Title: Fine-Grained Visual Prompting
Venue: NeurIPS 2023
Accepted: yes
Keywords: visual prompting

zero-shot

visual language model

referring expression comprehension
Abstract: Vision-Language Models (VLMs), such as CLIP, have demonstrated impressive zero-shot transfer capabilities in image-level visual perception. However, these models have shown limited performance in instance-level tasks that demand precise localization and recognition. Previous works have suggested that incorporating visual prompts, such as colorful boxes or circles, can improve the ability of models to recognize objects of interest. Nonetheless, compared to language prompting, visual prompting designs are rarely explored. Existing approaches, which employ coarse visual cues such as colorful boxes or circles, often result in sub-optimal performance due to the inclusion of irrelevant and noisy pixels. In this paper, we carefully study the visual prompting designs by exploring more fine-grained markings, such as segmentation masks and their variations. In addition, we introduce a new zero-shot framework that leverages pixel-level annotations acquired from a generalist segmentation model for fine-grained visual prompting. Consequently, our investigation reveals that a straightforward application of blur outside the target mask, referred to as the Blur Reverse Mask, exhibits exceptional effectiveness. This proposed prompting strategy leverages the precise mask annotations to reduce focus on weakly related regions while retaining spatial coherence between the target and the surrounding background. Our **F**ine-**G**rained **V**isual **P**rompting (**FGVP**) demonstrates superior performance in zero-shot comprehension of referring expressions on the RefCOCO, RefCOCO+, and RefCOCOg benchmarks. It outperforms prior methods by an average margin of 3.0\% to 4.6\%, with a maximum improvement of 12.5\% on the RefCOCO+ testA subset. The part detection experiments conducted on the PACO dataset further validate the preponderance of FGVP over existing visual prompting techniques. Code is available at https://github.com/ylingfeng/FGVP.

Fine-Grained Visual Prompting

Abstract

Vision-Language Models (VLMs), such as CLIP, have demonstrated impressive zero-shot transfer capabilities in image-level visual perception. However, these models have shown limited performance in instance-level tasks that demand precise localization and recognition. Previous works have suggested that incorporating visual prompts, such as colorful boxes or circles, can improve the ability of models to recognize objects of interest. Nonetheless, compared to language prompting, visual prompting designs are rarely explored. Existing approaches, which employ coarse visual cues such as colorful boxes or circles, often result in sub-optimal performance due to the inclusion of irrelevant and noisy pixels. In this paper, we carefully study the visual prompting designs by exploring more fine-grained markings, such as segmentation masks and their variations. In addition, we introduce a new zero-shot framework that leverages pixel-level annotations acquired from a generalist segmentation model for fine-grained visual prompting. Consequently, our investigation reveals that a straightforward application of blur outside the target mask, referred to as the Blur Reverse Mask, exhibits exceptional effectiveness. This proposed prompting strategy leverages the precise mask annotations to reduce focus on weakly related regions while retaining spatial coherence between the target and the surrounding background. Our F ineG rained V isual P rompting ( FGVP ) demonstrates superior performance in zero-shot comprehension of referring expressions on the RefCOCO, RefCOCO+, and RefCOCOg benchmarks. It outperforms prior methods by an average margin of 3.0% to 4.6%, with a maximum improvement of 12.5% on the RefCOCO+ testA subset. The part detection experiments conducted on the PACO dataset further validate the preponderance of FGVP over existing visual prompting techniques. Code is available at https://github.com/ylingfeng/FGVP .

1 Introduction

The usage of Vision-Language models (VLMs) [42, 31, 1, 30] has become increasingly prominent in various vision-related tasks, largely due to their notable impact. These models benefit from a vast amount of training data and parameters, demonstrating powerful performance in the training of fundamental visual model backbones [34, 15, 51]. Moreover, the transfer learning potential of VLMs has been employed in tasks such as open vocabulary detection [20, 64], visual grounding [32, 37], and image editing [6, 5], etc.

In the above-mentioned works, the VLMs generally require additional training, to adapt to each specific downstream task. However, the utilization of off-the-shelf Vision-Language Models (VLMs)

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Crop Box Mask -> Colorful Crop for leveraging their inherent image-text understanding, without additional tuning, remains largely unexplored. A notable challenge lies in the low sensitivity of VLMs to perform object spatial localization due to the presence of a large amount of weakly related background noise in the images [68, 3, 35]. The common practice involves cropping the region of interest [10, 62] to obtain a zoomed-in visual representation of a single object, at the expense of discarding valuable global information. As a result, existing training-free approaches for classification lack a comprehensive understanding of contextual information and spatial awareness across different objects.

