BiggerGait: Unlocking Gait Recognition with Layer-wise Representations from Large Vision Models
Authors: Dingqiang Ye
Chao Fan
Zhanbo Huang
Chengwen Luo
Jianqiang Li
Shiqi Yu
Xiaoming Liu
Venue: NeurIPS 2025 poster ()
Keywords: Gait Recognition
Large Vision Models
Primary area: applications

Abstract: Large vision models (LVM) based gait recognition has achieved impressive performance.
However, existing LVM-based approaches may overemphasize gait priors while neglecting the intrinsic value of LVM itself, particularly the rich, distinct representations across its multi-layers. 
To adequately unlock LVM's potential, this work investigates the impact of layer-wise representations on downstream recognition tasks.
Our analysis reveals that LVM's intermediate layers offer complementary properties across tasks, integrating them yields an impressive improvement even without rich well-designed gait priors.
Building on this insight, we propose a simple and universal baseline for LVM-based gait recognition, termed BiggerGait.
Comprehensive evaluations on CCPG, CAISA-B*, SUSTech1K, and CCGR_MINI validate the superiority of BiggerGait across both within- and cross-domain tasks, establishing it as a simple yet practical baseline for gait representation learning.
All the models and code are available at https://github.com/ShiqiYu/OpenGait/.

BitgerGait: Unlocking Gait Recognition with Layer-wise Representations from Large Vision Models
Dingqiang Yel.3+, Chao Fan?; Zhanbo Huang
Chengwen Luo", Jianqiang Li?, Shiqi Yu' Xiaoming Liu
1 Department of Computer Science and Engineering, Southern University of Science and Technology School of Artificial Intelligence, Shenzhen University
Department of Computer Science and Engineering, Michigan State University
11810121 @mail sustcch. chaofan9966 www.dd..c.. en, huang247@usu.cdu
[chengwen, lijq @ reszz.oduu mmm.odu.cn en, yusq sustech odu.en, en, liuxm ese mww.mmu..coooo
Abstract
Large vision models (LVM) based gait recognition has achieved impressive per- formance. However, existing LVM-based approaches may overemphasize gait priors while neglecting the intrinsic value of LVM itself, particularly the rich, distinct representations across its multi-layers. To adequately unlock LVM's po- tential, this work investigates the impact of layer-wise representations on down- stream recognition tasks. Our analysis reveals that LVM's intermediate layers offer complementary properties across tasks, integrating them yields an impres- sive improvement even without rich well-designed gait priors. Building on this insight, we propose simple and universal baseline for LVM-based gait recog- nition, termed BiggerGait. Comprehensive evaluations on CCPG, CAISA-B*. SUSTech1 K, and CCGR_ MINI validate the superiority of BiggerGait across both within- and cross-domain tasks, establishing it as a simple yet practical base- line for gait representation learning. All the models and code available https: //gbtttt....///////g com com//hiiqiYt/Openaaat..
Introduction
Gait recognition aims to identify an individual based on the unique patterns in the walk sequence. Unlike other biometric [23] modalities such face [53, 38, 27, 46, 45, 28, 18], fingerprint [3], or iris [51, 49], gait is unobtrusive and capable of identifying individuals from afar without their active involvement. These unique advantages make gait recognition especially effective for security applications, including suspect tracking and identity verification [47, 58, 39, 40, 29, 79, 22, 21].
To focus on pure gait patterns, early approaches suppress appearance noise by transforming each frame into pre-defined representations, like silhouettes [5, 11, 37, 57, 14]. skeleton landmarks [36, 63, 15), body parsing [41, 77, 80], SMPL meshes [34, 76, 43], before feature extraction, as shown in Figure/ (a), Although such explicit representations curb distractions from clothing and background, they also discard crucial cues: silhouettes erase body structure, skeletons remove shape information, and SMPL overly smooths personal idiosynerasies. capping accuracy. Alternatively, recent approaches [74, 72, 26] achieve substantial gains by guiding large vision models (LVM) with human priors such as feature smoothing [74], language guidance [72], and geometry-driven denoising [26] to extract rich and implicit gait features directly from RGB data.
"Equal contribution. Part of the work was conducted when Mr. Ye visited MSU.
i Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Pre-defined Representation
LVM Representation
Gait Representation
Light Gal Prica
Heavy
Profe
Prafei
Pelli
Diventty
Denajin
Goometris
Meat
(a) Pre-defined Gait
(b) Typical LVM Gait
(c) Layer-wise LVM Gait (ours)
Figure 1: Comparison of gait representation paradigms. (a) Pre-defined gait learning uses explicit gait patterns for prediction, losing essential identity information. (b) Typical LVM gait learning relies heavily gait priors and treats all LVM layers equally, underutilizing discriminative features. (c) Our layer-wise LVM gait learning fully utilizes intermediate LVM layers with minimal gait priors.
Despite recent advances, Misedaeed gait recognition methods [74, 26] still rely heavily on traditional gait priors, whether through supervised or self-supervised training, as shown in Fig 1(b). However, this dependence truly necessary? Through extensive experiments,
challenge this assumption, showing that LVMs [48, 30, 52] inherently possess rich, layer-wise representations that can support nigh-performance gait recognition with minimal reliance on human-designed priors. These findings suggest a simpler yet more robust baseline for LVM-based gait recognition, potentially redefining the role of domain knowledge in this field. Our experiments across various LVMs [52, 48, 30] and datasets 175, 56, 80, 25] further validate this remarkable observation, aligning similarly with related studies [7, 16, 59, 60, 62].
To make the above findings comprehensive, this work systematically investigates layer-wise rep- resentations in LVMs for gait recognition and uncovers three key insights: (1) Across range of LVM architectures and scales, intermediate layers consistently yield more discriminative features than the final layer, echoing trends observed in LLMs [7, 16, 60]. (2) Each layer contributes unique, task-dependent information. (3) Intermediate-layer features are highly complementary, and their fusion produces substantial performance gains. Based these findings, we propose simple and universal layer-wise baseline, termed BiggerGait. However, fully exploiting the advantages of BiggerGait remains challenge for GPU-limited scenes. The issue lies in the need add a dedicated gait encoder to every LVM layer, inflating the parameters and computation cost as depth grows.
To mitigate this issue, we propose optional grouping strategy for BiggerGait balance performance and efficiency. Previous work [62] suggests that residual connections in LVMs encourage intermediate layers to inhabit shared feature space. Based this insight, contend that single gait encoder can effectively process features from multiple LVM layers, eliminating the need for layer-specific encoders. Specifically, this grouping strategy merge neighboring similar LVM layers, replacing per-layer gait encoders with two shared ones: one for shallow layers and one for deep layers. This design delivers the similar performance gains as the standard BiggerGrii while saving considerable cost during gait representation extraction. Experiments on CCPG [33], CCGR_ [80], CASIA- B* [75] and SUSTech1K K [56] datasets validate the effectiveness of BiggerGait and this grouping strategy in both within- and cross-domain evaluations.
This paper makes two important contributions.
Comprehensive layer-wise analysis. We conduct the first systematic examination of LVM layer representations for gait recognition, detailing how different depths affect task-specific performance and discovering that fusing complementary intermediate features unlocks substantial accuracy gains.
A simple yet powerful baseline. We present BiggerGait, simple and universal framework for L-V-bbaedd gait recognition, along with a grouping strategy to balance performance- efficiency trade-offs. Extensive experiments show that BiggerGait establishes state-of-the-art results across multiple RGB-based gait benchmarks, achieving superior performance in both intra-domain and cross-domain evaluation settings.

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2
Related Work
Gait Recognition Gait recognition aims to extract subtle gait patterns that remain invariant to background clutter and clothing variations. Recent research has primarily focused addressing these challenges in RGB video-based gait analysis [33, 56, 19, 61, 69, 66, 20J. Existing approaches can be broadly grouped into two categories: pre-defined [64, 5, 11, 37, 14, 36, 63, 15, 4, 77, 41, 34, 76, 43, 71, 70, 73, 58] and LVMs representations [74, 72, 26]. Pre-defined methods explicitly extract gait-relevant components using segmentation [5, 37, 57, 14]. pose estimation [36, 63, 15, 4], and 3D modeling [34,7 76, 43], effectively suppressing irrelevant gait information. However, this often comes the cost of discarding
disrrminiisr cues. In contrast, LVMs methods [74, 72, 26] extract implicit gait features from large vision models guided by human priors, preserving richer semantics and achieving stronger performance. Building the LVMs paradigm, this paper introduces BiggerGait to delve deeper into its potential for gait representation.
Large Vision Models Motivated by the success of L.LMs [2, 9, 24, 31, 54), the vision community has increasingly turned its attention to building large-scale foundation models for visual understand- ing [48, 30J. These models aim to learn transferable and general-purpen purp-ss visual representations from massive web-scale datasets. Representative LVMs include CLIP [52]. which leverages language super- vision to guide visual representation learning: SAM [30], a promptable segmentation model trained on a large-scale annotated dataset for strong generalization; and DINOv2 [48], self-supervised model that learns highly transferable features from vast, diverse image collections. The features extracted from these LVMs termed all-purpose, they effectively transfer to range of downstream tasks, including image classification, semantic segmentation, and depth prediction. Our aim is to explore how these all- all-purpose representations can be adapted to the task of gait recognition, harnessing the broad advantages offered by LVMs. In this paper, we conduct a comprehensive investigation of how different types and scales of three representative LVMs (CLIP, SAM, and DINOv2) can be leveraged for downstream gait recognition. Further, we propose a unified baseline, BiggerGait, that consistently excels across multiple LVM architectures and model sizes.
