Bootstrapping MLLM for Weakly‑Supervised  Class‑Agnostic Object Counting
Authors: Xiaowen Zhang
Zijie Yue
Yong Luo
Cairong Zhao
Qijun Chen
Miaojing Shi
Venue: ICLR 2026 Poster (2026)
Keywords: Object counting
MLLMs
weakly-supervised
class-agnostic counting
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Object counting is a fundamental task in computer vision, with broad applicability in many real-world scenarios. Fully-supervised counting methods require costly point-level annotations per object. Few weakly-supervised methods leverage only image-level object counts as supervision and achieve fairly promising results. They are, however, often limited to counting a single category, \eg person. In this paper, we propose WS-COC, the first MLLM-driven weakly-supervised framework for class-agnostic object counting. 
Instead of directly fine-tuning MLLMs to predict object counts, which can be challenging due to the modality gap, we incorporate three simple yet effective strategies to bootstrap the counting paradigm in both training and testing: First, a divide-and-discern dialogue tuning strategy is proposed to guide the MLLM to determine whether the object count falls within a specific range and progressively break down the range through multi-round dialogue. Second, a compare-and-rank count optimization strategy is introduced to train the
MLLM to optimize the relative ranking of multiple images according to their object counts. Third, a global-and-local counting enhancement strategy aggregates and fuses local and global count predictions to improve counting performance in dense scenes. Extensive experiments on FSC-147, CARPK, PUCPR+, and ShanghaiTech show that WS-COC matches or even surpasses many state-of-art fully-supervised methods while significantly reducing annotation costs.

Published as a conference paper at ICLR 2026
BOOTSTRAPPING MLLM FOR WEAKLY-SUPERVISED CLASS-AGNOSTIC OBJECT COUNTING
Xiaowen Zhang', Zijie Yue', Yong Luo?, Cairong Zhao', Qijun Chen', Miaojing Shi!
'College of Electronic and Information Engineering, Tongji University
&Colleee of Computer Science, Wuhan University
CCollege of Computer Science, Tongji University
*State Key Laboratory of Autonomous Intelligent Unmanned Systems
Sparse
Pred:8
Pred:13
160
"scagulls" TM
G Sparse
O Dense
GT:9
Zero
Pred:30
Pred:50
140
MAE on sparse scenes
120
MAF on dease scenes
'strawberries'
100
CT:14
Pred:9
Pred:13
wS-CoC-
Dense
Base
Pred:1133
Pred:119
60
"apples"
(b)
40
4T:1
CRCO, GLCE, D strategies
Pred:99 14ed:14
"green
G7::155
Ws-coc
(c)
Pred:143 Pred: 183
MLLM: Zero
WS-COC-
Base
WS-COC T21Count CountGD
(d)
Figure 1: (a)-(c) Visualizations results for MILM-Zem, WS-COC-Bass and WS-COC. (d) MAE results of MLLM-Zero. WS-COC-Base, WS-COC, and state-of-art supervsue counting methods (T2ICount (Qian et al. 2025) and CountGD (Amini-Naieni et al. 2024)) on sparse (up to 20 instances per image) and dense (more than 100 instances per image) object scenes. WS-COC significantly outperforms MLLM-Zero and WS-COC-Base and yields competitive performance to T21Count and CountGD, Experiment is on FSC-147 (Ranjan al. 2021 and MLLM LLaVA- VersionnB (Li et al. 2024).