In recent studies, CPT [61] and ReCLIP [50] have leveraged visual prompting techniques to establish relations between partial instances. Visual prompting is a technique used in image-language tasks, where markers such as colorful boxes or circles are added directly onto an image to highlight specific targets. By utilizing appropriate visual prompts, the attention of VLMs can be effectively directed toward the desired targets while preserving the global context. Moreover, a study by RedCircle [49] demonstrated that drawing red circles enclosing the object on the entire image can effectively distinguish instances, where the circles correspond to inscribed ellipses derived from proposal boxes. This discovery suggests that Vision-Language Models (VLMs) may possess the inherent ability to understand the local object within an overall image. Therefore, a specifically designed visual prompt has the potential to explicitly invoke this capability of VLMs, thereby benefiting various tasks.

Despite the interest in visual prompting techniques, their unique designs have yet to be fully explored. The current approaches only rely on coarse markers like colorful boxes or circles, which can introduce ambiguity and fail to highlight the intended instance accurately. In this paper, we address this issue by systematically organizing and investigating various forms of visual prompting. Further, we propose a refined technique called Fine-Grained Visual Prompting (FGVP), which utilizes semantic masks to mark each target precisely, thereby enhancing zero-shot performance.

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LVLM 𝑡 1 apple on the left 𝑡 2 raspberries 𝑡 3 apple in the middle In our research, we examine different visual prompting designs showcased in Fig. 1, including those utilized in previous studies such as CPT [61], ReCLIP [50], and RedCircle [40]. To provide an overview of previous research, we summarize their strategies in Table 1. It is important to note that some approaches achieve their best results through prompt ensembles or post processing techniques, which are discussed extensively in Sec. 3.1. Despite the progress made in visual prompting, none of the existing methods have fully explored the use of more precise semantic masks as fine-grained markers, rather than relying on coarse boxes or circles. To verify the effectiveness, we begin by exploring the upper bound of these prompts. Our methodology assumes perfect accuracy of all annotations applied, ensuring that images are marked with ground-truth information, namely bounding boxes and masks. We confirm that a single Blur Reverse Mask [ D 4 ], which involves blurring all content outside the instance mask, significantly outperforms other types of visual prompts on multiple datasets. This indicates the effectiveness of our fine-grained visual prompt design.

In practical scenarios where ground-truth annotations are unavailable, there are two primary approaches for obtaining finegrained visual markings. The first approach is based on utilizing object proposals predicted by a pretrained detector, following a similar paradigm as employed in MAttNet [62]. This approach is commonly employed in zero-shot classification tasks [61, 50, 49]. To generate fine-grained masks, we employ Segment

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Anything Model (SAM) [26], which takes the aforementioned bounding boxes (refer to Fig. 2) as input prompts. Additionally, we propose a second approach that eliminates the dependency on a detector for prompt candidates. Our approach establishes a viable zero-shot pipeline by solely leveraging SAM, irrespective of specific tasks. Specifically, we prompt SAM with grid-wise keypoints and subsequently apply a non-maximum suppression (NMS) operation to obtain proposals with semantic masks. These fine-grained masks can then be employed for zero-shot classification (see Fig. 3).

Our contributions can be summarized as follows:

We propose Fine-Grained Visual Prompting (FGVP), employing Blur Reverse Masks to enhance the semantic localization capability of Vision-Language models such as CLIP (Fig. 1). To the best of our knowledge, we are the first to explore the use of Blur Reverse Masks as visual prompting, highlighting their remarkable effectiveness in zero-shot image-text comprehension.

We are the first to provide a comprehensive exploration of visual prompt formats, including crop, box, circle, mask with different line markings, colored masks, grayscale, and Gaussian blurring. Additionally, we thoroughly analyze and evaluate the impact of associated auxiliary attributes such as blur deviation, color, etc., ensuring a comprehensive understanding of their effects.

Our proposed Fine-Grained Visual Prompting (FGVP) achieves state-of-the-art (SOTA) zero-shot results on the referring expression comprehension task, surpassing ReCLIP and RedCircle by an average of 4.6% and 3.0% accuracy, respectively, across RefCOCO, RefCOCO+, and RefCOCOg benchmarks. Notably, our zero-shot pipeline demonstrates superior part detection accuracy on the PACO dataset compared to previous visual prompting methods.