Layer-wise Analysis in Large Models Recent works [60, 16, 62, 42, 17, 10. 1] have increasingly focused on layer- wise representation from large models, as intermediate features often show surprising robustness, challenging the traditional final layer representations. In NLP, researchers [42] have found that lower layers tend to encode more syntactic information, while higher layers specialize in semantic features. Others suggest that residual connections encourage layers to share common feature space while still specializing in distinct sub-tasks [62], or that attention sink effects may weaken final-layer performance [17]. Similar trends emerge in vision domains: Head2Toe [10] selects the most useful representations from intermediate layers in transfer learning. outperforming the final layer. A fresh work [1] LVMs again suggests that the final layer may not contain the most robust visual features, and addresses this by distilling optimal intermediate features back into the final layer. Unlike prior works [10, 1] that focus on coarse-grained vision tasks (classification, detection, and tracking), our study goes a step further by validating and advancing this insight in a significantly more demanding setting, i.e., highly ine-grained recognition task. Beyond broadening this insight, further reveal more interesting and unexplored findings unique to gait tasks in Sec. 3.4 & 3.5.
3 Layer-wise Representation Analysis
First of all, we hypothesize that the unique layer-wise heterogeneity observed in large language models [60, 62, 16, 7, 59] (LLM) may also exist in large vision models [48, 30, 52] (LVM), potentially influencing downstream gait recognition. To verify our conjecture, we introduce a simple layer- wise gait baseline, comprehensive experiment setting, and three key questions systematically explore the impact of different LVM layers on gait recognition. Eventually, we further analyse the computational overhead inherent in layer-wise methods and propose a mitigation option.
3.1 BiggerGait: A Layer-wise LVM- Gait Baseline
We construct a simple layer-wise gait baseline, called BiggerGait, illustrated in Fig. 2. Given an image x ~ p(x:) from walking video, a LVM projects it into multiple intermediate feature maps {ff i E {1,2,... N}} with the corresponding semantic hierarchy spanning from low to high levels.
3

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Human Silhouetle
Lrec
Ltri
Auto Encoder
Lee
Head
fr
Gail Extracttor 8.Ay
Guil Extractor
fx-1
fNN-
Gait Extractor
8x-1
B
LVM
Gaik Extractor 2
81
B.
Gait Extrettor E.4,
fi
Pr
Bi
B
Walking Video
Mask Selection
Trainable
Linear Projection B
Figure 2: Overview of the proposed BiggerGait. An LVM extracts multi-level features from RGB videos. Human silhouettes are generated using an unsupervised auto-encoder [74]. serving the only human-designed gait prior. Each level's features, with background noise removed, are processed by separate linear projection layers and gait extractors to obtain the final gait representations.
In BiggerGait, we set N 12, uniformly sampling 12 layers from the LVMs. Followed the design of BigGait [74], the last feature map fN which contains the highest-level semantic information, is fed into an auto-encoder to generate the human silhouette m
softmax (oftmax(E(fx)) fN D(m), Cree I|fN - 5x
(1)
where F. and D are 1 X convolution layers, F. outputs 2 channels, and 1) restores the original channel dimension. Notably, unlike traditional LVM gait methods [74, 26] reliant on heavy gait priors (e.g., geometric, denoising, and diversity constraints), this human mask is the only gait prior we employed. The softmax function is conducted along the channel dimension, and Cree presents the reconstruction loss, After masking the background noise in {fi), we obtain {
fT m Ji:
(2)
where denote the multiplication. To reduce the GPU memory consumption, each f. is fed into a lightwight linear projection layer:
= sigmoid(EE) (f?")))))
where Eς consists of two 1 x 1 convolutions, two natch-normalizaiee layers, GELU activation. Its output channel is set to C. Here the hyper-parameter C is set to 16, following [74]. Each f? is upsampled by bilinear interpolation size of 16 x 64 x 32 while the first dimension denotes the output channel of the linear projection. Finally, we feed the f. into gait extractors EV (GaitBase [14]) to obtain gait representation g:- Overall, the gait representation R of the BiggerGait can be formulated as:
improve the resolution, i.e., exhibited
3-D tensor with
R {g. = E (sigmoid E (f(")))
(4)
Consistent with recent works [76, 44, 67, 68, 81], triplet losses Lars and cross-entropy losses fee are used for gait training. The overall loss can be formulated as:
L = Leri + Lee + Lree-
(5)
In summary, the central innovation BiggerGait lies in treating intermediate layers independently to fully unlock the power of large vision models.
3.2 Experimental Setting
Separate Testing, To assess each layer's discriminative power, we adopt separate testing strategy at inference. Given a probe sample ~ X' and a gallery sample Y, layer produces gait features of and g. Their Fuclidean distance serves as the similarity score for layer
d, (a, y) || 12-
(6)
Since embeddings vary across depths, each layer yields distinct score for this pair (x, y).

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DINOV2-S
DINOv2-L
CLIP-S
CLIP-L
SAM-S
SAM-L
84
60.1
B1.6
26
78.9
810
85.8
67.8
833
03.5
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13.7
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78.5
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91.0
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ON
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BG
AVG
Figure Liser-wise Performance Across MsMs. This figure presents the gaít recognition accuracy of six [48], CLIP-Small/Larr Large [52], and SAM- Aa---mal/LLarge [30], across
large vision models, Le., JINOv2-Small/L.nn 12 intermediate layers, evaluated on the CCPG [33] dataset. Each cell lists the Rank-1 accuracy for full clothing (CL), top (UP), pants (DN), and bag (BG) changes, along with the average (AVG). A downward arrow the left indicates increasing network depth. In each column, the best score is shown in black color.
DINOv2-S
DINOv2-L
CLIP-S
CLIP-L
SAM-S
SAM-L
84.0
62.8
4
9.1
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68.6
39.9
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6638
64.3
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60.0
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70.5
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A
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Figure 4: Layer-wise Performance Across Datasets. The columns (A. B, C, D) represent the four test sets.
CCPG [33], SUSTechIK [56), CASIA-B* [75], and CCGR_MIMI [80]. All models are trained on CCPG datasct. Column reports within-domain performance, whereas columns B-D present cross dossin results.
Target LVMs. Three representative LVMs with distinct pretraining strategies evaluated in experiment: SAM [30] is trained under supervised segmentation objectives, CLIP [52] follows an image-text contrastive learning approach, and DINOv2 [48] adopts self-supervised knowledge distillation. In this paper, LVM-S and LVM-L refer to the small and large versions of LVM. We test every one of the 12 layers in LVM-S, but uniformly sample 12 layers from LVM-L.
Dataset. Subsequent experiments are mainly conducted on four widely used dothing-variation and multi-view gait datasets: CCPG [33], CASTA-B* [75], SUSTech1K [56], and CCGR_ M__ [80]. CCPG serves as the cornersoone benchmark, offering diverse full body clothing variations while masking faces and shoes to simulate real-world cloth-changing scenarios.
3.3 Do Middle Layers Outperform the Final Layer?
As shown in Fig. 3, to investigate whether middle layers offer stronger discriminative power than the final layer, evaluate gait recognition accuracy across 12 layers of three popular LVMs [30, 52, 48] in different model sizes. Clearly, all LVMs achieve peak performance at middle layers rather than at the deepest one, echoing similar findings in LLMS [60, 16, 7]. For instance, in DINOv2-S [48], the highest average accuracy of 89.5% occurs at Layer 6, while the final layer drop to 80.9%.
This similar trend is also observed in both CLIP [52]. which uses image-text pretraining, and SAM [30], trained with supervision, despite their different paradigms from the self-supervised DINOv2, highlighting the generality of this interesting phenomenon. Notably, even when model size increases, this middle-layer advantage remains, suggesting that deeper depth does not eliminate the representational superiority of intermediate layers. This effect is particularly evident in CLIP, where middle layers outperform both shallow and final layers by significant margin.
Our answer: Yes, middle layers consistently outperform the final layer for gait recogni- tion, regardless of LVM type and size.
5

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Peak Performance
LVM
Ensemble Testing
CCPG
SUSTecblk
CASLA-B
CCGR
Avg
Prodict
UP
DN
CL
NM
BG
CL
R-1
Predict
DINOv2-S
84.7
88.2
89.5
48.6
66.4
84.7
76.7
39.8
14.4
25.4
59.3
89.8
92.1
93.7
97.5
70.9
79.6
93.0
90.8
55.6
15.5
26.9
67.0
Predict
Predict
DINOv2-L
92.8
90.1
94.6
92.5
92.0
95.7
97.1
76.4
48.8
85.3
68.6
95.4
85.7
93.7
79.5
62.2
43.5
18.7
26.9
30.5
70.0
62.5
(a) Separate Testing
CLIP-S
81.0
86.2
77.3
89.7
96.9
67.0
84.2
91.5
87.8
47.3
32.4
15.2
26.1
29.2
65.8
55.5
CLIF-L
80.3
84.5
87.9
35.2
65.9
82.6
77.2
38.8
20,0
33.2
59.7
87.5
90.7
92.9
97.4
72.6
$8.3
95.0
92.6
51.7
18.3
30.4
69.6
Predict
SAM-
83.0
86.1
88.7
94.2
64.6
51.0
61.4
69.8
62.8
23.8
9,4
17.8
17.4
59.7
52.7
SAM-L
87.8
90.4
21.6
54.1
94.0
91.2
93.8
97.0
64.1
73.3
81.3
77.7
42.2
11.9
21.3
61.3
(b) Ensemble Testing
(c) Comparison of Scparate vs. Ensemble Testing
Figure 5: Separate ys. Ensemble Layer Testing (a) The red box highlights the peak performance achieved by these layers. This peak performance represents the final result of separate testing. (b) Ensemble testing works likes fair voting manner. (c) All models trained CCPG [33], and tested on four datasets [33, 80, 75, 56]. CCGR* indicates ECGR_MINI dataset.
3.4
Do Middle Layers Contribute Similarly?