ABSTRACT
Object counting is fundamental task in computer vision, with broad applicability in many real-world scenarios. Fully-supervised counting methods require costly point-level annotations per object. Few weakly-supervised methods leverage only image-level object counts as supervision and achieve fairly promising results. They are, however, often limited to counting single category, e.g. person. In this paper, we propose WS-COC, the first MLLM-driven weakly-supervised frame- work for class-agnostic object counting. Instead of directly fine-tuning MILMM to predict object counts, which can be challenging due to the modality gap, we in- corporate three simple yet effective strategies to bootstrap the counting paradigm in both training and testing: First, divide-and-discen dialogue tuning strategy is proposed to guide the MLLM to determine whether the object count falls within specific range and progressively break down the range through multi-round di- alogue. Second, a compare-and and-rank count optimization strategy is introduced to train the MILMM optimize the relative ranking of multiple images according their object counts. Third, a global-and-loca counting enhancement strategy ag- gregates and fuses local and global count predictions improve counting perfor- mance in dense scenes. Extensive experiments on FSC-147, CARPK. PUCPR+ and ShanghaiTech show that WS-COC matches or even surpasses many state-of- art sully-su methods while significantly reducing annotation costs. Code is available at
www.:hww.....c.cc.aeeeeooo...coe tong Mongji/We ji//s COC
CCoreeponding author: mshi @tongji en

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Published as a conference paper at ICLR 2026
INTRODUCTION
Object counting refers to the task of estimating the number of target instances within given image.
Conventional methods (Radford et al. 2021 Liu et al. 2022a Amini- Naieni et al., (023) mainly estimate a density map of an image where the integral over the density map gives the total number of objects. The density map is obtained by convolving Gaussian kernels with point annotations per object in the image. Despite that these fully-supervised methods have led to strong performance across various counting benchmarks (Ranjan al.) 2021| Hsieh et al.) 2017 Mundhenk et al. 2 acquiring point-level annotations is labor-intensive and unne-consuming, especially in dense scenes where hundreds or thousands of objects co-occur and even occlude each other.
To alleviate the high annotation cost, few studies (Wang et al. 2024 Yang et al. 2020 Lei et al, 2021 have explored the weakly-superviser learning paradigm. They leverage image-lever object counts as the ground truth to learn a mapping from visual features of an image to the total number of target instances in the image. Existing weakly-supervised counting methods are still in early stage and often limited to a single object category, i.e. person (Yang et al. 2020 Lei et al., 2021 Xiong al. 2022). Benefiting from large-scale pretraining on rision-language pairs, recent advances in Multmodal Large Language Models (MLLMs) present new opportunity for class-agnostic object counting in text-promptable way, where text prompts specify target categories of interest and are then converted to proper instructions. Combined with corresponding images, these multimodal instructions are fed into the MLLM to auto-regressively predict object counts. This presents a basic object counting pipeline based on the MLLM. We first evaluate this pipeline (i.e. MLLM-Zero) without any fine-tuning on a standard object counting benchmark, FSC-147 (Ranjan et al. 2021 As shown in Fig. Ta): observe that it indeed produces reasonable estimates in sparse scenarios yet its performance degrades significantly in dense scenes. This can be intuitively explained by the fact that MILMS mostly seen with sparse rather than dense object distributions in their pre- training corpora. Given the underlying counting ability of MILMMs, this study aims to explore how to extend such potentials accurate class-agnostic object counting with minimal fine-tuning cost.
In this paper, we introduce a Weakly-Supervised Class-agnostic Object Counting network, WS- COC, which bootstraps MLLMs for object counting using only image-level count supervision. A naive baseline (i.e. (S-COC-Bass) is to directly fine-tune mun-tune the MLLM to predict the object count from the given image. Nevertheless, as shown in Fig. [Tb), learning& direct mapping is difficult due to the absence of object distribution supervision; especially in dense scenes, this still leads to clear underestimation. To address it, we introduce three simple yet effective strategies:
First, instead of regressing the exact count in one shot, we introduce divide-and-disce dialogue tuning (D² strategy to guide the MLLM to judge whether the count falls within a certain range and progressively break down the range from big to small. This multi-step reasoning helps MLLM learn to count from easy to hard. When the range small enough, we ask the model predict the actual object count:
Next, directly optimizing the predicted count against the ground truth can still be challeng- ing due the modality gap between vision and text. Instead, guiding MILM to judge the relative count difference between images can be more visually probed. Motivated by this, we introduce a compare-and-rante count optimization (CRCO) strategy that trains the MLLM to predict relative ranking of multiple images according to their object counts. Last, during inference, to further mitigate the counting bias in dense crowds, we introduce a global-ande and-local counting enhancement (GLCE) strategy. We first predict a global count for the given image, and then partition the imput image into smaller sub-images, querying each sub-image independently to obtain local counts. The global and local counts are fused to obtain the final count.
