Test-Time Adaptation via Cache Personalization for Facial Expression Recognition in Videos

Masoumeh Sharafi; Muhammad Osama Zeeshan; Soufiane Belharbi; Alessandro Lameiras Koerich; Marco Pedersoli; Eric Granger

cs.CVMar 22, 2026

Test-Time Adaptation via Cache Personalization for Facial Expression Recognition in Videos

Authors: Masoumeh Sharafi, Muhammad Osama Zeeshan, Soufiane Belharbi, Alessandro Lameiras Koerich, Marco Pedersoli, Eric Granger

Organizations: LIVIA, Department of Systems Engineering, ´ETS Montreal, Canada · LIVIA, Department of Software and IT Engineering, ´ETS Montreal, Canada

Abstract

Facial expression recognition (FER) in videos requires model personalization to capture considerable variation across subjects. Vision-language models (VLMs) offer strong transfer through image-text alignment, but their performance can degrade under inter-subject distribution shifts. Test-time adaptation (TTA) can mitigate this challenge, yet most state-of-the-art methods rely on unsupervised parameter optimization, introducing computational overhead that limits real-world deployment. This paper introduces TTA through Cache Personalization (TTA-CaP), a gradient-free, cache-based method for cost-effective personalization of VLMs in video FER. Unlike prior cache-based TTA methods that rely solely on dynamic memories of test samples and may drift because of noisy pseudo-labels, TTA-CaP employs three complementary caches: a personalized static cache constructed through feature-statistics matching, a positive target cache containing reliable subject-specific samples, and a negative target cache containing low-confidence cases as negative evidence. A tri-gate mechanism prevents cache corruption by controlling updates according to temporal stability, confidence, and consistency with the personalized static cache. The caches jointly provide subject-matched positive and negative evidence for robust personalization. TTA-CaP further refines predictions through embedding fusion, supporting temporally stable video-level predictions. Experiments on BioVid, StressID, and BAH show that TTA-CaP outperforms state-of-the-art TTA methods under subject-specific and environmental shifts while maintaining low computational and memory overhead. Our code is publicly available at https://github.com/MasoumehSharafi/TTA-CaP.

Figures & tables

Figure 1: A comparison of SOTA cache-based TTA methods ( a ), which store pseudo-labeled test embeddings only in dynamic target caches and perform logit-level fusion, with our TTA-CaP ( b ). TTA-CaP adapts a VLM to a subject video at test time by combining a personalized static cache with dynamic target-domain caches, and refines predictions through embedding-level fusion. (c) Average weighted average recall ( WAR ) versus GFLOPs for test-time adaptation, over target subjects of the BioVid dataset. Marker shapes distinguish cache- vs. prompt-based methods, while marker sizes indicate runtime (ms) at test time.

Figure 2: Overview of TTA-CaP . At test time, frozen CLIP encoders produce visual and text embeddings and an initial prediction through image–text similarity. A personalized static cache provides stable subject-relevant references. While, a tri-gate evaluates temporal stability, prediction confidence, and consistency with the static cache to control updates to the positive and negative target caches. This design limits the accumulation and propagation of noisy pseudo-labels. Final predictions are obtained through embedding-level fusion of the temporally aggregated representation with retrieved cache representations, enabling cost-effective personalization without test-time training.

Protocol	Method	BioVid		StressID		BAH
Protocol	Method	WAR $\uparrow$	F1 $\uparrow$	WAR $\uparrow$	F1 $\uparrow$	WAR $\uparrow$	F1 $\uparrow$
ZS	CLIP-ViT-B/32 (ICML’21)	50.0	33.3	60.4	34.8	39.5	28.1
	CLIP-ViT-B/32 $\dagger$ (ICML’21)	69.7	66.6	67.0	44.5	60.4	39.8
	EmoCLIP [ 8 ] (FG’24)	67.7	63.4	63.5	35.9	56.2	36.5
	X-CLIP [ 20 ] (ECCV’22)	70.9	57.9	62.3	41.3	63.0	39.2
FT	Exp-CLIP [ 47 ] (WACV’25)	70.2	66.7	63.1	44.5	62.2	38.5

Table 1: Performance ( F1 score and WAR ) of zero-shot (ZS), fine-tuning (FT), and TTA on CLIP models for FER. Results are averaged over 10 target subjects for each dataset. CLIP-ViT-B/32

\dagger

denotes full CLIP fine-tuning.