To further evaluate the contribution of different LVM layers to gait recognition, we adopt additional testing datasets, each reflecting distinet characteristics. Specifically, SUSTechIK [56] emphasizes scenes, CASIA-B# [75] represents controlled indoor environments, CCPG [33] provides extensive clothing variations, and CCGR MINI [80] integrates diverse covariates.
We see two interesting finding in Fig. 4: (1) The best layers for cross cross-domain performance often differ from those for within-domall performance, occurring with probability of 83.3%. This means that, for domain-specific tasks, the contribution of layers is inconsistent, where the most effective layer may change. (2) Notably, SUSTech1 K achieves peak performance in shallow layers, most frequently at Layer (6),7)) while CCGR_MINI MINI performs better in deeper ones. We consider that deeper LVM layers capture stronger semantic features, while shallower ones preserve appearance details. To verify this, we carefully check its layer-wise performance on SUSTech K, where the optimal layer shifts from the 1st (Same-clothing condition, easy appearance task) to the 7th tthanging-clothing condition, harder semantic task). These results suggest that all LVM layers, from the shallowest to the deepest, may potentially benefit different domain-specifie recognition tasks.
Our answer: No, layer contributions vary by task, and should be treated independently.
3.5
Are Middle Layers Complementary?
To evaluate the complementarity of these layers, we introduce ensemble testing during inference and compare it with separate testing, as illustrated in Fig.
Ensemble Testing- Unlike separate testing in Sec. 3.2, which scores each layer independently, ensemble testing pools all layer distances and yields single unified result. For probe-galle pair (z. y) with per-layer scores (d,(x,, y) the final similarity of ensemble testing is their mean:
1 N Σ
D (z, ()
de (z, v).
Fig. 5 (c) shows that ensemble testing often offers an impressive performance gain in both within- and cross-domain settings, raising accuracy by +7.7% on DINOv2-S, +10.3% on CLIP-S, and +7.0% on SAM-S. Meanwhile, an abnormal observation arises in the CCGR MIGR,MINI, where the fusion results sometimes slightly drop compared to the separated ones. Sec. 3.4 shows that the challenging CCGR_MIN prefers deeper semantic-bard bassd layers due to its complex data variations. We further experimented with fusing only the deeper half of the layers, which alleviate this problem on CCGR but harm generalization on other datasets such as SUSTech Thus, our BiggerGait still adopts all layers for simplicity, consistency, and stronger generalization.

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Depth Group
Gait Group
Masked LVM Feature f (E(1.2...N))
Cos Similarity Matrix
1.0
Shured Gait Extractor B9p
0.2
1.0
8.7
04
0:
02
02
0.1
0.0
0.5
00
00
91
03
0.
8:
54
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0.3
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0.3
81
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40
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05
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Shured Gait Extractor E
8
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8:
6.5
0C
0.0
8.1
01
02
0.
0,4
0.5
06
04
1
Layer Indek
Depth Grouping
Gait Grouping
Ensemble Testing
(a) Layer Similarity & Grouping Regions
(b) The urchitecture of BiggerGait*
Figure 6: (a) Average pairwise cosine similarity across the layers of DINOv2-S [48], with depth and gait groups highlighted. (b) B;ggerGait*; A grouped based BiggerGait that clusters similar layers (J = 6), uses two shared gail encoders (P = 2) to replace original per-layer ones, and applies ensemble testing during inference.
Our answer: Yes, middle layers are highly complementary, and aggregating them often yields significant gains in both within- and cross-domain tasks.
3.6 Efficiency Discussion and Mitigation
Although multi-layer features are highly complementary, assigning a separate gait head to every LVM layer inflates computation and memory. Recent work [62] in LLMs shows that VIT residual connections encourage middle layers in a shared feature space. Fig. 6 (a) shows that the same pattern may also exist in LVM: for DINOv2-S [48], similarity remains strong between adjacent middle layers but decays quickly. To suit the need of GPU-limited scene, we equip BiggerGait with a grouping strategy inspired by this shared space hypothesis. To distinguish it from the standard version, denote this grouping-base baadeng variant as BiggerGait*. As shown in Fig. 6 (b), the strategy has two components: (1) depth grouping, which lowers the computational cost of gait heads; and (2) gait grouping, which reduces parameter overhead.
Depth Grouping. Adjacent, similar layer features {f,") are divided into J continuous depth groups, and the features within each group are concatenated:
f concat(f?" f - f!"))
E J1.
(8)
where f represents the concatenated feature for group j. The subseript 42,42,.., in refer to the specific layers. Depth grouping cuts the number of gait heads' forward passes from N to J.
Gait Grouping. The multiple gait encoders are also merged from (ET) into groups (E. I (1, 2,.,,.. }}}. Gait grouping reduces parameters of the gait encoders from Ns to Ps, where s size of one gait encoder. The gait results I'' of BiggerGait* is reformulated as:
R' [gg = F.g (sigmoid FFT E [
where J (1, J} and p = {1, 2,.., P}, denoting the number of depth groups and gait groups
The objective of the grouping paradigm is to minimize the number of groups J and P while preserving the fidelity of the output representations, i.e., ensuring that new gait result R' closely approximates its original counterpart R in Eq. (4). This objective can be expressed as a bi-level optimization problem:
min J.P
min (EF). (E))
| R211 <
(10)
In Sec. 4.3, we experimentally conclude that J 6 and P= represent an effective configuration. Our current grouping design prioritizes feasibility, reproducibilityy and efficiency. More sophisticated grouping strategies, like explicitly maximizing representational dissimilarity learnable grouping, also promising future directions worth specific exploration.
7

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Table 1: Performance comparison across methods and datasets. Yellow regions indicate within-domain
evaluations, others are cross-domain. The last column reports the overall average accuracy. CCGR indicates CCGR MINI dataset. BiggerGait** refers the grouping BaggerGait.
Training Danaset
Testing Dataset
Input
Method
1NM
Venue
CCPG
[33]
CCGR
[S0] SUSTechik [56]
CASIA-B [75]
CL
UP
DN
BG
R-1
R-5
CL
R-1
NM
BG
CL
Silh.
GaitSet [5]
P2
60.2
65.2
65.1
68.5
2.4
6.9
8.2
12.8
47.4
40.9
25.8
29.5
Sith.
GairPart [11]
CVPR
20
64.3
67
68.6
71.7
24
6.9
8.1
13.5
51.2
41.9
26,0
30.9
GaitGL [37]
762
67.0
Silh.
GaitBase [13]
CVPR'23
71.6
75.0
76.8
78.6
2,8
7.3
9.5
16.8
59.1
52.7
30.4
35.6
Sith.
Deep(iairV2 [12]
Antiv
78.6
8488
80.7
89.2
3.7
9.1
27.0
38.4
74.6
67.2
50.2
47.4
Sill.+Skel
BiFusion [50]
M3AA23
67.6
Silh.+Skeel
SkelesnGaaii [155]
AAA1224
79.1
83.9
81.7
89.9
Sith. a Pars.
XGaii [78]
MM
72.8
17.0
79.1
80.5
OCPO
MultiGaii++ [25]
2AA1'25
RGB+Siih.
GaidEdge [35]
ECCV
66.9
74.0
70.6
77.1
8.9
19.6
66.5
58.7
44.8
&GBb.
DeniiiiggGait [26]
SD [55]
CVPR
84.0
88.0
90.1
95.9
37.3
59.1
83.9
76.1
34,8
RGB
BigGnit [74]
DINOv2-S
CVPR224
77.4
RGB
BiggarGait
SAM-S [30]
Ours
86.8
89.5
91.8
95.9
91.5
17.8
64.6
75.5
80.1
76.2
31,8
59.7
RGB
BiggerGuit
CLIP-S
Ours
89.7
15.2
91.5
RGB
BiggerGair
DINOV2- (48)
Ours
89.8
92.1
93.7
97.5
15.5
26.9
70.9
79.6
93.0
90.8
55.6
67.0
RGB
BiggerGair
SAM-S
Ours
86.9
V2.3
95
17.2
74.4
79.7
74.9
28.9
59.0
RGB
BiggerGait
CLI-S
Outs
78.9
83.8
87.9
96.
13.9
24.2
63.1
81.5
92.3
87.1
12.9
61.0
RGB
BiggerGait
DINOv2-S
Ours
89.0
91.9
94.0
97.2
14.5
25.3
69.5
80.4
91.6
87.7
54.7
Silh.
GuilBasc
CVPR
20.8
31.3
38,3
70.2
21.1
37,8
28.7
48.0
66.3
57.7
33,9
40.5
DeepCuitV2
Ardiv
37.1
36.5
69.9
26.4
72.0
RGB
HigGiit
DINOv2-8
CVPR
22,9
42.2
25.0
80.5
88.0
95.9
71.9
85.6
90.1
87.9
58.4
73.7
RGB
RiggerGait
SAM-S
Ours
15.8
32.8
23,1
69.6
94.