As illustrated in Fig dd), through the implementation of these strategies, we significantly enhance the quantity awareness of MLLM, leading to substantial reductions in counting errors, especially un- der dense conditions. We conduct extensive experiments on four widely-used benchmarks: FSC-144
(Ranjan et al. 2021). CARPK (Hsieh et al. 2017). PUCPR+ (Hsieh et al. 2017). and Shanghai Tech (Zhang et al. 2016). Our method, as the first
weakly-supervel object counting method, yields surprisingly good results on par with state-of-the-an fully-supervise object count-
ing methods (Pelhan et al. 2024 Zhu et al. 2024 Hui al. 2024
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2
RELATED WORK
2.1 OBJECT COUNTING
Existing counting methods can be broadly divided into class-specific and -agnostic ones depending on whether they could count arbitrary object categories.
Class-specifi object counting aims to predict the number of objects for a specific category, such as persons (Ranasinghe et al.
2024 Guo et al. 2024, Peng & Chan 2024, Wu & Yangl 2023 Du
et al.
2023 Liu et al. [2022b]]
Shi al., 2019), vehicles (Hsteh et al. Mundhenk al. 2016),
cells (Xie et al.
2018).
fishes (Sun et al. 2023), etc. Conventional methods are typically detection-
based (Liu al. 2023
Liang et al. 2022] Liu et al. 2019) regression-
(Ranasinghe al.
2024 Guo et al.
2024
Peng & Chan [204]] Wu & Yang 2023; Li et al. [2033] Wang et al. 2020).
The former detects every individual instance with a bounding box and obtains object count as the
number of bounding boxes; the latter estimates a density map for given image and obtains object
count by integrating pixel values over the density map. Overall, class- class-specific methods are inherently restricted to certain object category, limiting their applications.
Recently, class-agnostic counting (Chattopadhyay et al. 2017 Lu et al., 2019) Xu et al., 2023) methods have been proposed to count objects of arbitrary categories within images. A common practice is to provide visual exemplars to indicate the target category (L.u et al. 2019 Liu et al.] 2022a Pelhan al. 2024), as in CounTR (Liu et al. 2022a), which fuses image and exemplar features for density map estimation. However, since manually selecting exemplars requires extensive
labeling cost, an emerging way is to replace them with text prompts that use class names to specify
target object categories (Xu et al.
2023] Ammin--aiien et al. 2024] Jiang et al.] 2023 Kang et al.) Zhu et al. 2024 Hui al.
2024
2024 Dai et al. 2024 Qian et al.) [025 Zhai al. [025 Shi
et al. 2025). For example, VLPG (Zhai et al. 2025
leverages CLIP (Radford et al.
202] to extract
visual embeddings from the given image and textual embeddings from the text prompt, then fuses
them via cross-attention for density map estimation.
So far, the majority of object counting methods adopts the fully-supervised learning paradigm, where models are trained with point-level annotations. To reduce the annotation cost, few methods (Yang al. 2020 Meng et al., 202T; Lei et al., 202T / Xiong al. 2022 Wang et al., 2024) have explored the weakly-supervice
learning paradigm, mm where only image-level object counts are provided as the ground truth. For example, Xiong al. (2022) leverage the vision transformer (ViT) to extract multi-scale visual features from images and directly map them to person counts. GCNet (Wang et al.) 2024) utilizes a pretrained ResNet (He et al., 2016) to count the most frequently occurring object category in images. These initial explorations, based on CNN or ViT, are limited to class-specific object counting; while we novelly leverage MILMM to achieve class-agnostic object counting.
