is set to le 5e 4 and le 4 for MITT-States, UTT-Zappss, and C-GQA, respectively. Following prior works (Nacem et al. 2021; Lu et al. 2023), we apply post-training calibration filter out infeasible compositions in the oprn-world setting during testing.
4.2
COMPARISONS WITH SoTAS
We evaluate the guantitative performance of EVA in comparison to previous CLIP-based CZSL
methods (Radford et al. 2021 Nayak al. 2022 Lu et al.] 2023 Xu et al. 2024 Huang et al.
2024 Li et al., 2024: Bao et al. 2204 Jing et al. 2024 u et al. 2025 Wu et al. 2025), in both
closed- and open-world settings, presented in Table
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Success Cases
Failure Cases
Young Ekphant
Ripe Apple
Kliches
New Bay
R: Young Blgghart
Boor
Dosk
Ki Small Dox
Small Hlppore
Hany llest
Liupe Apple
Large Recen
Thay
Tryy Eleghene
Indlaee Boat
Rips Poir
Clean Kitchen
New Tey
Bi Tiny Elophant
B: Lighnwsight
Ii Uniipe
It Clean Room
B: Tayy Dog.
Boal
Clay
Hage Torner
Canras BoottAAkkle
Meeden Houee
Desk
Enaded
Tower
Boots Anide Condaroy Boctuhalle
E: Small Poel
Dry Clty
Monées
Morden Bluso
Forpry flakk
Uroded Valley
New City
Pars Far lloors Arkie
Thee
Open Door
Dry Villey
Monken City
B: Condeny Boors Anide
Lage
Clean Dask
Figure 3: Qualitative Results. We present top-3 predictions from EVA (E) and top-1 prediction from BASELINE (B) in terms of success (Left) and failure cases (Right).
Evaluation in Closed-Worts Setting, EVA achieves remarkable improvements over other methods, with AUC gains over LOGICZSL (Wu et al. 2025), improvements +0.6%, +4.44, and +3.5% on MIT-States, UT-Zappos and C-GQA. respectively. Specifically, our EVA improves Unseen by at least +1.0% compared to previous CZSL methods on MIT-States. Notably, our method achieves the highest HM score of 36.9% in C-GQA, demonstrating enhanced compositional generalization compared to other methods (Huang et al. 2024, Li et al. 2024) Jing et al., 2024). These results underscore the advantage of domain-expert adaption in coken-aware concept representation learn- ing. In general, our approach EVA exhibits robust performance across all datasets and evaluation metrics, consistently outperforming previous state-of-the-ar methods, particularly in unseen sets, underscoring its potential for practical applications in closed-world settings.
Evaluation in World Setting. Open-world world results demonstrate that our method significantly outperforms state-of-the-ar approaches, achieving AUC gains over CLUSPRO (Qu et al. 2025). improvements of +0.1%, +0.7% and +2.6% on MIT-States, UT-Zappos and C-GQA, respectively. Specifically, our method attains notable Unseen score of 23.2% on MIT-States dataset, surpassing the previous highest score of 22.1%. Additionally, our Seen score of 71.6% exceeds that of sev- eral CZSL methods, highlighting a strong balance in handling both seen and unseen compositions. On the most challenging C-GQA dataset, our method surpasses other CZSL methods across all evaluation metrics, particularly in HM. The improvement of open- world performance demonstrates the effect of semantic variant alignment in establishing accurate mmage-primitives matching rela- tions. Overall, EVA distinguishes itself through its ability to handle unseen data, making it highly effective for worn-world tasks where new and unknown compositions are frequently encountered.
4.3 ABLATION STUDY
Effeet of Core Components. We conduct several ablation experiments to access the effect of key components in our EVA, as presented in Table 2 The BASELINE employs the frozen CLIP (Rad-) ford et al.) 202I) encoder without the proposed methods, learning compositional zero-shot capacity with learnable prompts P.. P. and P... Domain-expert adaption boosts the model's zero-shot perfor- mance across all datasets, e-g-, improving AUC from 20.2% to 23.0% on MIT-States, indicating that in-domain knowledge learning effectively strengthens primitive semantic modeling. Additionally, we observe that Semantic variant alignment leads significant improvement, such as an AUC increase from 10.4% to and 12.1% on C-GQA. The integration of both components achieves new state-of-the-ar performance, further demonstrating the effectiveness of our approach.
