Title: A Generalized Label Shift Perspective for Cross-Domain Gaze Estimation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Gaze estimation

Generalized label shift

Distribution estimation
Abstract: Aiming to generalize the well-trained gaze estimation model to new target domains, Cross-domain Gaze Estimation (CDGE) is developed for real-world application scenarios. Existing CDGE methods typically extract the domain-invariant features to mitigate domain shift in feature space, which is proved insufficient by Generalized Label Shift (GLS) theory. In this paper, we introduce a novel GLS perspective to CDGE and modelize the cross-domain problem by label and conditional shift problem. A GLS correction framework is presented and a feasible realization is proposed, in which a importance reweighting strategy based on truncated Gaussian distribution is introduced to overcome the continuity challenges in label shift correction. To embed the reweighted source distribution to conditional invariant learning, we further derive a probability-aware estimation of conditional operator discrepancy. Extensive experiments on standard CDGE tasks with different backbone models validate the superior generalization capability across domain and applicability on various models of proposed method.


Peer reviews:
comments: SUMMARY:
This paper proposes a method for cross-domain gaze estimation (CDGE), addressing key limitations of existing domain adaptation methods for label shift and conditional shift. It introduces the Generalized Label Shift (GLS) theory as the solution. Experiments between several datasets demonstrate the superior generalizability compared to existing methods.

STRENGTHS AND WEAKNESSES:
Strengths:
- Extensive experiments demonstrate its superior performance on various datasets.
- Sufficient theoretical support provided.

Weaknesses:
- While the abstract and introduction clearly state the contributions, the motivation for addressing Generalized Label Shift (GLS) in gaze estimation could be better justified. The authors should explicitly explain why existing domain-invariant methods fail in gaze estimation compared to other tasks. Current explanations mainly focus on the general difference between the proposed method and previous works, but are not related to task-specific problems. 

- It should also be explained why label shift and conditional shift are significant and inherent problems for gaze estimation and differ from in other tasks. In the descriptions of L128-137, the involved problems can also appear in other tasks like object detection and categorization. How Sec. 3.4 solves the problems specifically in gaze estimation is also unclear, which is more like a general solution. It's better to make the relationship between the task-specific problem analysis and the proposed solution more clearly. 

- Typos. e.g. L18, "appereance-based"

QUESTIONS:
See the weaknesses

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-07

comments: SUMMARY:
This paper addresses the cross-domain gaze estimation (CDGE) problem by introducing a novel perspective based on Generalized Label Shift (GLS) theory, modeling the domain shift as both label and conditional shifts. It proposes an importance reweighting strategy using a truncated Gaussian distribution to effectively handle the continuity challenges in label shift correction. Extensive experiments demonstrate that the method achieves superior generalization across domains and is applicable to various backbone models.

STRENGTHS AND WEAKNESSES:
Pros:
1. The paper proposes a new perspective for the domain adaptation in gaze estimation which is interesting.
2. This paper is theoretically sound.
3. Experiments are extensive and support the conclusion well.

Cons:
1. The proposed method seems effective but complicated. I am curious about the time cost for the adaptation process.
2. Is it too simple to modelize the gaze label distribution as a bivariate truncated Gaussian distribution? Do you have better choices?
3. PXY = PX|Y*PY or PY|X*PX. Does your approach also work for PY|X*PX? Why not choose PY|X*PX?
4. Figure 1 is not clear for illustrating the difference among different methods. I advise to including three subfigures to show the main ideas of the three methods.
5. How many images from the target domain are used for domain adaptation? It should be included in Table 2 and 3.

QUESTIONS:
Check Cons above

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-23

comments: SUMMARY:
This paper focuses on Cross-domain Gaze Estimation (CDGE) task, which helps apply gaze estimation to various environments. This work introduce Generalized Label Shift (GLS) theory to gaze estimation, consisting of shifts in both the label and conditional distributions. Evaluations are conducted on different datasets and network structures, showing that the proposed method has achieved state-of-the-art performance.

STRENGTHS AND WEAKNESSES:
Strength:
1. The tasking definition is clear and easy to understand its value.
2. The authors clearly outline limitations in existing GLS correction methods for gaze estimation and propose appropriate solutions.
3. Comprehensive experiments have been conducted, which helps confirm the method's effectiveness.

Weakness:
1. The rationale for modeling the label distribution as a bivariate truncated Gaussian is unclear and needs stronger justification.
2. The relationship between sections in Sec. 3 lacks clarity. Specifically, Sec. 3.2 appears to describe previous methods, making it more suitable for inclusion in the related work section. From my point of view, it would be useful to have an overview at the beginning of Sec. 3.
3. It should address how GLS correction methods have benefited other tasks and clarify their value specifically in gaze estimation. Additionally, an overview of the current state of gaze estimation research should be provided to better contextualize the contribution.

QUESTIONS:
Please refer to the weaknesses.

LIMITATIONS:
NA
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-27

comments: SUMMARY:
This paper proposes to introduce GLS into the CDGE task, and designs a design that simultaneously utilizes GLS, UDA, and DG techniques to improve the performance of CDGE. The paper introduces a lot of theoretical analysis and derivations to introduce GLS into CDGE tasks.

STRENGTHS AND WEAKNESSES:
**Strengths**

1.  The paper has a detailed ablation study, feature visualization and hyper-param visualization.
2.  The paper includes detailed propagation, which makes the paper more theoretically sound.

**Weaknesses**

1.  There are too many notations in the paper, which makes the paper not easy to follow.
2.  The results from other domains to EyeDiap are not good.
3.  Although the paper puts the enhancement of GLSGE by Res-18, Res50 and GazeTR, the enhancement is not easy to see compared with the enhancement of other methods on these backbones, if we can put the comparison of different methods on GazeTR+X (X is other methods), we can understand this paper better.
4.  Although the paper puts a lot of analysis of the introduced GLS theory, the results on the EyeDiap dataset are not so good, which makes me concern whether the theory is suitable enough for this estimation task.

**Minors**

Minors.

1.  L8: a → an
2.  Equations 2, 3, 8, 12, 19 lack the necessary punctuation

QUESTIONS:
Alignment in Fig.1 and Probability-aware learning with ... in Fig.3 are in different colors, kind of confuse me, does this have a special meaning?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 1
IMPACT: 2
ORIGINALITY: 3
DATE: 2025-06-30