Weakly Supervised Continuous Micro-Expression Intensity Estimation Using Temporal Deep Neural Network

• URL: http://arxiv.org/abs/2512.01145v1
• Date: Sun, 30 Nov 2025 23:47:47 GMT
• Title: Weakly Supervised Continuous Micro-Expression Intensity Estimation Using Temporal Deep Neural Network
• Abstract summary: We propose a unified framework for continuous micro-expression intensity estimation using only weak temporal labels.<n>A simple triangular prior converts sparse temporal landmarks into dense pseudo-intensity trajectories.<n>Experiments on SAMM and CASME II show strong temporal agreement with the pseudo-intensity trajectories.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Micro-facial expressions are brief and involuntary facial movements that reflect genuine emotional states. While most prior work focuses on classifying discrete micro-expression categories, far fewer studies address the continuous evolution of intensity over time. Progress in this direction is limited by the lack of frame-level intensity labels, which makes fully supervised regression impractical. We propose a unified framework for continuous micro-expression intensity estimation using only weak temporal labels (onset, apex, offset). A simple triangular prior converts sparse temporal landmarks into dense pseudo-intensity trajectories, and a lightweight temporal regression model that combines a ResNet18 encoder with a bidirectional GRU predicts frame-wise intensity directly from image sequences. The method requires no frame-level annotation effort and is applied consistently across datasets through a single preprocessing and temporal alignment pipeline. Experiments on SAMM and CASME II show strong temporal agreement with the pseudo-intensity trajectories. On SAMM, the model reaches a Spearman correlation of 0.9014 and a Kendall correlation of 0.7999, outperforming a frame-wise baseline. On CASME II, it achieves up to 0.9116 and 0.8168, respectively, when trained without the apex-ranking term. Ablation studies confirm that temporal modeling and structured pseudo labels are central to capturing the rise-apex-fall dynamics of micro-facial movements. To our knowledge, this is the first unified approach for continuous micro-expression intensity estimation using only sparse temporal annotations.

