GaitSnippet: Gait Recognition Beyond Unordered Sets and Ordered Sequences
Authors: Saihui Hou
Chenye Wang
Wenpeng Lang
Zhengxiang Lan
Yongzhen Huang
Venue: ICLR 2026 Poster (2026)
Keywords: Gait Recognition
Snippets Sampling
Snippet Modeling
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We propose a novel perspective that conceptualizes human gait as a composition of snippets, allowing for the integration of both short-range and long-range temporal context, thereby facilitating more comprehensive gait feature learning.

Abstract: Recent advancements in gait recognition have significantly enhanced performance by treating silhouettes as either an unordered set or an ordered sequence. However, both set-based and sequence-based approaches exhibit notable limitations. Specifically, set-based methods tend to overlook short-range temporal context for individual frames, while sequence-based methods struggle to capture long-range temporal dependencies effectively. To address these challenges, we draw inspiration from human identification and propose a new perspective that conceptualizes human gait as a composition of individualized actions. Each action is represented by a series of frames, randomly selected from a continuous segment of the sequence, which we term a snippet. Fundamentally, the collection of snippets for a given sequence enables the incorporation of multi-scale temporal context, facilitating more comprehensive gait feature learning. Moreover, we introduce a non-trivial solution for snippet-based gait recognition, focusing on Snippet Sampling and Snippet Modeling as key components. Extensive experiments on four widely-used gait datasets validate the effectiveness of our proposed approach and, more importantly, highlight the potential of gait snippets. For instance, our method achieves the rank-1 accuracy of 77.5% on Gait3D and 81.7% on GREW using a 2D convolution-based backbone.

Published as a conference paper at ICLR 2026
GAITSNIPPET: GAIT RECOGNITION BEYOND UN- ORDERED SETS AND ORDERED SEQUENCES
Saihui Hou', Chenye Wang', Wenpeng Lang', Zhengxiang Lan', & Yongzhen Huang 1.24 School of Artificial Intelligence, Beijing Normal University ?WATRIX.AI (housaihui, Saangyonnzheen@bninn edu.
ccenye.. wang, wenpenglang, beenngxianggaaneenann iou.iiiiiuu..... edu. cn
ABSTRACT
Recent advancements in gait recognition have significantly enhanced performance by treating silhouettes as either an unordered set or an ordered sequence. How- ever, both set- et-based and sequence- requence-based approaches exhibit notable limitations. Specifically, set-based methods tend to overlook short-range temporal context for individual frames, while sequence-basee methods struggle to capture long-range temporal dependencies effectively. To address these challenges, we draw inspira- tion from human identification and propose human gait as a composition of individualized actions. Each action is represented by series of frames, randomly selected from continuous segment of the se- quence, which we term a snippet. Fundamentally, the collection of snippets for given sequence enables the incorporation of multi-scale temporal context, fa- cilitating more comprehensive gait feature learning. Moreover, we introduce a non-trivial solution for snippet-based gait recognition, focusing on Snippet Sam- pling and Snippet Modeling as key components. Extensive experiments on four widely-used gait datasets validate the effectiveness of our proposed approach and, more importantly, highlight the potential of gait snippets. For instance, our method achieves the rank- I accuracy of 77.5% on Gait3D and 81.7% on GREW using a 2D convolution-basee backbone.
perspective that conceptualizes
INTRODUCTION
Gait recognition aims to identify individuals based on their unique walking patterns. This technique can performed at a distance without the explicit cooperation of the subjects, making it highly applicable in areas such social security Rida et al. (2019), hwwan- humman-computer interaction Zhu et al. (2022), and health monitoring Bortone et al. (2021). 1), etc. Silhouettes are commonly used as input, as they effectively eliminate clothing texture while remaining robust under low-resolution conditions.
In the gait recognition literature, early studies typically aggregated silhouettes into a template, such Gait Energy Image Han & Bhanu (2005), which, although simple, inevitably sacrifices fine- grained details. Recent research predominantly treats silhouettes either as an unordered set or an ordered sequence, leveraging deep neural networks extract gait features. Specifically, set-based methods Chao et al. (2019); Hou et al. (2000) 2021 2022b) assume that the appearance of sil- houette inherently contains its positional information, rendering the order information unnecessary. The pioneering GaitSet Chao et al. (2019), representative of this category, significantly improves performance over template-bane
methods and demonstrates resilience frame permutations. In contrast, sequence-based methods Lin et al. (2020) 2) Huang al. (202 a) treat sequence of silhouettes as video, utilizing 3D Tran et al. (2015) or P3D Qiu et al. (2017) convolutions, along with their variants Lin et al. 2020). extract both spatial and temporal features.
Despite the significant performance gains of recent advancements, both set-based and sequence- based paradigms exhibit notable limitations. First, in set-based methods, feature extraction in the backbone, typically performed using 2D convolution, processes each silhouette independently, lack- ing awareness of short-range temporal context between adjacent frames. Second, in sequence-based
"orresponning author.

---

Published as a conference paper at ICLR 2026
Unordened Set
Ordered Sequence
Snippet
Snippet:
Snippetz
Figure 1: viewed in color.
Illustration of gait snippets in comparison to unordered sets and ordered sequences. Best
methods, feature extraction primarily relies on 3D/P3D convolutions their variants, with limited number of continuous frames (e.g., 30) sampled from each sequence during training. This approach significantly hinders the ability to model long-range temporal dependencies, especially in long se- quences (e.g., those with more than 200 frames in real-world benchmarks [heng et al. (2022)) This raises critical question: Is there an alternative paradigm for extracting gait features from silhou- ettes that addresses these limitations?
In this work, we propose new perspective on gait recognition inspired by human cognition, ar- guing that identification often depends on key actions in few adjacent frames-not full cycle. This aligns with the biological finding that "recognition possible for stimuli lasting a fraction of full walking cycle" "Giese & Poggio (2003). Motivated by this insight, we propose conceptualize human gait as a composition of individualized actions. Specifically, as illustrated in Figure[] we rep- resent action using several frames randomly selected from a continuous segment of the sequence, which we term a snippet. This approach allows an individual's walking pattern to be described the union of snippets derived from the same sequence. Gait snippets offer notable concep- tual advantages: (1) Compared to unordered sets, snippets facilitate the incorporation of short-range temporal context for frame-level feature extraction. (2) Compared to ordered sequences, snippets enable the capture of long-range temporal dependencies within a long sequence.
Building on these insights. we focus on snippet-based gait recognition and address two critical challenges: (a) How to sample snippets during the input phase for training and inference? (b) How to effectively model snippet-based inputs for gait recognition? In this work, we propose an efficient yet effective solution, marking the first attempt to systematically tackle these challenges.
Regarding Snipper Sampling, given a sequence of silhouettes, we treat it as non-continuous due to imperfect upstream processing and various occlusions Fan et al. (203b), but we assume that the relative order of frames preserved. This order used divide the sequence into non-overlapping segments of equal length. For training, we randomly select subset of frames from each segment to form snippet representing an individualized action, with the number of snippets generally fewer than the number of segments. For inference, all frames from each segment are used to construct snippet, and all snippets from a sequence are utilized to match the probe and gallery. In ferms of Snip- pet Modeling, we design efficient framework to address three core challenges: (1) Intra-Snippet Modeling: We introduce a Snippet Block where a nonpaaraetriic pooling operation captures local temporal context within snippet, merging with frame-level features through residual connec- tion. (2) Cross-Snippet Modeling: We treat all snippets within a sequence as an unordered set, employing Set Pooling to derive sequence-level representations based on intra-snippet modeling. (3) Snippet-Level Supervision: Representing gait through snippets enables hierarchical represen- tations both the sequence and snippet levels. addition to sequence-level loss, we introduce snippet-level supervision to further enhance training.
In summary, the main contributions are threefold:
(1) We introduce new perspective on gait recognition, organizing sequence of silhouettes union of snippets to characterize the walking pattern.
(2) We pioneer snippet-based gait recognition, designing a comprehensive solution that includes Snippet Sampling and Snippet Modeling.
(3) Extensive experimental results demonstrate the potential of gait snippets, with approach achieving the rank-1 accuracy of 77.5% on Gait3D Zheng et al. (2022) and 81.7% on CREW/Zhu et al. (202T) using a 2D convolutional backbone.
2