72.7
89.2
87
85.3
48
70.8
CCGR
RGB
BiggerGait
CLIP-S
Ours
21.7
46.5
28.6
88.4
80.7
92.0
81.2
93.3
94.2
92.8
60.9
75.7
RGB
BiggerCait
DINOv2-S
Ours
23.2
44.5
29.5
86.7
85.7
94,1
82.1
93.8
97.2
96.4
66.7
78.1
RGB
Bagg
SAM-S
Ours
15.2
33.3
24.8
71.1
86.3
95.0
73.7
88.2
84.2
82.0
46.9
70.4
BiggerGait*
CLIP-S
Ours
42.3
29.3
92.4
92.9
RGB
GaigerGait*
DINOv2-$
Ours
22.6
44.9
31.5
85.8
87.8
95.8
83.7
93.8
96.9
96.3
65.6
78.5
Experiments
We conduct our experiments four widely used clothing-variation and multi-view gait datasets: CCPG [33], CASIA-B* [75], SUSTechIK [56], and CCGR CCGR_MINI [80]. CCPG acts as our cornerstone benchmark, since it offers the richest wardrobe diversity, covering an arra of coats, trousers, and bags in assorted color and styles, and faces and shoes are masked to emulate real-world cloth-changing scenarios. Although outfit changes viewpoints, complex ground conditions, different walking speeds, and composite covariate scene. The full CCGR set is too large for quick testing, we use the official mini version, which keeps the challenge but significantl drops the bulk. Therefore, the challenge CCPG and CCGR_ MINI sets supply the training data, whereas evaluation is performed on all four datasets. Every experiment strictly follows the official protocols released by the owner. Gait evaluation protocols is reported for nie settings, and rank- I accuracy serves as the principal metric.
few in CCGR, it excels in covariate variety: abundant
4.1 Implementation Details
All input frames are resized to 448 x 224 for DINOv2 [48], 224 x 224 for CLIP [52] and 512 x 256 for SAM [30]. The training runs for 30k iterations using SGD (momentum 0.9, weight-decay = 5 x 10-4) with an initial learning rate of 0.1, which is dropped by 10x at 15k and 25k steps. Each mini-batei adopts the tuple (p, k. l) (8, 4, 30). which is 8 identities, 4 sequences per identity, and 30 frames per sequence. Frame sampling follows the protocol of GaitBase [13], and the sole augmentation is random horizontal flip applied consistently to every frame within sequence. Using eight 24GB RTX 6000 GPUs, the DINOv2-S-bar S-ased BiggerGait requires approximately 8.8 hours to train on CCPG. Ensemble testing method presented in Sec. 3.5 is used during inference.
4.2
Main Results
To show our superiority, BiggerGait and its grouping-based variant are compared with diverse SoTA
methods, including the silhouette-based [5, 11, 37, 13, 12), multimodal- [50, 15, 35), and RGB-based [74, 35] gait methods. Due to the lack of multimodal data, cross-domain results for multimodal-basee methods are unavailable.
8

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Table 2: All models are evaluated on CCPG [33]. (a) & (b) Hyperparameter search for BiggerGait*. (c) Ablation
study on larger LVMs. (d) Efficiency comparison across gait methods. The yellow cells in (a) & (b) mark the final setting for BiggerGait*. FLOPs are computed for an input resolution of 148 x 224.
(a) Ablation Gait Group (J
12)
(b) Ablation of Depth Group (P 2, 2.333
AParams
DN
INM
F1.OPS
DN
BG
209:10 7M
86.8
89.5
91.8
95.9
95.16
55.8
91.8
96.3
SAM-S [30]
2
112.
85.8
89.5
91.8
96.3
SAM-S
6
79.24
56.9
39.4
92.3
95.8
101.7M
85.2
88.6
91.3
95.8
71.20
54.6
87.5
90.5
94,4
12
208 6M6
81.0
86.3
59.7
96.9
12
49.34
90.1
85.5
89.4
96.3
CLIPS [52]
110SM
89.4
CLIPS
6
39.46
96.1
100.6M
78.4
57.7
95.0
25.50
75.4
86.5
95.8
12
142.2M
89.8
92.1
93.7
91.5
12
45.7G
89.5
92.5
94,0
97.6
DINO S [18]
43.6M
89.5
92.5
94.0
97.6
DINOx2-S
6
29,86
80.0
91.9
94.0
97.2
33.4M
86.3
90.3
92.5
97.1
21.96
88.8
91.7
93.1
97.5
(d) Parameter and GFLOPs Comparison
(c) Ablation of Scaling LVM's Size
Mothod
Upstream
Downstroam
#Parums
FLOPss
1VM
Matiedd
#Params
F1.0P%
CT.
UP
DN
BG
Parsing-h based
Siih.-based
Dee
SCHP [33)
CaitBase
Gairtaase
34,2M
74.0M
35.20
45,41
SAM-L
DiggerGant
BiggerCiai
436.4M
337.7M
246.1G
258.
89.0
90.0
91.2
91.2
93.5
93.8
97.0
96.6
Skeleton-hasg baseed
BigGant (74]
HRNes-9/33 9/32 655
DINOv2
Tai--TR
Gaitilase
30.8M
20, MM.0
12.7G
31,20
CLIP-L
CagperGaite
Biggertiait
430.9M
111.305
97.00
97.4
BiggerCaii
CLJP-
Ganttase
208
3G
DINO
BiggerGair
BiggerCait
399.6M
129.9M
203.4G
190.7
92.7
92.8
94.6
93.9
96.2
95.7
97.8
96.2
BaggerGair
BaggerCait
DINOx2-8
SAM-S
12: x GaliBuse
x GaitBase
112.2M
142.2M
29,20
45,7G
Gagh
CLIPS
2% GailBlase
110.8M
33.4G
BiggerGait
DINOx2-8
2x Gaillase
43.6M
29.80
Cross-domain Evaluation. The final column of Tab. highlights BiggerGait's impressive results. Trained on CCPG, the SAM-S-, CLIP-S-, and DINOv2-S-based BiggerGait impressively boost rank-1 by +6.7%, +12.8% and +14.0% over prior work. With CCGR MINI as the train set, CLIP-S- and DINOv2-S-base BiggerGait push SoTA up by +2.0% and -4.4%, respectively. Such huge jumps confirm that BiggerGait learns a robust gait embedding that travels well across datasets.
Like BigGait [74], BiggerGait also shows data-bias limitation, i.e., the distribution of training data influences outcomes. Trained on CCGR MINI and tested on CCPG, Rase- methods exhibit less impressive results in some cases, e.g., performing well in the ups-changing (UP) and bag changing (BG), but poorly in the full-changing (CI.) and pants-changing (DN). The limited clothing diversity in CCGR_ __NN (9.4% of pairs, exclusively UP changes) probably accounts for this result.
Within-domain Evaluation. As highlighted in the yellow block of Tab. BiggerGait shines on the challenge CCPG dataset. SAM-S- and DINOv2-S-basee BiggerGait outperforms other methods BiggerGait shows significant improvements of +5.9%
on every metric. Notably, DINOv2-S-bass on CL, +3.1% on UP, +6.6% on DN, and +4.4% on BG. These results highlight BiggerGait's effectiveness in learning subtle clothing-irrelevall gait representations.
On the CCGR _INI, BiggerGait slightly struggles. As discussed on Sec. 3.4 and 3.5, CCGR_MINI with diverse covariant only prefers deeper semantic-based layers, and other datasets prefers shal- low appearance-based layers. For simplicity, consistency, and stronger generalization, BiggerGait adopts all layers, resulting slightly drop on MINI. We consider that this performance gap is acceptable: with the same DINOv2-S backbone, BiggerGait and BiggerGait* achieves 2.3% and 0.2% lower rank-1 accuracy than BigGait, respectively, comparing their larger overall performance gains of +4.4% and +4.8%.
Comparing Different LVMs. A clear domain pattern emerges: (1) the text-aligned CLIP [52] excels in cross-domain tests but lags dobbinn-oman;; (2) the eegmentation-superviee SAM [30] exhibits the reverse trend: (3) the self-supervised DINOv2 [48] balances both. This implies that LVM supervision strategies probably shape their domain adaptation properties thereby affecting gait recognition.
4.3 Ablation Study
All experiments in this section are performed on the CCPG benchmark [33]. We systematically evaluate: (1) an effective configuration for BiggerGait*; (2) the efficiency issue of BiggerGait.
Gait & Depth Group. Tab. 2(a) shows, using just two gait encoders delivers an accuracy similar to that of using twelve. Remarkably, even with all layer-wise features share one single gait encoder, the DINOv2-S- and SAM-S- BiggerGait still delivers SOTA results, outperforming the methods listed in Tab. 1. Tab, 2(b) reveals that six depth groups achieve an accuracy comparable to the

---

Table 3: Model size and computation cost comparison across methods. All methods trained CCPG, and lested on four datasets. This is supplement for Tab. This report follows the settings Table (d), with FLOPs computed at an input resolution of 448 x 224.
Testing Datacet
Input
Method
Upstream
Param
FLOPs
CCPG
CCGR
SUSTochIk
CASIA-B*
Avg
CL
UP
DN
BG
R-1
R-5
CL
R-1
NM
BG
CL
Sith.
GaitSet
29.4M
50.30
60.2
65
65.1
68.5
2.4
6.9
8.2
12.8
47.4
40.9
25.8
29.5
Silh.
GaitPart
DeepLabV34
31.6M
Sith.
GaitGL
DacpLabV3+
30.1M
88.7G
68.3
76.
67.0
76.7
3.3
8.4
25.4
33.6
63.1
58.5
46.3
41.2
Sith.
GoätBase
DeepLabV3+
34.2M
45.4G
71.6
75.0
76.8
78.6
2.8
7.3
9.5
16.8
59.1
52.7
30.4
35.6
Sith.
DeepGaitV2
DoepLabV3+
35.2M
93.2G
78.6
84.8
80.7
89.2
3.7
9.1
27.0
38.4
74.6
67.2
50.2
47.4
Silb. +Skel.
BiFusion
62.6
67.6
66.3
56.0
Silb.+SSoel.
SkcletonGail++
89.9
Silh. Pars.
XGait
72.8
77.0
79.1
80.5
Silb, Gilb.+Parr-ff Pars.++ +Flow
MultiGeat++
83.9
89.0
86.0
91.5
RGB Silh.
Gaitlige
74.0
70.6
77.1
19.6
RGBB-Sith.