2.2 MULTIMODAL LARGE MODELS
Early multimodal large models, commonly referred to vision languala models (VLMs), such as CLIP (Radford et al. 2021) and BLIP (Li et al.. 2022). Iearn cross-modal representations through large-scale pretraining vision-language pairs. They typically perform vision-language contrastive learning and vision-language matching to align vision and text modalities, and have been widely used in multimodal downstream tasks (Jiang al. 2023 Kang et al. 2024| Monsefi et al.. 2024). In recent years, multimodal large language models (MLLMs) (Liu et al. 2024a Li et al., 2024 Bai et al. 2025) have emerged as an upgrade of VLMs with multimodal reasoning and generative capability. By incorporating large language models (LLMs) as the core semantic understanding and reasoning engine, MLLMs have achieved remarkable success across a broad range of tasks, such as
visual question answering (Goyal et al. 2017) and image captioning (Agrawal et al.) 2019).
Several studies (Jiang et al. to address object counting in a text-promptable manner. For instance, CLIP-Count (Jiang et al.) 2023), VLPG (Zhai et al.) 20225) and VLCounter (Kang et al., (024) finetune the pretrained VLM, L.e. CLIP Radford et al. (2021). for class-agnostic counting. CLIP and other VLMs are mainly discriminative models where object count has to be obtained by devising an additional counting head. Our WS-COC, on the other hand, relies solely on MLLM where object count be auto- regressively generated. Notably, some VLM-based counting method, such as CrowdCLIP (Liang et al. 2023). adopts ranking strategy that appears similar to our proposed compare-and-rant count
2023 Qharabagh et al. 2024) have leveraged multimodal large models
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optimization strategy, but there key differences. First, Liang al. (2023) constructs image sets by cropping a single image into sub-images of different sizes, assuming that larger crops contain more objects. In contrast, WS-COC samples different images with varying object counts within the same category. Second, [iang et al. (2023) relies on the contrastive optimization while WS-COC directly generates ranks using the language modeling loss.
PRELIMINARY
In this section, first provide brief background for MLLMs and then devise a baseline model upon them for weakly-supervised object counting.
Multimodal Large Language Models. MLLMs, specifically for vision and language, consist of visual encoder, a projection module, and a LLM. They take the image I and text instruction Tinst as input and output text response 7 Specifically, the visual encoder extracts visual features from J. The projection module transforms these features into ones that can be fed into the LLM. The LLM then performs semantic understanding and reasoning based on the projected visual features and instruction tokens of 7 to auto-regressively generate 7 MLLMs are trained using a language modeling loss that minimizes the cross-entropy between 7 and ground truth response T8
Baseline. We devise a baseline model (i.e. WS-COC-Base) in which we finetune a MLLM, LLAVA- Version (Li et al. 2024) by default, for weakly-superviser object counting. Specifically, for each image I and its global object count c, we construct the text instruction T and the ground truth response 78 using fixed templates. Tiust is formulated as "How many [ohj] are there in the image?". while 7 follows "a photo of [num] [obj]", where the [num] token is replaced with and the [obj] token denotes a specific object category. We then follow the LoRA-based finetuning paradigm (Hu et al. 2022) to optimize the MLLM. SS--COO-Base's performance is shown in Fig d).
4
METHOD
Fig? 2 illustrates the framework of our proposed WS-COC, in which we bootstrap the MLLM for reekky-suupevvise object counting using only image-level count supervision. Building upon the baseline model, we introduce three simple yet effective strategies: divide-and-discem dialogue tun- ing, compare-and-rank count optimization, and global-local counting enhancement.
4.1 DIVIDE-AND-DISCERN DIALOGUE TUNING
Learning to predict absolute object count from image is difficult for MLLMs, especially with- out explicit supervision on individual objects. To cope with it, we reformulate the count prediction task series of range judgment tasks: instead of regressing the exact count in shot, guide the model to iteratively determine whether the count falls within specific ranges through multi-round dialogue. By progressively breaking down the range from coarse to fine, the model is able to learn to count in a curriculum manner, moving from easy to hard.
