arXiv:2512.01495v4 Announce Type: replace Abstract: Video instance segmentation (VIS) for low-light content remains highly challenging for both humans and machines alike, due to noise, blur and other...
The paper introduces FaVOS, a benchmark for Video Object Segmentation (VOS) that focuses on scenarios where target objects appear only intermittently over long videos. It demonstrates that the standard J&F metric can be gamed by empty predictions, allowing trivial models to outperform strong ones like SAM 3. To address this, the authors propose Volumetric J&F, which treats mask sequences as spatio‑temporal volumes, reducing the influence of target‑absence rewards while maintaining sensitivity to segmentation quality and temporal structure.
arXiv:2610.33478v1 Announce Type: cross Abstract: Marine video understanding is crucial for a wide range of real-world applications. However, existing marine video datasets suffer from limitations, i...
arXiv:2604. 27975v2 Announce Type: replace-cross Abstract: Traditional Shot Boundary Detection (SBD) inherently struggles with complex transitions by formulating the task around isolated cut points, frequently yielding corrupted video shots.
DiDA introduces a lightweight video object segmentation framework that leverages Distillation Learning of Deformable Attention. The method uses deformable attention to adapt key and value positions across frames, enabling object representations that are responsive to spatial and temporal changes. Experiments on DAVIS and YouTube‑VOS benchmarks show state‑of‑the‑art performance and efficient memory usage.
Video panoptic segmentation (VPS) aims to jointly detect, segment, and track all objects while partitioning the video into semantically consistent regions. We introduce the task setting of unsupervised VPS, omitting any human supervision.
arXiv:2602.19202v3 Announce Type: replace Abstract: Event cameras excel at high-speed, low-power, and high-dynamic-range scene perception. However, as they fundamentally record only relative intensit...
arXiv:2606. 29531v1 Announce Type: cross Abstract: We propose MotionAtlas, a system for detailed captioning of motion-centric videos, comprising (1) a dedicated human-annotated benchmark, (2) a scalable, high-quality pipeline to construct training samples, and (3) a family of powerful Video-MLLMs.
arXiv:2609.39785v1 Announce Type: new Abstract: The Segment Anything Model (SAM) relies heavily on massive manual annotations, creating a fundamental bottleneck for model scaling. While unsupervised...
arXiv:2501.12375v4 Announce Type: replace Abstract: Depth Anything has achieved remarkable success in monocular depth estimation with strong generalization ability. However, it suffers from temporal...
arXiv:2609.40356v1 Announce Type: cross Abstract: Recent video generation is increasingly realistic and controllable, yet video editing remains less developed, particularly for precise local edits th...
The paper presents a lightweight, training‑free framework for real‑time unsupervised object discovery from asynchronous event camera streams. It introduces a linear‑time Spatio‑Temporal Probabilistic Event Filter (SPEF) that adaptively distinguishes salient motion from noise, and an Event Morton Code Clustering (EMCC) module that efficiently groups events without costly distance calculations. Experiments on E‑MLB, FRED, and eTraM datasets show SPEF outperforms classical filters and competes with learning‑based methods, while EMCC achieves the highest accuracy and fastest execution among density‑based clustering baselines.