📈 Time Series

🎞️ ECCV2024 · 3 paper notes

📌 Same area in other venues: 📷 CVPR2026 (7) · 🔬 ICLR2026 (121) · 💬 ACL2026 (8) · 🧪 ICML2026 (45) · 🤖 AAAI2026 (31) · 🧠 NeurIPS2025 (54)

Multi-person Pose Forecasting with Individual Interaction Perceptron and Prior Learning

This paper proposes IAFormer (Interaction-Aware Pose Forecasting Transformer). By designing the Interaction Perceptron Module (IPM) to evaluate the level of individual interaction with events, and introducing the Interaction Prior Learning Module (IPLM) to accumulate prior knowledge of high-frequency interaction patterns, it achieves semantic-level multi-person pose forecasting, significantly outperforming existing methods on multiple multi-person scene datasets.

OmniSat: Self-Supervised Modality Fusion for Earth Observation

This paper proposes OmniSat, a unified framework that fuses heterogeneous remote sensing data—including multi-spectral time-series (S2), SAR time-series (S1), and high-resolution single-temporal images (SPOT/Aerial)—into a unified representation using modality-specific encoders and cross-modal contrastive self-supervised pre-training. It outperforms all unimodal and multimodal baselines on semantic segmentation and crop classification.

Semantically Guided Representation Learning For Action Anticipation

The S-GEAR framework is proposed, which learns visual action prototypes and utilizes the semantic associations of language models to guide the geometric relationships among these prototypes. This enables the model to comprehend the semantic interconnectedness among actions, thereby enhancing action anticipation performance. S-GEAR achieves SOTA or highly competitive results across four benchmarks: Epic-Kitchens 55/100, EGTEA Gaze+, and 50 Salads.