Specifically, given an image and its global object count e, we set the initial range [L, U1] for the first round dialogue, e.g. minimal and maximum object counts [1, 2000) in the FSC-147 dataset (Ran- jan al. 2021p. We calculate the midpoint of this range TΙ | (1x+))) and construct the text instruction Qi using the template "Are there more than [obj] in the image?", where [obj] token replaced by the target object category. The ground truth response R&
"yes" if Ti and "no" otherwise. We then query the MLLM using and Q1 to predict the response R1, which is optimized against R using the language modeling loss.
We progressively half the range and repeat the above process for multiple rounds. For example, in the /-th round, we update [T,, U.] according to the ground truth response R²_1 of (1 1)-th round:
LL. Unl = f [T-- + U.11),
if [ Yes,
( [4,-1, 1e if R No.
(1)
The dialogue terminates when U, L, is lower than 8 = 0.2 x c. Then, the MLLM is asked to predict the actual object count.
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(n) Divide-and-Disent Dialogue Tuning
"Are there mure than 1000 birds in the imuge""
"Are there mure thon 500 birds the image"
"Are there mure than 250 birds the image""
"Are there more than 312 birds the image""
Image
Instruction
Instruction
Instruction
Inxtruction
Multimodal Large Language Model
LoRA adapter
Answer:
Answer: No.
Answer: Yes.
Answer:: No.
Count range: [1,1000]
Count raage: [1,500]
Count range: (250,500)
Count (312,355]
(b) Compare-and Rann Count Optimization
(e) Global-and-Loce Counting Enhancement
How many the image?
grouping
sampling
Image
Sub-images
Instruction
Images
Dataset
+
MILM + LoRA adapter
"Given images in ascending order on their counts of pills."
"Answer:
Imagee2
MILM LoRA adapter
158
Imagell
Imagee,.
Global
Final count
Local
Instruction
prediction
prediction
Figure 2: Overview of the proposed WSSCOC for weakly-supervised class-agnostic object counting. (a) Divide-and-disceni dialogue tuning (Sec. 4.1 guides the MLLM to determine whether the object count falls within a specific range and progressively break down the range through multi-round dialogue. (b) Compare-and-rank count optimization (Sec. trains the MLLM to rank images by their object counts. (c) Global-and-loce counting enhancement (Sec. 4.3) aggregates and fuses the global and local count predictions to improve the performance in dense scenes during inference.
4.2 COMPARE-AND-RANE COUNT OPTIMIZATION
Visual features are high- ditb-iiiensooall representations, whereas the ground truth count is discrete scalar expressed as a text token. Directly optimizing the predicted count against the ground truth can be challenging due the modality gap. This makes it difficult for the model establish robust mapping. In contrast, guiding the model to judge the relative count difference between images can be intuitively more accessible. Therefore, we propose compare-and-rante count optimization strategy that trains the model to compare multiple images and optimize their relative ranking according to object counts.
First, we need to sample multiple images for count comparison. Since counting datasets typically exhibit a long-tailed distribution object counts, dense scenes with large object counts are rare and unlikely to be sampled through random sampling. Therefore, we introduce simple scheme to cluster images according to their counts. Specifically, we obtain the minimum and maximum counts (i.e. Ic, π)) for each object category in the training set, and then partition [c, into K (4 by default) equal-length intervals. Accordingly, the images of each category are divided into four groups based on intervals in which their object counts fall. During each training iteration, we randomly sample one image from each group to construct an image set = {4 Iz, Iz. I1]. where the corresponding counts inherently satisfy e₁ C2 C3 C₁. In this way ensure that both sparse and dense scenes can be covered. We randomly shuffle the order of images in I I and treat it the visual input to the MILM.