---

Published as a conference paper at ICLR 2026
2 RELATED WORK
Gait Recognition We address the fundamental challenges in the modeling paradigm for gait recognition by using silhouettes as input. early studies Bandies/Ha&& & Bhanu (2005): Wang et (2010).
silhouettes were usually aggregated into templates. More recent advancements have treated si silhou-
ettes as either unordered sets Chao et al. (2019); Hou et al. (020 2021: 2022b); Fan et al. (2023c)
or ordered sequences Lin et al. (2020); Fan et al. (2020) Lin et al. (202 b: Huang et al. (2021b
Ma et al. ): Dou et al. (2023); Wang et al. (2023 c) for feature learning. Below, we briefly
review representative methods within these two subcategories.
(I) Unordered Sets: GaitSet Chao et al. (2019) is the first to introduce set-based feature learning for silhouettes, using horizontal splits of feature maps learn multiple part representations. GLN Hou et al.] (2020) merges multi-stage features for set-based modeling, focusing on reducing feature di- mensionality to enhance recognition performance. GaitBase Fan et al. (2023c) and its deeper variant, DeepGaitV2-2D Fan et al.] (223a)), present a robust ResNet-like 2D backbone, achieving competi- tive performance across various benchmarks.
(2) Ordered Sequences: GaitGL Lin et al. (2021) utilizes 3D convolution to blend local and global feature extraction in its architecture. GaitGCI Dou et al.] (2023) introduces counterfactual in- tervention to mitigate the effects of confounding factors while using dynamic convolution for factual/couutteraatuaal attention generation. DyGait Wang et al. (2023c) captures dynamic fea- tures by leveraging differences between frame-level and template features. DeepGaitV2-3D and DeepGaitV2-P333 Fan et al. (2023a) are variants of GaitBase Fan et al. (2023c) that utilize ordered input with 3D/P3D convolutions. VPNet Ma et al. (2024) employs a ResNet50-like backbone for gait recognition and introduces visual prompts to handle complex variations in gait patterns.
Snippet Paradigm We noticed that the term "snippet" has been previously used in the action recognition literature WWng et al. (2016); Duan et al. (202)), and we compare those approaches with our own. For instance, TSN Wang et al. (2016) constructs RGB snippets but mandates that snippets be sampled from all segments and lacks intra-snippet modeling, which we consider crucial for snippet-based gait recognition. SkeleTR Duan et al. (2023) processes short skeleton sequences snippets but requires continuity within each snippet. In our study, we extend the concept of snippets to gait recognition, where neither the frames within a snippet nor the snippets in sequence need to be strictly continuous. Moreover, approach diverges significantly from these methods by emphasizing snippet modeling, which will be elaborated in the next Section 3.2
similar fashion
3 OUR APPROACH
In this work, we investigate fundamental paradigm for gait recognition that addresses the limita- tions of unordered sets and ordered sequences. Specifically, we propose a new perspective that treats human gait composition of individualized actions, with each action represented by a snippet, which consists of a few frames randomly selected from a continuous segment of the sequence. This snippet paradigm allows the model leverage both short-range and long-range temporal contexts during training, enhancing its capability for comprehensive gait feature learning.
In the following sections, we will first describe our strategy for organizing a sequence of silhouettes into snippets. Subsequently, we will present an effective approach to conduct snippet-based gait recognition.
3.1 SNIPPET SAMPLING
The underlying principles of sampling strategies for gait recognition can generally be summarized from two perspectives: (1) During training, a limited number of frames are typically sampled to represent a sequence due to the trade-off between computational cost and sampling diversity. (2) During inference, all frames of a sequence are utilized to ensure accurate recognition. Below, we briefly highlight the distinctions in sampling strategies when treating silhouettes as either unordered sets ordered sequences. Specifically, in the training phase, set-based methods randomly select sequences discontinuous frames from the entire sequence Chao et al. (2019). whereas sequence- based methods select continuous or nearly continuous frames for temporal modeling Fan et al. 22020)
3

---

Published as a conference paper at ICLR 2026
Gr
Snippet
Snippet,
1) Slice into Segments
Sumple
G
3 Sample
M segments
N fromes
Snippet sampling for training. {G1,-.. ..G represent the total segments of sequence, where L is the segment length and L1 for the first segment a random integer to enhance sampling diversity. [G], G represent the sampled segments. M and denote the number of sampled snippets per sequence and the number of sampled frames per snippet, respectively.
Figure 2:
Our snippet-based sampling strategy influences both the training and inference phases, as described in detail in this section. It is noteworthy that we assume the relative order of frames in a sequence reliable, even though the frames themselves may not necessarily be continuous, condition that aligns well with practical applications Sopas-Moghaddam & Etemad (202T): Shen et al. (2022).
3.1.1 TRAINING PHASE
During the training phase, we first partition sequence into non-overlapping segments of equal duration, preserving the relative order, and then design the snippet sampling strategy based on three guiding principles: (a) Gíven the constraints of computational resources and the need for sampling diversity, the total number of frames selected from a sequence should be limited, denoted as S. (b) The fundamental unit within the sampled S frames a snippet, where each snippet consists of N frames randomly selected from segment to capture an individualized action. (c) To increase sampling diversity and enhance model robustness, the segment partition for a sequence should vary across iterations.
Our approach is illustrated Figure 2 (1) A sequence of silhouettes is divided into K segments, denoted as {G1, G2. Gk each of length L, where L typically approximates the number of frames in a gait cycle (e.g., L = 16 in most cases Ma et al.] (2024)) the sequence length is additional segment. (2) When
not perfectly divisible by L, the remaining frames treated processing a sequence in mini-batch, we randomly sample M segments from it and then randomly select N frames from each chosen segment construct the snippets. Sampling with replacement allowed when the number of segments or the number of frames in a segment is limited. We ensure that M x N, assigning each snippet a segment label k E (1, K}) ) for subsequent modeling, (3) To enhance sampling diversity within a sequence, the initial frames are treated as special segment, with length L1 randomly chosen from (1,2,-.. L}.
3.1.2 INFERENCE PHASE
The snippet sampling strategy for the inference phase is also developed based on three guiding principles: (a) All frames in sequence should be utilized ensure precise matching between the probe and gallery. (b) To maintain consistency with the training phase, sequences are divided into segments, with all frames in each segment forming a snippet. (c) The segment partition should remain fixed to produce stable predictions.
Accordingly, our inference strategy involves the following three aspects: (1) A sequence of silhou- ettes is divided into K segments of equal length L, as previously defined in the training phase (e.g., L= 16). (2) Each snippet comprises all frames within a segment, and prediction features are ex- tracted using all snippets from the sequence, which is equivalent to setting M K and N = L during inference.
(3) The length of the first segment L, is fixed to L, thereby eliminating the need for multiple forward passes and reducing inference overhead.
3.2 SNIPPET MODELING
Snippets provide a new paradigm for modeling silhouettes in gait recognition. However, fully ex- ploiting the potential advantages of snippets remains open question. In this work, propose
4

