September 2026

Spiking neural networks (SNNs) have garnered significant attention in reinforcement learning tasks for their low power consumption. However, traditional spiking reinforcement learning (SRL) methods, w

Temporal Action Detection (TAD) in untrimmed videos requires effective spatial feature extraction for precise action classification and temporal feature modeling for accurate boundary localization. To

Knowledge distillation (KD) has become a pivotal technique for transferring knowledge from large-scale teacher models to lightweight student models. However, traditional feature-based distillation met

Unsupervised cross-modal hashing (UCMH) has attracted considerable attention owing to its minimal reliance on manual annotations and low retrieval latency. However, existing UCMH methods based on cont

Adverse weather conditions, such as rain, fog, and snow, degrade visual information, posing significant challenges for image restoration frameworks that adapt across diverse scenarios. Unified models

Accurate medical image segmentation plays a vital role in clinical diagnostics by facilitating the precise delineation of anatomical structures and pathological regions. However, the performance of ex

Egocentric video-language understanding demands both high efficiency and accurate spatial-temporal modeling. Existing approaches face three key challenges: 1) Excessive pre-training cost arising from

Event cameras are increasingly used for Multiple Object Tracking (MOT), but their asynchronous event output often requires specialized methods. Existing processing methods primarily follow two paradig

Traditional temporal action localization (TAL) methods rely on large amounts of detailed annotated data, whereas few-shot TAL reduces this dependence by using only a few training samples to identify u

Computational optical imaging reconstructs signals from limited and noisy measurements, leading to ill-posed inverse problems that require regularization. Graph regularization (GR) has shown promise f