Method	Sub-1	Sub-2	Sub-3	Sub-4	Sub-5	Sub-6	Sub-7	Sub-8	Sub-9	Sub-10	Avg.
TPT [ 29 ] (NeurIPS’22)	92.0	51.5	40.0	49.6	79.0	87.0	79.5	43.4	99.0	54.0	67.5
TDA [ 12 ] (CVPR’24)	94.9	50.3	43.5	48.0	80.8	86.2	80.2	43.5	100.0	55.0	68.2
DPE [ 45 ] (NeurIPS’24)	90.4	51.5	43.5	73.1	72.0	83.6	79.8	48.5	100.0	54.4	69.6
PromptAlign [ 1 ] (NeurIPS’23)	94.2	60.1	42.5	61.5	83.5	90.5	80.2	43.5	100.0	60.0	71.6
ReTA [ 17 ] (ACMMM’25)	94.2	60.1	43.5	60.8	83.5	88.0	80.2	43.5	100.0	60.0	71.3
MCP [ 5 ] (ICCV’25)	94.5	62.0	43.5	64.0	86.0	92.0	82.0	44.0	100.0	62.0	73.0

Table 2: F1 score per subject on the BioVid dataset for TTA-CaP and competing TTA methods. Best results are shown in bold .

Method	Sub-1	Sub-2	Sub-3	Sub-4	Sub-5	Sub-6	Sub-7	Sub-8	Sub-9	Sub-10	Avg.
TPT [ 29 ] (NeurIPS’22)	70.7	43.6	86.0	83.1	36.4	78.7	41.1	50.0	40.0	50.0	57.9
TDA [ 12 ] (CVPR’24)	45.5	40.0	50.1	81.0	35.0	80.0	29.6	45.6	42.9	50.0	49.9
DPE [ 45 ] (NeurIPS’24)	41.0	40.6	49.0	81.0	49.8	80.0	56.0	46.0	42.9	56.6	54.2
PromptAlign [ 1 ] (NeurIPS’23)	41.3	45.0	45.6	82.3	48.1	80.0	44.0	58.0	42.9	45.5	53.2
ReTA [ 17 ] (ACMMM’25)	43.2	41.0	53.0	80.9	36.1	80.0	39.1	53.3	42.9	59.3	52.8
MCP [ 5 ] (ICCV’25)	55.0	44.5	60.0	78.3	43.6	79.1	52.2	61.2	44.0	65.0	58.2

Table 3: F1 score per subject on StressID for TTA-CaP and other TTA methods.

Personalization protocol	WAR (%)	F1 (%)	Time
Subject-level	81.0	78.6	$\textbf{1.0}\times$
Video-level (online)	80.8	78.0	$\sim 4.0\times$

Table 4: Comparison of subject-level and video-level personalization for TTA-CaP on BioVid .

Panel of Figure 3 — Figure 3: (a) Effect of number of selected source subjects on WAR . (b) Top- $3$ source subjects selected by diagonal Fréchet distance.

Method	WAR (%)	F1 (%)
TTA-CaP	81.0	78.6
w/o Positive Cache	79.2	76.8
w/o Negative Cache	80.0	77.6
w/o Tri-Gate	78.7	76.0
w/o Temporal Encoder	80.6	78.1
w/o Personalized Static Cache	78.1	77.9

Table 5: Component ablation of TTA-CaP on BioVid . Each row removes one component from the full method.

Panel of Figure 4 — Figure 4: Leave-one-out ablation of the tri-gate mechanism on BioVid . Removing any gate reduces WAR , with the largest drop observed when removing the prototype gate.

Appendix figures & tables17 assets

Supplementary material from the paper’s appendix.

