Papers Video Object Segmentation
“Video Object Segmentation” 태그가 달린 논문 608편 · 필터 해제
MLLM-Assisted Audio VOS: A 3rd Place Report for the MeViS-Audio Track, 8th LSVOS Challenge
In this technical report, we present a training-free framework for audio-guided video object segmentation, which integrates Multimodal Large Language Models (MLLMs) with SAM-based segmentation models. We decompose the ta…
Video Object SegmentationMultimodal ReasoningVideo SegmentationSAM2Dual: Training-Free, Dual Memory for Long-Term Video Object Segmentation
Long-term video object segmentation (VOS) remains challenging due to error accumulation under extended occlusions, re-appearance, and scene changes. Although SAM2 provides strong zero-shot performance, its streaming memo…
Video Object SegmentationRRTrack: Robust and Recoverable Object 6D Pose Tracking for Dynamic Scenes
Robust object 6D pose tracking is critical for robotic systems operating in dynamic and occluded scenes. Per-frame estimators are accurate but computationally expensive, while current trackers struggle with fast motion a…
Video Object SegmentationPose TrackingREMIND: RE-Identification with Memory for INDoor Navigation
Mobile robots operating indoors must re-identify previously observed objects after long temporal gaps, significant viewpoint changes, and severe illumination variations. This remains a challenging problem: multi-object t…
Video Object SegmentationVehicle Re-IdentificationMulti-Object TrackingSAM-MT: Real-Time Interactive Multi-Target Video Segmentation
Modern Video Object Segmentation (VOS) involves tracking and segmenting user-specified targets. While recent approaches have achieved remarkable performance in single-target scenarios, extending them to multi-target sett…
Video Object SegmentationVideo Segmentation`Attention-Guided Cross-Temporal Clustering for Self-Supervised Video Object Segmentation
Video object segmentation (VOS) is a fundamental task in video understanding, requiring accurate delineation and consistent tracking of objects across frames. While supervised methods achieve strong performance, they rel…
Video Object SegmentationSelf-Supervised LearningG$^2$TAM: Geometry Grounded Track Anything Model
Human spatial understanding arises from jointly perceiving geometry and semantics, enabling consistent object identification and localization across viewpoints and time. Current video segmentation models depend on explic…
Video Object SegmentationVideo SegmentationSpatial Reasoning3D ReconstructionSelective Mask Propagation for Multi-Object Tracking
In multi-object tracking, most frames are easy for a lightweight base tracker while a small fraction is intrinsically hard. Video object segmentation (VOS) models can often preserve identity through the hard frames where…
Video Object SegmentationMulti-Object TrackingVideoSEG-O3: A Multi-turn Reinforcement Learning Framework for Reasoning Video Object Segmentation
Reasoning Video Object Segmentation (RVOS) demands a sophisticated integration of temporal dynamics, spatial details, and linguistic reasoning to achieve precise pixel-level localization. Existing methods are limited to …
Video Object SegmentationReinforcement LearningGMOS: Grounding Moving Object Segmentation in 3D Space and Time
Moving Object Segmentation (MOS) aims to discover, segment, and track objects that move independently of the camera. Current MOS methods, however, exhibit two fundamental limitations: they rely on pre-computed 2D auxilia…
Video Object SegmentationWeighted Reverse Convolution for Feature Upsampling
Pre-trained vision foundation models (VFMs) provide strong semantic representations, yet their patch-level features are inherently coarse, limiting their effectiveness on tasks requiring fine-grained localization, dense …
Video Object SegmentationComputational EfficiencyFeature UpsamplingDepth EstimationRobust Promptable Video Object Segmentation
The performance of promptable video object segmentation (PVOS) models substantially degrades under input corruptions, which prevents PVOS deployment in safety-critical domains. This paper offers the first comprehensive s…
Video Object SegmentationFeaturising Pixels from Dynamic 3D Scenes with Linear In-Context Learners
One of the most exciting applications of vision models involve pixel-level reasoning. Despite the abundance of vision foundation models, we still lack representations that effectively embed spatio-temporal properties of …
Video Object SegmentationSemantic Segmentation2nd of the 5th PVUW MeViS-Audio Track: ASR-SaSaSa2VA
Audio-based video object segmentation aims to locate and segment objects in videos conditioned on audio cues, requiring precise understanding of both appearance and motion. Recent audio-driven video segmentation methods …
Video Object SegmentationSpeech RecognitionVideo SegmentationSynMulti: Synthetic-to-Real Learning for Multimodal Video Understanding
Training multimodal large language models (MLLMs) for video understanding requires large-scale annotated data spanning diverse tasks such as object counting, question answering, and segmentation. However, collecting and …
Video Object SegmentationSynthetic Data GenerationVisual Question AnsweringVisual GroundingOnline Reasoning Video Object Segmentation
Reasoning video object segmentation predicts pixel-level masks in videos from natural-language queries that may involve implicit and temporally grounded references. However, existing methods are developed and evaluated i…
Video Object SegmentationPanoSAM2: Lightweight Distortion- and Memory-aware Adaptions of SAM2 for 360 Video Object Segmentation
360 video object segmentation (360VOS) aims to predict temporally-consistent masks in 360 videos, offering full-scene coverage, benefiting applications, such as VR/AR and embodied AI. Learning 360VOS model is nontrivial …
Video Object SegmentationUniSurgSAM: A Unified Promptable Model for Reliable Surgical Video Segmentation
Surgical video segmentation is fundamental to computer-assisted surgery. In practice, surgeons need to dynamically specify targets throughout extended procedures, using heterogeneous cues such as visual selections, textu…
Video Object SegmentationVideo SegmentationAdvancing Complex Video Object Segmentation via Tracking-Enhanced Prompt: The 1st Winner for 5th PVUW MOSE Challenge
In the Complex Video Object Segmentation task, researchers are required to track and segment specific targets within cluttered environments, which rigorously tests a method's capability for target comprehension and envir…
Video Object SegmentationColon-Bench: An Agentic Workflow for Scalable Dense Lesion Annotation in Full-Procedure Colonoscopy Videos
Early screening via colonoscopy is critical for colon cancer prevention, yet developing robust AI systems for this domain is hindered by the lack of densely annotated, long-sequence video datasets. Existing datasets pred…
Video Object SegmentationVisual Question Answering