---

Published as a conference paper at ICLR 2026
Intra, Snippet Gathering
Gross: Snippes Moddling
Stope
(P586)
Stage (R504)
Stage 4 (P58k)
HPW
BNNeck
Snippet,
Snippet
Sequence- Branch
RSBs
Stock of Residual Srippet Blocks
BNNNiccle
Lep Luee
HPK
Snippet- level Branch
HPM
Horizontal Pynamid Mapping
Illustration of GaitSnippet. (1) Residual Snippet Block integrating Intra-Snippet Model- ing as shown in Figure (b) serves the basic component to construct the backbone. (2) the end of the backbone, we first apply Intra-Snippet Gathering (the Gathering step for Intra-Snippet Mod- eling) to derive snippet-level representations and then perform Cruss-Snippet Modeling to obtain sequence-level representations. (3) In addition to sequence-level supervision, an auxiliary branch is introduced to enforce supervision on snippet-level features only for training.
Figure 3:
4b)
an efficient yet effective solution address this issue. Specifically, we identify three primary chal- lenges in snippet modeling for gait recognition: Intra-Snippet Modeling, Cross-Snippet Model- ing, and Snippet-Level Supervision. In the following sections, we systematically address these challenges through our proposed approach, which we term GaitSnippet. The pipeline is illustrated Figure
3.2.1 INTRA-SNIPPER MODELING
In GaitSnippet, we address intra-snippet modeling with the objective of capturing local temporal context enhance frame-level feature extraction through three-step process:
(1) Gathering:
Considering that the frames within snippet not necessarily continuous, treat a snippet as an unordered set. Based on this formulation, we utilize the efficient Set Pooling technique aggregate the features of snippet, which is implemented through non-parametric Temporal Max Pooling operation Chao et al. (2019).
(2) Smoothing: To mitigate the negative effects of noise within a snippet and reduce the semantic gap between different levels of features, we apply a smoothing layer, typically implemented using convolution, following the Gathering step.
(3) Residual: To make frame-level feature extraction aware of local temporal context in snippet, we incorporate residual connection to merge the snippet-level output after smoothing with the frame-level features of the corresponding snippets.
As illustrated in Figure 4 a), these steps are formulated into basic block called Snippet Block.
Furthermore, recent advancements in gait recognition have demonstrated that a plain 2D residual backbone FFan al. (2023c]a) can achieve highly competitive performance in both constrained and unconstrained environments, while maintaining significantly lower computational costs compared to their 3D counterparts. The spatial convolution, specifically applied along the height and width dimensions, plays critical role in extracting frame-level features. To facilitate effective collabo- ration between intra-snippet modeling and spatial convolution, draw inspiration from P3D Qiu et al. (2017) and integrate a Snippet Block between two spatial convolutional layers within stan- dard residual block. The rationale behind this approach enable each frame become aware of local temporal context within snipper during successive stages of frame-level feature extraction. Ultimately, the architecture illustrated Figure(4b)) called Residual Snippet Block, serves as the basic component to construct the backbone for GaitSnippet as shown in Figure
3.2.2 CROSS-SNIPPER MODELING
For cross-snippet modeling, objective is acquire robust global representation for gait sequence based on the snipper-level features. As a pionecring attempt and to ensure a fair compar- ison with the base models Fan et al. (202c conduct cross-snippet modeling the output
featurr
5

---

Published as a conference paper at ICLR 2026
X M xNx Cx
Spatial
Gathering
Mx W
Snippet Block
Smoothing
Mi Nx. Hx W
xx Cx Hx W
Spatial Conv
Repeat
x N. C=
Mi No Cx
Mx Cx
(a)
(b)
Figure 4: (a) Snippet Block. (b) Residual Snippet Block. M and N denote the number of snippets and the number of frames per snippet in sequence, while C, H, and W represent the dimensions of channel, height, and width.
of the backhone which corresponds the frame-level features. Specifically, first apply Intra- Snippet Gathering (the Gathering step for intra-snippet modeling) on the frame-level features to derive snippet-level representations. Subsequently, we treat all snippets from a sequence as un- ordered set and employ another Set Pooling Chao et al. (2019) to perform cross-snippet modeling. In practice, this is implemented using Temporal Max Pooling on all snippet-level representations within sequence.
is crucial highlight that (1) GaitSnippet involves two hierarchical unordered sets: frames within snippet and all snippets within sequence. However, the snippet-based modeling approach is not permutation-invaric to the frame order, distinguishing it from methods that exclusively rely on unordered sets Chao al. (2019); Fan et al. (2023c). Unlike unordered sets, the use of snippets enables the exploitation of local temporal context in frame-level feature extraction, which vital for learning discriminative and complementary features for individual silhouettes. (2) At the end of the backbone, Temporal Max Pooling employed for both intra-snippet and cross-snippet modeling makes the sequence-level features equivalent the maximum of all frames. Yet the intermediate output from intra-snippet modeling is essential for enabling Snippet-l Supervision. Further discussion about the role of Temporal Max Pooling provided in Section A.4.2 of the appendix.
3.2.3 SNIPPET-LEVEL SUPERVISION
The snippet-based modeling of gait conveniently facilitates the extraction of two hierarchical repre- sentations for sequence, namely, sequence-level and snippet-level representations. For supervision on the sequence-level representationss, we adopt the typical approach outlined in Fan et al. (023c)..
Initially. Horizontal Pyramid Mapping Fu et al. (2019): Chao al. (2019) (including linear layers for separate parts) is utilized to horizontally split the features for obtaining fine- ine-grained part repre- sentations efficiently. Then, for each part, we employ triplet loss Ltp and cross-entropy loss L.s. assisted by BNNeck | luo et al. (2019), for training. Formally, these losses are defined as follows:
anchor
U
V
Cip Ntp
D(J.,v D(Fu.o,
uml mmm
b=l Gifft
(I)
batck
No qu log Parnyo
Loe
UXV