Expert Split. We evaluate the impact of expert split, as depicted in Table 3a configurations 0 + 8 and + 8, we observe that shared expert enhances compositional recognition performance. However, increasing the number of shared experts leads to decline in model per- formance, suggesting that retaining a certain proportion of routed experts crucial. Furthermore, when the number of shared and routed experts is set to be equal, performance is lower than that of + 8. This indicates that dynamic routing plays a more significant role than constant collaboration in complex semantic learning.
By comparing
Semantic Variant Alignment. Table 36 provides ablation results for semantic variant alignment on C-GQA dataset. Compared to BASELINE without semantic variant alignment. text-to-image (12i) alignment effectively improves compositional zero-shot performance, e.g., +0.8% improve- ment in AUC. We observe that progressively incorporating the remaining modules (i.e., inter- and
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intra-modal affinity) further increases overall performance. The best results are achieved when all components are utilized, demonstrating the effectiveness of the proposed method.
Expert Number. Table 3e demonstrates that the model with 8 experts achieves the highest per- formance across all metrics, highlighting that a moderate level of expert diversity is most effective for capturing semantic heterogeneity and enabling compositional reasoning. Models with too few experts tend to underfit the complex variations in semantics, failing to disentangle meaningful com- ponents, whereas models with excessive number of experts suffer from routing sparsity, which reduces the effectiveness of each expert and weakens overall specialization. This indicates that balancing the number of experts crucial for achieving both expressive power and efficient special- ization in semantic decomposition.
Domain-expert Adaption. To further investigate the impact of semantic isolation on multi-expert learning, we conducted comparative experiment. Specifically, we employed two identical MoE- based adapters for states and objects, respectively. Each adapter consists of one shared expert and eight routed experts, mirroring the architecture of EVA. We evaluated the models the most com- plex dataset, C-GQA, with results summarized Table 3d
Introducing semantic isolation leads to slight performance degradation, suggesting that strictly isolating experts does not improve compositional generalization. This implies that mixture of attributes and objects may help experts capture semantic relationships, whereas arbitrary divisions offer little benefit. Considering the potential effect of imbalanced expert usage, we further applied a balancing loss mitigate it. However, even with balanced token loads, performance remains inferior to EVA. This is likely because load balancing enforces uniform expert usage, which contradicts the natural semantic distribution of primitives. Forcing equal routing effectively homogenizes experts, thereby reducing their specialization.
4.4 QUALITATIVE ANALYSIS
Expert
Expert
Expert
Expert
Expert
Expert 6
Expart
Expert
Figure 4: Patch visualization handled by different visual experts.
As illustrated in Fig. 3 we present the success (left) and failure (right) cases of EVA and BASE- LINE, which adopts LoRA-based adapters rather than MoE adapters in both image and text en- coders. Compared to BASELINE, EVA demonstrates strong compositional recognition capabili- ties, accurately identifying complex semantic relationships between visual instances, such as Huge Tower and Eroded Clay. Furthermore, the top-3 predictions are semantically closely related the Ground Truth, indicating that EVV establishes a robust and reliable cross-modal alignment for rec- ognizing compositional relationships between states and objects. In failure cases, while EVA and BASELINE both make wrong predictions, EVA produces semantically related predictions when in- terpreting holistic concepts (Huge Kitchen) and ambiguous subjects (Modern House). This indicates that although EVA handle semantic ambiguity well, its ability to perceive targets in complex vi- sual scenes still requires improvement in future, which may be due to the lack of explicit supervision regarding visual objects.
