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SAM2Long: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory Tree
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SAM2Long: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory Tree
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The Segment Anything Model 2 (SAM 2) has emerged as a powerful foundation model for object segmentation in both images and videos, paving the way for various downstream video applications. The crucial design of SAM 2 for video segmentation is its memory module, which prompts object-aware memories from previous frames for current frame prediction. However, its greedy-selection memory design suffers from the "error accumulation" problem, where an errored or missed mask will cascade and influence the segmentation of the subsequent frames, which limits the performance of SAM 2 toward complex long-term videos. To this end, we introduce SAM2Long, an improved training-free video object segmentation strategy, which considers the segmentation uncertainty within each frame and chooses the video-level optimal results from multiple segmentation pathways in a constrained tree search manner. In practice, we maintain a fixed number of segmentation pathways throughout the video. For each frame, multiple masks are proposed based on the existing pathways, creating various candidate branches. We then select the same fixed number of branches with higher cumulative scores as the new pathways for the next frame. After processing the final frame, the pathway with the highest cumulative score is chosen as the final segmentation result. Benefiting from its heuristic search design, SAM2Long is robust toward occlusions and object reappearances, and can effectively segment and track objects for complex long-term videos. Notably, SAM2Long achieves an average improvement of 3.0 points across all 24 head-to-head comparisons, with gains of up to 5.3 points in J&F on long-term video object segmentation benchmarks such as SA-V and LVOS. The code is released at https://github.com/Mark12Ding/SAM2Long.
Forward citations
Cited by 11 Pith papers
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3AM: 3egment Anything with Geometric Consistency in Videos
3AM integrates MUSt3R 3D features into SAM2 via a Feature Merger and FOV-aware sampling to deliver geometry-consistent video object segmentation from RGB alone, with large gains on wide-baseline datasets.
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Backdoor Attacks on Prompt-Driven Video Segmentation Foundation Models
BadVSFM is the first effective backdoor attack on prompt-driven video segmentation foundation models, using a two-stage encoder-decoder strategy to achieve high attack success rates with limited clean performance loss.
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SAM 3: Segment Anything with Concepts
SAM 3 introduces promptable concept segmentation that doubles accuracy of prior systems on images and videos while improving standard SAM segmentation performance.
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SAM 2++: Tracking Anything at Any Granularity
SAM 2++ unifies video tracking across mask, box, and point granularities via task-specific prompts, a unified decoder, task-adaptive memory, and a new multi-granularity dataset, reporting state-of-the-art results.
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Efficient Tracking and Understanding Object Transformations
FluxGraph detects object transformations reactively via SAM2's multi-mask disagreement, cutting TubeletGraph's inference cost by 3.3–10.7x with comparable tracking and state-graph quality.
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REMIND: RE-Identification with Memory for INDoor Navigation
REMIND combines frozen DINOv3 features, dual-bank multi-prototype memory, part/background descriptors, neighbour co-occurrence, and joint Hungarian assignment to re-identify generic indoor objects after long gaps, rea...
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SENTRY: SAM2-Enhanced Neighbor-Aware and Temporally Reasoned Memory for Visual Tracking
SENTRY is a plug-and-play module that replaces confidence-based memory writes with neighbor-aware cycle-consistent validation in SAM2 trackers, yielding new zero-shot SOTA results on LaSOT, GOT-10k and other benchmarks.
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TrianguLang: Geometry-Aware Semantic Consensus for Pose-Free 3D Localization
TrianguLang achieves state-of-the-art feed-forward text-guided 3D localization and segmentation by using predicted geometry to gate cross-view semantic correspondences without ground-truth poses.
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Zero-Shot Multi-Animal Tracking in the Wild
A zero-shot multi-animal tracker combining Grounding DINO + SAM 2 with three hand-designed heuristics beats prior methods on four animal-tracking benchmarks with fixed hyperparameters.
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FreeVPS: Repurposing Training-Free SAM2 for Generalizable Video Polyp Segmentation
FreeVPS pairs a per-frame polyp segmenter with frozen SAM2 tracking and two filtering modules to reduce error accumulation, improving in-domain and out-of-domain video polyp segmentation.
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SAMIDARE: Advanced Tracking-by-Segmentation for Dense Scenarios
SAMIDARE improves segmentation-based multi-object tracking in dense sports videos, gaining 2.5 HOTA and 4.2 IDF1 over baseline on SportsMOT validation through adaptive mask control and state-aware association.
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