RMP-SAM: Towards Real-Time Multi-Purpose Segment Anything

## Abstract

Abstract Advanced by transformer architecture, vision foundation models (VFMs) achieve remarkable progress in performance and generalization ability. Segment Anything Model (SAM) is one remarkable model that can achieve generalized segmentation. However, most VFMs cannot run in real-time, which makes it difficult to transfer them into several products.
On the other hand, current real-time segmentation mainly has one purpose, such as semantic segmentation on the driving scene.
We argue that diverse outputs are needed for real applications.
Thus, this work explores a new real-time segmentation setting, named all-purpose segmentation in real-time, to transfer VFMs in real-time deployment. It contains three different tasks, including interactive segmentation, panoptic segmentation, and video segmentation. We aim to use one model to achieve the above tasks in real-time. We first benchmark several strong baselines. Then, we present Real-Time All Purpose SAM (RAP-SAM). It contains an efficient encoder and an efficient decoupled decoder to perform prompt-driven decoding. Moreover, we further explore different training strategies and tuning methods to boost co-training performance further. Our code and model are available at https://github.com/xushilin1/RAP-SAM/ .

## 1 Introduction

Figure: Figure 1: We present real-time all-purpose segmentation to segment and recognize objects for image, video, and interactive inputs. In addition to benchmarking, we also propose a simple yet effective baseline, named RAP-SAM, which achieves the best accuracy and speed trade-off among three different tasks. Both real-time panoptic segmentation and video instance segmentation are shown at the bottom.
Refer to caption: extracted/5355738/figs/teaser.jpg

Recent advancements in computer vision, propelled by transformer architectures [, , ], focus on developing a singular, large-scale model versatile enough for various tasks, including detection and segmentation, with a general-purpose design. Specifically, several studies [, ] adopting identical architectures have demonstrated superior performance compared to models designed exclusively for specific tasks. Furthermore, numerous studies [, ] investigate the foundation model design using extensive data. A notable example, the Segment Anything Model (SAM) [], introduces an interactive model driven by visual prompts for versatile segmentation. Concurrently, for semantic-level segmentation, numerous studies [, , , , ] employing a universal design have surpassed previous models tailored for specific image and video segmentation tasks.

However, the majority of these studies face challenges in achieving real-time performance or compatibility with mobile devices, primarily due to their heavy encoders and cascaded decoders. This constraint significantly limits the applicability of these advanced architectures in practical vision applications, although they can achieve high performance. On the contrary, various studies explore real-time segmentation using distinctive designs. Yet, these efforts predominantly concentrate on singular application purposes, including autonomous driving scenes, the COCO dataset for panoptic segmentation (PS) [], and real-time video instance/object segmentation (VIS) [].
Currently, no studies investigate real-time, all-purpose segmentation, wherein a model is capable of performing universal segmentation tasks, encompassing image segmentation, video instance/video segmentation, and SAM-like interactive segmentation.

Inspired by the aforementioned challenges, this study investigates the novel issue of real-time, all-purpose segmentation. We pose a critical inquiry: given the limited computational resources and model capacity, how can we develop an efficient, all-purpose segmentation model? This entails creating a single model capable of segmenting, tracking, and classifying each pixel in real-time, akin to performing interactive segmentation similar to SAM.

Initially, we define this research problem by integrating existing benchmarks. Specifically, we use the COCO [] and YouTube-VIS 2019 [] datasets for joint co-training, employing identical hyper-parameters. Beyond the semantic queries used for semantic-level segmentation in prior research, our approach additionally incorporates visual prompt queries in SAM for interactive segmentation. However, there are still several challenges: 1), No previous works explore joint co-training for three different purposes. Thus, extensive experiments are needed to search for a better meta-architecture. 2), Interactive segmentation and semantic-level segmentation need better strategies to balance each other.
3), Under the real-time setting, several previous architectures [, ] for multi-task segmentation may not be practical.

To address these challenges, we benchmark several recent works [, , ] in a real-time setting.
Then, we initially investigate a range of meta-architectures suitable for all-purpose segmentation.
Unlike previous works, we avoid cascaded architecture and only use the pyramid feature once when performing dynamic convolution.
We explore four different architectures in the case of various decoder designs, where we adopt a single decoder with multiple queries.
Via extensive experiments, we adopt pooling-based dynamic convolution to replace per-pixel cross-attention to achieve better accuracy and speed trade-off.
To balance interactive and semantic-level segmentation, we present an effective dual adapter design that better adapts shared knowledge of the same decoder.
Finally, we term our best model as Real-time All-Purpose SAM (RAP-SAM). To the best of our knowledge, it is the first real-time all-purpose segmentation model. Moreover, due to the semantic level and interactive level segmentation ability, our model can also create more new applications, such as interactive video segmentation. Extensive experiments show the effectiveness of our design. The main contributions of this work are:

- •
We introduce all-purpose segmentation, a multi-task segmentation that aims to segment objects for image, video, and interactive inputs in real-time.
- •
We benchmark several real-time transformer-based segmentation approaches for the new settings.
- •
We present a simple yet fast baseline named RAP-SAM. It contains a lightweight feature extractor, a unified decoder, and two asymmetric adapters.
- •
Extensive experiments demonstrate that RAP-SAM achieves the best speed and accuracy trade-off in the proposed benchmark and regular real-time semantic and panoptic segmentation benchmarks. We also show scalability across datasets and application demos.

## 2 Related Work

Universal Segmentation. Prior research has focused on designing segmentation models tailored for specific tasks. Recent developments in this field [, , , , , , , , , , , , , ] have shifted towards employing mask classification architectures, coupled with an end-to-end set prediction objective, to facilitate universal segmentation. This approach has outperformed specialized models [, , , , , , , , , , ] across various segmentation tasks, including those involving images, videos, and point clouds [, , , , , , ]. Specifically, Mask2Former [] uses a masked-attention mechanism and surpasses the performance of prior specific image segmentation models. Similarly, Tube-Link [] outperforms earlier models [, , ] tailored for specific video segmentation tasks. Semantic-SAM [], built on Mask-DINO [], employs a hybrid query design to facilitate both panoptic and interactive segmentation within a single framework. However, despite the strong performance of these works, their complexity, stemming from the heavier encoder and the cascaded decoder designs, poses significant challenges for integration into actual products. These are primarily hindered by slow processing speeds and substantial parameter overhead.