Vision Expert Analysis. In Fig. We observe that different experts attend to distinct semantic regions: some focus on object-level cues (e.g., "bus", "tower"), while others highlight state-related

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Published as a conference paper at ICLR 2026
attributes (e.g., "wet surface," "eroded road"). This specialization validates the intended design of MoE, where experts capture complementary semantic factors. Moreover, experts demonstrate partial cettinddanymmulippi experts sometimes capture overlapping local regions-white reflects collaborative encoding rather than isolated specialization.
uctest city
anscart
EPICANSE Beslding
discaeet Aossee
Jencent Boets
APOAEC clice
City
chastennt
BReRO Ciyy
0.75
0:30
0.41
0.24
0.73
604
332
6.39
61
0.22
1.46
0.27
4.6
0.24
0.66
6.0
913
62-
0.44
0.30
431
0.31
0.00
0.3
4.12
2.20
62
4.02
234
2.05
033
4.11
354
035
0:55
0.50
42-
3.40
433
6.5
344
0.25
43
855
0.4
0.4
4.65
0.64
0.71
136
4.53
5.19
64
922
0.49
92
2.00
0.15
0.00
+5
-0
25
231
4.54
200
4.31
-0.50
224
4.7
422
4.71
30
0.20
0.58
8.75
0.33
0.9
+0
0.28
535
0.39
835
0.30
337
2.64
0.9
-0.75
(a). State "ancient" concien""similarii" similarity analysis
(b). Object "city" similarity analysis
Figure 5: Analysis of the similarity between primitive features from different experts and composition features.
Text Expert Analysis. Fig. further analyzes the cosine similarity between primitive text features from different experts and composition text features. For example, when evaluating the state ancient, certain experts consistently align with ancient city, while others align with ancient library, showing context-sensitive partitioning of primitive semantics. Similarly, for the object city, experts diverge towards modern city versus ancient city, indicating that experts encode fine-graines semantic vari- ants. This decomposition confirms that EVA structurally separates semantic subspaces, mitigating the entanglement present in single-prototyy baselines.
Fig. 6 displays the visualization results of at- tribute and object semantic variants. terms of state, Semantic variants belonging to differ- ent categories are distinguishable. Variant clus- ters with similar semantic meanings closer in distance, e.g., large-huge.and the overall dis- tribution of variant clusters corresponds to the correlations of their respective states. The same phenomenon is also observed in the distribution of object variants. Specifically, dog, tiger, and cat all belong to the category of animals, shar- ing closer proximity each other, with dis- tinct boundary separating them from other categories. This indicates that EVA achieves clear se- mantie separation, with the distribution of semantic variants from different primitives exhibiting semantic plausibility.
Figure 6: Visualization of semantic variants from text encoder term state(left) and object(right).
CONCLUSION
In this work, we propose Mixture-of-Experr Semantic Variant Alignment framework (EVA) to address the challenges of concept learning and composition divergence within primitives. Inspired by distributed processing system of the human brain, we leverage MoE adapters to enable an end- to-end model. Through dynamic token allocation, experts specialize as effective in-domain learners, enhancing the modeling of primitive features. Moreover, we introduce semantic variant alignment to enable fine- -ne-grained and accurate -eiiiiprimitivee mappings. The resulting well well-structured primi- tive representation space facilitates the establishment of discriminative improving compositional generalization. In future work, we aim to explore strategies to enhance the understanding of abstract concepts and the ability distinguish object subjects effectively.
composiionni relations,
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ACKNOWLEDGMENTS
This work was supported by the Brain Networks and Brain-Inspired Intelligence Science Break- through Pilot Project under Grant No. JYB2025XDXM500 and the Brain Science and Brain- like Intelligence Technology National Science and Technology Major Project under Grant No.
20222D0208800.
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APPENDIX
The detailed statistics the dataset splits are provided in Table
Train |C*|
Validation
Test
Dataset
[S]
|0]
[C]
[C*|
|Cd4
[C*]
[C*]
MIT-Sttaee (Isola et al. 2015
115
245
28175
1262
300
300
400
400
UT-Zappos Yu & Grauman 2014
16
12
192
83
15
15
18
18
C-GQA (Nacem al.