DenoisingGait
SD & DevpLabV3+
84.0
88.0
90.1
95.9
37.3
59.1
83.9
76.1
34,8
RGB
BigGuit
DINOv2.8
30.8M
12.7G
82.6
85.9
87.1
93.1
7,4
16.3
43.7
56.4
77
71.5
33.6
RGB
BiggerGiat
SAM
209.7M
93.10
86.8
89.5
91.8
93.9
9.5
17.8
64.6
75.3
80.1
76
31.8
39.7
RGB
BiggorGiait
CLIP-S
208. 6M
49.3G
81.0
86.2
89.7
96.9
15.2
26.1
67.0
84.2
91.5
87.8
47.3
65.8
RGB
BiggerGait
DINOv2.8
45.7G
89.8
93.7
15.5
26.9
70.9
79.6
RGB
BiggerCaai
SAMS
112.2M
79.20
86.9
89.4
92.3
95.8
9.1
17.2
60.8
74.4
79.7
74.9
28.9
59.0
RGB
BiggerGuut
CLIP-
110.8M
33.4G
78.9
13.9
RGB
BiggerGuit
DINOV2-S
43.6M
29.8G
89.0
91.9
94.0
97.2
14.5
25.3
69.5
80.4
91.6
87.7
54.7
66.5
ungrouped setup, except the CLIP-S-based one. Therefore, we set P = 2 and J 6 for BiggerGait*, cutting roughly 108.8M and 23.8G FLOPs for DINOv2-S-base one. In this setting, DINOv2-S-based BiggerGait* achieves a 44% speedup (29.89 ms image), approaching BigGait (21.64 ms).
Scaling the LVM Size Tab. 2(c) shows BiggerGait** offers marginal benefits, saving FL.PPs limited while hurting performance. We consider that the upstream LVM dominates computation ( 87.5% for DINOv2-L), basicly making BiggerGait's overhead negligible compared to the expensive LVM's cost. Therefore, for larger LVM cases, the standard BiggerGait is recommended.
Efficiency Comparison. Tab. 2(d) shows that BiggerGait*, especially for DINOv2-S S-aseddbdddd-- one, has similar FLOPs as the popular gait methods. This result indicates that the BiggerGait's superiority stems not from increased parameters or FLOPs, but from diverse layer-wise LVM features.
Parameter & FLOPS Comparison. Tab. further confirms that despite its significant performance gains, the DINOv2-S-based BiggerGiait* maintains FL.OPs and size comparable to popular gait methods. We include the cost DeepLabV3+ (26.8M parameters, 43.7 GFLOPs) for mask extraction in silhouette- based methods. Statistics for multimodal approaches are unavailable due to reproduction difficulty, yet they clearly incur higher computation costs from additional preprocessing models.
5 Conclusions
This work shifts the attention of LVM-based gait research from well-designed gait priors to the fun- damental properties of LVMs itself. Our comprehensive study shows that layer-wise representations in LVM contain rich, distinct gait semantics. Without relying elaborate gait priors, integrating these diverse layer-wise features delivers substantial gains. Building on these insights, we propose BiggerGait, simple yet universal layer-wise LVM framework for gait recognition. We systematically analyze the inherent efficiency challenges of layer- wise methods and introduce an optional mitigation strategy. Comprehensive evaluations CCPG, CASIA-B* SUSTech1K and CCGR MINI reveal BiggerGait's ss advance most within- and cross-domain tasks. The work may also provide inspiration for employing the layer-wise knowledge produced by LVMs for other vision tasks.
Limitation. While BiggerGait sets impressive results in gait tasks, its feature extraction mainly at the image level. The temporal feature remains underexplored in this work and deserves further study, Meanwhile, predicting the most effective layers for new, unseen datasets remains an open challenge, as the optimal layer often shifts with training data and task types.
Acknowledgement. This work was supported partially by National Natural Science Foundation of China (Grant 62476120, 62325307, 62422312, and 62506236) and partially by National Key R&D Program of China (Grant 7020YFA0908700 and 2023YFB4704900).
10

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Reviews:
[{"review_id": "l0oURjw9nV", "reviewer": "Reviewer_r6GY", "summary": "This paper presents BiggerGait, a gait recognition framework that exploits intermediate representations from LVMs. The core observation lies in that middle layers yield stronger and richer representations than final layers and propose fusing them to enhance recognition accuracy without relying on handcrafted gait priors. The method approaches gait recognition from a representation learning perspective instead of designing gait-specific priors, aiming to unlock the discriminative capacity of LVMs. Experiments on four datasets show state-of-the-art performance in both within- and cross-domain settings.", "questions": "1. Clarify technical novelty beyond prior representation works. The authors should clarify what new insight or mechanism your work contributes beyond simply applying known findings to gait recognition.\n2. Strengthen Justification for Grouping Strategy Design. Performing an ablation comparing similarity-based grouping vs. diversity-based grouping (e.g., maximizing representational dissimilarity), or explore learnable grouping strategies.\n3. Inference time. Considering including actual inference time.\n4. Clarify which parts of the architecture are original versus reused from prior work, and provide a step-by-step comparison with related baselines to make the contribution more explicit.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths \n1. This paper takes a perspective by leveraging intermediate representations from large vision models instead of relying on handcrafted gait priors.\n2. Comprehensive analysis of layer-wise representations across three LVMs and four datasets. The visualizations are particularly effective, such as Figure 3-6.\n3. Well written and easy to understand.\n\nWeaknesses\n1. Technical novelty. The core idea of fully exploiting intermediate layers representation (Line 40, Sec 3.3) in gait recognition is somehow limited. The insight is well established in existing visual representation learning literature. For example, DINOv2 shows that intermediate layers outperform final layers on a range of vision tasks. And also, ref[1][2] suggest that intermediate layers can encode even richer and more transferable representations.\n  [1] Evci, Utku, Vincent Dumoulin, Hugo Larochelle, and Michael C. Mozer. Head2toe: Utilizing intermediate representations for better transfer learning. In International Conference on Machine Learning, 2022: 6009-6033.\n  [2] Raghu, Maithra, Thomas Unterthiner, Simon Kornblith, Chiyuan Zhang, and Alexey Dosovitskiy. Do vision transformers see like convolutional neural networks?. Advances in neural information processing systems, 2021: 12116-12128.\n2. Architecture design. The proposed framework largely builds upon and modestly extends existing works BigGait.\n3. Grouping strategy. The introduced “grouping strategy” is heuristic: it relies on pairwise cosine similarity between layers to merge the most similar layers. However, this contradicts the authors’ own claim in Sec 3.5 that middle layers are complementary. Obviously, this strategy is suboptimal. No ablation is provided to justify whether similarity-based grouping is optimal, leaving the design choice weakly supported.\n4. Efficiency Analysis. Lack of inference time comparisons in Sec 3.6. The model parameters and flops are insufficient to prove efficiency.", "was_revised": true, "final_justification": "Thanks for the detailed response, I would raise the rating to 4.", "rebuttal": "### **\\#W1/Q1: Clarify New Insights and Technical Novelty.**\n\nThanks for your comments. We would like to clarify our contribution in three aspects:\n\n- **(a)** Our BiggerGait works on gait recognition, a fine-grained video-based vision task, but most related works [1,2] are for coarse-grained and image-based tasks. And we argue that adapting a promising insight to the highly fine-grained and challenging task of gait recognition substantially enriches both the insight and the task itself. In particular, as shown in Lines 143–155, we demonstrate that for the fine-grained problem of gait recognition, intermediate-layer features consistently outperform final-layer features. As noted in Lines 36–37, while this trend aligns with related works [6, 14, 43, 44, 46]—including the references [1,2] you provided, which primarily investigate coarse-grained classification tasks—our study goes a step further by validating and advancing this insight in a significantly more demanding setting. This not only reinforces the generality of the idea but also establishes its previously unexplored practical value for fine-grained gait recognition.\n\n- **(b)** Beyond broadening this general insight, we further reveal two novel findings unique to gait recognition. In Lines 157-170, we observe that the contributions of intermediate layers vary significantly across different gait recognition scenarios (i.e., datasets). Specifically, as shown in Fig. 4 and Lines 164-165, on the SUSTech1K dataset, shallow layers often outperform deeper ones. For example, the first layer of DINOv2-L achieves 68.6% accuracy, significantly surpassing the 44.4% of the final layer. Interestingly, this trend reverses on the CCGR\\_MINI dataset, where the final layer of DINOv2-L achieves 16.2%, compared to just 7.7% for the first. To our knowledge, this scenario-dependent reversal has rarely been reported in the visual representation learning literature, highlighting a new perspective on the role of intermediate layers in domain-specific recognition tasks.\n\n- **(c)** Based on this second finding, in Lines 171-181, we further reveal that these intermediate layers are highly complementary across most recognition scenarios. Aggregating them often yields significant gains in both within- and cross-domain scenarios. As shown in Fig. 5, the plain ensemble testing strategy significantly boosts the average Rank-1 accuracy of four testing datasets: DINOv2-S improves by 7.7%, CLIP-S by 10.3%, and SAM-S by 7.0%. On SUSTech1K specifically, these gains are even more substantial (e.g., +13.2%, +19.9%, and +14.1%, respectively). This demonstrates not only the practical benefits of exploiting intermediate layers in gait recognition but also a new and effective methodology for enhancing generalization in fine-grained recognition tasks.\n\nTaken together, these contributions indicate that our work goes beyond reiterating the general usefulness of intermediate layers by systematically validating and extending this insight in the fine-grained and highly challenging domain of gait recognition. By revealing scenario-dependent reversals, highlighting their cross-domain complementarity, and introducing a practical layer grouping strategy, we provide new evidence and perspectives that enrich the understanding and application of large vision models for gait recognition.\n\n[1] Evci, Utku, et al. \"Head2toe: Utilizing intermediate representations for better transfer learning.\" International Conference on Machine Learning. PMLR, 2022.