2 Related Work

Vision-Language Models Large Language Models (LLMs), such as GPT-3 [7], GPT-4 [41], LLaMA [53], and PaLM [12], have demonstrated strong zero-shot transfer abilities in natural language processing. In recent years, vision-language Models (VLMs) that leverage image-text data pairs from the web have gained prominence in computer vision (CV) tasks. CLIP [42] and ALIGN [31] learn image-text alignment through contrastive learning. Furthermore, models like Flamingo [1] have shown impressive few-shot learning capabilities. BLIP-2 [30, 29] proposes joint multimodal task handling through transfer learning. Notably, VLMs have excelled in image-text generation as demonstrated by DALL-E [46, 45]. However, instance-level tasks such as referring expression comprehension and part detection typically require tuning of vision and text encoders in existing open vocabulary methods [20, 64] and image grounding approaches [32, 37]. In contrast, this paper proposes a zero-shot architecture for instance-level tasks using off-the-shelf VLMs.

Prompt Engineering Prompt engineering is a well-developed practice in NLP [43, 7]. AutoPrompt [48] and CoOp [69] automate the generation of prompt templates, leveraging gradient-based methods instead of manual crafting. Language prompting is then extended to open vocabulary detection [14, 16] and segmentation [59]. While language prompting has been extensively explored, visual prompting has received less attention. Previous works [35, 22, 2, 4] use visual prompt tuning to adapt to VLMs. In terms of zero-shot solutions, CPT [61] introduces colorful boxes as markers on images, and RedCircle [49] demonstrates the effectiveness of a circle mark for visual attention during zero-shot classification. However, existing visual prompting methods lack fine-grained precision. In contrast, we propose leveraging semantic masks derived from segment models like Segment Anything [26] for more precise visual marking. It is worth noting that CPT [61] also employs semantic masks as colored prompts, but only after cropping the region using bounding boxes. In our approach, we directly mark the fine-grained mask on the entire image to preserve the global visual content.

Image Segmentation Image segmentation is a common task in computer vision [52], involving predicting region segment masks by labeling pixels. This task encompasses various sub-tasks such as instance segmentation [21, 13, 55], semantic segmentation [38, 67, 11], and panoptic segmentation [25, 25]. Recent advancements include SegGPT [56], which facilitates performing diverse segmentation tasks through in-context visual learning. Another notable approach, Segment Anything Model (SAM) [26], introduces spatial prompts for segmenting arbitrary objects. SAM is trained using an extensive dataset of over 1 billion segmentation masks. In our study, we employ the off-the-shelf SAM framework to generate precise semantic masks for fine-grained visual prompting.

Referring Expression Comprehension This task involves providing captions for specific objects in images and localizing them with bounding boxes. Proposal-based methods use object detectors like Mask R-CNN [21] to detect instance regions. Each proposal is then cropped and used for subsequent categorization. Examples include MAttNet [62], UNITER [10], OSCAR [33], and VinVL [66]. Proposal-free methods, such as MDETR [23] and ViLT [24], train Vision-Language Models end-toend. Recent zero-shot methods [61, 50, 40] combine proposal boxes from MAttNet [62] with VisionLanguage Models for image-caption matching. Our work follows this setup for fair comparisons.

Part Detection Part Detection is a sub-field of object detection [21, 47, 17, 8], primarily focused on fine-grained classification [65, 54] and human parsing [58, 18] that require precise region localization. Notable datasets include CUB-200 [54] for annotated bird parts, LIP [19] for semantic human part labels, PASCAL-Part [9] for common objects, and PACO [44], which introduces a comprehensive benchmark with jointly annotated part masks. Previous zero-shot works typically detect objects and parts using pre-annotated visual attributes to transfer the knowledge [28]. In contrast, we leverage the transferability of Vision-Language Models to perform part detection in this study.

3 Method

In this section, we begin by presenting a comprehensive overview of the visual prompting pipeline for zero-shot object recognition and part detection tasks. Subsequently, we delve into the details of our Fine-Grained Visual Prompting (FGVP) architecture. Finally, we examine the effectiveness and rationale behind our design.