Vision Foundation Model. Recent advancements in the vision research community have been propelled by the development of Vision Foundation Models (VFMs), including pure-visual pre-training [], vision-language pre-training [, ], and multi-modal models employing visual prompting [, ]. SAM [] stands as a seminal work targeting general interactive segmentation. Subsequent studies [, , ] have adapted SAM for diverse applications, predominantly employing it as a segmentation tool. However, these Vision Foundation Models typically employ substantial visual encoders, like ViT-base [], which poses challenges for deployment on real-time or mobile devices due to their size.

Efficient Model Design. This research direction primarily concentrates on the development of efficient CNNs [, , , , , , ], transformers [, , ], and hybrid architectures [, , , , ], aimed at advancing visual representation learning. Most research investigates backbone design within the constraints of real-time processing and parameter efficiency. These studies are orthogonal to our own, which primarily explores the impact of data, task, and decoder design. Additionally, our work employs efficient models as encoders and presents detailed benchmarks and analyses of the encoder’s effects.

Efficient Segmentation. Previous studies [, , , , , , , ] on efficient segmentation have predominantly concentrated on closed-set and specific domains. In particular, a significant portion of this research [, , ] is dedicated to driving scenarios. Recently, various studies [, , ] have developed efficient segmentation techniques that facilitate model execution on mobile devices. Mobile SAM [] introduces a streamlined encoder distillation method. Fast SAM [] employs a single-stage instance segmentation framework that directly decodes class-agnostic masks. Recently, multiple studies have been conducted on efficient panoptic segmentation [, , ] and rapid video instance segmentation [, ].
However, these real-time segmentation methods are limited to specific tasks. We contend that an all-purpose model capable of real-time performance would have wide-ranging applications in editing, tracking, and segmentation functionalities within various products.

## 3 Problem Setup

Settings. Our proposed real-time all-purpose segmentation contains three visual inputs, including image, video, and visual prompts (boxes and points). It outputs corresponding masks, labels, and instance ID. We adopt panoptic segmentation for image segmentation, which unifies semantic and instance segmentation. For video segmentation, we adopt video instance segmentation, where we aim to segment and track each foreground instance. For interactive segmentation, we follow the SAM-like setting, where we perform class-agnostic segmentation according to the user’s inputs. Compared with previous settings, as shown in Tab. [1](#S4.T1), we can achieve more flexible input and output, which is more challenging. Combined with interactive segmentation and video segmentation, we can also achieve interactive video segmentation as video object segmentation.

Datasets. For image segmentation and interactive segmentation, we use the well-known COCO dataset [] for benchmarking. We adopt partially SAM data [] for testing. Due to the limited resources and limited model capacity in real-time settings, we do not use SAM data for training. We take COCO data as SAM data for training and testing, where the boxes and random points sampled from masks are used for visual prompting. For video segmentation, we adopt the widely used YouTube-VIS 2019 dataset [] for training. We do not use the SAM data for our benchmarking. There are several reasons: (1), SAM data is not well annotated as the COCO dataset. (2), We only focus on object-level and scene-level segmentation, while SAM data contain more granularity. (3), SAM data has no semantics, while our goal is to predict mask labels for real applications.

Figure: Figure 2: RAP SAM overview. Our method contains three visual inputs: image, video, and visual prompts. Utilizing positional encoding, we generate prompt queries from these visual prompts. The learnable object queries, alongside the prompt queries and the feature map $F$, are directed to the multi-stage decoder. This process generates multi-stage predictions and refined queries. These refined queries engage in cross-attention with $F$, resulting in the final prediction.
Refer to caption: x1.png

## 4 Proposed Method

Motivation. Employing the same formulation, we first revisit three tasks: panoptic segmentation, video instance segmentation, and interactive segmentation. When combined with a query-based mask transformer, object queries can represent all entities within the scene, encompassing image-level objects, video-level tracked objects, and prompt-based specific objects. Each task’s formulation is as follows:

For panoptic segmentation [], given an input image $I\in\mathbb{R}^{H\times{W}\times 3}$, the objective is to generate a group of masks $\{y_{i}\}_{i=1}^{N}=\{(m_{i},c_{i})\}_{i=1}^{N}\,$ where $c_{i}$ denotes the ground truth class label of the binary mask $m_{i}$ and $N$ is the number of masks, ${H}\times{W}$ indicates the spatial dimensions.

For video instance segmentation [], given a video clip input $V\in\mathbb{R}^{T\times H\times{W}\times 3}$, where $T$ denotes the number of frames, the process aims to generate an instance mask tube $\{y_{i}\}_{i=1}^{N}=\{(m_{i},c_{i},d_{i})\}_{i=1}^{N}\,$, where $N$ represents the number of the tube masks $m_{i}\in{\{0,1\}}^{{T}\times{H}\times{W}}$, $c_{i}$ indicates the class label of the tube $m_{i}$, and $d_{i}$ identifies the instance ID for each tube.

**Table 1: Real-Time Segmentation Settings. Our all-purpose segmentation supports more tasks in one framework.**
| Property | Image Seg | Video Seg | SAM-Like | Ours |
| --- | --- | --- | --- | --- |
| Image Masks | ✓ | ✗ | ✓ | ✓ |
| Video Masks | ✗ | ✓ | ✗ | ✓ |
| Interactive | ✗ | ✗ | ✓ | ✓ |
| Semantic Labels | ✓ | ✓ | ✗ | ✓ |
| Multi tasks | ✗ | ✗ | ✗ | ✓ |

**Table 2: Comparison of Segmentation Methods. Our proposed RAP-SAM supports various segmentation tasks, and it can run in real time.**
| Methods | SS | PS | VIS | Interactive | Multi-Task in One Model | Real Time |
| --- | --- | --- | --- | --- | --- | --- |
| ICNet [] | ✓ | ✗ | ✗ | ✗ | ✗ | ✓ |
| Bi-Seg [] | ✓ | ✗ | ✗ | ✗ | ✗ | ✓ |
| YOSO [] | ✓ | ✓ | ✗ | ✗ | ✗ | ✓ |
| Mobilie-VIS [] | ✗ | ✗ | ✓ | ✗ | ✗ | ✓ |
| SAM [] | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ |
| Mask2Former [] | ✓ | ✓ | ✗ | ✗ | ✗ | ✗ |
| Video K-Net [] | ✗ | ✓ | ✓ | ✗ | ✗ | ✗ |
| OneFormer [] | ✓ | ✓ | ✗ | ✗ | ✓ | ✗ |
| RAP-SAM (Ours) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |

For SAM-like interactive segmentation [], both the image $I$ and visual prompts $P\in\mathbb{R}^{K\times 4}$ (box prompts are used here) are taken as inputs. These prompts, which include points and boxes, lead to the outputs of corresponding binary image masks $\{y_{i}\}_{i=1}^{K}=\{m_{i}\in\{0,1\}^{H\times W}\}_{i=1}^{K}$, where $K$ denotes the number of visual prompts. Each visual prompt is encoded into one object query, which naturally can be the input of the decoder, as implemented in  [, ].