\n\n[2] Raghu, Maithra, et al. \"Do vision transformers see like convolutional neural networks?.\" Advances in neural information processing systems 34 (2021): 12116-12128.\n\n---\n\n### **\\#W2/Q4: Architecture Difference between BigGait v.s. BiggerGait.**\n\nWe believe that Fig. 1 may have led to some misunderstandings.\n\nBigGait rely on heavy gait priors but BiggerGait does not. In fact, the heavy gait priors introduced by BigGait (Fig. 1 (b)) rely on many carefully designed modules, including the mask branch, appearance branch, and denoising branch (with smoothing and diversity constraints), as well as specific strategies to combine them [3].\n\nIn contrast, BiggerGait reuses only the mask branch from BigGait [3], where employing the human foreground is a common practice in gait recognition [3, 4]. We therefore regard this as light gait priors, as illustrated in Fig. 1 (c). As shown in Fig. 2, the central innovation of BiggerGait lies in treating intermediate layers independently to fully unlock the power of large vision models. Furthermore, BiggerGait* (Fig. 6 (b)) introduces a straightforward layer grouping strategy that strikes a balance between performance gains and computational efficiency.\n\nMoreover, we deliberately term our method BiggerGait for two reasons:\n- **(a)** to highlight that it achieves greater (bigger) performance improvements while relying on fewer priors.\n- **(b)** to acknowledge and pay respect to BigGait, the first LVM-based gait recognition method.\n\nMany thanks for your comments, we will improve Fig.1 and Sec. 3.1 for better clarification.\n\n[3] Ye, Dingqiang, et al. \"Biggait: Learning gait representation you want by large vision models.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2024.\n\n[4] Jin, Dongyang, et al. \"On Denoising Walking Videos for Gait Recognition.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.\n\n---\n\n### **\\#W3/Q2: Strengthen Justification for Grouping Strategy Design.**\n\nAs discussed in Lines 182–189, the grouping strategy of BiggerGait* is designed as a straightforward yet effective approach to achieve a balanced trade-off between accuracy and efficiency. Importantly, while middle layers are indeed complementary (Sec. 3.5), our cosine-similarity-based grouping does not aim to collapse their complementarity. Instead, it clusters the most correlated representations into groups, allowing the network to still capture diversity across groups while avoiding redundant computation within each group.\n\nAs shown in Tab. 2 (a) and (b), BiggerGait* (J=6, P=2) achieves accuracy very close to the full version (J=12, P=12) — with only marginal differences (-0.8% CL, -0.2% UP, +0.3% DN, -0.3% BG on CCPG for DINOv2-S-based cases) — while reducing the parameter size by 98.6M and computation by 15.9 GFLOPs. This demonstrates that the proposed grouping strategy is not only simple and reproducible but also practically effective for scaling large vision models in gait recognition.\n\nWe agree with the reviewer that there may exist more sophisticated grouping strategies — for example, explicitly maximizing representational dissimilarity or employing learnable grouping as you mentioned. These are promising future directions worth specific exploration. Nevertheless, our current design prioritizes feasibility, reproducibility, and efficiency, which aligns with the main goal of maintaining strong accuracy while significantly reducing computational cost.\n\nThanks for your suggestion. We will revise the manuscript to explicitly discuss these considerations in the grouping strategy section.\n\n---\n\n### **\\#W4/Q3: Inference Time.**\n\nThe inference time is measured on a single 24GB RTX 6000 GPU using float32 precision, with input resolution of 448×224. We conducted 50 inference runs per model—using the first 30 as warm-up and the remaining 20 to compute the average inference time. The results are as follows:\n\n- Without grouping, DINOv2-S-based BiggerGait requires 53.39 ms / image—significantly slower than BigGait[3] (21.64 ms). However, with the grouping strategy, BiggerGait* achieves a 44% speedup (29.89 ms), dramatically reducing this gap.\n- Silhouette-based methods (GaitBase[5] / DeepGaitV2[6]) spend 13.77 ms on segmentation (DeepLabV3[7]) preprocessing alone, with total times of 21.35 / 22.09 ms, respectively.\n\nThese results demonstrate that our grouping strategy is pivotal in bridging the efficiency gap with SoTA approaches, and we will include these data in the manuscript.\n\n[5] Fan, Chao, et al. \"Opengait: Revisiting gait recognition towards better practicality.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2023.\n\n[6] Fan, Chao, et al., \"OpenGait: A Comprehensive Benchmark Study for Gait Recognition Towards Better Practicality,\" in IEEE Transactions on Pattern Analysis and Machine Intelligence, doi: 10.1109/TPAMI.2025.3576283.\n\n[7] Chen, Liang-Chieh, et al. \"Rethinking atrous convolution for semantic image segmentation.\" arXiv preprint arXiv:1706.05587 (2017)."}, {"review_id": "Er0pTbvxGU", "reviewer": "Reviewer_jAUv", "summary": "This paper presents a comprehensive investigation into the layer-wise representations of large Visual models (LVMs) for gait recognition. Building on these findings, the authors propose BiggerGait, a simple yet effective and universal layer-wise baseline for gait recognition. The paper makes two major contributions: (1) it offers the first in-depth, systematic analysis of LVM layer representations in the context of gait recognition, revealing how feature depth influences task performance and how combining intermediate features leads to accuracy improvements; and (2) it introduces BiggerGait, a practical and high-performing framework that achieves state-of-the-art results across multiple RGB-based gait recognition benchmarks, excelling in both intra-domain and cross-domain scenarios while offering a balanced performance-efficiency trade-off through a proposed grouping strategy.", "questions": "None", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths\n-The paper presents meaningful insights regarding intermediate-layer features in large Vision Models (LVMs), particularly highlighting that these layers yield more discriminative and complementary features for gait recognition than final layers. These findings are in line with trends in other domains (e.g., large language models), which gives the study credibility and relevance.\n-The introduction of BiggerGait, a simple yet effective layer-wise baseline, contributes a practical advancement to RGB-based gait recognition systems. The systematic analysis of layer-wise feature contributions appears to be a first in the domain, adding to the paper's value.\n-The paper claims extensive experiments across multiple benchmarks and evaluation settings (intra- and cross-domain), which strengthens the reliability and generalizability of its results.\nWeaknesses\nThere is insufficient discussion of prior work on layer-wise analysis in other domains. A section reviewing how such analysis has been applied in areas like NLP or vision (e.g., CNNs, Transformers) would make the paper’s positioning clearer and more compelling.\nThe use of “video” versus “image” to describe inputs is inconsistent, particularly in Figure 2 versus the text. This inconsistency could confuse readers about the nature of the data used and should be resolved for clarity.\nThe paper’s structure could be improved. Presenting key experimental results (e.g., Section 3.3) before formally introducing the experimental setup (Section 4) breaks the logical flow and could hinder comprehension for readers unfamiliar with the dataset or methodology.", "was_revised": false, "final_justification": "", "rebuttal": "### **\\#W1: Insufficient discussion of prior work on layer-wise analysis in other domains.**\n\nAppreciate your warm and helpful reminders.  \nThe following discussion will be updated into the \"Layer-wise Analysis in Large Models\" of Sec. 2.\n***\nRecent works[1-7] have increasingly focused on layer-wise representation from large models, as intermediate features often show surprising robustness, challenging the traditional final layer representations.\n\nIn NLP, researchers[4] have found that lower layers tend to encode more syntactic information, while higher layers specialize in semantic features. Others suggest that residual connections encourage layers to share a common feature space while still specializing in distinct sub-tasks[3], or that attention sink effects may weaken final-layer performance[5].\n\nSimilar trends emerge in vision domains: Head2Toe[6] selects the most useful representations from intermediate layers in transfer learning, outperforming the final layer. A fresh work[7] on LVMs again suggests that the final layer may not contain the most robust visual features, and addresses this by distilling optimal intermediate features back into the final layer to boost performance.\n\nUnlike prior works[6,7] that focus primarily on coarse-grained vision tasks (classification, detection, and tracking), our study goes a step further by validating and advancing this insight in a significantly more demanding setting, i.e., a highly fine-grained recognition task. Beyond broadening this general insight, we further reveal more unexplored findings unique to gait recognition, as shown in Sec. 3.4 \\& 3.5.\n\n[1] Skean, Oscar, et al. \"Layer by layer: Uncovering hidden representations in language models.\" arXiv preprint arXiv:2502.02013 (2025).  \n[2] Fan, Siqi, et al. \"Not all layers of llms are necessary during inference.\" arXiv preprint arXiv:2403.02181 (2024).  \n[3] Sun, Qi, et al. \"Transformer layers as painters.\" Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 39. No. 24. 2025.  \n[4] Liu, Nelson F., et al. \"Linguistic knowledge and transferability of contextual representations.\" arXiv preprint arXiv:1903.08855 (2019).  \n[5] Gu, Xiangming, et al. \"When attention sink emerges in language models: An empirical view.\" arXiv preprint arXiv:2410.10781 (2024).  \n[6] Evci, Utku, et al. \"Head2toe: Utilizing intermediate representations for better transfer learning.\" International Conference on Machine Learning. PMLR, 2022.  \n[7] Bolya, Daniel, et al. \"Perception encoder: The best visual embeddings are not at the output of the network.\" arXiv preprint arXiv:2504.13181 (2025).\n\n---\n\n### **\\#W2: The use of \"video\" versus \"image\" to describe inputs is inconsistent.**\n\nAppreciate you pointing out this confusion.  \nRGB-based gait recognition uses video data as input.  \nAll ambiguous uses of \"image\" will be revised to \"video,\" and Fig. 2 will be updated to better reflect this.