---

Published as a conference paper at ICLR 2026
Here, pos, neg, and sub stand for positive, negative, and subjects, respectively. V) denote the number of subjects and the number of sequences per subject in a mini batch. Nip serves as a normal- ization coefficient accounting for the non-zero triplet terms. ô is a margin threshold and []+ works as the ReL.U function. F denotes the sequence-level representations and 'D measures the Euclidean distance. (Fu,,, Fu,a) and (Fu, Fb.e) represent positive and negative pairs, respectively. Ne is the number of subjects in the training set, while and denote the predicted probabilities and the one-hot ground-truth identity labels.
With the snippet-based approach to gait, we can conveniently obtain snippet-level representations in addition sequence-lever representations, motivating us introduce the following fine- tin-grained supervision. Specifically, we add separate branch to process snippet-level representations prior to cross-snippet modeling, using Horizontal Pyramid Mapping to obtaín part-level features and incor- porating BNNeck analogous to the sequence-level branch. Formally, for each part, the snippet-level triplet loss Ctp and cross-entropy loss L are computed as follows:
pos
neg
batck
Lip
Focs)]
1p
(2)
batch
anp
sub
Co UXVXM
Dun logPs,,,m.c
where snp denotes snippets, M is the number of sampled snippets per sequence for training, and F* refers to snippet-level representations. The remaining symbols are similar to those in Eq. with the superscript * indicating snippet-level computations.
We then define the integrated objective for one of the part representations as:
Lall Ctp + Lee a x (Cip + (te)
(3)
where is a hyperparameter balance the two levels of supervision signals. The final loss is computed by averaging the above losses across all parts, which is used to train the entire network.
is important to emphasize that the additional branch for snippet-level supervision is employed only the inference complexity unaffected. For evaluation, we utilize the features extracted before BNNeck in the sequence-level branch to compute similarities between the probe and gallery sequences.
during the training phase, thereby leaving
EXPERIMENTS
4.1 SETTINGS
We conduct experiments on four widely-used gait datasets: Gait3D Zheng et al. (2022) and GREW Zhu et al. (202T), CCPG al. (2023) and CCGR-MINI /ou et al. (2024). In train- ing phase, we adopt T.= 16 to approximate the number of frames depicting gait cycle Ma et al. (024) for segment partition, and L1 is a random integer sampled from {1, 2, ... 166. To sample sequence, randomly select 44 snippets and N = frames per snippet, i.e., sample S 32 frames for each sequence. For evaluation, we set L1 = L = 16 for segment partition. All frames in segment treated snippet, and all snippets for sequence are used to extract gait features. Detailed dataset statistics and implementation details are provided in the appendix.
4.2 PERFORMANCE COMPARISON
The emergence of Gait3D and GREW has advanced gait recognition research from controlled laboratory settings real-world environments. In Table present performance comparison on in in-the-wild benchmarks. The methods are categorized into three groups based on how they treat the input: Ordered Sequences, Unordered Sets, and the brand-new Snippets.
Gait3D & GREW
7

---

Published as a conference paper at ICLR 2026
Cate- gory
Back- bone
Gaird
GREW
Method
R1
mAP
R1
R5
GaitPart Fan al
2020
2D
28.2
21.6
47.6
60.7
GaitGL Lin et
2021
3D
29.7
22.3
47.3
63.6
GairGCI
Dou et
2123
3D
50.3
39.5
68.5
80.8
DyGait Wang al.
2023c
3D
66.3
56.4
71.4
HSTL Wang
3D
61.3
55.5
62.7
76.6
SwinCGai-331Faa
12023
Seq
Swin
75.0
67.2
79.3
88.9
DeepGaiVV233DD Tran
4025a
3D
72.8
63.9
79.4
88.9
DeepGaitV22PPDD Fan al.
20258
P3D
74.4
65.8
77.7
87.9
VPNer[ al. 2024
3D>
75.4
80.0
89.4
CLTDiXiong Xrong ci 2204
3D
69.7
78.0
GaitMoE Hoang
2024
3D
73.7
66.2
79.6
GaitSet Chao
2019
2D
36.7
30.0
48.4
63.6
GaitBase Fan
2025c
2D)
64.6
55.3
60.1
75.5
SwinGail-22 2D al 202.3g
Set
Swin2D
69.4
61.6
70.8
83.7
DeepGaitV2 2a
2025a
2D
68.2
60.4
68.6
82.0
GaitSnippet (Ours)
Snippet
2D
77.5
69.4
81.7
90.9
Table 1:
Performance comparison on Gait3D Zheng et al. 2022) and GREW Zhu et al. (2021). The
results are reported in rank-1 (R1, %), rank-5 (R5, %). and mean Average Precision (mAP, %). The best results in each category are marked in red, blue, and bold, respectively.
From the results in Table the following observations can be made: (1) Sequence- methods achieve state-of-the-ar performance and mostly employ 3D or P3D convolution in the backbone, which generally entails higher computational costs compared to 2D convolution- based backbones. (2) DeepGaitV222DD Fan et al.) (2023a), despite their simplicity, achieve highly competitive per- formance these benchmarks. (3) GaitSnippet outperforms advanced methods on both bench- marks using a 2D convolutional backbone. Specifically, the performance gains compared to 2D convolution-ban based baselines (e.g.. RI: +9.3%, mAP: +9.0% over DeepGaitV2-2D Gait3D with the same network depth) effectively demonstrate the effectiveness of snippet- gait recognition.
With the increasing interest in gait recognition, several new datasets have recently been introduced Li al. 2023); [Shen (2023 Li at. (2024); Zou et al. (2024), aiming to address more diverse and challenging scenarios. To further demonstrate the generalizabiligy of GaitSnippet, additionally. evaluate it on representative emerging datasets: CCPG L et al. 2023 and CCGR-MINY Zou et al. (2024). As shown in Table 2 GaitSnippet achieves state-of- the-art performance on both datasets, further validating the effectiveness and adaptability of snippet- based modeling for gait recognition.
CCPG & CCGR-MINI
4.3
ABLATION STUDY
4.3.1
ABLATION STUDY ON SNIPPET SAMPLING
In Table
we present an ablation study on Snippet Sampling from two perspectives.
First, evaluate the overall effect of the sampling strategy using base model (i.e., Vee- 2D) in the first part, including set-based, sequence-based, and snippet-based strategies. Interestingly, from the first three
observe that Snippet Sampling during training also benefits recognition performance with DeepGaitV2-2D which is based on unordered sets. A likely reason for this is that sampling strategy enhances the robustness of DeepCaitVV22DD by narrowing the distribution gap between discontinuous frames during training and continuous sequences during testing.
Second, analyze the effect of dyper-parametenn for Snippet Sampling in the second part. Specif- ically, during the training phase, there are four hyper-parameters for sampling snippets from a se- quence: L-the segment length, S-the total number of frames sampled from sequence, M-the number of snippets sampled from sequence, and N-the number of frames sampled per snippet from segment. Note that = M x N is always maintained. During our experiments with Snippet Sampling, we fix S= = 32, considering computational cost and ensuring fair comparisons. In Table
We re-ran the experiments on CCPG using the same preprocessing and protocols the baselines Fan et al. 2023a ensure fair comparisons. The results differ slightly from those in the initial submission, but they remain state of the
8