2021
413
674
278362
5592
1252
1040
888
923
Table 4: The detailed data split statistics.
Expert dim r. In Table 5a we investigate the influence of expert dim P on compositional general- ization performance. The optimal dimension found to be 64, achieves the best performance while maintaining training efficiency. When the dimension is gradually reduced below 64, decline in performance observed, attributable to information loss caused by dimensional compression. Conversely, increasing the expert dimension to 128 does not yield performance improvements, sug- gesting that larger dimensions introduce information redundancy.
Number K Unseen Seen 1 AUC
HM +
Expert dim
Unseen
Seen f
AUC
HM
K
42.4
45.3
16.7
34.7
r
42.0
45.9
16.8
34.7
K
43.8
47.1
18.0
36.1
42.8
46.2
17.1
35.1
K
44.6
47.1
18.8
36.9
r 32
44.2
46.8
18.2
36.5
K
43.2
46.9
17.8
35.9
18.8
128
43.8
46.5
18.0
36.1
K 42.9
46.4
17.5
35.7
(a) Hidden Dim of Expert Adapter
(b) Activated Expert Number
Table 5: Ablation experiments on C-GQA dataset.
Activated Expert Number. 5bbleob the impact of activated expert number K. The optimal K is 2, which achieves a win-win situation in terms performance and computational cost. When we reduce K to 0, i.e., only utilizing the sharing expert, the MoE adapter becomes MLP-based adapter, resulting in a performance decline. We next set one activated expert and observe the im- provement of CZSL performance, which suggests that the dynamic token routing is beneficial to primitives representation modeling. Larger number causes the lower performance, indicating the low efficiency in collaboration among multiple experts.
Analysis hyperparameter a. The coefficient a is introduced to balance inter- and intra-model affinity. Analogous to weighting terms in loss functions, it enables comprehensive consideration of both types affinity when selecting appropriate feature variants. The sensitivity analysis results Across the range from
presented in Table
all metrics vary only slightly,
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Qk
Unseen t
Seen
AUC t
HM
0
43.8
46.2
18.0
35.8
0.3
44.2
46.9
18.6
36.6
0.5
44.6
47.1
18.8
36.9
0.7
44.0
46.8
18.5
36.3
1.0
43.8
46.8
18.2
36.0
Table 6: The balancing coefficient @
indicating that EVA is largely insensitive to the exact choice of Performance remains stable a broad interval, suggesting that the model is robust to this hyperparameter.
Effect of Semantic Variant Alignment. SVA is proposed to address semantic divergence and achieve fine-grained image-primitive alignment. Table 7 shows that Semantic Variants Alignment
(SVA) achieves higher primitive prediction accuracy and AUC than BASELINE. It demonstrates that our variants-basee primitive alignment is more effective than previous single-prototype method.
MIT-States obj
C-GQA
Method
att
AUC
att
obj
AUC
BASELINE
41.0
50.0
23.0
48.5
60.0
17.2
+SVA
42.7
52.1
24.0
50.0
63.1
18.8
Table 7: Ablation on Semantic Variants Alignment
Efficiency Comparison. Additionally, Table g shows the efficiency comparison on UT-Zappos dataset. Since utilize efficient strategy to design MoE adapters and primitives alignment method, our model achieves superior performance and inference speed without massive trainable parameters.
Method
#Params(M) | Training Time | Inference Time
AUC
RAPR
Jing et al. 2024
37.9M
3min32s
28.4ms
44.5
Ours
36.7M
3min30s
12.0ms
50.2
Table 8: Efficiency comparison with SoTA method.
Design Comparison. Table further demonstrates the effect of our design, where suffix module has same structure with MoË adapter. Compared to independent suffix modules, the LoRA-based intra-layer adapter enables a flexible end-to-end model with higher AUC and HM.
UT-Zappos AUC
C-GQA
Method
HM
AUC
HM
Suffix Module
45.2
52.7
15.8
32.8
LoRA (Ours)
50.2
60.2
18.8
36.9
Table 9: Design comparison.