\n\n---\n\n### **\\#W3: The paper’s structure could be improved.**\n\nAppreciate this valuable suggestion.  \nTo enhance readability and logical coherence, dataset descriptions and implementation details from Sec. 4 will be previewed in the experiment setting in Sec. 3.2."}, {"review_id": "RCgmirLxrz", "reviewer": "Reviewer_zEBF", "summary": "This paper proposes a gait recognition method, BiggerGait, an efficient transformer-based framework using multi-layer features from large vision models (LVMs). It aggregates both intermediate and high-level visual representations through a lightweight Groupwise Head for robust performance across diverse datasets. Experiments on GREW, SUSTech1K, and CCGR show state-of-the-art or competitive results with high scalability and generalization.", "questions": "1) The proposed method is worse than BigGait in the evaluation using CCGR-MINI. It is still not clear why. \n2) Is there any way to predict what kind of LVMs and which layers are essential for a new, unknown dataset?\n3) How robust is the method to video quality degradations (e.g., blur, noise)?", "limitations": "yes.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "The paper provides a comprehensive analysis of layer-wise LVM representations for gait recognition, revealing their complementary strengths. This method achieved state-of-the-art results in most datasets. The technique is clearly explained, and the evaluation experiments are well-designed. \n\nOn the other hand, the critical layer differs in each dataset, and therefore, it may be challenging to predict which layers are essential in a new task. This means generalization is still insufficient. Also, the proposed method focuses on frame-level image features, disregarding temporal dynamics, which are crucial in gait recognition.", "was_revised": true, "final_justification": "As the authors' response addressed most of my concerns, I kept my original score.", "rebuttal": "### **\\#Q1: The proposed method is worse than BigGait in the evaluation using CCGR\\_MINI.**\n\nAppreciate this meaningful question.\n\n- **(a) From BiggerGait aspect:** Fig. 4 and Lines 164-165 show that different senses favor different layer-wise representations. CCGR\\_MINI tends to prefer deeper layer representations more strongly than other domain datasets, as it poses a more challenging data distribution, with extreme viewpoint changes (e.g., overhead probe vs. frontal gallery) and 53 covariates, including complex combinations of five primary factors (Carrying, Clothing, Road, Speed, and Walking Style). For uniformity, BiggerGait employs a straightforward design that consistently uses all layers for different datasets. When ensemble testing is applied only to the deeper half of the layers on BiggerGait*, increasing rank-1 accuracy from 87.8% to 88.1%, surpassing BigGait’s 88.0%. However, such an operation caused the cross-domain rank-1 performance on SUSTech1K to drop from 93.8% to 92.8%, meanwhile disrupting BiggerGait's uniformity. Therefore, the final version of BiggerGait opted to use all layers, ensuring simplicity, consistency, and better generalization. As shown in Lines 249-251, we also believe that its benefits outweigh the drawbacks in most practical situations (+4.8% cross-domain gains versus -0.2% within-domain loss).\n\n- **(b) From BigGait aspect:** We consider that BigGait incorporates abundant gait-specific designs, such as meticulously crafted gait prior loss functions (including diversity loss and smoothness loss). While these undoubtedly enhance its within-domain performance, its generalization capability still falls short of our BiggerGait approach.\n\nMany thanks for your careful observation and valuable questions.  \nThe above viewpoint will be revised into the \"Within-domain Evaluation\" in Sec. 4.\n\n---\n\n### **\\#Q2: Is there any way to predict what kind of LVMs and which layers are essential for a new, unknown dataset?**\n\nMany thanks for this interesting question.\n\n- **(a) For best LVMs:**  As shown in Tab. 1 and Lines 252-255, text-aligned CLIP excels in cross-domain tasks, segmentation-supervised SAM performs well within-domain tasks, while self-supervised DINOv2 delivers strong performance on both. Based on this, we recommend using text-aligned or self-supervised LVMs for new, unknown datasets.\n\n- **(b) For best layers:**  As you mentioned, predicting which layers are essential in a new, unknown task is challenging. This remains an open question beyond our current scope, calling for future research. Actually, our further analysis shows that even on the same known test dataset, the most effective layer can shift when the training data changes. Specifically, the 6th layer of DINOv2-S-based BiggerGait achieves peak performance on SUSTech1K when trained on CCPG, but the optimal layer shifts to the 10th when trained on CCGR\\_MINI. Fortunately, even though the optimal layer is dynamic, we also observe that easier recognition tasks consistently utilize shallower layers than more challenging ones. Specifically, on the SUSTech1K test set, the normal setting (no clothing change, easier task) achieves peak performance on the 1st and 7th layers when trained on CCPG and CCGR\\_MINI, respectively, while the clothing setting (with clothing changes, harder task) reaches its best performance on deeper layers, i.e., 7th for CCPG and 11th for CCGR\\_MINI.\n\nAppreciate your insightful comments again.  \nWe will append the above discussion to the Limitation section.\n\n---\n\n### **\\#Q3: How robust is the method to video quality degradations (e.g., blur, noise)?**\n\nPerformance of DINOv2-S-based BiggerGait* on CCPG under different perturbations:\n\n- **(a) Gaussian blur** (kernel=5, sigma=1.0) causes notable performance drops:  \n**CL**: 89.0% → 84.3% (**-4.7%**)  \n**UP**: 91.9% → 88.8% (**-3.1%**)  \n**DN**: 94.0% → 91.4% (**-2.6%**)  \n**BG**: 97.2% → 97.0% (**-0.2%**)  \n\n- **(b) Additive noise** (std=0.1) leads to relatively milder degradation:  \n**CL**: 89.0% → 84.7% (**-4.3%**)  \n**UP**: 91.9% → 89.4% (**-2.5%**)  \n**DN**: 94.0% → 91.4% (**-2.6%**)  \n**BG**: 97.2% → 96.9% (**-0.3%**)  \n\nAs mentioned in line 222, BiggerGait's training data augmentation only includes flipping, so this performance drop is reasonable. Remarkably, even when degraded by perturbations, BiggerGait still outperforms other state-of-the-art methods using clear and undisturbed inputs (e.g., BigGait's 82.6% CL, 85.9% UP, 87.1% DN, and 93.1% BG on Tab. 1), further demonstrating BiggerGait's robustness and superior capability. These results align with the core idea of our paper: by fully leveraging the capabilities of large vision models, excellent performance can still be achieved even relying on fewer gait priors."}, {"review_id": "lUXJNsEEG1", "reviewer": "Reviewer_AzAN", "summary": "The paper introduces BiggerGait, a simple yet powerful framework for gait recognition using large vision models (LVMs). It reveals that intermediate layers of LVMs contain rich and complementary gait-relevant features that outperform final layers, enabling high accuracy without heavy reliance on traditional gait priors. The authors propose a grouping strategy to mitigate computational overhead by sharing encoders across similar layers. The authors have also explore the performance of different layers of LVMs for gait recognition task. Extensive experiments across multiple benchmarks demonstrate BiggerGait’s superior within- and cross-domain performance, establishing it as a new baseline in RGB-based gait recognition.", "questions": "1.Can the authors provide more in-depth feature-level analysis to explain why intermediate layers of LVMs consistently outperform final layers in gait recognition? Are there any visualizations or theoretical explanations that support this phenomenon?\n\n\n2. How robust are the layer-wise performance observations to variations in the architecture of the Gait Extractor? Additionally, how does the quality of the human silhouette (used for masking) affect the consistency and validity of the results?\n\n\n3. The experiments suggest that earlier layers of larger models (e.g., DINOv2-L) can yield stronger performance, can the authors provide deeper analysis or hypotheses as to why larger models show this behavior? \n\n\n4. In cross-domain analysis, why do deeper layers perform better on more complex datasets? Can the authors offer a clearer justification for how dataset complexity interacts with layer depth in LVMs for gait-specific semantics?\n\n\n5. Can the authors elaborate on whether the proposed layer grouping strategy in BiggerGait can generalize to other vision tasks such as action recognition or pose estimation? Have any preliminary tests been conducted to verify the transferability of the observed layer-wise contributions?\n\n\n6. Table 1 lacks a report of model sizes and computation costs, can the authors provide a detailed comparison of parameters and FLOPs across methods, and discuss the tradeoff between accuracy and efficiency more thoroughly?", "limitations": "The authors mentioned limitation of the proposed method on Line 237.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. It is an very interesting study to explore the contribution of different LVM layers for the gait recognition tasks, which could deliver valueable insights to the community.\n\n2. The proposed BiggerGait model achieves good performance across various evaluation settings of the gait recognition task, which can benefit the community.\n\n3. The proposed BiggerGait model is verified to be effective across different LVMs.\n\nWeaknesses:\n\n1. In Section 3.3, the authors provide experimental details to illustrate that middle layers of LVM outperform the final layer. It is an interesting observation, however, the authors are encouraged to introduce more regarding why this phenomenon happens. More insights analysis of feature level should be provided by the authors.\n\n2. I have one concern regarding the performance of different layers of LVMS, will they be effected by different structure of the Gait Extractor? (e.g., GaitExtractor with different layers). How sensible the observation is with respects to the human silhouette quality?\n\n3. Another interesting analysis that could be added would be the layer wise analysis towards LVMs with different sizes, e.g., Dinov2-S / Dinov2-L, where we could observe that for Dinov2 and SAM, earlier layers of larger models could provide higher gait recognition performances. The authors are encouraged to add more in-depth analysis regarding why it happens.