---

Published as a conference paper at ICLR 2026
CCPG
CCGR-MINI
Method
Category
Backbone
CL
UP
DN
BG
AVG
R1
mAP
DeepGaitV2 2eepGaitV2-P3DFFBB
GaitPart Fan el al. 2020
2023a
Seg
P3D
2D
90.5
79.2
96.3
85.3
91.4
86.5
96.7
88.0
84.8
93.7
39.4
8.0
36.0
10.1
GaitSet Chao et 2019
2D
77.5
85.0
82.9
87.5
83.2
13.8
15.4
GaitBase Fan et at.
20252
2D
88.5
92.7
93.4
93.2
92.0
27.0
24.9
GaitSmippel (Ours)
Snippet
2D
91.5
96.6
94.6
97.7
95.1
42.4
39.5
Table 2:
Performance comparison on CCPG Li et al. (0023) and CCGR-MINI Zou et al. 2024))
The results on CCPG are reported in rank-1 (RI, %) accuracy, while those on CCGR-MINI are reported in rank-1 I accuracy (RI, %) and mean Average Precision (mAP, %). CL. UP, DN, BG, and AVG refer changing full outfits, changing top clothes, changing pants, walking with bags, and mean accuracy, respectively. The best performance in each category is highlighted in red, blue, and bold, respectively.
we conduct ablation studies on the other sampling parameters. (1) We L= 16 approximate the number of frames in gait cycle Ma et al. (2024). and we also tried L E {8, (2) in the fourth/fifth rows. (2) We set N 8, which half of gait eycle, and also tried N (4, 16) in the sixth/seventh rows. (3) Given that 5 = M X N, M varies with different values of N in each case (i.e., M = 4 for
N 8, M 8 for N = 4, and M =2 2 for 16).
4.3.2 ABLATION STUDY ON SNIPPET BLOCK
In this section, we analyze the three steps involved in intra-snippet modeling, namely Gathering, Smoothing, and Residual, The results are presented the first part of Table
To clarify the results: (1) If none of the techniques in Table is applied, GaitSnippet is equivalent to DeepGait V2-2DD with Snippet Sampling. (2) In the first row, when Gathering is removed for each stage of intra-snippet modeling, snippet-level supervision becomes inapplicable. (3) In the second row, the experiment highlights the importance of the smoothing layer, which acts as a bridge between frame-level features and those aggregated from snippet. When Smoothing is removed, the model for inference does not introduce any additional parameters, as the snippet-level branch is only employed during training. In this case, the local context modeling within each Snippet Block still works but is more susceptible to the noise within a snippet and the semantic gap between different levels of features. (4) For the third row, worth emphasizing that Residual Table 4 refers to the integration of local contextual information from a snipper with frame-level features as depicted Figure fa), rather than the standard residual connection shown in Figure 4b). When Residual is removed, only the contextual information from the snippet is used for subsequent layers, which inevitably results in the loss of fine mna-graindd details from each silhouette after the first Snippet Block.
4.3.3 ABLATION STUDY ON SNIPPET-LEVEL SUPERVISION
Benefitting from the snippet paradigm, we can easily incorporate snippet-level losses to provide fine-grained supervision for gait feature learning. In the second part of Table we perform ablation studies analyze the effect of snippet-level supervision. We can observe that: (1) When shown in the fifth row, only the sequence-level loss used to train the entire model, and our approach still achieves highly competitive performance. (2) Snippet-level supervision improves recognition performance with varied loss weights.
0, as
Additionally, in the last row of Table 4 we experiment with sharing weights between the sequence- level and snippet-level branches shown in Figure consisting of Horizontal Pyramid Mapping and BNNeck. The corresponding results show moderate performance degradation, likely due to the semantic gap between the sequence-level and snippet-level features, making weight sharing between the branches inappropriate.
5 CONCLUSION
In this work, explore paradigm for gait recognition that integrates the strengths of un- ordered sets and ordered sequences. Motivated by the observation that human identification does not necessarily rely complete gait cycle Giese & Poggio 2003), we conceptualize human gait

---

Published as a conference paper at ICLR 2026
Sampling Strategy
Model
T.
M
N
R1
mAP
Set
68.2
60.4
DeepGaitV2-2D
Seq
66.0
58.7
Snipper
16
4
8
69.5
61.5
8
8
76.4
69.0
32
4
8
74.7
67.5
GaitSnippet
Snippet
16
74.3
66.7
16
75.2
66,9
16
77.5
69.4
Table 3: Ablation study on snippet sampling. L, M, and N denote the segment length for sequence partition, the number of snippets sampled per sequence, and the number of frames sampled per snippet, respectively. The results are reported on Gait3D.
Snippet Block
Snippel Supervision
R1
mAP
Gathering
Smoothing
Residual
SW
73.3
63.7
0.75
74.8
66.6
0.75
72.5
63.7
V
0.75
77.5
V
0.00
75.8
68.5
0.50
76.4
76.6
0.75
75.5
68.8
Table 4: Ablation study on snippet modeling Gait3D. Gathering. Smoothing, and Residual the three steps Snippet Block. a represents the loss weight for snippet-level losses, and SW denotes sharing weights between the sequence-level and snippet-level branches in Figure The experiments are conducted on Gait3D.
combination individualized actions, with each action represented by few frames that are adjacent but not necessarily continuous. In essence, gait snippets enable the model to simultaneously exploit both short-range and long-range temporal contexts, which is beneficial for learning identity-related features from entire walking sequences. Furthermore, provide non-trivial solution based gait snippets, addressing the challenges of Snippet Sampling and Snippet Modeling. Extensive experi- ments across various benchmarks demonstrate that approach consistently improves performance and achieves state-of-the-art results the wild, effectively verifying the potential snippet-based gait recognition.
ETHICAL STATEMENT
The datasets used in experiments widely adopted in the literature, with informed consent obtained from all subjects during data collection. Additionally, no personal identifiers are accessible. We strongly advocate that research in this field should be conducted with strict privacy protection measures place.
REPRODUCIBILITY STATEMENT
All experiments are conducted on publicly available gait recognition datasets (Gait3D, GREW, CCPG and ECGR-MINE following the official splits and evaluation protocols. Detailed hyper- parameters and training procedures are provided in the paper. The complete code and pre-trained models will be released.
ACKNOWLEDGEMENT
This work is jointly supported by National Natural Science Foundation China (62276025, 62476027) and the Fundamental Research Funds for the Central Universities (2253200026).
10