Appendix

Method	Time (ms)	Memory (MB)	WAR (%)
TPT	771.2	2800	71.1
PromptAlign	900.0	3100	75.3
TDA	97.8	2124	71.4
DPE	263.9	1700	73.1
MCP	145.0	1450	75.7
ReTA	174.0	2390	75.1

Appendix

Table 1: Complexity of TTA methods. Run time per batch (B=16), memory, and average WAR for BioVid target subjects.

Representation	Current-class	Non-current-class	$\Delta$
Pseudo-label: Stress
$\mathbf{z}^{\mathrm{tgt}}$	0.61	0.47	0.14
$\mathbf{z}^{\mathrm{tgt}}-\mathbf{z}^{\mathrm{tgt}-}$	0.58	0.33	0.25
Pseudo-label: Neutral
$\mathbf{z}^{\mathrm{tgt}}$	0.63	0.45	0.18
$\mathbf{z}^{\mathrm{tgt}}-\mathbf{z}^{\mathrm{tgt}-}$	0.60	0.31	0.29

Appendix

Table 2: Effect of negative-cache subtraction in TTA-CaP on StressID . Class scores are cosine similarities between target representations and class-text embeddings, averaged over all target samples for each pseudo-label. The non-current score is the highest score among classes other than the current pseudo-label, and

\Delta

denotes the margin between the current- and highest non-current-class scores.

Method	WAR (%)	F1 (%)
MCP	75.7	73.0
MCP + Temporal	75.9	73.4
MCP + Temporal + Personalized Cache	77.6	75.3
TTA-CaP	81.0	78.6

Appendix

Table 3: Fair comparison with MCP on BioVid . Temporal modeling and personalized cache denote the corresponding components used in TTA-CaP .

Backbone	Method	WAR (%)
RN101	Source FT	64.8
RN101	TTA-CaP	76.9
ViT-B/16	Source FT	66.1
ViT-B/16	TTA-CaP	79.6
ViT-B/32	Source FT	67.7
	TTA-CaP	81.0

Appendix

Table 4: Performance of TTA-CaP with different backbones on BioVid .

$m$	Time (ms)	Memory (MB)	WAR (%)
1	104	870	76.2
2	107	885	78.2
3	110	904	81.0
4	113	921	81.0
5	116	938	81.0

Appendix

Table 5: Accuracy–efficiency tradeoff of TTA-CaP for different personalized cache sizes (

m

) on BioVid .

Window size	TTA-CaP w/o PSC	TTA-CaP
16	41.1	41.0
24	42.0	43.5
32	42.2	43.8

Appendix

Table 6: Effect of temporal window size on the F1 (%) of TTA-CaP on BAH . PSC denotes the personalized static cache.

Figure 1: Representative neutral and pain samples from an easier subject (Sub-9) and a more challenging subject (Sub-3). Sub-9 exhibits more visually distinguishable expression changes between neutral and pain, whereas the two classes are less separable for Sub-3, consistent with their subject-wise F1 results.

Method	Logit ECE $\downarrow$	Embed ECE $\downarrow$
TDA	20.7	15.2
ReTA	19.2	14.0
TTA-CaP	17.8	12.3

Appendix

Table 7: Calibration comparison on BioVid

Weighting Strategy	WAR (%) $\uparrow$
Conf.-weighted	78.9
Attention-based	77.4
Equal-weight	81.0

Appendix

Table 8: Ablation of cache-weighting strategies for TTA-CaP on BioVid . Results report the average WAR over target subjects.

Prompt Template	WAR	F1 score
a photo of a [CLS]	68.7	63.3
a_photo_of_the_[CLS]_face	66.5	59.3
a_photo_of_one_[CLS]_face	64.2	55.8
a_close-up_photo_of_the_[CLS]_face	68.2	62.6
a_low_resolution_photo_of_a_[CLS]_face	68.7	64.2
a_good_photo_of_a_[CLS]_face	69.2	65.4

Appendix

Table 9: Ablation of class-prompt templates for the CLIP text encoder on BioVid using CLIP ViT-B/32. Performance varies noticeably across prompt formulations, and the template “a person with an expression of [CLS]” achieves the best WAR and F1 score.