3.1 Framework

The zero-shot framework takes as input an image I ∈ R 3 × H × W , N box proposals Φ ∈ R N × 4 and M caption texts T ∈ Ω M = { t 1 , t 2 , ..., t M } , where Ω denotes the set of textual sentences. The goal is to find the best matching image-text pairs. A common practice is to get each image input by cropping or RoIAlign [21] according to Φ . However, with the introduction of visual prompting, one can mark the regions on the whole image, which highlights each target instance while keeping the background knowledge. For simplicity, we regard cropping as an ordinary type of visual prompting. Then the image input I Φ ∈ R N × 3 × H × W = { i 1 φ , i 2 φ , ..., i N φ } for VLMs can be generated as:

where VP concludes visual prompting, such as cropping, drawing colorful boxes or circles, or employing fine-grained masks, etc. Then the cosine similarity S = { s ( i n φ , t m ) } N × M between each image proposal I Φ and text T can be derived by the VLM, e.g., CLIP [42]. For the referring expression comprehension task, T is a gathering of short sentences that describe the related instances in the image, and one needs to predict their referring location. Note that there exist some post processing techniques for this task, namely 'Relations' [50] and 'Subtraction' [49], respectively. The 'Relations' considers spatial relations R = { r ( i n 1 φ , i n 2 φ ) } N × N between every two proposals, w.r.t. left, right, above, below, bigger, smaller, and inside. Then the parsed final scores are updated as:

Besides, the 'Subtraction' deals with positive and negative text set to weigh down the score. By randomly sampling Q negative captions ˜ T that related to no instances on the image, all negative scores are calculated as ˜ S ∈ R N × Q = ˜ s ( i n φ , ˜ t q ) . And then use it to penalize S :

Once the final scores are obtained, the referring region ̂ Φ can be given by:

For the part detection task, the text is the object or part label names. Considering the general case, each part only appears in one region (all eyes of a bird are covered by a single box). Thus we indicate a post processing strategy for this task with the Hungarian algorithm [27], to find a bipartite matching between image proposals and part labels. Otherwise, the label predicted corresponding to each proposal is derived as:

3.2 Fine-Grained Visual Prompting

Existing visual prompts primarily use proposal boxes from the detector. The Crop [ P ] and Colorful Crop [ A 2 ] prompts involve extracting cutouts of the original image based on the location of the box. Prompting with Keypoint [ A 1 ] entails placing a small circle around the box center. The box-based [ B ∗ ] prompts naturally use the box as the marking boundary, while the circle-based [ C ∗ ] prompts are essentially similar to [ B ∗ ], except for replacing the box with its inscribed ellipse.

However, these prompts are too coarse to emphasize key targets. Marking inaccuracies introduce irrelevant information and lead to sub-optimal performance based on empirical evidence. We illustrate previous visual prompting approaches as coarse prompting on top of Fig. 1. In contrast, we investigate fine-grained visual prompting using semantic masks, which can accurately highlight the target instance, reduce background interference, and retain global knowledge. Nonetheless, obtaining semantic masks may pose a challenge when only proposal boxes are provided. To tackle this issue, we propose the use of a robust image segmentation model called Segment Anything Model (SAM) [26] to perform class-agnostic segmentation based on the given boxes (Fig. 2):

where SAM denotes Segment Anything Model [26], and M ∈ R N × H × W denotes the semantic masks. We updated Eq. (1) with Eq. (6), (7) to produce fine-grained visual prompting.

In addition, We innovatively proposed a zero-shot classification pipeline, which does not require pre-processed box proposals but directly generates fine-grained markers. The key idea lies in that

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SAM can propose an extremely exhaustive prediction of almost any object of parts on the images taken a grid-wise set of keypoints G ∈ R g 2 × 2 as inputs (Fig. 3), where g is the point number along one side of the image. Then the mask is generated through SAM prompted by the grid-wise keypoints:

where NMS denotes Non-Maximum Suppression to filter out duplicate proposals. Notably, the regression box Φ is obtained by calculating the smallest box containing the object mask.

Following the FGVP illustration, we provide an overview of all visual prompting variants. In addition to the categorization based on box, circle, and mask, they can also be sorted based on different markers, such as line [ ∗ 1 ], color [ ∗ 2 ], grayscale [ ∗ 3 ], and blur [ ∗ 4 ]. Specifically, line-based methods utilize closure lines to prompt the image, while color-based prompting involves drawing colorful masks on the target. These two types can be classified as positive marking, which aims to highlight the target area. Conversely, the reverse-based prompting serves as a form of negative marking by grayscaling or blurring the background area, thereby reducing the impact of weakly related information.