---

Published as a conference paper at ICLR 2026
REFERENCES
llaria Bortone, Rodolfo Sardone, Luisa Lampignano, Fabio Castellana, Roberta Zupo, Madia Lozupone, Biagio Moretti, Gianluigi Giannelli, and Francesco Panza. How gait influences frailty models and health-related outcomes in clinical-based and population-based studies: systematic review. Journal of Cachexia, Sarcopenia and Muscle, 12(2):274-277, 2021.
Hanqing Chao, Yiwei He, Junping Zhang, and Jianfeng Feng. GaitSet: Regarding gait set for cross-view gait recognition. In AAAI, volume 33, pp. 812 8126-81333 -133-,133, 2019.
Yufeng Cui and Yimei Kang. Multi-modal gait recognition via effective spatial-temporal feature fusion. In CVPR, pp. 17949-17957, 2023.
Huanzhang Dou, Pengyi Zhang, Wei Su, Yunlong Yu, Yining Lin, and Xi Li. Gaitgei: Generative counterfactual intervention for gait recognition. In CVPR, pp.
2023.
Haodong Duan, Mingze Xu, Bing Shuai, Davide Modolo, Zhuowen Tu, Joseph Tighe, and Alessan- dro Bergamo. Skeletr: Towards skeleton-based action recognition in the wild. In ICCV, pp- 13644-13644, 2023.
Chao Fan, Yunjie Peng, Chunshui Cao, Xu Liu, Saihui Hou, Jiannan Chi, Yongzhen Huang, Qing Li, and Zhiqiang He. GaitPart: Temporal part-based model for gait recognition. In CVPR, pp- 14225-14233, 2020.
Chao Fan, Saihui Hou, Yongzhen Huang, and Shiqi Yu. Exploring deep models for practical gait recognition. arXiv preprint arXiv:2303.03301, 2023a.
Chao Fan, Saihui Hou, Jilong Wang, Yongzhen Huang, and Shiqi Yu. Learning gait representation from massive unlabelled walking videos: A benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023b.
Chao Fan, Junhao Liang. Chuanfu Shen, Saihui Hou, Yongzhen Huang, and Shiqi Yu. OpenGait: Revisiting gait recognition towards better practicality. In CVPR, pp. 9707-9716, 2023c.
Chao Fan, Jingzhe Ma, Dongyang Jin, Chuanfu Shen, and Shiqi Yu. Skeletongait: Gait recognition using skeleton maps. In AAAI, volume 38, pp. 1662-1669, 2024.
Yang Fu, Yunchao Wei, Yuqian Zhou, Honghui Shi, Gao Huang, Xinchao Wang, Zhiqiang Yao, and Thomas Huang. Horizontal pyramid matching for person re-identification. In AAAI, volume 33, pp. 81888888800 2019.
Yang Fu, Shibei Meng, Saihui Hou, Xuecai Hu, and Yongzhen Huang. Gpgait: Generalized pose- based gait recognition. In ICCV, 2023.
Martin Giese and Tomaso Poggio. Neural mechanisms for the recognition of biological move- ments. Nature Reviews Neuroscience, 4(3):179-192. 2003.
Hongji Guo and Qiang Ji. Physics-augmenter autoencoder for 3d skeleton- gait recognition. In ICCV, pp. 19627-19638, 2023.
Ju Han and Bir Bhanu. Individual recognition using gait energy image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(2):316-32,, 2005.
Xiao Han, Peishan Cong, Lan Xu, Jingya Wang, Jingyi Yu, and Yuexin Ma. Licamgait: Gait recognition in the wild by using lidar and camera multi-modal visual sensors. arXiv preprint arXiv:2211.12377 2022.
Saihui Hou, Chunshui Cao, Xu Liu, and Yongzhen Huang. Gait lateral network: Learning discrimi- native and compact representations for gait recognition. In ECCV, 2020.
Saihui Hou, Xu Liu, Chunshui Cao, and Yongzhen Huang. Set residual network for silhouette-basee gait recognition. IEEE Transactions Biometrics, Behavior, and Identity Science, 2021.

---

Published as a conference paper at ICLR 2026
Saihui Hou, Chao Fan, Chunshui Cao, Xu Liu, and Yongzhen Huang. A comprehensive study on the evaluation of silhouette- based gait recognition. IEEE Transactions on Biometrics, Behavior, and Identity Science, 2022a.
Saihui Hou, Xu Liu, Chunshui Cao, and Yongzhen Huang. Gait quality aware network: Toward the interpretability of silhouette-baase gait recognition. IEEE Transactions on Neural Networks and Learning Systems, 2022b.
Hung-Min Hsu, Yizhou Wang, Cheng- Yen Yang, Jenq-Neng Hwang, Hoang Le Uyen Thuc, and Kwang-Ju Kim. Gaittake: Gait recognition by temporal attention and keypoint-guided embed- 2022.
ding. In ICIP, pp.
Panjian Huang, Yunjie Peng, Saihui Hou, Chunshui Cao, Xu Liu, Zhiqiang He, and Yongzhen Huang. Occluded gait recognition with mixture of experts: An action detection perspective. In ECCV, pp. 380-397, 2024.
Xiaohu Huang. Duowang Zhu, Hao Wang, Xinggang Wang, Bo Yang. Botao He, Wenyu Liu, and Bin Feng. Context-sensitive temporal feature learning for gait recognition. In ICCV, pp. 12909- 12918, 2021a.
Zhen Huang, Dixiu Xue, Xu Shen, Xinmei Tian, Houqiang Li, Jianqiang Huang, and Xian-Sheng Hua. 3d local convolutional neural networks for gait recognition. In ICCV, pp. 14920-14929, 2021b.
Lily Lee and Eric I. Grimson. Gait analysis for recognition and classification. In IEEE Interna- tional Conference on Automatic Face Gesture Recognition, pp. 155-162, 2002.
Aoqi Li, Saihui Hou, Chenye Wang, Qingyuan Cai, and Yongzhen Huang. Aerialgait: Bridging aerial and ground views for gait recognition. In ACM MM, pp. 1139-1147, 2024.
Weijia Li, Saihui Hou, Chunjie Zhang, Chunshui Cao, Xu Liu, Yongzhen Huang, and Yao Zhao. An in-depth exploration of person re-identification and gait recognition in dothhcchagging conditions. In CVPR, pp. 13824-13833, 2023.
Xiang Li, Yasushi Makihara, Chi Xu, Yasushi Yagi. Shiqi Yu, and Mingwu Ren. End-to-end model- based gait recognition. In ACCV, 2020.
Xiang Li, Yasushi Makihara, Chi Xu, and Yasushi Yagi. Multi-view large population gait database with human meshes and its performance evaluation. IEEE Transactions on Biometrics, Behavior, and Identity Science, 2022.
Junhao Liang, Chao Fan, Saihui Hou, Chuanfu Shen, Yongzhen Huang, and Shiqi Yu. GaitEdge: Beyond plain end-to-end gait recognition for better practicality. In ECCV, 2022.
Rijun Liao, Shiqi Yu, Weizhi An, and Yongzhen Huang. A model-based gait recognition method with body pose and human prior knowledge. Pattern Recognition, 98:107069, 2020.
Beibei Lin, Shunli Zhang, and Feng Bao. Gait recognition with multiple-tempoorl-ssaale 3d convo- lutional neural network. In ACM MM, pp. 3054-3062, 2020.
Beibei Lin, Shunli Zhang, and Xin Yu. Gait recognition via effective global-local feature represen- tation and local temporal aggregation. In ICCV, pp. 14648-14656, 2021.
Hao Luo, Youzhi Gu, Xingyu Liao, Shenqi Lai, and Wei Jiang. Bag of tricks and a strong baseline for deep person re-identification. In CVPR Workshops, pp. 0-0, 2019.
Kang Ma, Ying Fu, Dezhi Zheng, Chunshui Cao. Xuecai Hu, and Yongzhen Huang. Dynamic aggregated network for gait recognition. In CVPR, pp. 22076-22085, 2023.
Kang Ma, Ying Fu, Chunshui Cao, Saihui Hou, Yongzhen Huang, and Dezhi Zheng. Learning visual prompt for gait recognition. In CVPR, pp. 593-603, 2024.
Yunjie Peng, Kang Ma. Yang Zhang, and Zhiqiang He. Learning rich features for gait recognition by integrating skeletons and silhouettes. Multimedia Tools and Applications, pp. 1-22, 2023.
12