Single Object Query For All Segmentation. As mentioned above, by combining all three settings into one, we can represent all the output segmentation entities using the same query-based mask classification framework. In particular, one object query corresponds to one mask $m_{i}$, one label $c_{i}$, and one ID $d_{i}$. We aim to design a simple, efficient, and parameter-sharing framework to support these tasks.

Compared Baselines. In Sec. [5.1](#S5.SS1), we benchmark several baselines for our all-purpose segmentation. We use the prompt encoder (proposed in SAM []) to encode the visual prompt and jointly learn with object query for both PS and VIS on a shared decoder. Although we adopt the lightweight design to speed up, their performance is still limited or not balanced, as shown in Tab [3](#S4.T3).

### 4.1 RAP-SAM Architecture

Overall Architecture. Our RAP-SAM is a simple encoder and decoder architecture. As shown in Fig. [2](#S3.F2), it contains a backbone, a lightweight neck, and a shared multitask decoder. Visual prompts $P$ are also the input of the decoder. Following SAM, we also adopt the prompt encoder to encode visual prompts into a query. We adopt the same decoder for both visual prompts and initial object queries to share more computation and parameters. However, the goals of both are different. The former pays more attention to the local details, while the latter also considers the scene and temporal features. To better balance the results for interactive segmentation and image/video segmentation, we design a prompt adapter and an object adapter in the end of the decoder.

Lite Feature Extractor. Due to computation cost limitation, we avoid the design of previous methods, such as a large backbone and heavier transformer encoder. We explore lightweight backbones, including ResNet18 [], STDC-v1 [], and SeaFormer []. We adopt feature pyramid networks with deformable convolutions to fuse multi-scale features and obtain more aligned feature representation, motivated by previous works [] on real-time semantic segmentation. For video input, we extract the spatial-temporal features. For simplicity, we use $F_{img}\in\mathbb{R}^{\frac{H}{4}\times\frac{W}{4}\times d}$ for image inputs and $F_{vid}\in\mathbb{R}^{T\times\frac{H}{4}\times\frac{W}{4}\times d}$ for video inputs.

Unified Dynamic Convolution Decoder. As in previous works, the goal of the decoder is to refine the object query. However, many of these approaches are impractical for real-time settings due to their reliance on heavily cascaded layers and pixel-wise cross-attention mechanisms. In contrast, our method employs a pooling-based dynamic convolution framework [, ] to enhance the decoder’s efficiency.

Given object query $Q_{i}\in\mathbb{R}^{N\times d}$ and prompt query $P_{i}\in\mathbb{R}^{K\times d}$, the initial masks $M_{i}$ are obtained by dot product with $F_{img}$ or $F_{vid}$. $i$ denotes the index of the decoder stage. According to the inputs, the masks can be image masks for panoptic segmentation $M_{i}^{pan}\in\mathbb{R}^{N\times\frac{H}{4}\times\frac{W}{4}}$ and interactive segmentation $M_{i}^{iter}\in\mathbb{R}^{K\times\frac{H}{4}\times\frac{W}{4}}$ or tube masks $M_{i}^{tube}\in\mathbb{R}^{N\times T\times\frac{H}{4}\times\frac{W}{4}}$. Then, we can obtain the corresponding query features $X_{i}^{pan}\in\mathbb{R}^{N\times d}$, $X_{i}^{iter}\in\mathbb{R}^{K\times d}$, $X_{i}^{tube}\in\mathbb{R}^{N\times d}$ via mask pooling:

$$ $X_{i}^{pan}=\sum_{u}^{W}\sum_{v}^{H}M_{i-1}^{pan}(u,v)\cdot F_{img}(u,v).$ (1) $$

For video tasks, we group the spatial-temporal features as follows:

$$ $X_{i}^{tube}=\sum_{t}^{T}\sum_{u}^{W}\sum_{v}^{H}M_{i-1}^{tube}(t,u,v)\cdot F_ {vid}(t,u,v),$ (2) $$

where $u$, $v$ are the indices of spatial location, $t$ is index of input frame. Since the dynamic convolution performs similarly for each task, we use panoptic segmentation $X_{i}^{pan}$ as an illustration. For interactive segmentation, we adopt the same equation (Equ. [1](#S4.E1)). In our implementation, the learned query is shared for both $Q_{i}^{pan}$ and $Q_{i}^{tube}$.

In particular, we adopt the design [, , ] to refine input queries $Q_{i}^{pan}$ with features $X_{i}^{pan}$ which are grouped from their masks,

$$ $\hat{Q}^{pan}_{i}=\mathrm{DynamicConv}(X_{i}^{pan},Q^{pan}_{i-1}),$ (3) $$

where the dynamic convolution uses the query features $X^{pan}_{i}$ to generate parameters to weight input queries $Q_{i-1}^{pan}$.
Specifically, $\mathrm{DynamicConv}$ uses input query features $X^{pan}_{i}$ to generate gating parameters via a multilayer perceptron (MLP) and multiply back to the original query input $Q^{pan}_{i-1}$. We adopt the same design [, ] by learning gating functions to update the refined queries.
The $\mathrm{DynamicConv}$ operation is based on:

$$ $\hat{Q}^{pan}_{i}=\mathrm{Gate}_{x}(X^{pan}_{i})X^{pan}_{i}+\mathrm{Gate}_{q}( X^{pan}_{i})Q^{pan}_{i-1},$ (4) $$

where $\mathrm{Gate}$ is implemented with a fully connected (FC) layer followed by Layer Norm (LN) and a sigmoid layer. We adopt two different gate functions, including $\mathrm{Gate}_{x}$ and $\mathrm{Gate}_{q}$. They are implemented by MLP.
The former is to weigh the query features, while the latter is to weigh corresponding queries.
Next, we adopt one self-attention layer with feed-forward layers [, ] to learn the correspondence among each query and update them accordingly.
This operation leads to the full correlation among queries, shown as follows:

