📈 Time Series

🎞️ ECCV2026 · 5 paper notes

📌 Same area in other venues: 🧠 NeurIPS2026 (2) · 📷 CVPR2026 (7) · 🔬 ICLR2026 (121) · 💬 ACL2026 (8) · 🧪 ICML2026 (45) · 🤖 AAAI2026 (31)

Video2Reaction: Mapping Video to Audience Reaction Distribution in the Wild

Addressing the challenge of predicting diverse and non-stationary viewer emotional responses to video content in the wild, this paper presents Video2Reaction—the first large-scale benchmark comprising 10,348 movie clips (389 hours) paired with 21-class fine-grained audience reaction distributions derived from social media comments—supported by a scalable two-stage multi-agent LLM annotation pipeline and comprehensive evaluations showing that finetuned vision-language models substantially outperform classical distribution baselines.

Forecasting Animal Motion

By establishing dense point trajectories as visual tokens for behavior modeling and combining local DINOv3 visual features with a camera motion compensation pipeline, this work introduces a diffusion transformer (DiT) architecture over unordered trajectory sets to forecast complex non-rigid wild animal motion across diverse species without category-specific 3D priors.

Geometry-Aware Visual Representation for Remaining Useful Life Prediction

Addressing the failure of conventional time-frequency representations to capture intrinsic system dynamical geometry, this paper introduces Phase Space Density Images (PSDI) with Globally Anchored Reconstruction (GAR) to model degradation as attractor dispersion, achieving robust RUL prediction via sliding-window temporal aggregation and compact knowledge distillation.

NeuralDMD: Interpretable Untrained Neural Network for Imaging from Sparse and Noisy Observations

Proposes NeuralDMD, a simulation-free, untrained per-instance reconstruction framework that parameterizes dynamic mode decomposition (DMD) spatial modes as continuous neural fields with spectral time evolution, reconstructing and extrapolating continuous spatio-temporal dynamics directly from extremely sparse and noisy observations via differentiable forward operators.

PMGC-SimVP: Parametric Multi-scale Gated Convolution for Global Ionospheric TEC Prediction

To tackle severe non-stationarity and pronounced latitudinal heterogeneity in global ionospheric Total Electron Content (TEC) under space weather disturbances, this paper proposes PMGC-SimVP, a purely convolutional framework featuring a two-stage low-rank temporal adapter, latitudinally structured parameter modulation, and dual-baseline residual learning, slashing parameters by over 90% while significantly boosting storm-time forecasting accuracy and long-horizon stability.