Cache construction	WAR (%) $\uparrow$
Baseline (k-means, $k{=}1$ )	78.8
Two-stage (k-means)	79.3
Two-stage (DBSCAN)	79.5
Personalized (k-means)	79.6
Personalized (DBSCAN)	81.0

Appendix

Table 10: Effect of different static source-cache construction strategies. Average WAR (%) over target subjects in BioVid using TTA-CaP .

Panel of Figure 2 — Figure 2: Ablation of source-cache construction on BioVid . Results report the average WAR across target subjects. “Prototypes” denotes the personalized source cache built from source subject prototypes.

Figure 3: t-SNE visualization of the embedding space before and after cache fusion for two subjects from the StressID dataset.

Method	Sub-1	Sub-2	Sub-3	Sub-4	Sub-5	Sub-6	Sub-7	Sub-8	Sub-9	Sub-10	Avg.
TPT [ 29 ] (NeurIPS’22)	40.6	35.7	43.7	45.9	45.6	43.1	35.0	34.7	40.7	32.9	39.7
TDA [ 12 ] (CVPR’24)	40.0	39.7	44.4	46.0	42.0	42.6	35.0	35.5	40.7	33.2	39.9
DPE [ 45 ] (NeurIPS’24)	34.8	37.1	44.2	46.0	45.3	43.0	37.2	32.5	41.0	33.2	39.4
PromptAlign [ 1 ] (NeurIPS’23)	25.0	37.0	46.9	51.0	46.1	43.5	41.0	32.5	41.0	33.2	39.7
ReTA [ 17 ] (ACMMM’25)	25.0	36.9	41.9	51.0	46.1	51.2	40.0	32.5	41.0	33.2	39.8
MCP [ 5 ] (ICCV’25)	38.5	37.8	44.5	48.0	45.5	45.0	39.0	33.5	40.7	28.5	40.1

Appendix

Table 11: F1 score per subject on BAH for TTA-CaP and other TTA methods.

Method	Sub-1	Sub-2	Sub-3	Sub-4	Sub-5	Sub-6	Sub-7	Sub-8	Sub-9	Sub-10	Avg.
TPT	91.9 $\pm$ 0.12	53.0 $\pm$ 0.31	50.0 $\pm$ 0.00	61.5 $\pm$ 0.36	79.0 $\pm$ 0.19	88.9 $\pm$ 0.14	76.0 $\pm$ 0.12	50.0 $\pm$ 0.00	98.9 $\pm$ 0.05	62.0 $\pm$ 0.28	71.1
TDA	93.0 $\pm$ 0.10	52.6 $\pm$ 0.29	50.0 $\pm$ 0.00	63.9 $\pm$ 0.32	79.5 $\pm$ 0.17	87.5 $\pm$ 0.16	76.3 $\pm$ 0.18	50.0 $\pm$ 0.00	100.0 $\pm$ 0.00	61.6 $\pm$ 0.26	71.4
DPE	91.7 $\pm$ 0.13	55.0 $\pm$ 0.27	50.0 $\pm$ 0.00	85.0 $\pm$ 0.18	73.0 $\pm$ 0.24	84.7 $\pm$ 0.20	80.0 $\pm$ 0.16	50.0 $\pm$ 0.00	100.0 $\pm$ 0.00	61.8 $\pm$ 0.20	73.1
PromptAlign	93.8 $\pm$ 0.09	65.2 $\pm$ 0.21	50.0 $\pm$ 0.00	70.0 $\pm$ 0.24	85.0 $\pm$ 0.14	92.0 $\pm$ 0.10	80.0 $\pm$ 0.15	50.0 $\pm$ 0.00	100.0 $\pm$ 0.00	67.6 $\pm$ 0.20	75.3
ReTA	93.8 $\pm$ 0.08	65.2 $\pm$ 0.19	50.0 $\pm$ 0.00	69.5 $\pm$ 0.22	85.0 $\pm$ 0.13	90.2 $\pm$ 0.12	80.0 $\pm$ 0.14	50.0 $\pm$ 0.00	100.0 $\pm$ 0.00	67.6 $\pm$ 0.18	75.1
MCP [ 5 ]	93.9 $\pm$ 0.08	65.8 $\pm$ 0.20	50.0 $\pm$ 0.00	72.0 $\pm$ 0.20	85.5 $\pm$ 0.13	92.5 $\pm$ 0.10	81.5 $\pm$ 0.14	50.0 $\pm$ 0.00	100.0 $\pm$ 0.00	66.0 $\pm$ 0.18	75.7