3.3 Discussion

In fact, both part detection and referring tasks require a mutual understanding between instances and backgrounds. In the referring expression comprehension task, captions are used to describe the interrelationships between multiple objects. Similarly, in part detection, a local part can be hard to classify regardless of the object. Successful performance of these tasks thus necessitates a network's ability to handle global information and accurately comprehend the relationships between instances. For this reason, implementing more precise visual prompting is helpful, typically with the Blur Reverse Mask Prompting [ D 4 ]. This is because the web-scale dataset on which VLM is trained contains a large amount of photography.

These images prefer to employ 'Bokeh' to blur the background and highlight the subject (Fig. 4). As a result, VLMs may have prior knowledge of visual prompting with background blurring.

4 Experiments

In this section, we first evaluate individual visual prompting performance. Then, we compare FGVP with previous zero-shot methods on the referring expression comprehension and part detection tasks to show our effectiveness. For more experimental analysis refer to the Supplementary Materials.

4.1 Dataset

We conduct the experiments on several visual datasets, i.e., RefCOCO [63], RefCOCO+ [63], RefCOCOg [39], COCO [36], and PACO [44]. The RefCOCO, RefCOCO+, and RefCOCOg datasets are subsets selected from COCO, containing bounding boxes and masks corresponding to captioned

instances. The COCO dataset is annotated with boxes and masks for objects, while PACO additionally incorporates annotations of corresponding parts for each object.

4.2 Implementation Details

We follow Timm [57], CPT [61], and ReCLIP [50] to construct our pipelines. By default, we use ViT-B/32, ViT-L/14@336px, and RN50x16 backbones trained in CLIP [42] by OpenAI as our visionlanguage model. To simplify reference to these models in subsequent experiments, we refer to them as ViT-B, ViT-L, and RN. Additionally, for improved mask generalization, we employ SAM-ViT-H, a variant of Segment Anything Model [26]. Concerning part detection on the PACO [44] dataset, we begin by cropping the object according to the annotated boxes and then perform part detection on each object crop. All experiments are conducted on 8 × Tesla V100.

4.3 Ablation Study on Visual Prompt

In this section, we elaborate on the zero-shot performance of individual visual prompts defined in Fig. 1 with two experiment settings. Firstly, we assume all prompting representations, i.e., bounding boxes and semantic masks, to be as precise as possible, in order to explore the upper bound of each visual prompt. Therefore, we acquire them from ground truth annotations of all datasets. Then we evaluate the imagelabel matching accuracy following the zero-shot pipeline described in Sec. 3.1. More specifically, instance-label pairs are represented as objectobject name, part-part name, and object-caption in COCO, PACO, and RefCOCO series datasets, respectively. The left part of Table 2 reveals that the Blur Reverse Mask [ D 4 ] offers the best overall performance.

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In the second part, we ablate on the referring expression comprehension task under the codebase of ReCLIP [50] with proposal boxes provided by MAttNet [62]. Note that in this setting, semantic

masks in FGVP are derived following our proposed framework presented in Fig. 2. Consequently, the Blur Reverse Mask [ D 4 ] shows a consistent superiority demonstrated in the right part of Table 2. Notably, in line-based [ ∗ 1 ] prompting, we employ a default red line with a thickness of 2 pixels, as described in the RedCircle [49]. As for color-based methods [ ∗ 2 ], we utilize a green color with a transparency level of 0.5 following CPT [61]. Next, we ablate the standard deviation of the Gaussian blur kernel for blur-based prompting [ ∗ 4 ] (Fig. 5), and a value of 100 achieves the best. More ablation study details on the prompting properties are provided in the Supplementary Materials.

4.4 Referring Expression Comprehension

In this section, we compare our FGVP with previous zero-shot methods in Table 3. For fair comparisons, we also implement FGVP upon the original codebases of each compared method, namely CPT [61] and ReCLIP [50].

We conduct experiments following consistent settings with each compared work. To be specific, with the CPT and our codebase, we focus on individual visual prompting performance without post-processing. We use proposals from UNINEXT and MAttNet to demonstrate the robustness of our enhancements. It's important to note that different proposal selections solely affect the box candidates, which are equitably shared among all the comparison prompting methods. Next, with the ReClip codebase, ReClip employs cropping and colorful boxes as visual prompts, with default spatially-relations post-processing. To ensure a fair comparison, we first add cropping as an ensemble to all experiments. Next, to facilitate comparison with RedCircle (which inherently uses Score Subtraction as post-processing and primarily ensembles based on three circle prompts), we adopt the same three types of prompt formats but based on semantic masks. Finally, we explore higher performance possibilities. The preprocessing procedure for text inputs, including the prefix and clear-text principle, remains consistent across all codebases. Additionally, we compare zero-shot

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referring accuracy under unified CLIP models. Unless otherwise stated, all existing method results are reported according to their original papers.

