Learning Time in Static Classifiers

2025-11-15

AI Summary

Traditional static classifiers neglect the temporal dynamics inherent in visual data, rendering them inadequate for modeling gradual evolutionary patterns. To address this, we propose a temporal enhancement framework that introduces strong temporal inductive bias solely through the loss function—without modifying the underlying network architecture. Our method establishes a support-example-query (SEQ) learning paradigm and employs a differentiable soft dynamic time warping (DTW)-based temporal alignment loss. It integrates pre-extracted features, class-specific temporal prototype modeling, multi-objective optimization, semantic consistency constraints, and temporal smoothing regularization. Evaluated on fine-grained and ultra-fine-grained image classification as well as video anomaly detection, the approach achieves significant improvements in both classification accuracy and temporal consistency. The framework is modular, data-efficient, and exhibits strong generalization across diverse temporal vision tasks.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Planning, Routing, and Scheduling: Temporal Planning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Real-world visual data rarely presents as isolated, static instances. Instead, it often evolves gradually over time through variations in pose, lighting, object state, or scene context. However, conventional classifiers are typically trained under the assumption of temporal independence, limiting their ability to capture such dynamics. We propose a simple yet effective framework that equips standard feedforward classifiers with temporal reasoning, all without modifying model architectures or introducing recurrent modules. At the heart of our approach is a novel Support-Exemplar-Query (SEQ) learning paradigm, which structures training data into temporally coherent trajectories. These trajectories enable the model to learn class-specific temporal prototypes and align prediction sequences via a differentiable soft-DTW loss. A multi-term objective further promotes semantic consistency and temporal smoothness. By interpreting input sequences as evolving feature trajectories, our method introduces a strong temporal inductive bias through loss design alone. This proves highly effective in both static and temporal tasks: it enhances performance on fine-grained and ultra-fine-grained image classification, and delivers precise, temporally consistent predictions in video anomaly detection. Despite its simplicity, our approach bridges static and temporal learning in a modular and data-efficient manner, requiring only a simple classifier on top of pre-extracted features.

Problem

• Enhancing static classifiers to handle temporal data evolution
• Learning temporal prototypes without architectural modifications
• Bridging static and temporal learning through loss design

Innovation

• SEQ paradigm structures temporal training trajectories
• Soft-DTW loss aligns prediction sequences dynamically
• Multi-term objective ensures semantic-temporal consistency