$$ $Q_{i}=\mathrm{FFN}(\mathrm{MHSA}(\hat{Q}_{i})+\hat{Q}_{i}),$ (5) $$

where $\mathrm{MHSA}$ means Multi Head Self Attention, $\mathrm{FFN}$ is the Feed Forward Network commonly used in the current version of Transformers [, ]. Finally, the refined masks are obtained via dot product between the refined queries $Q^{pan}_{i}$, and the input features $F_{p}$, $F_{s}$.
For mask classification, we adopt several feed-forward layers on $Q^{pan}_{i}$ and directly output the class scores.
Then, we perform the inner product between the learned queries $Q^{pan}_{i}$, $Q^{iter}_{i}$, and their features $F_{vid}$ and $F_{img}$. For the video segmentation task, the only difference is that we predict the tube mask rather than the image mask. The entire process (Equ. [1](#S4.E1), Equ. [2](#S4.E2), Equ. [3](#S4.E3) and Equ. [5](#S4.E5)) is repeated three times in total.

Moreover, we also explore various decoupled designs, as shown in Fig. [3](#S5.F3). In particular, as previous works suggest, we also explore decoupled decoders and their corresponding adapters. Via our detailed experiments, we do not find extra gains in the cases of decoupled decoders that introduce more computation and parameter costs. Please refer to Sec. [5.2](#S5.SS2) for the detailed decoder design.

**Table 3: Real-Time All-Purpose Segmentation benchmark. Our proposed RAP-SAM achieves the best accuracy and speed trade-off on three tasks.**
| Method | Backbone | COCO-Panoptic | COCO-SAM | YouTube-VIS 2019 | Flops | Parameters | FPS |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  | PQ | SQ | $\mathrm{PQ_{th}}$ | $\mathrm{PQ_{st}}$ | mIoU | mAP |  |  |  |  |  |
| Mask2Former [] | R18 |  | 35.6 | 77.5 | 39.0 | 30.3 | 54.7 | 38.6 | 89.8G | 18.6M | 31.2 |
| Mask2Former [] | R50 |  | 42.9 | 79.8 | 47.6 | 35.6 | 58.0 | 42.1 | 153G | 45.2M | 26.6 |
| MaskFormer [] | R18 |  | 31.0 | 76.0 | 33.2 | 27.7 | 55.6 | 34.1 | 79.7G | 18.5M | 38.0 |
| MaskFormer [] | R50 |  | 37.4 | 77.3 | 41.2 | 31.8 | 58.8 | 40.0 | 143.0G | 45.2M | 34.3 |
| MaskFormer [] | TopFormer [] |  | 31.6 | 75.9 | 33.5 | 28.6 | 56.1 | 38.2 | 60.0G | 12.8M | 29.9 |
| kMaX-DeepLab [] | R18 |  | 27.8 | 71.0 | 30.3 | 24.1 | 16.9 | 23.1 | 87.1G | 18.7M | 15.0 |
| kMaX-DeepLab [] | R50 |  | 36.9 | 75.7 | 42.7 | 28.4 | 20.1 | 26.4 | 280.0G | 51.4M | 14.9 |
| YOSO [] | R18 |  | 31.6 | 76.8 | 36.4 | 24.4 | 45.7 | 31.4 | 57.3G | 18.7M | 41.0 |
| YOSO [] | R50 |  | 37.3 | 76.9 | 42.7 | 29.2 | 49.2 | 40.7 | 119.0G | 45.1M | 36.3 |
| YOSO [] | TopFormer [] |  | 31.0 | 75.9 | 33.0 | 26.9 | 45.1 | 27.9 | 36.8G | 12.9M | 31.1 |
| RAP-SAM | R18 |  | 39.9 | 78.6 | 43.3 | 34.8 | 52.7 | 38.7 | 60.5G | 22.8M | 40.3 |
| RAP-SAM | R50 |  | 46.9 | 80.8 | 51.6 | 39.8 | 57.9 | 46.2 | 123.0G | 47.2M | 35.1 |
| RAP-SAM | TopFormer [] |  | 34.6 | 77.0 | 37.8 | 29.8 | 53.3 | 41.7 | 40.1G | 15.0M | 30.7 |

Light-Weight Decoupled Adapter. After the shared decoder, we also add two lightweight adapters, $A_{obj}$ and $A_{prompt}$ to adapt the shared decoder’s knowledge better.
In particular, we use the asymmetric design. $A_{obj}$ uses the same dynamic convolution design to refine the object query further, while $A_{prompt}$ uses pixel-wise cross-attention design. Our key insights are: (1) For panoptic segmentation and video segmentation, the interaction of the scene feature is necessary, but not for interactive segmentation. (2) despite the decoder being shared, the details are still missing for interactive segmentation due to the pooling effect in Equ. [1](#S4.E1), where we even find inferior results after adopting a pooling-based adapter. More detailed adapter design can be found in Sec. [5.2](#S5.SS2).

### 4.2 Training and Inference

Joint Image and Video Segmentation Co-training. Our goal is to train all segmentation tasks only once jointly. All training targets are one entity label and mask for all three different cases. The entity can be thing, stuff, class-agnostic masks, and their corresponding labels. Note that the instance masks with the same ID $d$ form the tube masks. During training, we apply Hungarian matching between the predicted and ground-truth entity masks to assign object queries to video/image entities and then supervise their predicted masks and classification. The classifier is replaced by CLIP text embedding to avoid cross-dataset taxonomy conflicts.

Following previous works [, ], the final loss function is given as $L=\lambda_{cls}L_{m_cls}+\lambda_{ce}L_{m_ce}+\lambda_{dice}L_{m_dice}$. Here, $L_{m_cls}$ is the Cross-Entropy (CE) loss for mask classification, and $L_{m_ce}$ and $L_{m_dice}$ are mask Cross-Entropy (CE) loss and Dice loss [, ] for segmentation, respectively. By default, we set $\lambda_{cls}=2,\lambda_{ce}=5,\lambda_{dice}=5$.

Inference. we follow the same inference procedure of Mask2Former [] for image segmentation. We merge the things and stuff for PS according to the sorted scores. The scores are generated by CLIP text embedding. For video instance segmentation, we use query matching rather than introducing extra tracking components to generate instance ID, following previous work [, , ]. We adopt a near-online method for inference. For interactive segmentation tasks, we follow the origin SAM [], by providing box and point prompts and obtaining the binary masks. All the parameters are shared across three different tasks.