Next, construct the associated text instruction T using the template "Given four images, rank them in ascending order based on their counts of [obj]". and the ground truth response T specifies the correct ranking in the form "Image i Image j", where "Image i" and "Image j" denote

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the i-th and j-th image in I, respectively. We use T and I to query the MLLM to obtain the response T". then optimize it with respect to 7 using the language modeling loss.
4.3 GLOBAL-AND-LOCAA COUNTING ENTIANCEMENT
Once the model is fine-tuned, we observe that it still tends to underestimate object counts in dense scenes, although its performance is much improved compared to that before fine-tuning- Therefore, during the inference stage, to further mitigate the counting bias dense crowds, we propose to partition the images with dense scenes into smaller sub-images and query the MLLM each sub- image independently. The local predictions are aggregated and combined with the global prediction produce the final output.
Specifically, we first query the MLLM to predict the global count c for the given image I. If c lower than the threshold ch, directly treat e the final prediction. Otherwise, the image I is considered as a dense scene. In this case, we evenly partition / into L x (2 x 2 by default) non-overlapping sub-images and obtain local predictions {c from the MLLM by respectively querying each sub-image. We then sum the local predictions to obtain c¹. Due to the edge effect when aggregating sub-images, c' often overestimates the total count compared ct, Empirically, we obtain the final prediction by simply averaging c' and c In this way. they complement each other in estimating the final count in dense scene (see Sec. 5.3 GL.CE).
4.4 TRAINING AND INFERENCE
On top of the baseline (Sec. 3). divide-and-discem dialogue tuning and compare-and-rank count optimization are additionally applied only during training, while the global-and counting en- hancement is activated only during inference. Text instruction for inference is formulated "How many [obj] are there in the image?", where the [obj] token denotes the target object category.
5 EXPERIMENTS
5.1 EXPERIMENT DETAILS
Datasets. Our experiments are primarily conducted on the FSC- 147 dataset (Ranjan et al.) which contains 6,135 images spanning 147 object categories. We report the model performance on both the validation and test sets of FSC-147. Following prior works (Jiang et al. 2023 Kang 2024). we also investigate cross-dataset generalization performance by testing the trained model from 147 onto CARPK (Hsich et al. 2017), PUCPR+ (Hsieh et al., (017). and Shang- hai'Tech (Zhang et al., 2016). CARPK consists nearly 90,000 annotated cars captured from four parking lots via drones, while PUCPR+ contains 16,456 car instances in bad weather. The Shang- haiTeeh dataset divided into SHA (482 internet images) and SHB (716 street-view images from Shanghai), covering diverse crowd densities and scene layouts.
2021)
Implementation details. We adopt LLaVA-OneVisioon (Li et al. 2024) as the default MLLM and follow MM-RAIT (Liu et al. 2025) to optimize it using the LoRA- based finetuning paradigm: the rank of LoRA adapter is set to 128 with a scaling factor 256. The count threshold ch set to 100. We set L = 2 in the global-and-locat counting cahancement strategy. The batch size is 4, and training is conducted on a NVIDIA 1,20 GPU.
Evaluation metrics. Following common practice in (Jiang et al. 2023 Kang al. 2024), evaluate performance using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE).
5.2 COMPARISON WITH STATE-OF-TIIE-ART METHODS
we compare the proposed method with eight fully-supervised text-promptable object counting methods and three weakly-superviset counting methods on the FSC-147 dataset. Methods marked with "*" denote weakly-superviee supervised counterparts: we replace the last layers of their decoders, which originally predict density maps, with a linear layer for direct count prediction. Morcover, we denote MLLM- Zero as a variant that directly evaluates the MLLMM in the zero-shot setting. Notably, our proposed WS-COC achieves
As shown Tab
variants that we adapted from their fully-

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Quantitative comparisons on FSC-147. The best fully-supervised approach is marked in shadow while the best weakly-supervised approach is marked in bold. Methods marked with "*" denote weakly-supervised variants that we adapted from their fully-supervised counterparts.