Analysis of hyperparameter sensitivity. We further study the sensitivity of hyperparameter À and Ag for loss functions, and for composition inference. In Fig. the parameters values change, the fluctuation range of the AUC remains within 1%, which validates the robustness our method.
Token load. To evaluate the impact of MoE adapter in-domain knowledge learning, analyze the computational load of each expert in learning state (Fig. g (a)) and object (Fig, (b). We observe an imbalanced distribution of token processing the state domain, experts & and Eg process the majority of tokens, while the remaining experts han- dle similar token load. In the object domain, experts &, and E, exhibit the highest computational
experts in both state and object domains. In
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load. Since single text encoder is utilized, this imbalance suggests knowledge separation, with certain experts specializing in specific domains, i.c., expert & excels in state-related tasks, while expert E performs well in object-related tasks.
Visualization of variant alignment. Fig. 2 demonstrates that primitive visual variants extracted from different expert models encode diverse semantic information. For instance, V1 and V3 (local) highly relevant to primitive-level semantics and therefore are more suitable for primitive alignment, while image-level features (global) aggregate high-level contextual information across the entire scene, resulting in loss of fine spatial details that are critical for distinguishing primitive object or object-specific attributes.
Feature Analysis. Fig. I0 visualizes the image features learned by BASELINE and EVA. Lever- aging domain-expert adaption and semantic variant alignment, EVA constructs a well-structured representation space, where features corresponding to identical states objects are more tightly clustered, and class boundaries are more distinct. This structured representation enhances composi- tional generalization to unseen instances.
24.
24.50
253
21.4
233
23.9
23.05
232
232
23.50
0.2
0.4
0.5
0.5
1.0
0.6
0.6
0.8
1.0
0.2
0.6
0.6
0.8
1.0
Ag
Ag
Figure 7: Hyperparameter sensitivity analysis MIT-States dataset in terms of AUC.
85
85
215
215
200
70-
Token Number
60-
Token Number
100
33
30
30
30
20
50
21
20
23
21
26
20
2
7
5
Expert
Expert
(a) State Domain
(b) Object Domain
Figure 8: The token load of various experts in state and object domains.
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0.8
ancient library
ancient library
v1
06
v2
0.4
v3
v4
v5
v5
anclent
empty
huge
large
old
wet
library
building
house
book
window
cat
Figure 9: Visualization of the affinity between images feature "ancient library" and variations (vl...., v5) with states and objects.
dog
cookie
creek
halloon
fire
dog
cookie creek balloon fire
car
(a) BASELINE
(b) Ours
Figure 10: Visualization of image features learned by BASELINE and method.
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Reviews:
[{"review_id": "LRd64FzR3s", "reviewer": "Reviewer_8hsj", "summary": "This paper proposes a mixture-of-experts framework for semantic variant alignment (EVA) to solve compositional zero-shot learning. The proposed method introduces two main modules: domain-expert adaptation and semantic variant alignment. The extensive experiments on three benchmarks demonstrate significant improvements over previous SOTA.", "questions": "Addressing weaknesses and improving writing quality.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1) This paper introduces MoE paradigm to model the heterogeneous nature of primitive concepts.\n2) The experimental results are promising.", "weaknesses": "1) The novelty is incremental. Specifically, many CZSL methods enable the primitive embeddings to dynamically adapt to diverse semantic variants by fine-grained learning[3], clustering-based prototypes[1], distribution learning[2] et al. The introduction of MoE to facilitate the adaptation of features is sound but this is merely a simple application without promoting the development of CZSL.\n2) Some parts of the paper are difficult to understand. Terms like \"domain-expert adaption,\" “in-domain knowledge”, \"variant-based method\" are used with little prior explanation. \n2) The inclusion of a balancing coefficient $\\alpha$ is also empirically motivated rather than theoretically justified, and it is unclear how sensitive results are to this choice.\n3) In lines 258-269, the feature variants V is not described clearly, and the selection of state/object image features from feature variants lacks rigorous mathematical support or analysis. In addition, A_S \\in R^{N_e+1}, why i\\in [0,N_e] in equation (11)? \n4) In lines 267-268, is {v_i}_{i=0}^{N_e} feature variants or semantic variants?