Extension and Application. Since our model can perform various segmentation tasks, RAP-SAM can be extended to more datasets and tasks, including semantic segmentation on ADE20k [] or video panoptic segmentation on VIPSeg []. Moreover, our method can support prompt-driven video object segmentation by combining interactive segmentation with a video instance segmentation framework. We put the details of the extension and application for RAP-SAM in the supplementary.

## 5 Experiments

Datasets. Our benchmark uses COCO [] and YouTube-VIS 2019 [] datasets for benchmarking. COCO-SAM is constructed by using ground truth boxes and masks as visual prompt inputs, following the SAM-1B dataset []. In addition, to verify the effectiveness and generality of RAP-SAM, we also use other datasets, including ADE-20K [] and VIP-Seg dataset [] in Sec. [5.1](#S5.SS1).

Evaluation Metrics and Devices. For image segmentation, we adopt panoptic quality (PQ), which can be further decomposed into the segmentation quality (SQ) and the recognition quality (RQ). For the VIS task, mAP is adopted. We further report the semantic segmentation results (mIoU) and video panoptic segmentation results (STQ [] and VPQ []). We adopt one A100 GPU for speed testing.

Re-implemented Baselines. Since no previous works explore joint co-training for image, video, and interactive segmentation in one framework. We re-implement several representative baselines using the same codebase, including non-real-time models (Mask2Former [], kMaX-DeepLab []) for reference. In particular, we benchmark over ten different methods, including different backbones and decoders.

Implementation Details. We implement our models and all other baselines in PyTorch [] with the MMDetection toolbox []. We use the distributed training framework with 16 A100 GPUs. Each mini-batch has two images per GPU. In particular, we adopt pseudo video training on COCO by moving image masks with random directions. All the models are trained with 24 epochs. For data augmentation, we adopt large-scale jitter as previous works [] to build strong baselines. For all models, we adopt training steps and optimizers. Refer to the supplementary for more details.

### 5.1 Main Results

Our Benchmark Results. We list our benchmark results in Tab. [3](#S4.T3). We benchmark recent state-of-the-art methods using the same training and test settings. From the table, our proposed RAP-SAM achieves the best speed and accuracy trade-off on all three different visual segmentation tasks, under the various backbones. Mask2Former [] achieves similar or partially stronger results than our method. However, their speed is still limited, which is 7-8 FPS slower than our methods. YOSO [] runs as fast as our method. However, the performance gap is still significant. Thus, our proposed RAP-SAM is a simple yet effective baseline for real-time and all-purpose settings.

Promptable Results Compared with SAM. In Tab. [4](#S5.T4), we verify our trained model as a promptable segmenter, like SAM. We compare our method with SAM-Huge, which is much larger. Despite there being a gap, our method can run fast and with much less GFlops.

Comparison with Specific Design Models on VIP-Seg. We also verify the effectiveness of RAP-SAM on a more challenging video segmentation task, video panoptic segmentation on the VIP-Seg dataset. Compared with recent works [, ], RAP-SAM also archives the best speed and accuracy trade-off.

**Table 4: Comparison with SAM [] as a promptable segmenter on various detectors and instance segmenters.**
| Method | Detectors | mAP | AP50 | AP75 | Parameters | Flops |
| --- | --- | --- | --- | --- | --- | --- |
| SAM-Huge | Mask-RCNN (R50) | 35.6 | 54.9 | 38.4 | 641M | 3000G |
| Ours | Mask-RCNN (R50) | 26.7 | 43.0 | 27.9 | 47M | 188G |
| SAM-Huge | Mask-RCNN (X-101-64x4d) | 39.0 | 60.5 | 42.1 | 641M | 3000G |
| Ours | Mask-RCNN (X-101-64x4d) | 28.5 | 46.4 | 29.9 | 47M | 188G |

Figure: Figure 3: Meta-architecture exploration. (a), Simple shared decoder design. (b), Decoupled decoder design with two heads. (c), Shared decoder with decoupled adapter. (d), Decoupled decoder with the decoupled adapters. Best viewed in color and zoom in.
Refer to caption: x2.png

**Table 5: Comparison with real-time video segmentation method on VIP-Seg validation set.**
| Method | backbone | $\rm VPQ^{1}$ | $\rm VPQ^{2}$ | $\rm VPQ^{4}$ | $\rm VPQ^{6}$ | VPQ | STQ | FPS |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Clip-PanoFCN [] | R50 | 24.3 | 23.5 | 22.4 | 21.6 | 22.9 | 31.5 | 8 |
| Video K-Net [] | R50 | 29.5 | 26.5 | 24.5 | 23.7 | 26.1 | 33.1 | 10 |
| Tube-Link [] | STDCv1 | 32.1 | 31.3 | 30.1 | 29.1 | 30.6 | 32.0 | 14 |
| Tube-Link [] | STDCv2 | 33.2 | 31.8 | 30.6 | 29.6 | 31.4 | 32.8 | 12 |
| Ours | R18 | 34.2 | 32.1 | 30.8 | 30.2 | 32.5 | 33.7 | 30 |

**Table 6: Comparison with real-time panoptic segmentation method on ADE-20k validation set.**
| Method | Backbone | PQ | $\mathrm{PQ^{th}}$ | $\mathrm{PQ^{st}}$ | Parameters | Flops |
| --- | --- | --- | --- | --- | --- | --- |
| PanSegFormer [] | R50 | 36.4 | 35.3 | 38.6 | 51M | 214G |
| MaskFormer [] | R50 | 34.7 | 32.2 | 39.7 | 45.2M | 181G |
| Mask2Former [] | R50 | 39.7 | 39.0 | 40.9 | 45.2M | 226G |
| YOSO [] | R50 | 38.0 | 37.3 | 39.4 | 45.1M | 176G |
| RAP-SAM | R50 | 38.3 | 36.6 | 39.7 | 47.2M | 179G |

Comparison with Specific Design Models on ADE-20k. We further transfer RAP-SAM on ADE-20k datasets. Since our method is real-time model, we only list several representative works for reference. Again, compared with recent work [], our method still achieves stronger results.

### 5.2 Ablation Study and Visual Analysis

Ablation on Shared Decoder Design. In Tab. [7](#S5.T7), we find using simple pooling-based dynamic convolution performs well under real-time settings, where we adopt R18 as the backbone and keep the adapter unchanged. In particular, there is no difference in panoptic segmentation results and a slight gap for interactive segmentation. However, per-pixel cross-attention introduces extra computation costs.