Table 1:
VAL SET
TEST SET
Method
Supervision
MAB4
RMSEL
MAE
RMSEL
ZSOC Xu al.
2023)
point-level
26.93
88.63
22.09
115.17
CLIP-Coune Jiang et al. 2023
point-level
18.79
61.18
17.78
106.62
VLCounter Kang et al. 2024
point-level
18.06
65.13
17.05
106.16
CountDiff Huu al. 2024
point-level
15.50
54.33
14.83
103.15
Grounding (Dai et 2024
point-level
10.06
58.62
10.12
107.19
CountGD (Amini- -ainni et al. 2024
point-level
12.14
47.51
14.76
120.42
T21Count (Qnan et al. 2025
point-level
13.78
58.78
11.76
97.86
VLPG (Zhai et al.
2025
point-level
16.05
53.49
17.60
97.66
GCNet (Wang et al. 2024)
image-level 19.50
63.13
17.83
102.89
CLIP-Count Jiang et al 2023
image-level
29.86
81.68
30.01
124.93
T21Count*
Qian et al.
2025
image-level
26.64
83.00
28.55
131.52
MLLM-Zerro WS-COC-Base WS-COC
x 38.92
119.26
38.19
145.42
image-level
21.70
87.53
21.08
122.18
image-level
14.77
54.24
13.91
97.28
Table 2: Cross-datased evaluation on CAPRK and PUCPR+.
CARPK MAEІ RMSE
PUCPR+
Method
Supervision
MAEJ RMSEL
CLIP-Count (Jiang et al.
2023
point-level
11.96
16.61
VLCounter
Kang al. 2024
point-level
6.46
8.68
48.94
69.08
CountDiff
Huui et al. 2024
point-level
10.32
12.92
VLPG Zhai et 2025
point-level
10.14
13.79
T21Count (Qian et al.
2025
point-level
8.61
13.47
CLIP-Coume Jiang et al.
2023)
image-level
24.90
29.12
63.28
86.63
T21Count* * Qian et al.
2025
image-level
19.42
23.48
58.43
77.24
MLLM-Zero
x
26.24
55.11
82.40
133.50
WS-COC-Bai -ase WS-COC
lmage-level
14.06
27.60
60.40
84.64
image-level
10.39
15.83
42.30
54.06
comparable superior performance some recent fully-supervised methods. For instance, WS-COC surpasses two very recent methods CountDiff (Hui et al. (2024) and VLPG (Zhai et al. 2025). It also achieves superior performance weakly-
methods, e.g. decreases MAE by -3.92 and RMSE by -5.61 from GCNet (Wang et al. 2024) on the test set. Moreover, WS-COC significantly outperforms MLLM-Zero and the baseline model WS-COC-Base. It is worth not- ing that, as shown in Fig. dd), WS-COC substantially reduces the counting error (MAE) in dense scenes (more than 100 instances per image) from 149 69 (MLLM-Zero), and 82.44 (WS-COC-Barl to 54.37 (WS-COC). Some examples shown in Fig further illustrate that WS-COC can produce accurate predictions.
Next, we follow previous methods (Jiang et al. 2023 Zhai al. 2025 Hui et al. 2024 to conduct cross-dataset validation on the CARPK, PUCPR+ and Shanghai Tech datasets. As shown in Tab, 2 and Tab. B WS-COC achieves the best results PUCPR+ and SHA, and exhibits competitive performance to most fully-supervised methods on CARPK and SHB. These results demonstrate the strong generalizability of method in unknown scenarios.
Last, we compare the training and inference cost between WS-COC and some state-of saiee-of-thhe of-tth-an art meth- ods (Jiang al. 2023 Aimin--aient al.] 2024 Qian et al. 2025) in Tab, 4.b ensuring fair compar- ison by using the same GPU (i.e. NVIDIA L20). Although MLLMs are generally big, we adopt the LoR A-based finetuning paradigm, which helps significantly reduce the training cost. The training
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Table 3: Cross-dataset evaluation Shanghai
SHA
SHB
Method
Supervision
MAE.