Appendix

Table 12: Subject-wise WAR score on the BioVid dataset for TTA-CaP and competing TTA methods. Best results are shown in bold .

Method	Sub-1	Sub-2	Sub-3	Sub-4	Sub-5	Sub-6	Sub-7	Sub-8	Sub-9	Sub-10	Avg.
TPT	74.7 $\pm$ 0.18	51.6 $\pm$ 0.21	90.0 $\pm$ 0.09	88.0 $\pm$ 0.12	52.2 $\pm$ 0.24	77.5 $\pm$ 0.11	66.6 $\pm$ 0.22	63.4 $\pm$ 0.19	54.0 $\pm$ 0.23	91.9 $\pm$ 0.08	70.9
TDA	66.9 $\pm$ 0.22	45.2 $\pm$ 0.28	75.6 $\pm$ 0.17	90.1 $\pm$ 0.10	49.6 $\pm$ 0.24	80.0 $\pm$ 0.05	41.2 $\pm$ 0.31	75.3 $\pm$ 0.18	81.8 $\pm$ 0.02	91.9 $\pm$ 0.07	69.7
DPE	64.0 $\pm$ 0.23	55.0 $\pm$ 0.20	73.9 $\pm$ 0.18	90.0 $\pm$ 0.09	52.6 $\pm$ 0.22	80.0 $\pm$ 0.05	50.9 $\pm$ 0.27	73.0 $\pm$ 0.19	81.8 $\pm$ 0.07	92.0 $\pm$ 0.07	71.3
PromptAlign	69.9 $\pm$ 0.20	61.6 $\pm$ 0.17	78.6 $\pm$ 0.14	90.9 $\pm$ 0.08	51.2 $\pm$ 0.21	80.0 $\pm$ 0.15	60.4 $\pm$ 0.19	81.3 $\pm$ 0.13	81.8 $\pm$ 0.06	90.9 $\pm$ 0.09	74.6
ReTA	64.5 $\pm$ 0.21	50.5 $\pm$ 0.23	82.0 $\pm$ 0.12	90.0 $\pm$ 0.09	46.8 $\pm$ 0.25	80.0 $\pm$ 0.12	49.3 $\pm$ 0.29	80.0 $\pm$ 0.04	81.8 $\pm$ 0.10	93.7 $\pm$ 0.06	71.8
MCP [ 5 ]	72.5 $\pm$ 0.19	61.0 $\pm$ 0.18	81.5 $\pm$ 0.13	90.5 $\pm$ 0.09	55.0 $\pm$ 0.21	80.0 $\pm$ 0.08	65.0 $\pm$ 0.20	78.0 $\pm$ 0.14	81.8 $\pm$ 0.06	89.7 $\pm$ 0.07	75.5

Appendix

Table 13: Subject-wise WAR score on the StressID dataset for TTA-CaP and competing TTA methods.

