This paper proposes a simplified Temporal Convolutional Network-based estimator to improve channel estimation in vehicular communication.

This paper proposes Supervised Memory Training (SMT), a method for training nonlinear RNNs that sidesteps recurrent credit propagation entirely.

This paper proposes an improved CNN-LSTM model for IoT intrusion detection, achieving high accuracy by combining spatial and temporal feature learning from network traffic.

This paper proposes a 3D CNN detector that leverages temporal artifacts to accurately identify high-quality deepfake videos, demonstrating robust detection even after social media re-encoding.

The paper introduces ConTrans, a novel local-global multi-scale encoder that combines convolutional and transformer features to significantly improve zero-shot temporal action localization by capturin…

The paper introduces Multi-temporal Referring Segmentation (MTRS), a new task requiring models to segment language-described temporal changes, and proposes MTRefSeg-R1, a specialized framework that ac…

MORPHOS is a novel autoregressive framework that generates dynamic 3D assets (like meshes and radiance fields) from videos by using a unified 4D representation to ensure temporal consistency and handl…

The paper introduces the Temporal Contrastive Transformer (TCT) for financial crime detection, demonstrating that its self-supervised embeddings capture meaningful temporal behavioral patterns, though…

The paper introduces residualized temporal Sparse Autoencoders (SAEs) to analyze the full spatiotemporal structure of activations generated during the iterative denoising process of diffusion models,…

The paper introduces BabyCL, a continual multimodal learning framework that processes egocentric video data in a single chronological pass, demonstrating that meaningful word-referent mappings can be…

The paper proposes Dual-Scale Retentive Dynamics (DSRD), a unified framework that improves representation learning on dynamic graphs by jointly modeling evolving temporal and structural dependencies.

The paper introduces QuITE, a plug-and-play embedding module that uses learnable query tokens to effectively embed irregular multivariate time series data into latent representations compatible with e…

CLANE presents an end-to-end continual action recognition system deployed on neuromorphic hardware (Intel Loihi 2) using event cameras, achieving high accuracy with massive reductions in energy and la…

SHARP proposes a novel sleep-based hierarchical replay framework to efficiently learn long-range non-stationary temporal patterns in streaming data, achieving improved context retention and predictive…

CART introduces a parameter-efficient recurrent transformer architecture that reuses a core block multiple times, but its performance does not surpass a dense baseline, suggesting that weight sharing…

MARS proposes an encoder-agnostic aggregation operator that explicitly models multi-scale temporal structure in sequential recommendation, achieving state-of-the-art performance across both sparse and…

LongLive-RAG proposes a novel Retrieval-Augmented Generation (RAG) framework to stabilize and improve the quality of long-horizon video generation by treating the entire generated history as a searcha…

The paper introduces Moment-Video, a new benchmark that diagnoses the ability of video MLLMs to understand brief, critical visual events, revealing that current models struggle significantly with temp…

The paper introduces MusTBENCH, a new benchmark, and MusT, an optimization recipe, to rigorously test and improve the ability of Large Audio-Language Models (LALMs) to accurately ground their musical…

The paper establishes the first theoretical framework for analyzing the learnability of Test-Time Adaptation (TTA) under non-stationary data streams by introducing Recovery Complexity, which quantifie…