---

Published as a conference paper at ICLR 2026
Ekkasit Pinyoamuntapong, Ayman Ali, Pu Wang, Minwoo Lee, and Chen Chen. Gaitmixer: skeleton- based gait representation learning via wide-speetrum multi-axial mixer. In /CASSPP pp. 1-5, 2023.
Zhaofan Qiu, Ting Yao, and Tao Mei. Learning spatio-temporal representation with pseudo-3d residual networks. In ICCV, pp. 1533-5541, 2017.
Imad Rida, Noor Almaadeed, and Somaya Almaadeed. Robust gait recognition: comprehensive survey. IET Biometrics, 8(1):14-28, 2019.
Alireza Sepas-Moghaddamm and Ali Etemad. Deep gait recognition: A survey. arXiv preprint 10X1v:2/02.09546. 2021.
Chuanfu Shen, Shiqi Yu, Jilong Wang, George Q Huang, and Liang Wang. A comprehensive survey on deep gait recognition: algorithms, datasets and challenges. arXiv preprint arXiv:2206.1377 13732, 2022.
Chuanfu Shen, Chao Fan, Wei Wu, Rui Wang, George Q Huang, and Shiqi Yu. LidarGait: Bench- marking 3d gait recognition with point clouds. In CVPR, pp. 1054-1063, 2023.
Torben Teepe, Ali Khan, Johannes Gilg, Fabian Herzog, Stefan Hörmann, and Gerhard Rigoll. GaitGraph: Graph convolutional network for skeleton-based gait recognition. ICIP, pp. 2314 2318, 2021.
Torben Teepe, Johannes Gilg, Fabian Herzog, Stefan Hörmann, and Gerhard Rigoll. Towards deeper understanding of skeleton-based gait recognition. In CVPRW, pp. 1569-1577, 2022.
Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri, Learning spa- tiotemporal features with 3d convolutional networks. In ICCV, pp. 4489-4497, 2015.
Chen Wang, Junping Zhang, Jian Pu, Xiaoru Yuan, and Liang Wang. Chrono-gait image: A novel temporal template for gait recognition. In ECCV, pp. 257-270. Springer, 2010.
Lei Wang, Bo Liu, Fangfang Liang, and Bincheng Wang. Hicrarchical spatio-temporal representa- tion learning for gait recognition. In ICCV, 2023a.
Lei Wang, Yinchi Ma, Peng Luan, Wei Yao, Congcong Li, and Bo Liu. Hih: A multi-moda hier- archy in hierarchy network for unconstrained gait recognition. arXiv preprint arXiv:2311.112//1 2023b.
Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaoou Tang, and Luc Van Gool. Temporal segment networks: Towards good practices for deep action recognition. In ECCV, pp. 20-36, 2016.
Ming Wang, Xianda Guo, Beibei Lin, Tian Yang, Zheng Zhu, Lincheng Li, Shunli Zhang, and Xin Yu. Dygait: Exploiting dynamic representations for high-performance gait recognition. In ICCV, 2023c.
Yanxiang Wang, Bowen Du, Yiran Shen, Kai Wu, Guangrong Zhao, Jianguo Sun, and Hongkai Wen. EV Gait: Event- based robust gait recognition using dynamic vision sensors. In CVPR, pp- 6358-6367, 2019.
Zeggbin Wang, Saihui Hou, Man Zhang, Xu Liu, Chunshui Cao, Yongzhen Huang, Peipei Li, and Shibiao Xu. Qagait: Revisit gait recognition from quality perspective. In AAAI, 2024.
Haijun Xiong, Bin Feng, Xinggang Wang, and Wenyu Liu. iausalit--inspired discriminative feature learning in triple domains for gait recognition. In ECCV, 2024.
Dingqiang Ye, Chao Fan, Jingzhe Ma, Xiaoming Liu, and Shiqi Yu. Biggait: Learning gait repre- sentation you want by large vision models. In CVPR, pp. 200-210, 2024.
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan R Salakhutdinov, and Alexander J Smola. Deep sets. In NeurlPS, pp. 3391-3401, 2017.
Cun Zhang, Xing-Peng Chen, Guo-Qiang Han, and Xiang-Jic Liu. Spatial transformer network on skeleton-based gait recognition. Expert Systems, pp. el e13244, 2023.
13

---

Published as a conference paper at ICLR 2026
Rong Zhang, Christian Vogler, and Dimitris Metaxas. Human gait recognition at sagittal plane. Image and Vision Computing, 25(3):321-33,,22 2007.
Jinkai Zheng, Xinchen Liu, Wu Liu, Lingxiao He, Chenggang Yan, and Tao Mei. Gait recognition in the wild with dense 3d representations and benchmark. In CVPR, pp. 20228-20237, 2022.
Yongsheng Zhu, Fengxin Sun, Changjun Jia, Chaorui Huang, Kuo Wang, Ying Li, Liping Chou, and Yupeng Mao. A 3d printing triboelectric sensor for gait analysis and virtual control based on human- computr interaction and the internet of things. Sustainabilliyy 14(17):10875, 2022.
Zheng Zhu, Xianda Guo, Tian Yang, Junjie Huang, Jiankang Deng, Guan Huang, Dalong Du, Jiwen Lu, and Jie Zhou. Gait recognition in the wild: A benchmark. In ICCV, pp. 14789-14799, 2021.
Shinan Zou, Chao Fan, Jianbo Xiong, Chuanfu Shen, Shiqi Yu, and Jin Tang. Cross-covariate gait recognition: A benchmark. In AAAI, volume 38, pp. 7855-7863, 2024.
14


