S3VD is a new video deraining framework that leverages semantic guidance and spatio‑temporal scanning to improve performance over existing State Space Models such as Mamba. It introduces a Multi‑Scale Semantic Fusion module that uses DINOv2 priors to preserve 2D spatial semantics, and a Spatio‑Temporal Scanning Fusion module that incorporates a Decoupled‑Gating Mamba layer to better model intra‑ and inter‑frame correlations. Experiments on video deraining benchmarks show that S3VD achieves state‑of‑the‑art results, improving PSNR by an average of 0.84 dB over Mamba‑based baselines.
arXiv:2607. 13421v1 Announce Type: cross Abstract: Spatio-Temporal Video Grounding (STVG) aims to retrieve the visual trajectory of a specific object from a video stream as described by a natural language expression.
arXiv:2608.22861v1 Announce Type: new Abstract: State Space Models (SSMs) have surfaced as a promising architecture in Video Frame Interpolation (VFI), as they can capture long-range dependencies wit...
arXiv:2602. 21819v3 Announce Type: replace-cross Abstract: Reconstructing dynamic visual experiences from brain activity provides a compelling avenue for exploring the neural mechanisms of human visual perception.
arXiv:2605. 20301v2 Announce Type: replace-cross Abstract: In autonomous driving, 3D object detection is essential for accurate perception and reliable decision-making.
arXiv:2508.03077v2 Announce Type: replace Abstract: Feedforward 3D Gaussian Splatting (3DGS) overcomes the limitations of optimization-based 3DGS by enabling fast and high-quality reconstruction with...
FluidRain is a lightweight video deraining model that leverages a divergence‑free rain flow field to guide Loop‑in‑Loop attention across scales and neighboring frames, eliminating the need for explicit motion alignment. By projecting estimated rain‑flow onto a divergence‑free subspace, the method steers window attention along rain streaks, enabling efficient temporal aggregation with only 0.80 M parameters. Experiments on four benchmarks demonstrate competitive performance against larger models, and the authors introduce a new RainSyn‑Gust dataset and a physics‑based no‑reference metric for evaluating real‑rain removal.
arXiv:2607. 02551v1 Announce Type: cross Abstract: Video multimodal large language models have made strong progress on open-ended video understanding, but they still lack precise local spatiotemporal perception.
The paper introduces Driving with DINO (DwD), a framework that uses Vision Foundation Module (VFM) features to bridge simulation and real-world domains for autonomous driving video generation. It addresses the consistency‑realism dilemma by projecting VFM features onto a principal subspace, dropping high‑frequency texture elements, and applying a Random Channel Tail Drop to preserve structural detail. Additional components— a learnable Spatial Alignment Module and a Causal Temporal Aggregator— enhance control precision, spatial alignment, and temporal stability, reducing motion blur and ensuring realistic, consistent outputs.
arXiv:2606. 16278v1 Announce Type: cross Abstract: Long-tail hazardous scenarios are essential for safety-oriented autonomous driving, yet they are difficult to collect and reproduce at scale.
arXiv:2609.36929v1 Announce Type: new Abstract: Recent event-based depth estimation methods successfully transfer geometric priors from vision foundation models via cross-modal distillation. However,...
arXiv:2608.21136v1 Announce Type: new Abstract: Recently, open-vocabulary zero-shot 3D scene understanding using vision foundation models has emerged as a promising alternative to data-intensive supe...
arXiv:2608.29003v1 Announce Type: cross Abstract: In dynamic and unstructured environments, conventional SLAM systems generally suffer from significant accuracy degeneration due to their static assum...