Table 3 shows that the single fine-grained visual prompt, i.e., Blur Reverse Mask can surpass previous works. With a ViT-L model, our FGVP surpasses RedCircle by an average of 3.4% accuracy in our codebase and 5.5% ∼ 8.1% with the CPT [61] codebase. Further, we implement FGVP under the ReCLIP [50] codebase, which investigates the ensemble of multiple models, prompts, and post processing. The results show that the improvement brought by FGVP is orthogonal to the ensemble techniques. Under equal comparisons, our FGVP shows better performance than previous works with a consistent gain of an average of ∼ 3% and a max of 12.5% in the RefCOCO+ testA set.

4.5 Part Detection

The PACO [44] dataset features annotations for boxes and masks for common objects and their corresponding parts. For the part detection task, models need to locate the part within its object. Different from the referring task, there is no prior information indicating where the parts are. Our proposed pipeline (Fig. 3) is capable of performing zero-shot part detection without box proposals and utilizing only image inputs in these circumstances. Different from the localization keypoints operation employed in RedCircle [40] which only predicts the center location, we instead predict precise semantic masks of the target. Same with the metric in referring expression comprehension, a predicted box is considered correct only when the intersection over union with the ground truth box exceeds 0.5. As shown in Table 4, our proposed method, FGVP, outperforms other coarse prompts for part detection. Notably, we set the grid size to 16 along one side of the image and used an NMS threshold of 0.7 by default. However, better performance can be achieved by including more proposals through the use of a larger grid size and NMS threshold. We conduct an ablation study on them by varying one while fixing the other, as illustrated in Fig. 6. A visualization of the FGVP results is depicted in Fig. 7.

In addition, the referring expression comprehension task can be seen as part detection (each referring instance can be seen as a part of the image) when no prior box proposals are provided. We conducted zero-shot experiments on the validation set of RefCOCO, RefCOCO+, and RefCOCOg datasets, utilizing only images as inputs. As shown in Table 4, the Blur Reverse Mask Prompting without prior box proposals even surpasses certain coarse visual prompting methods with box proposals.

4.6 Limitations

Firstly, FGVP takes longer for inference than other methods because it involves the segmentor to produce semantic masks. To be specific, we compare the inference costs in terms of computation and

speed between our method and others. Further, we conduct an ablation study on the scalability of SAM. Notably, the post-processing technique to filter small disconnected regions and holes in masks can further improve performance at the cost of speed. Disabling the mask-filter post-processing will greatly improve the speed without losing too much performance (Table 5). Experiments are run on RefCOCO with a CLIP pre-trained ViT-L/14@336px on 8 × NVIDIA A100. Above all, speeding up the architecture is an important direction for future improvement.

Secondly, the current implementation does not couple the visual prompt with a specifically designed language prompt. Therefore, the image-text alignment comprehension ability of VLM has not been fully explored appropriately. Another consideration is that there are more possible tasks that can adopt the proposed method for zero-shot transfer, such as referring image segmentation. We leave it to our future work.

5 Conclusion

In this paper, we focus on the visual prompt, a technique to highlight the target instances in the image content using visible markings such as colorful boxes and circles. The visual prompt is beneficial in invoking potential spatial comprehension within VLMs like CLIP on instance-level tasks. However, existing prompting designs are often too coarse for locating the target instance, leading to unrelated information that may harm performance. Since the topic of visual prompting is rarely explored, we systematically summarize various typical prompting formats and propose Fine-Grained Visual Prompting (FGVP), which utilizes precise semantic masks of target instances derived via Segment Anything (SAM). We discover that the Blur Reverse Mask prompting, which blurs the background, achieves the best performance. Furthermore, we construct two zero-shot classification architectures for regular referring expression comprehension tasks using boxes as prior proposals and for part detection utilizing only input images. We evaluate the effectiveness of FGVP on referring expression comprehension and achieve state-of-the-art (SOTA) performance.

Broader Impacts

Further research and careful consideration are necessary when utilizing this technology, as the presented proposed method relies on statistics derived from training datasets that may possess biases and could potentially result in negative societal impacts.