\n5) In lines 262-263, why is A_v computed based on V and image features f_c, while A_s and A_o are computed based on V and text features t_s/t_o?\n[1] LEARNING CLUSTERING-BASED PROTOTYPES FOR COMPOSITIONAL ZERO-SHOT LEARNING\n[2] Prompting Language-Informed Distribution for Compositional Zero-Shot Learning\n[3] Leveraging sub-class discimination for compositional zero-shot learning", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "k6ocsbJ936", "reviewer": "Reviewer_BePU", "summary": "This paper addresses Compositional Zero-Shot Learning (CZSL) focusing on the challenge that primitive features (states, objects) vary semantically across contexts. The authors propose EVA (Expert-based Variant Alignment), a MOE framework that learns context-aware primitive representations and performs fine-grained cross-modal alignment with carefully designed traning objective. EVA introduces two key components:(1) Domain-Expert Adaption, which employs MoE adapters in image and text encoders to capture diverse semantic facets of primitives through dynamic token routing; and(2) Semantic Variant Alignment (SVA), which aligns fine-grained visual and textual variants via both text-to-image and image-to-text matching. Comprehensive quantitative and qualitative experiments on the three most commonly used CZSL datasets  demonstrate that EVA achieves superior performance  in both closed- and open-world settings. The analyses further show that different experts specialize in distinct semantic aspects, validating the effectiveness of the proposed design.", "questions": "1. Figure 7 shows that the token load across text experts is highly unbalanced. Do these experts correspond to particular semantic groups or patterns? Could you provide further analysis, and would introducing a load-balancing loss help?\n\n2. In the SVA module, the image-to-text alignment require computing affinities of experts. How is this objective applied or adapted to the baselines reported in Table 2?\n\n3. Table 8 reports efficiency analysis on UT-Zappos, but this dataset has relatively few state and object categories. Since the efficiency is sensitive to the number of primitives, could you report or discuss results on other datasets and under open-world settings?\n\n4. Could you provide a more detailed analysis of the vision experts? For example, showing how the same expert allocates patches across different images?\n\n5. It would be valuable to include a more detailed investigation of text experts. For instance, analyzing the impact of using separate experts for states, objects, and compositions.\n\n6. Which dataset was used for the qualitative analyses in Figures 7–8?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. The paper introduces an MoE-based framework to address the semantically heterogeneous of primitives in CZSL. By dynamically routing tokens to domain-specific experts, the proposed approach effectively captures context-dependent primitive semantics.The proposed model achieves SOTA performance in both closed-world and open-world settings without any suffix modules.\n\n\n2. The proposed SVA module overcomes the limitation of previous all-to-one alignment  by introducing a global-to-local cross-modal alignment from both image and text perspectives. This design enables more accurate and context-sensitive alignment of visual and textual primitives, leading to better compositional discrimination. \n\n3. The paper is well-structured and easy to follow, with thorough quantitative and qualitative evaluations.", "weaknesses": "1.  Figure 2(c) does not clearly illustrate how the SVA module works, and Figure 2(a) lacks explicit description for the text-to-image alignment pathway $\\mathcal{L}_s^v ,\\mathcal{L}_o^v$.\n2. The paper does not include an ablation study on the number of experts.\n3. The paper lacks deeper analysis of expert specialization, such as overall statistical distributions or further exploration of text experts.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "jn7eRsq7cO", "reviewer": "Reviewer_Fu3k", "summary": "This paper addresses the problem of primitive polysemy in Compositional Zero-Shot Learning (CZSL) and proposes an expert-based dynamic feature decoupling framework called EVA (Expert-based Variant Alignment). The authors point out that primitives can have different meanings under different semantic contexts (e.g., “old” in “old man” vs. “old building”), which are difficult to model using a single embedding. As a result, traditional attribute–object composition approaches cannot fully capture semantic variations.\n\nEVA mainly consists of two core modules:\n\n1.