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Fast Segment Anything
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The recently proposed segment anything model (SAM) has made a significant influence in many computer vision tasks. It is becoming a foundation step for many high-level tasks, like image segmentation, image caption, and image editing. However, its huge computation costs prevent it from wider applications in industry scenarios. The computation mainly comes from the Transformer architecture at high-resolution inputs. In this paper, we propose a speed-up alternative method for this fundamental task with comparable performance. By reformulating the task as segments-generation and prompting, we find that a regular CNN detector with an instance segmentation branch can also accomplish this task well. Specifically, we convert this task to the well-studied instance segmentation task and directly train the existing instance segmentation method using only 1/50 of the SA-1B dataset published by SAM authors. With our method, we achieve a comparable performance with the SAM method at 50 times higher run-time speed. We give sufficient experimental results to demonstrate its effectiveness. The codes and demos will be released at https://github.com/CASIA-IVA-Lab/FastSAM.
Forward citations
Cited by 35 Pith papers
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Segment This Thing: Foveated Tokenization for Efficient Point-Prompted Segmentation
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TestMate fuses FastSAM mask proposals with a segmentation network via size-ordered soft refinement to achieve backpropagation-free, first-frame TTDA gains on semantic segmentation benchmarks.
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StateScribe: Towards Accessible Change Awareness Across Real-World Revisits
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ViRefSAM: Visual Reference-Guided Segment Anything Model for Remote Sensing Segmentation
A few-shot segmentation framework that injects reference-image prototypes into SAM's decoder and image encoder, eliminating per-image manual prompts and improving remote sensing segmentation accuracy.
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LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment
LoD-Loc v2 localizes aerial cameras by aligning predicted building silhouettes with rendered low-detail city-model silhouettes, achieving accurate 4-DoF pose without textured maps.
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SAM4D: Segment Anything in Camera and LiDAR Streams
SAM4D is a promptable model that segments and tracks objects across camera and LiDAR streams with cross-modal prompts, trained on pseudo-labels generated by an automated data engine.
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T-Rex: Task-Adaptive Spatial Representation Extraction for Robotic Manipulation with Vision-Language Models
A zero-training framework that adaptively selects spatial representation extractors per object and per task stage improves real-world robot manipulation success and efficiency over fixed-representation baselines.
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Lean-SAM2: Target-Anchored Memory and Encoder Acceleration for SAM2
Lean-SAM2 combines target-anchored memory pruning, condensed insurance memory, and risk-aware window routing to accelerate SAM2.1 inference ~1.4× with better accuracy than Efficient-SAM2.
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MuViSeg: Multi-View Segment Correspondences from Dense Geometry Priors
A joint multi-view self-attention matcher over SAM segments beats pairwise matchers at wide baselines and lifts HM3D navigation success from 50% to 70%, while a LightGlue-style pairwise head wins outdoors and at narro...
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Accurate and efficient zero-shot 6D pose estimation with frozen foundation models
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Dynamic Sub-region Search in Homogeneous Collections Using CLIP
Dynamic region detection plus IoU-based geometric constraints roughly doubles recall over static grid partitioning for underwater known-item search, given accurate query boxes.
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Towards Terrain-Aware Task-Driven 3D Scene Graph Generation in Outdoor Environments
An outdoor 3D scene graph pipeline using LiDAR-camera fusion, CLIP embeddings, and per-terrain Voronoi graphs is demonstrated on a campus dataset with qualitative results.
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Perceive Anything: Recognize, Explain, Caption, and Segment Anything in Images and Videos
PAM extends SAM 2 with a frozen LLM and a Semantic Perceiver to jointly segment and describe regions in images, videos, and streaming video, and contributes a 0.6M-sample region-level streaming video caption dataset.
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Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data
Adding monocular depth to EfficientViT-SAM improves point-prompted segmentation at 3 and 5 clicks after fine-tuning on 11.2k images, but universal gains and data-efficiency are not established.
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SAM-MI: A Mask-Injected Framework for Enhancing Open-Vocabulary Semantic Segmentation with SAM
SAM-MI improves open-vocabulary segmentation by injecting aggregated SAM masks as low- and high-frequency guidance into CLIP cost maps, with sparse text-guided point prompts for speed.
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Multi-modal video data-pipelines for machine learning with minimal human supervision
An open-source video pipeline automatically extracts 13+ visual modalities from raw video with no human annotation, and a sub-1M-parameter distilled model reaches near-Mask2Former accuracy on an aerial scene benchmark.
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SegDINO: An Efficient Design for Medical and Natural Image Segmentation with DINO-V3
A frozen DINOv3 backbone plus a simple MLP head reportedly beats specialized segmentation models on six benchmarks, but the evidence lacks statistical rigor.
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Object-Centric Cropping for Visual Few-Shot Classification
The supplied full text (arXiv:2508.00225) is a different paper from the claimed metadata (arXiv:2508.00218), so no claim about few-shot classification is backed by the manuscript.
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PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens
PlantSAM, a pipeline that uses YOLOv10 bounding boxes to prompt SAM2, segments plants from herbarium backgrounds with IoU 0.94 and improves trait classification accuracy by up to 4.36%.
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FastSmoothSAM: A Fast Smooth Method For Segment Anything Model
FastSmoothSAM smooths FastSAM's jagged segmentation boundaries through a four-stage B-Spline fitting pipeline, with a modest runtime overhead.
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Objectomaly: Objectness-Aware Refinement for OoD Segmentation with Structural Consistency and Boundary Precision
A training-free, SAM-based score-calibration and boundary-refinement pipeline reports SOTA OoD segmentation results, but internal inconsistencies prevent the claim from standing.
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Experimental Evaluation of Static Image Sub-Region-Based Search Models Using CLIP
Static spatial grids over CLIP image embeddings improve text-to-image retrieval on homogeneous underwater keyframes, while text-only position suffixes do not.
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Foundation Models for Zero-Shot Segmentation of Scientific Images without AI-Ready Data
GroundingDINO plus SAM, wrapped in an interactive web tool, achieves high segmentation scores on FIB-SEM catalyst images, although the evaluation is limited and ground-truth creation is unexplained.
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Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges
A structured survey of prompt engineering methods for the Segment Anything Model, covering geometric, textual, and multimodal prompts and their applications.
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