Related papers

• Discretizing Continuous Time Series for Imputation with Masked Diffusion Training [3.978640858147917]
  Time series imputation is a crucial area for reliable time series analysis, yet it remains challenging due to the complex temporal dynamics and noise of real-world data.<n>Existing approaches, however, exhibit two limitations: missing and observed values are embedded within the same representation space without explicit structural separation.<n>We propose the Masked Diffusion Time-series Imputation Model (MDTIM), which leverages the training paradigm of masked diffusion model for imputation tasks.<n>Our experiments confirm MDTIM achieves superior robustness and scalability, consistently outperforming state-of-the-art deterministic and generative baselines across various missing scenarios.
  arXiv  Detail & Related papers  (2026-08-19T17:16:27Z)
• In-Context Forcing: Uncovering Context Effects in Autoregressive Video Diffusion [52.18702395026718]
  We introduce In-Context Forcing, a progressive autoregressive paradigm that utilizes contexts with decreasing noise levels.<n>By applying less masking to distant frames and more masking to adjacent ones, this approach provides adaptive guidance.<n>Our method significantly outperforms state-of-the-art approaches in both visual fidelity and inference speed.
  arXiv  Detail & Related papers  (2026-08-05T13:54:31Z)
• From Snapshots to Trajectories: Learning Single-Cell Gene Expression Dynamics via Conditional Flow Matching [40.46237203947526]
  Single-cell Flow Matching (scFM) is a latent generative framework based on coupling-resolved flow matching.<n>scFM consistently improves distributional prediction performance for both temporal and temporally coherent visualizations.<n>scFM yields more accurate trajectory reconstruction and temporally coherent visualizations where intermediate time points are absent.
  arXiv  Detail & Related papers  (2026-05-21T11:26:19Z)
• A Progressive Training Strategy for Vision-Language Models to Counteract Spatio-Temporal Hallucinations in Embodied Reasoning [49.61652671596548]
  "Multi-image hallucination reasoning", where a massive performance drop between forward and temporal queries reveals a dependence on superficial shortcuts instead of genuine understanding.<n>To mitigate this, we develop a new dataset: Chain-of-temporal building on detailed reasoning into Chain steps and definitive judgments.<n>Our experiments demonstrate that this approach not only improves accuracy but also the forward-backward performance gap from over 70% to only 6.53%.
  arXiv  Detail & Related papers  (2026-04-12T07:48:44Z)
• Dual-Gated Epistemic Time-Dilation: Autonomous Compute Modulation in Asynchronous MARL [0.0]
  Multi-Agent Reinforcement Learning (MARL) algorithms achieve unprecedented successes across complex continuous domains.<n>Their standard deployment strictly adheres to a synchronous operational paradigm.<n>This dense throughput acts as a fundamental barrier to physical deployment on edge-devices where thermal and metabolic budgets are highly constrained.<n>We propose Epistemic Time-Dilation MAPPO (ETD-MAPPO), augmented with a Dual-Gated Epistemic Trigger.
  arXiv  Detail & Related papers  (2026-03-24T21:19:06Z)
• Generalization and Memorization in Rectified Flow [2.230929655489877]
  Generative models based on the Flow Matching objective, particularly Rectified Flow, have emerged as a dominant paradigm for efficient, high-fidelity image synthesis.<n>We investigate the memorization behaviors of RF through the test statistics of Membership Inference Attacks (MIA)<n>We show that replacing uniform timestep sampling with a Symmetric Exponential (U-shaped) distribution effectively minimizes exposure to vulnerable intermediate timesteps.
  arXiv  Detail & Related papers  (2026-03-12T21:10:39Z)
• MRI Contrast Enhancement Kinetics World Model [8.691568608551444]
  Applying world models to simulate the contrast enhancement kinetics in the human body enables continuous contrast-free dynamics.<n>The low temporal resolution in MRI acquisition restricts the training of world models, leading to a sparsely sampled dataset.<n>We propose MRI Contrast Enhancement Kinetics World model (MRI CEKWorld) with SpatioTemporal Consistency Learning (STCL)
  arXiv  Detail & Related papers  (2026-02-22T17:39:31Z)
• EDIT: Early Diffusion Inference Termination for dLLMs Based on Dynamics of Training Gradients [6.736735746633275]
  Diffusion-based large language models (dLLMs) refine token generations through iterative denoising, but answers often stabilize before all steps complete.<n>We propose EDIT, an inference-time criterion that adaptively stops denoising once sufficient reasoning stability relative to training-time reasoning is detected.
  arXiv  Detail & Related papers  (2025-11-29T23:47:47Z)
• Point-Supervised Facial Expression Spotting with Gaussian-Based Instance-Adaptive Intensity Modeling [18.418576406317538]
  We propose a two-branch framework for point-supervised facial expression spotting (P-FES)<n>First, to mitigate the limitation of hard pseudo-labeling, we propose a Gaussian-based instance-adaptive intensity modeling (GIM) module.<n>Second, we design a class-aware apex classification branch that distinguishes macro- and micro-expressions solely based on their pseudo-apex frames.
  arXiv  Detail & Related papers  (2025-11-21T04:59:54Z)
• PPMStereo: Pick-and-Play Memory Construction for Consistent Dynamic Stereo Matching [51.98089287914147]
  textbfPick-and-textbflay textbfMemory (PM) construction module for dynamic bfStereo matching, dubbed as bftextPPMStereo.<n>Inspired by the two-stage decision-making process in humans, we propose a textbfPick-and-textbflay textbfMemory (PM) construction module for dynamic bfStereo matching, dubbed as bftextPPMStereo.
  arXiv  Detail & Related papers  (2025-10-23T03:52:39Z)
• Time Is a Feature: Exploiting Temporal Dynamics in Diffusion Language Models [57.474294329887236]
  Diffusion large language models (dLLMs) generate text through iterative denoising.<n>Current decoding strategies discard rich intermediate predictions in favor of the final output.<n>We introduce two complementary methods that exploit temporal consistency.
  arXiv  Detail & Related papers  (2025-08-12T17:59:57Z)
• Hierarchical Temporal Context Learning for Camera-based Semantic Scene Completion [57.232688209606515]
  We present HTCL, a novel Temporal Temporal Context Learning paradigm for improving camera-based semantic scene completion. Our method ranks $1st$ on the Semantic KITTI benchmark and even surpasses LiDAR-based methods in terms of mIoU.
  arXiv  Detail & Related papers  (2024-07-02T09:11:17Z)
• Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks [50.2033914945157]
  We present a representation learning framework, Intensity Profile Projection, for continuous-time dynamic network data. The framework consists of three stages: estimating pairwise intensity functions, learning a projection which minimises a notion of intensity reconstruction error. Moreoever, we develop estimation theory providing tight control on the error of any estimated trajectory, indicating that the representations could even be used in quite noise-sensitive follow-on analyses.
  arXiv  Detail & Related papers  (2023-06-09T15:38:25Z)
• RCL: Recurrent Continuous Localization for Temporal Action Detection [33.05225035315206]
  State-of-the-art methods mostly rely on a dense anchoring scheme, where anchors are sampled uniformly over the temporal domain with a discretized grid. In this paper, we introduce Recurrent Continuous Localization (RCL), which learns a fully continuous anchoring representation. RCL achieves 52.92% mAP@0.5 on THUMOS14 and 37.65% mAP on ActivtiyNet v1.3, outperforming all existing single-model detectors.
  arXiv  Detail & Related papers  (2022-03-14T13:56:12Z)
• Short and Long Range Relation Based Spatio-Temporal Transformer for Micro-Expression Recognition [61.374467942519374]
  We propose a novel a-temporal transformer architecture -- to the best of our knowledge, the first purely transformer based approach for micro-expression recognition. The architecture comprises a spatial encoder which learns spatial patterns, a temporal dimension classification for temporal analysis, and a head. A comprehensive evaluation on three widely used spontaneous micro-expression data sets, shows that the proposed approach consistently outperforms the state of the art.
  arXiv  Detail & Related papers  (2021-12-10T22:10:31Z)
• Semi-supervised Facial Action Unit Intensity Estimation with Contrastive Learning [54.90704746573636]
  Our method does not require to manually select key frames, and produces state-of-the-art results with as little as $2%$ of annotated frames. We experimentally validate that our method outperforms existing methods when working with as little as $2%$ of randomly chosen data.
  arXiv  Detail & Related papers  (2020-11-03T17:35:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.