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Peer reviews:
comments: SUMMARY:
This paper proposes Fine-Grained Visual Prompting (FGVP) that incorporates Blur Reverse Mask to improve the semantic localization capability of VLMs, like CLIP. It provides a comparison to other possible methods for highlighting the different parts/objects in the image, based on SAM and other techniques. The resulting method improves the performance on RefCOCO* datasets.

STRENGTHS:
The paper is written in a clear way and describes the algorithm well. The evaluation shows clear improvements and the ablation study is convincing. The idea to blur and mask the background of different object/part proposals is both innovative and significant.

WEAKNESSES:
The related work section missing two relevant works in the visual prompting domain:

1. Bhang et al., "Exploring Visual Prompts for Adapting Large-Scale Models", 2022

2. Bar et al., "Visual Prompting via Image Inpainting", NeurIPS, 2022


Moreover, while SAM is a powerful method for segmentation, it requires running the model with a relatively dense grid of keypoints. This runtime can be significant and should be discussed in the limitations section.

QUESTIONS:
Can SAM be replaced with a simpler segmentation model, like color-based unsupervised segmentation methods? 
Any ablation of SAM will be useful here for understanding the scale/quality that is required from the segmentation network for achieving good results.

LIMITATIONS:
The limitation section is present and addresses some unexplored directions. I suggest adding runtime estimates.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2023-06-30

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a new “visual prompting” method. Visual prompting refers to the idea of altering images to guide the “attention” of a vision-language model when the model is used to embed the image. For example, to obtain an embedding for an object in an image that contains many objects, the user could draw a red circle around the object of interest and then embed the image. Since the vision-language model might have seen images during training in which important objects are highlighted with red circles, the resulting embedding might focus on the circled object.\n\nThis approach can be used to partially solve tasks such as referring expression comprehension: Given a set of bounding boxes and text descriptions, visual prompting can be used to find the best-fitting description for each box. A separate object detector is needed to obtain the bounding boxes first.\n\nThe paper proposes a visual prompting method that consists of blurring everything in the image except for the object of interest. This is done by first using a pretrained segmentation model (Segment Anything) to obtain a mask for the object of interest, and then blur everything outside of the mask. The motivation for this approach is that it simulates the shallow depth of field seen in photographs taken with a large aperture, which are commonly found in VLM training data.\n\nThe blur method is compared to the “red circle” method and several variants in object detection and referring expression tasks. The blur method consistently performs best. In addition, hyperparameter sweeps for the blur radius and other hyperparameters are shown.\n\nSTRENGTHS:\n1. The proposed visual prompting method consistently improves over other methods.\n\n2. The blurring approach is well motivated by the abundance of photographs with shallow depth of field. This is a clever way to exploit “natural supervision” present in large-scale web image training data.\n\nWEAKNESSES:\n1. The proposed method is significantly more complex than the “red circle” method, since it relies on a large segmentation model. The inference cost of the proposed method is therefore much higher than the “red circle” method. This should be acknowledged in the discussion and/or limitation sections.\n\n2. While the proposed method works well, it is an incremental improvement over the “red circle” idea (https://arxiv.org/pdf/2304.06712.pdf) and the evaluation is not as comprehensive as in the “red circle” paper. For example, no analysis of the relative biases of red circle vs blur methods are performed, and no failure cases are discussed. It is not clear if the contribution is substantial enough for NeurIPS.\n\nQUESTIONS:\nPlease use descriptive method names (keypoint/box/circle/…) instead of just A/B/C in Table2 and elsewhere, so that the reader doesn't have to refer to Figure 1 all the time.\n\nLIMITATIONS:\nThe runtime cost of the proposed method needs to discussed further.\n\nThe biases of the method compared to the \"red circle\" method need to be evaluated and discussed.\n\nA generic \"broader impact\" statement is given in the appendix.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2023-07-03"}

comments: SUMMARY:
This paper works on visual prompting. They proposed FGVP, together with Blur Reverse Mask, to improve the semantic localization ability of the vision-language model.

STRENGTHS:
Solid experiments showing the effectiveness of their method.

WEAKNESSES:
In general, this is a good work. However, there’re several things concerning me:

- The novelty of this work is not well established. Seems like an engineering combination of previous works.
- The discussion of the upper bound is based on the assumption, while the legitimacy of the assumption is not well discussed.

QUESTIONS:
No further questions.