Ablation on Meta-Architecture Design. In Tab. [8](#S5.T8), we further explore the meta-architecture design, as shown in Fig. [3](#S5.F3). We use R50 as the backbone and keep the adapter unchanged. From the table, we find using a shared decoder architecture (in Tab. [7](#S5.T7)) achieves the best parameter and performance trade-off. These findings may conflict with recent multi-task works [] since the decoupled decoders may have more capacity. We argue that since our feature extractors are weaker than those works and the representation power is far from the heavier backbone such as Swin [] or ViT-large [], adding more capacity in the decoder may not boost the final performance.

**Table 7: Ablation on shared decoder design.**
| Setting | COCO-Panoptic | COCO-SAM | FPS |
| --- | --- | --- | --- |
| Per-Pixel Cross-Attention [] | 45.0 | 55.3 | 28.0 |
| Pooling + Dynamic Convolution [] | 43.8 | 55.7 | 36.2 |
| Pooling + Dynamic Convolution with Gate [] | 44.6 | 56.7 | 34.5 |

**Table 8: Ablation on meta-architecture.**
| Setting | COCO-Panoptic | COCO-SAM | Parameters |
| --- | --- | --- | --- |
| Fig. [3](#S5.F3) (a) | 43.8 | 54.2 | 46.3M |
| Fig. [3](#S5.F3) (b) | 44.0 | 55.2 | 53.6M |
| Fig. [3](#S5.F3) (c) | 44.6 | 56.7 | 47.3M |
| Fig. [3](#S5.F3) (d) | 45.2 | 56.2 | 54.6M |

**Table 9: Ablation on adapter design. CA: cross-attention. DC: dynamic convolution.**
| $\mathrm{A_{obj}}$ | $\mathrm{A_{prompt}}$ | COCO-Panoptic | COCO-SAM |
| --- | --- | --- | --- |
| - | - | 44.2 | 53.2 |
| CA | CA | 42.6 | 54.3 |
| DC | DC | 44.7 | 52.1 |
| DC | CA | 44.6 | 56.7 |
| CA | DC | 44.5 | 56.2 |

**Table 10: Ablation on joint co-training. (a), COCO-Panoptic. (b), YT-VIS 2019. (c), COCO-SAM.**
| Setting | COCO-Panoptic | YT-VIS 2019 | COCO-SAM |
| --- | --- | --- | --- |
| a | 36.6 | - | - |
| b | - | 21.5 | - |
| a + b | 36.2 | 36.0 | 50.7 |
| a + b + c | 35.7 | 35.3 | 50.9 |

Figure: Figure 4: The visualization results of our model on the YouTube-VIS 2019 dataset and the COCO dataset. The first three rows visualize five frames of inputs. The same instances are shown with the same color. The fourth row shows the interactive segmentation results with a single-point prompt (shown in green color). The last row shows the panoptic segmentation results.
Refer to caption: x3.png

Ablation on Adapter Design. Then, we explore the adapter design in Tab. [9](#S5.T9). In particular, we use a pre-trained model without adapters for initialization to see the effect of different adapters. In particular, we find using asymmetric adapters works well for balanced results for object queries and prompt queries, since the goals of the two queries are different. The former needs the spatio-temporal and scene-level context, while the latter only cares about the region context with the input position as the guidance.

Effectiveness of Joint Co-training. We further explore the effect of each dataset in Tab. [10](#S5.T10). We find joint co-training with image and video data leads to better performance for video instance segmentation. Adding COCO-SAM training leads to a few performance drops. This is because the learning targets are different for object queries and prompt queries.

Visualization.
In Fig. [4](#S5.F4), we give the three different visualization results on the YouTube-VIS 2019 dataset and the COCO dataset. We generate these visualization results using only a single model thanks to the joint co-training strategy. The first three rows in Fig. [4](#S5.F4) show the video instance segmentation. The fourth row shows the interactive segmentation results with a single-point prompt (shown in green color), and the last row shows the panoptic results.

## 6 Conclusion

In this work, we explore a challenging real-time setting, all-purpose segmentation, to transfer more segmentation tasks into one model. To solve this problem, we introduce RAP-SAM, a real-time segmentation model, that can segment and track objects in image, video, and interactive modes. We design a simple shared decoder with two key designs: shared dynamic convolution and asymmetric adapters, which lead to the best trade-off between speed and accuracy on three segmentation tasks. We hope our proposed benchmark inspires future research on this new setting.

Overview. In this supplementary, we first present more detailed results to support both the benchmark and our proposed RAP-SAM in Sec. [A](#A1). Then, in Sec. [B](#A2), we present a more detailed visual comparison using our model. Finally, in Sec. [C](#A3), we discuss several failure cases and potential future work.

## Appendix A More Experiment Results

Benchmark More Methods. In Tab. [11](#A1.T11), we benchmark more models and backbones [, , ] as the supplementary to main paper. Again, for various backbones and different heads, our model can achieve better or comparable results across three different tasks, while running in real-time. In particular, compared with the dynamic kernel-based method, K-Net, our approach achieves better performance on the R18 and R50 backbones and comparable results on the lightweight backbones.

Scale Up Joint Co-training. In the bottom of Tab. [11](#A1.T11), we further scale up our model on a large backbone, ConvNeXt large []. From the table, we find there are significant gains in all settings while still running with 8 FPS. This means our proposed all-purposed segmentation still has room to balance the accuracy and inference speed.