\n\n4. In Section 3.4, the authors provided cross-domain evaluations, where the contribution of different layers show inconsistency. For the second finding, the authors claim that \"This phenomenon may stem from dataset complexity.\" More analysis should also be provided here. Why complex dataset benefits from deeper representations especially in terms of gait recognition? The current analysis described in the paper is very general.\n\n5. The proposed layer grouping strategy seems can be also implemented to other computer vision tasks beyond gait recognition, e.g., action recognition, how does it work on other computer vision task? Will the observations of the contribution of different layers keep consistent?\n\n6. In Table 1, the number of parameters of different approaches should also be reported. The authors are encouraged to conduct discussion regarding the tradeoff between accuracy and efficiency of different approaches.", "was_revised": true, "final_justification": "My concerns are well solved by the authors during the rebuttal, and I learn towards accepting this paper.", "rebuttal": "### **\\#W1/Q1: Can the authors provide more in-depth feature-level analysis to explain why intermediate layers of LVMs consistently outperform final layers in gait recognition?**\n\nAppreciate this valuable question. \n\nThis interesting phenomenon has been similarly observed across various vision tasks[1], such as classification, detection, and tracking, yet its underlying mechanism remains unclear. A potential theoretical explanation provided by LLM researchers is the attention sink effect [2–4], where deeper layers exhibit a disproportionate focus on a single token.  \nIn contrast, intermediate layers avoid these extreme attention sinks and engage in more balanced, meaningful information processing compared to shallow or deep layers [5]. \n\nSincere thanks for your deep curiosity and thoughtful thinking on this counterintuitive phenomenon. To help first-time readers better understand this anomaly, the above viewpoint will be updated in the revised manuscript. \n\n[1] Bolya, Daniel, et al. \"Perception encoder: The best visual embeddings are not at the output of the network.\" arXiv preprint arXiv:2504.13181 (2025).  \n[2] Xiao, Guangxuan, et al. \"Efficient streaming language models with attention sinks.\" arXiv preprint arXiv:2309.17453 (2023).  \n[3] Brunner, Gino, et al. \"On identifiability in transformers.\" arXiv preprint arXiv:1908.04211 (2019).  \n[4] Gu, Xiangming, et al. \"When attention sink emerges in language models: An empirical view.\" arXiv preprint arXiv:2410.10781 (2024).  \n[5] Barbero, Federico, et al. \"Why do LLMs attend to the first token?\" arXiv preprint arXiv:2504.02732 (2025).\n\n---\n\n### **\\#W2/Q2(1): How robust are the layer-wise performance observations to variations in the architecture of the Gait Extractor? (e.g., Gait Extractor with different layers)**\n\nThanks for asking!\n  \nOur Gait Extractor is a widely used GaitBase [6], adopted in many related gait works[7][8]. In our BiggerGait, we use the simplest version of GaitBase, which is based on a ResNet9 backbone. According to your suggestion, we double its depth to ResNet18 and then find that the CL performance of DINOv2-S-based BiggerGait* on CCPG drops from 89.0% to 83.7%. Although it still surpasses other SoTA methods (82.6% on BigGait), we observe that a deeper Gait Extractor tends to overfit the training data, ultimately degrading performance. This occurs because Gait Extractor's inputs are already highly refined representations from powerful LVMs, making a lightweight Gait Extractor architecture sufficient. Therefore, we consider that BiggerGait holds substantial robustness to the Gait Extractor architecture, and now the lightweight version presents a good choice.\n\n[6] Fan, Chao, et al. \"Opengait: Revisiting gait recognition towards better practicality.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2023.  \n[7] Ye, Dingqiang, et al. \"Biggait: Learning gait representation you want by large vision models.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2024.  \n[8] Jin, Dongyang, et al. \"On Denoising Walking Videos for Gait Recognition.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.\n\n---\n\n### **\\#W2/Q2(2): How does the quality of the human silhouette (used for masking) affect the consistency and validity of the results?**\n\nPerformance of DINOv2-S-based BiggerGait* on CCPG under degraded human silhouette quality:\n\n- **(a) Gaussian blur (kernel=5, sigma=1.0)** introduces moderate degradation:  \n**CL**: 89.0% → 87.4% (**-1.6%**)  \n**UP**: 91.9% → 90.8% (**-1.1%**)  \n**DN**: 94.0% → 93.5% (**-0.5%**)  \n**BG**: 97.2% → 97.2% (**-0.0%**)  \n\n- **(b) Additive noise (std=0.5)** results in more noticeable drops:  \n**CL**: 89.0% → 83.5% (**-5.5%**)  \n**UP**: 91.9% → 87.0% (**-4.9%**)  \n**DN**: 94.0% → 91.3% (**-2.7%**)  \n**BG**: 97.2% → 95.9% (**-1.3%**)  \n\n- **(c) Dilation (kernel=3)** shows a similar degradation pattern as noise:  \n**CL**: 89.0% → 82.9% (**-6.1%**)  \n**UP**: 91.9% → 86.6% (**-5.3%**)  \n**DN**: 94.0% → 91.4% (**-2.6%**)  \n**BG**: 97.2% → 95.9% (**-1.3%**)  \n\nWe acknowledge that low-quality human silhouettes can impact BiggerGait’s performance to some extent. However, even under such bad conditions, BiggerGait still outperforms other SoTA methods that rely on clean and undisturbed human silhouettes (e.g., BigGait achieves 82.6% CL, 85.9% UP, 87.1% DN, and 93.1% BG as shown in Table 1), further highlighting BiggerGait’s robustness. Notably, this result reinforces our core idea: BiggerGait’s performance gains mainly come from its unique inter-layer design, rather than from the high-quality gait prior (i.e., human silhouette).\n\n---\n\n### **\\#W3/Q3: The experiments suggest that earlier layers of larger models (e.g., DINOv2-L) can yield stronger performance.**\n\nMany thanks for your question!  \nIt seems there might be a small misunderstanding, as we haven't observed such a situation.  \n\nOur results show similar performance patterns across different LVM sizes:\n- **(a)** In Fig. 3, intermediate layers always achieve optimal performance even in larger LVMs, with DINOv2-L and SAM-L exhibiting slightly deeper optimal layers.\n- **(b)** Fig. 4 demonstrates that performance distributions remain similar between large LVMs and their smaller counterparts. On the contrary, we observe that variations across test datasets are more significant than those provided by LVM size. Specifically, on SUSTech1K (Column B), shallower layers tend to outperform deeper ones, while on CCGR\\_MINI (Column D), deeper layers perform better. \n\nWe hope the above discussion helps clarify your doubt.\n\n---\n\n### **\\#W4/Q4: Why do deeper layers perform better on more complex datasets?**\n\nThanks for this insightful question.  \n\nWe consider that deeper layers in LVM exhibit stronger semantic capabilities with progressive feature transformation, while shallower layers retain more direct appearance features. We conducted an additional experiment to verify this by testing the DINOv2-S-based BiggerGait (trained on CCPG) on SUSTech1K, observing the optimal layer depth for different sub-tasks.  \n\n- For the **Normal condition** (same clothing in probe/gallery, appearance-based easy task), best performance occurs at the shallowest 1st layer.  \n- For the **Clothing condition** (clothing changing in probe/gallery, semantic-based hard task), the 7th layer performs best.  \n\nThese results align well with the observations in Lines 164-165, again revealing that hard recognition tasks prefer deeper layers than easy tasks.  \n\nYour thoughtful questions on the characteristics of the different layers are truly appreciated.  \nThe above enlightening discussion will be added to Sec. 3.4.\n\n---\n\n### **\\#W5/Q5: Can BiggerGait\\* generalize to other vision tasks such as action recognition or pose estimation?**\n\nWe appreciate this engaging question.  \nOur work primarily focuses on gait recognition, and your comments highlight an important open issue that warrants further exploration. All models and code will be made publicly available, and we would be thrilled to see researchers in other vision domains exploring the applications of the findings provided by BiggerGait*. \n\n---\n\n### **\\#W6/Q6: Table 1 lacks a report of model sizes and computation costs.**\n\nThanks for your kind reminders.  \n\nThis report follows the settings in Table 2(d), with FLOPs computed at an input resolution of 448 × 224. Since silhouette-based methods require a segmentation model to extract binary masks from RGB inputs, we include the cost of DeepLabV3+, a widely used segmentation model, introducing 26.8M parameters and 43.7 GFLOPs.  \n\nDue to the difficulty of reproduction, statistics for multimodal-based methods are temporarily unavailable. Although its data is difficult to obtain, there is certainly more computation cost compared to the single-modal silhouette method, as it requires extra, expensive models for more modality preprocessing.  \n\nDetailed information is summarized below, and will be updated in Tab. 1.\n\n| Method         | Upstream    | #Params | FLOPs |\n|:--------------|:-----------:|:-----------:|:------:|\n| GaitSet        | DeepLabV3+  | 29.4M        | 50.3G   |\n| GaitPart       | DeepLabV3+  | 31.6M        | 53.0G   |\n| GaitGL         | DeepLabV3+  | 30.1M        | 88.7G   |\n| GaitBase       | DeepLabV3+  | 34.2M        | 45.4G   |\n| DeepGaitV2     | DeepLabV3+  | 35.2M        | 93.2G   |\n| BigGait        | DINOv2-S    | 30.8M        | 12.7G   |\n| **BiggerGait**   | SAM-S       | 209.7M       | 95.1G   |\n| **BiggerGait**   | CLIP-S      | 208.6M       | 49.3G   |\n| **BiggerGait**   | DINOv2-S    | 142.2M       | 45.7G   |\n| **BiggerGait***  | SAM-S       | 112.2M       | 79.2G   |\n| **BiggerGait***  | CLIP-S      | 110.8M       | 33.4G   |\n| **BiggerGait***  | DINOv2-S    | 43.6M        | 29.8G   |"}]

Author rebuttal:

Meta-review: The paper introduces BiggerGait, which is a simple baseline for LVM-based gait recognition that leverages complementary representations from intermediate layers of large vision models rather than relying heavily on gait-specific priors. The paper has gone through various discussions between the reviewers and authors in the rebuttal phase, and has eventually received the scores of BA, A, BA, BA. All reviewers acknowledge the contributions of the work to the gait community (e.g., systematic layer-wise analysis), while the key weaknesses are the lack of significant novelty at the core of the method, dependence on dataset/task complexity, and limited temporal modeling.
Decision: Accept (poster)