LIMITATIONS:
No notable limitations.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2023-07-07

comments: SUMMARY:
This paper proposes a visual prompting method that exploits the segmentation masks of interested objects in images to generate more fine-grained visual prompts. Experiments show that the proposed methods achieve competitive results on zero-shot referring expressions comprehension and part detection.

STRENGTHS:
1. This paper is well-organized. The motivation and the framework is clearly presented.  
2. The experiments confirm the effectiveness of the visual prompt design.

WEAKNESSES:
1. The ablation studies of different VLMs are missing. Since visual prompting is a zero-shot framework, it is natural that the performance of the visual prompts differ on VLMs trained on different data. The paper adopts the CLIP as the VLM in all experiments. How do different visual prompts perform on other VLMs?  
2. Figures 2 & 3 are very similar and thus redundant. The authors should consider merging them into one figure.

QUESTIONS:
1. Basically, I like the section 3.3 of the discussion of the prior knowledge introduced by the large amount of photography images in the large-scale image-text dataset. Could the authors provide more discussions on the analysis of the training data of the VLMs, and the effect of better alignments between the visual prompt design and the VLM training data?  
2. How good are the zero-shot results compared with the few-shot / many-shot methods? The few-shot / many-shot methods may be provided for general reference.

LIMITATIONS:
The proposed visual prompts can be further verified on more object-based tasks.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2023-07-10

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes Fine-Grained Visual Prompting (FGVP), which uses precise semantic masks from SAM as visual prompts to improve spatial localization of vision-language models (VLMs) like CLIP for instance-level tasks. The key contributions are:\n\n- Systematically study different visual prompting techniques like cropping, boxes, circles, masks, etc. Show that blurring background outside target mask (Blur Reverse Mask) works best.\n- Achieve state-of-the-art results on referring expression comprehension benchmarks RefCOCO/RefCOCO+/RefCOCOg, outperforming prior works.\n- Demonstrate FGVP can enable zero-shot part detection on PACO dataset without needing any box proposals, again outperforming other prompting methods.\n\nSTRENGTHS:\nOriginality: The paper presents a novel idea that using SAM to generate semantic masks from detected bbox as better visual prompts of specific instance in the image, which is an original idea not explored in prior works.\n\nQuality: The overall approach is technically sound. The experiments follow standard protocols and are extensive in studying different visual prompting design. The results demonstrate benefits over existing methods.\n\nClarity: The paper is well-written and easy to follow. The problem context, proposed method, experiments are clearly explained. Figures and tables aid understanding.\n\nSignificance: FGVP pushes state-of-the-art in two important vision-language tasks - referring expression and part detection. The analysis may inspires more research on the properties of VLMs regarding spatial understanding.\n\nWEAKNESSES:\nWhile the paper presents a novel fine-grained visual prompting technique and achieves state-of-the-art results, there are some aspects where the analysis could be strengthened:\n\nMore Insights from Study: The paper performs an extensive set of experiments on different visual prompt designs. However, it could provide a more detailed analysis of the inferences and insights derived from this study. For instance, comparing the gap between using ground truth and predicted bboxs would give insights into the impact of mask quality. Explaining the differences in various design choices (VP, PP, proposals etc.) in Table 3 would be informative. Attention visualizations could help reveal why fine-grained prompting is more beneficial.\n\nComputational Overhead: The paper proposes generating semantic masks using SAM models. However, the computational overhead this introduces is not analyzed. Reporting inference times, scalability, etc. would provide a better understanding of its practical viability.\n\nJoint Language and Visual Prompting: The study is currently limited to exploring only visual prompts. Evaluating prompts on both visual and textual modalities could offer a more comprehensive understanding of VLMs. It is good that authors discussed this point in the limitation though.\n\nGranular and Failure Analysis: Providing per-class performance breakdowns and detailed failure analysis compared to other methods through examples would provide useful insights into where the improvements come from.\n\nIn summary, while the core ideas are promising, performing a more thorough empirical analysis along the above dimensions would strengthen the paper and provide a better understanding of the factors behind the efficacy of fine-grained visual prompting.\n\nQUESTIONS:\nSee the main suggestions in weakness. \n\n- For the RegCOCO/RefCOCO+/RefCOCOg results in Table 4, which subsets are the numbers reported on?\n- Line 288-290 if there are better grid size / NMS thresholds, how do we still use the suboptimal default ones?\n\nLIMITATIONS:\nLimitations are discussed in the paper", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2023-07-14"}