**Table 11: Benchmark More Models on Real-Time All-Purpose Segmentation. The GFlops are obtained with $1333\times 800$ inputs.**
| Method | Backbone | COCO-Panoptic | COCO-SAM | YouTube-VIS 2019 | Flops | Parameters | FPS |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  | PQ | SQ | $\mathrm{PQ_{th}}$ | $\mathrm{PQ_{st}}$ | mIoU | mAP |  |  |  |  |  |
| YOSO [] | EdgeNexT [] |  | 31.8 | 74.5 | 35.6 | 26.1 | 47.4 | 35.4 | 40G | 15.3M | 28.6 |
| K-Net [] | R18 [] |  | 33.1 | 75.7 | 36.8 | 27.4 | 53.6 | 34.8 | 124G | 25.6M | 30.4 |
| K-Net [] | R50 [] |  | 39.2 | 78.2 | 43.9 | 31.6 | 56.3 | 41.7 | 171G | 38.5M | 20.9 |
| K-Net [] | EdgeNexT [] |  | 38.0 | 76.7 | 42.5 | 31.4 | 55.7 | 44.0 | 108G | 19.5M | 30.5 |
| Mask2Former [] | EdgeNexT [] |  | 39.8 | 78.8 | 43.6 | 34.0 | 54.2 | - | 73G | 12.0M | 25.6 |
| RAP-SAM | R18 |  | 39.9 | 78.6 | 43.3 | 34.8 | 52.7 | 38.7 | 60.5G | 22.8M | 40.3 |
| RAP-SAM | R50 |  | 46.9 | 80.8 | 51.6 | 39.8 | 57.9 | 46.2 | 123.0G | 47.2M | 35.1 |
| RAP-SAM | EdgeNexT [] |  | 38.0 | 77.7 | 42.1 | 31.9 | 54.8 | 46.3 | 44G | 14.3M | 31.5 |
| RAP-SAM | ConvNeXt-L [] |  | 52.3 | 82.5 | 58.6 | 42.8 | 61.1 | 55.6 | 700G | 239M | 8.6 |

Explore adapter Design on More Methods.
We explore the effectiveness of our adapter design on Mask2Former [] and add the adapter module to the last decoder layer of Mask2Former. As shown in Tab. [12](#A1.T12), Mask2Former with adapter will improve 1.1 mIoU score compared with origin Mask2Former for interactive segmentation and only drop 0.3 PQ for panoptic segmentation. This result indicates that the adapter module not only applies to our method but can also be generalized to other methods

Table: Table 12: Explore adapter Design on More Methods.

More Implementation Details.
We implement our proposed method and benchmark methods using MMDetection toolbox [], and we train all of these methods in the same environment. For kMaX-DeepLab [], we excluded the auxiliary semantic segmentation loss, instance discrimination loss, and preserving PQ-style loss alignment with Mask2former [] for a fair comparison. We utilize the AdamW [] optimizer with a weight decay of 0.05, and the learning rate is set to 1e-4 for all methods. We warm up the learning rate in the first 500 iterations using a linearly increased strategy and decay the learning rate at 8 and 11 epochs by a factor of 10. For data augmentation, we use the large-scale jittering (LSJ) augmentation [] with a random scale sampled from the range of 0.1 to 2.0 and followed by a fixed size crop to 1024×1024.

More Inference Details. For video instance segmentation, we adopt simple object query matching [, ] for all methods to keep the fair comparison. For interactive segmentation, we mainly use point prompts, which is more challenging than box prompts. For panoptic segmentation, all hyperparameters are the same. The FPS is obtained on one A100 card for both the main paper and supplementary.

## Appendix B Visual Comparison

Comparison on Youtube-VIS dataset.
In Fig. [5](#A2.F5), we compare our RAP-SAM (right) with a strong baseline Mask2Former (left) with the same ResNet50 backbone. Our methods achieve more consistent tracking and segmentation results in these examples.

Figure: Figure 5: When compared to the Mask2Former(right) on the YouTube-VIS 2019 dataset, our RAP-SAM(left) demonstrates superior performance in recognizing and segmenting objects in certain scenarios, and it also holds an advantage in detailing edges.
Refer to caption: x4.png

Comparison on COCO Panoptic Segmentation dataset.
In Fig. [6](#A2.F6), We demonstrate that our RAP-SAM model surpasses K-Net in panoptic segmentation tasks. RAP-SAM exhibits enhanced accuracy in processing complex scenes and maintaining fine details, especially in segmenting object edges and managing overlapping objects.

Figure: Figure 6: When compared with K-Net (bottom) on the COCO dataset, our RAP-SAM (top) model shows a significant advantage, achieving more accurate segmentation of foreground and background, with edges that are smoother.
Refer to caption: x5.png

More Interactive Segmentation Results.
In Fig. [7](#A2.F7), We present further visualizations of interactive segmentation, showcasing segmentation outcomes for ’things’ and ’stuff’. In these visualizations, green dots signify manually specified prompts. Our model exhibits precise segmentation abilities for ’things’ and ’stuff’. Notably, even when prompt points are situated on object boundaries, our model accurately identifies and segments the object.

Figure: Figure 7: Here are additional results of interactive segmentation. For point prompts or box prompts, RAP-SAM segments out the corresponding object based on the spatial location of the prompt. We have provided both segmentation results for ’things’ and ’stuff’, demonstrating that RAP-SAM is capable of segmenting both while maintaining strong performance.
Refer to caption: x6.png

## Appendix C Challenges and Future Work

Failure Cases Analysis.
We also analyze failure cases in video/image segmentation and identify two typical failure modes of RAP-SAM. Firstly, as illustrated in Fig. [8](#A3.F8), when faced with multiple objects with high overlap, RAP-SAM struggles to fully recognize them, resulting in the omission of several masks. Secondly, in crowded scenarios, RAP-SAM faces challenges in recognizing and segmenting all instances with a limited number of instance kernels.

Figure: Figure 8: Failure modes of RAP-SAM in image/video segmentation.
Refer to caption: x7.png

Future Work Discussion. There are several directions to explore for real-time all-purpose segmentation. The first direction is to achieve better-balanced performance on image, video, and interactive segmentation (since there is still a lot of room for performance, as shown in the last row of Tab. [11](#A1.T11)). The second direction is to speed up the model and deploy the model on the edge device, such as iPhone. The third direction is to explore different knowledge distillation approaches to transfer the vision foundation model in real-time all-purpose models. Moreover, more visual prompts can be explored, such as mask prompts or box prompts. Our method currently only supports point prompts. This will be our future work.

## Peer Reviews

confidence: 3
contribution: 3
initial_rating: 6
presentation: 3
questions: 1. Do you have the results for TopFormer in Table 3? Additionally, please bold the results in all comparison tables for clarity.
soundness: 3
strengths: 1. The model achieves multi-purpose segmentation through an efficient structure and unified training approach.
2. The paper is well-written and easy to follow.
3. The experiments on panoptic segmentation, interactive segmentation, and video segmentation are solid, comprehensive, and persuasive, effectively demonstrating the model's contribution.
summary: The paper presents a real-time, versatile segmentation model capable of interactive segmentation, panoptic segmentation, and video instance segmentation. 
While retaining the SAM encoder-decoder structure, the model incorporates an efficient encoder and adapter to enhance performance.
In the decoder, RAP-SAM introduces a three-stage pipeline that leverages novel pooling-based dynamic convolutions to refine mask tokens. Following the decoder, two additional prompt adapters are implemented to improve interaction between visual prompts and segmentation tokens.
RAP-SAM demonstrates efficiency and generalizability across various segmentation benchmarks.
weaknesses: 1. The paper lacks a detailed comparison with other SAM-like methods. A single COCO instance segmentation comparison in Table 4 is insufficient to substantiate claims of superiority over SAM. The results presented in Table 4 are not particularly outstanding. Additional experiments, such as on the SegAny task, with detailed metrics (AP for small, medium, large objects) on COCO instance segmentation, and evaluations with different object detectors, would strengthen the case.