Method	Sub-1	Sub-2	Sub-3	Sub-4	Sub-5	Sub-6	Sub-7	Sub-8	Sub-9	Sub-10	Avg.
TPT	62.0 $\pm$ 0.18	53.7 $\pm$ 0.19	73.9 $\pm$ 0.14	80.0 $\pm$ 0.11	81.2 $\pm$ 0.10	71.0 $\pm$ 0.16	58.0 $\pm$ 0.17	55.0 $\pm$ 0.18	69.0 $\pm$ 0.15	53.0 $\pm$ 0.20	65.6
TDA	58.0 $\pm$ 0.20	53.7 $\pm$ 0.18	75.0 $\pm$ 0.13	80.3 $\pm$ 0.10	79.0 $\pm$ 0.12	71.6 $\pm$ 0.15	58.0 $\pm$ 0.16	55.9 $\pm$ 0.17	69.0 $\pm$ 0.14	51.7 $\pm$ 0.22	65.2
DPE	58.6 $\pm$ 0.19	59.6 $\pm$ 0.16	79.6 $\pm$ 0.10	80.5 $\pm$ 0.10	83.4 $\pm$ 0.09	67.0 $\pm$ 0.18	59.0 $\pm$ 0.15	54.0 $\pm$ 0.19	71.9 $\pm$ 0.12	54.0 $\pm$ 0.18	66.7
PromptAlign	59.1 $\pm$ 0.18	59.1 $\pm$ 0.16	72.0 $\pm$ 0.15	84.5 $\pm$ 0.09	84.1 $\pm$ 0.08	72.4 $\pm$ 0.14	58.5 $\pm$ 0.16	55.9 $\pm$ 0.17	71.5 $\pm$ 0.12	54.9 $\pm$ 0.17	67.1
ReTA	61.4 $\pm$ 0.17	60.0 $\pm$ 0.15	72.0 $\pm$ 0.14	84.0 $\pm$ 0.09	84.1 $\pm$ 0.08	72.4 $\pm$ 0.13	58.5 $\pm$ 0.15	55.9 $\pm$ 0.16	72.0 $\pm$ 0.11	56.0 $\pm$ 0.16	67.6
MCP [ 5 ]	61.0 $\pm$ 0.17	59.5 $\pm$ 0.16	74.0 $\pm$ 0.14	84.5 $\pm$ 0.09	83.5 $\pm$ 0.09	73.0 $\pm$ 0.13	59.0 $\pm$ 0.15	55.5 $\pm$ 0.17	71.5 $\pm$ 0.12	54.5 $\pm$ 0.17	67.6

Appendix

Table 14: Subject-wise WAR score on the BAH dataset for TTA-CaP and competing TTA methods.

Test-Time Adaptation

Test-time adaptation updates or recalibrates pretrained models at inference to handle distribution shifts, often without target labels.

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Test-Time Adaptation with Online Personalized Energy-Based Cache for Fine-Grained Video Expression Recognition

Masoumeh Sharafi, Muhammad Osama Zeeshan, Soufiane Belharbi, Alessandro Lameiras Koerich+2

LIVIA, Department of Systems Engineering, École de technologie supérieure, Montreal, Canada · LIVIA, Department of Software and IT Engineering, École de technologie supérieure, Montreal, Canada

Test-Time Adaptation VLM Adaptation Facial Expression Recognition

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Facial expression recognition (FER) in videos is challenging because models must identify subtle, temporally evolving affective states that vary across individuals. Although vision-language models provide transferable visual-semantic representations, models trained on subject-independent data often degrade under subject-specific distribution shifts at inference time. Existing test-time adaptation (TTA) methods commonly update model parameters during inference, increasing computational cost and latency. Cache-based methods avoid parameter updates, but they usually require enough target samples to form reliable class prototypes, which is difficult early in adaptation and for rarely observed classes. We introduce Energy-Based Cache Personalization (EB-CaP), a subject-based online TTA method for video FER that generates class-specific prototypes personalized to each target video. EB-CaP uses a lightweight energy-based model to sample prototypes from the current unlabeled video and populate a personalized cache online, without accumulating large amounts of target data or storing diverse source prototypes. Its energy function relies only on pretrained CLIP: similarities between the target video embedding and class text embeddings guide prototype sampling. In parallel, positive and negative caches store reliable and uncertain target embeddings. An adaptive entropy gate controls cache updates according to the evolving confidence distribution, while a diversity gate limits redundant samples. Final predictions combine cache-derived scores with the current CLIP scores. Experiments on BioVid, StressID, and BAH show that EB-CaP outperforms state-of-the-art TTA methods while maintaining low computational and memory overhead. Code is available at https://github.com/MasoumehSharafi/EB-CaP.

Abstract

Test-Time Adaptation

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