Reviews:
[{"review_id": "fbaHmZpNYm", "reviewer": "Reviewer_fFgr", "summary": "This paper presents a novel snippet-based gait recognition method designed to overcome the limitations of traditional approaches: specifically, the lack of long-range modeling in sequence-based methods and the poor short-range temporal context in set-based methods. The core innovation lies in the sampling strategy, which divides the full gait sequence into $M$ equal-length sub-sequences (snippets), from which $N$ frames are uniformly sampled per snippet. For modeling this input, the method introduces intra-snippet and cross-snippet modules, which primarily adopt the structure of set-based methods, allowing for a fully 2D network architecture. Although the concept of snippet-based modeling is inspired by techniques from action recognition, its application in the gait domain yields a model that is both straightforward and efficient.", "questions": "Please refer to Weaknesses", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. The proposed method achieves improvements in both retrieval performance and inference speed.\n2. This paper introduces a simple approach to periodic modeling in gait recognition, which only requires an average estimation of frame length to guide snippet sampling. This is practical for gait recognition;\n3. Detailed experiments demonstrate the effectiveness of the sampling strategy and modeling design.", "weaknesses": "1. Regarding the framework design, the approach appears incremental and lacks clear differentiation from previous methods. The snippet sampling essentially extends uniform sampling, while the modeling constrains the receptive field through various blocks.\n2. Unlike TSN, which targets general video understanding, gait data exhibits inherent periodicity. Is there any analysis examining how this characteristic influences the snippet design?\n3. Is the 32-frame setting standard in gait recognition tasks, such as among other SOTA methods in Table 1? What is the length of one complete sequence during inference? How is L_1 determined at inference time?", "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": "7isOupXJ6i", "reviewer": "Reviewer_6tkX", "summary": "This paper presents GaitSnippet, a framework for silhouette-based gait recognition that bridges the gap between unordered set-based and ordered sequence-based approaches. The provided motivation is that traditional set-based models excel at computational efficiency but neglect short-range temporal cues, while sequence-based models capture temporal continuity at the expense of scalability and long-range dependencies. The authors reconceptualize gait as a collection of “snippets” and design a snippet-based pipeline encompassing Snippet Sampling, Snippet Modeling, and Snippet-Level Supervision for gait recognition. Experiments on four public datasets demonstrate consistent performance improvements, achieving state-of-the-art accuracy while maintaining efficiency comparable to 2D models and outperforming more complex 3D backbones.", "questions": "Please address Weaknesses O1-O4.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "S1) The paper introduces a seemingly new paradigm that redefines how temporal context is encoded in gait recognition. By treating gait as a composition of snippets rather than full sequences or unordered sets, the method unifies short- and long-range temporal modeling.\n\nS2) The pipeline is coherently structured—sampling, modeling, and supervision are all systematically justified and experimentally validated. \n\nS3) The experiments are extensive, spanning multiple datasets and including ablation studies on all major design elements (sampling hyperparameters, snippet block components, supervision weights). The performance gains over both 2D and 3D baselines are convincing and consistent.\n\nS4) The proposed approach achieves better accuracy than heavier 3D models while maintaining a lower computational cost, making it attractive for deployment in real-world gait recognition systems where efficiency is critical.", "weaknesses": "O1) While snippets are conceptually appealing, the paper provides limited theoretical justification for why random snippets generalize better than full sequences or unordered sets. The argument about mimicking human perception (recognition from partial cycles) is plausible but not formalized or empirically isolated. It remains unclear whether the improvement stems from better regularization, temporal diversity, or implicit data augmentation.\n\nO2) Although the results are strong, the paper reads as primarily an engineering contribution. There is little analysis of why snippet-level modeling yields gains—e.g., visualization of learned temporal attention, error distribution over gait conditions, or qualitative comparisons of temporal coherence. Such insights would bolster understanding of the snippet paradigm’s internal dynamics.\n\nO3) The architecture largely extends existing 2D residual frameworks by inserting pooling-based temporal operations. The core novelty thus lies more in data organization than in model architecture. \n\nO4) While technically detailed, the paper is verbose and occasionally repetitive (e.g., reiterating motivations for snippets across sections). Also a reader may struggle to identify the primary technical novelty amidst extensive procedural detail.\n\nO5) All experiments are silhouette-based. The paper does not explore how the snippet paradigm could extend to multimodal inputs (e.g., skeletons, RGB) or varying frame rates, which would be important for demonstrating broader impact. Moreover, robustness to occlusion, viewpoint shifts, or missing frames—scenarios where snippet sampling could be advantageous—are not analyzed.", "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": "SrI93jHOZ7", "reviewer": "Reviewer_P9Zy", "summary": "This paper focuses on the gait recogntion. Authors introduced a new representation of the gait, snipplet, instead of using the current set or sequence representation. Authors argue that with the new representation, it can both capture both local difference in temporal representation as well as the global shape and pose related difference. Authors also introduces specific snipplet modeling for it and build the gait snipplet model. Authors have compared this method with various of different baselines and show promising performance.", "questions": "In addition to comparison with some other representations that yields the best performance, it would be great if authors are able to provide time cost and resource analysis that is required for training for at least one of the dataset, such as GREW.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "+ The paper is well motivated and easy to follow. The introduced gait snipplet is able to combine both the sequential information as well as the set-level unordered information for understanding.\n\n+ Authors have provided pretty useful information in Table 3 for different sampling policy for both set, sequence and snipplet. It is interesting to see that the snipplet is able to yield the best performance across three modalities.\n\n+ Authors also compared the gait snipplet on multiple dataset, which they show best results compared with the other methods listed in the paper.", "weaknesses": "- Some of the latest results are not included in the paper, e.g., in [1], authors showed a 77.6/70.3 for R1 and mAP on Gait3D, while gait snipplet shows 77.5 and 69.4. Numbers on GREW is also 85.8/92.6 for R1 and R5 on [1] while it is 81.7/90.9. [1] is introducing a skeleton map for gait recognition, which is also a similar new representation that can be compared with snipplet, and frankly speaking, I think the snipplet idea can also be used on the skeleton map. It would be interesting for authors to apply the idea on [1] both for comparison with the latest state-of-the-art method as well as showing the generalization ability for the introduced method.\n\n[1] Skeletongait: Gait recognition using skeleton maps", "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": "aqrrpM9lDj", "reviewer": "Reviewer_yKZm", "summary": "This paper proposes a snippet-based gait recognition method aiming to resolve the drawbacks of current mainstream methods, i.e., set-based and sequence-based methods. Specifically, this paper proposes to represent a gait sequence as a combination of individualized snippets which contain several randomly selected frames from a continuous segment of the sequence. The randomly selected neighboring frames make the utilization of local context information better than that of set-based methods, while the randomly selection itself relax the constraint on continuity in sequence-based methods which is difficult to be satisfied in practical situation. Furthermore, this paper proposes rational approaches to snippet sampling and snippet modeling. Through extensive experiments, the effectiveness of this method is demonstrated.", "questions": "1. From my perspective, the main distinction between GaitSnippet and other multiscale temporal modeling methods lies in its integration of set-based and sequence-based approaches. Although the experimental results demonstrate its effectiveness, my concern is why such a combination is necessary, given that many prior studies have already shown the superiority of sequence-based methods over set-based ones.\n\n2. What would the performance be if a sequence-based modeling approach were also applied within each snippet?\n\n3. The authors propose to model a gait sequence as a composition of individual actions, each represented by an equal-length segment. Would it be more reasonable to represent each action with segments of variable length instead?\n\n4. Sampling with replacement is applied to both segment and frame selection. Could this strategy potentially lead to a loss of temporal information?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tThe paper is well-written and easy to follow. Most relevant details are described with enough depth providing the reader with enough information to properly grasp the overall idea and the rationale behind the choice of the implemented components. The implementation details seem to be enough to replicate the proposed solution. \n2.\tThe approach seems quite simple, yet it shows interesting performance.\n3.\tThe proposed approach seems can be applied on top of many other gait recognition methods.", "weaknesses": "See the Questions below.", "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)