2. Efficiency benchmarks are insufficiently detailed. For a model promoting efficiency, there should be a more comprehensive evaluation across different GPU platforms, such as the 3090 and V100, testing throughput and latency. Additionally, plotting latency versus performance compared to other SAM-like methods would provide a clearer visualization of the model's efficiency.

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: See weakness.
soundness: 3
strengths: 1.Demonstrates impressive performance and inference speed.

2.Filling the gap in real-time multi-purpose segmentation.

3.The whole method is very simple and easy to understand.

4.Code is provided for easy reproduction by the reader.
summary: This work addresses the need for real-time multi-purpose segmentation by introducing a novel setting that encompasses interactive, panoptic, and video instance segmentation, striving for a single end-to-end model capable of handling all tasks in real-time. The proposed Real-Time Multi-Purpose SAM (RMP-SAM) utilizes an efficient encoder and a decoupled adapter for prompt-driven decoding, along with innovative training strategies and adapter designs, demonstrating effectiveness and strong generalization across benchmarks and specific semantic tasks while achieving an optimal balance between accuracy and speed.
weaknesses: 1.Based on existing technology development, the entire pipeline is not novel.

2.Differences with SAMv2 should be further clarified, especially in terms of claimed semantic labels?

---

confidence: 4
contribution: 2
initial_rating: 5
presentation: 2
questions: Please see above.
soundness: 3
strengths: - RMP-SAM unifies interactive segmentation, panoptic segmentation, and video instance segmentation within a single model.
- RMP-SAM offers a good trade-off between speed and accuracy.
- Extensive experiments demonstrate the model's effectiveness.
summary: This paper presents a real-time multi-purpose segmentation model called RMP-SAM. RMP-SAM handles various tasks such as interactive segmentation, panoptic segmentation, and video instance segmentation using a single model. To balance the accuracy and speed, RMP-SAM utilizes a lightweight encoder and a dynamic convolution-based decoder. RMP-SAM achieves fast inference while maintaining satisfactory performance.
weaknesses: - The authors do not provide detailed information for joint training. Joint training for multiple tasks can be complex. How do the authors train RMP-SAM for some potential problems, such as avoiding the model being dominated by a single task and performance degradation by conflicts between different tasks?

- This paper ignores some related methods, making it difficult to assess the model's performance relative to existing SOTA approaches. For example, some universal methods[1,2,3] obtain better results than RMP-SAM using ResNet50. The authors should make a comprehensive comparison with other methods. 

[1] Tube-Link: A flexible cross tube framework for universal video segmentation. CVPR 2023.

[2] Dvis: Decoupled video instance segmentation framework. CVPR 2023.

[3] Univs: Unified and universal video segmentation with prompts as queries. CVPR 2024.

---

confidence: 3
contribution: 3
initial_rating: 6
presentation: 2
questions: - What does the dot size in Fig. 1b indicate?
- The abstract says "generalization ability of these models across diverse scenarios", a learnable classifier with CLIP text embeddings is also used and “segment anything” is in the title. Is there a connection to open-vocabulary?
soundness: 3
strengths: - Large models can perform many tasks, but are not real-time capable because of the large encoders, while real-time models are often specialized in only one task. The method presented here aims to combine the two things, i.e., "the first real-time multi-purpose segmentation model".
- Precise implementation details are given and the comparisons with the other methods appear to be fair.
- The method achieves good results in the trade-off between performance and speed across the various tasks and datasets.
- The ablation studies are useful and show interesting insights.
summary: The authors explore a novel real-time segmentation setting called real-time multi-purpose segmentation. It contains three fundamental sub-tasks: interactive segmentation, panoptic segmentation, and video instance segmentation. In contrast to previous methods that use a separate design for each task, the authors use only a single end-to-end model to handle all these tasks in real time. To fulfill the real-time requirements and balance multitask learning, a new dynamic convolution-based method, Real-Time Multi-Purpose SAM (RMP-SAM), is introduced. They benchmark several strong baselines by extending existing work to support multi-purpose segmentation.
weaknesses: - Many architectural elements were adopted from other works, it is not clear to me if there are already similar architectures as proposed here, or where exactly is the innovation (except the jointly training).
- In the related work section, many works are cited and also compared at the task level, but I also miss a comparison at the architectural level.
- The tables, especially table 3, are difficult to read because nothing is in bold print and you have to search for the trade-off here. A plot like Fig. 1b would be more useful.
- The references to the appendix could be a little more precise and there is no reference to Table 2.

Meta-review: This paper presents RAP-SAM, a real-time, versatile segmentation model capable of handling interactive segmentation, panoptic segmentation, and video instance segmentation. The model retains the SAM encoder-decoder structure while incorporating an efficient encoder and adapter to enhance performance. In the decoder, RAP-SAM introduces a three-stage pipeline leveraging pooling-based dynamic convolutions to refine mask tokens. Additionally, two prompt adapters are implemented to improve interactions between visual prompts and segmentation tokens. RAP-SAM demonstrates strong efficiency and generalizability across various segmentation benchmarks, filling the gap in real-time multi-purpose segmentation.

The reviewers unanimously praised the paper for its innovative approach to multi-purpose segmentation, including:
1.The ability to perform interactive segmentation, panoptic segmentation, and video instance segmentation through a unified training approach.
2.A well-written and easy-to-follow presentation.
3.Comprehensive experiments on multiple segmentation benchmarks, demonstrating the model's contributions and performance.
4.Strong inference speed and efficiency, meeting real-time requirements.



Therefore, I recommend the acceptance based on the unanimous support from reviewers. Since this research fills the gap in real-time multi-purpose segmentation and achieved impressive performance and inference speed, I would like to recommend for an oral presentation.

Decision: Accept (Oral)