\tDomain Expert Adaptation: Inserts a Mixture-of-Experts (MoE) module into the CLIP encoder. Through a token-level routing mechanism, it selects the appropriate expert to model semantic variations across contexts.\n\n2.\tSemantic Variant Alignment: Designs a cross-modal dynamic alignment strategy that combines global and local feature matching to achieve fine-grained consistency between visual and textual semantic spaces.\n\nExperiments on standard CZSL datasets — MIT-States, UT-Zappos, and C-GQA — show that EVA outperforms several recent methods (e.g., GIPCOL, Troika, IVLP) under both closed-set and open-set settings. The authors claim EVA achieves more stable semantic generalization and better interpretability.", "questions": "1. Does the expert module experience imbalance during training? Have you introduced a load balancing loss?\n\n2. What is the performance drop if the alignment module is removed or only the MoE structure is retained?\n\n3. Does the EVA framework rely on CLIP’s pretrained semantic structure? Would it still work with non-CLIP backbones?\n\n4. Can you provide more interpretability results (e.g., clustering distributions of experts across semantic attributes) to demonstrate the effectiveness of semantic decoupling?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1.\tThe paper proposes a logically coherent and complete MoE + alignment framework, effectively integrating expert modeling with semantic disentanglement.\n\n2.\tExperiments cover multiple mainstream CZSL datasets, and the reported performance exceeds several existing methods on certain metrics, showing credible results.", "weaknesses": "1. The work is essentially an architectural extension of existing CLIP-based compositional alignment methods.\n2. The core mechanisms (expert routing and alignment strategy) lack quantitative validation.\n3. The paper does not analyze computational cost, parameter scale, or inference latency, making it difficult to assess practicality.\n4. Some figures and text repeat information, reducing clarity.\n5. Differences between variants are small, making it unclear how much each module contributes.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Y3AnxCNcmJ", "reviewer": "Reviewer_nDeF", "summary": "This paper studies the Compositional Zero-Shot Learning (CZSL) problem, which aims to recognize unseen combinations of state and object primitives.\nExisting methods usually assume a single static prototype for each primitive (e.g., old, young), ignoring the semantic polysemy that arises across different contexts (e.g., old man vs. old book). This leads to semantic entanglement and limited generalization.\nTo address this, the authors propose EVA (Expert-based Variant Alignment), a Mixture-of-Experts framework that explicitly models semantic variability at the primitive level.\nIt consists of two main components:\n(1) Domain-Expert Adaption, which inserts lightweight LoRA-based MoE adapters into a frozen CLIP backbone to dynamically route tokens to specialized experts, enabling context-aware primitive representations; and\n(2) Semantic Variant Alignment, which selects semantically relevant feature variants from both text and image perspectives to achieve fine-grained cross-modal matching.\nExperiments on MIT-States, UT-Zappos, and C-GQA datasets demonstrate that EVA consistently outperforms state-of-the-art methods in both closed- and open-world settings, achieving up to +3.5% (closed-world) and +2.2% (open-world) AUC improvements while remaining efficient and interpretable.", "questions": "1.\tAre the experts activated evenly during training, or does routing collapse occur?\n2.\tCan the authors provide quantitative or visual evidence showing that different experts learn complementary semantics?\n3.\tWould EVA still work without explicit state/object labels in open-vocabulary or unsupervised settings?\n4.\tHow consistent are the improvements across different vision–language backbones (e.g., BLIP, SigLIP)?\n5.\tDoes the MoE module truly enhance semantic reasoning, or mainly increase representational capacity?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. Clear motivation addressing primitive polysemy in CZSL.\n2. Strong empirical results across three benchmarks.\n3. Lightweight and interpretable design built on frozen CLIP.", "weaknesses": "1. The paper lacks a deeper analysis of why MoE improves compositional classification — explanations remain intuitive without quantitative evidence of expert complementarity or routing diversity.\n2. The observed gain may stem from implicit feature reparameterization rather than genuine semantic disentanglement.\n3. No study of routing stability or expert utilization balance; potential risk of expert collapse.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)