RMSE
MAE.
RMSEL
RCC (Hobley & Prisacariu
2023
point-level
240.1
366.9
66.6
104.8
CLIP--Count (Jiang et al. 2023
point-level
192.6
308.4
45.7
77.4
VLPG Zhai et al. 2025
point-level
178.9
284.6
42.4
71.6
GCNet (Wang et al. 2024
ievee-level
148.9
260.7
38.6
53.9
CLIP-Count* (Jang ct al. 2023
image-level
237.4
355.5
52.3
73.8
T2ICount*
Qian et 2025
image-level
222.2
367.6
48.9
76.9
MLLM-Zero
336.8
486.6
75.7
108.0
WS-COC-Base
image-level
190.4
343.0
43.6
71.8
WS-COC
image-level
128.9
232.9
34.2
57.0
"apples"
dderr
"cashew nuts" "markers"
"green peas"
"paper rolls"
Query Image
GT:17
GT:39
GEII
GT:309
GT:449
GT:907
&Ground Truth
Pred:44.37
Pd:89.16
Pred:291.22
Pred:313.09
Pd:630.03
CLIP-Count
Pred:17.03
Pred:46.63
Pd:
1d:1111.5
Pred:3400.22
Pred:472.32
Pd:942.39
T21Count
Pred:17
Pred:35
Pred:114
Pred:316
Pred:407
Pred:636
CountGD
Pred:17
Pred:40
Pred:10k 108
Pred:319
Pred:437
437
Pred:976
WS-COC
Figure 3: Visualization results among ground truth, CLIP-Count (Jingg al. 2023). T21Count Qian
ct al. 2025), CountGD (Amini-Naicni et al. 2024). and WS-COC.
time of our WS-COC is only 3.44 hours, substantially lower than that of CountGD and T2ICount. Notice the primary computation cost of WS-COC is due to the inherent cost of the MLLM back- bone IWS-CCC-Base rather than proposed components. In practice one may also adopt model compression techniques such as pruning and distillation to further reduce the computation overhead.
5.3 ABLATION STUDY
We conduct ablation study on the validation and test sets of FSC-147 dataset.
Divide-and-discers dialogue tuning (D²T). In Tab. 5 which D is removed from WS-COC. The results show a significant increase in MAE, rising by +3.36 on the validation set and +3.21 1 on the test set, indicating the effectiveness of the D strategy.
we denote WS-COC w/o D&T as variant
Next, we vary the stopping threshold in D from 0.1 to 0.3. As shown Fig, 4a), the default 5 0.2 performs the best on the validation set, and concurrently the best in testing.
Last, we apply D&T strategy in the testing stage named WS-COC D As shown in Tab. this variant performs notably worse, inferior to WS-COC w/o DT. This is because, during training, we construct multi-round dialogue based on ground truth counts. However, at test time, any incorrect judgment in intermediate round can mislead the MILM into entirely wrong direction, resulting in severely degraded performance.
Compare-and-rank count optimization (CRCO). In Tab denote WS-COC w/o CRCO variant in which CRCO is removed. The results show a significant increase in MAE, rising by +2.62 on the validation set and +2.84 the test set, indicating the effectiveness of this strategy.
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Table 4: Comparison training and inference cost.
Trainable Params (M)
Training Time (hours)
Memory usage (GB)
Method
GFLOPs
FPS
CLIP-Coum (Jiang et al. 2023)
17.30
2.43
78.72
1.94
15.50
CountGD (Amini-arent el
2024)
37.00
11.73
3589.03
8.12
1.13
T21Count (Qian et al. 2025)
908.42
23.84
1100.75
6.77
5.09
WS-COC-Base WS-COC
339.94 339.94
1.84
1845.09
18.70
2.47
3.44
1845.09
18.70
2.16
VAL SET
TEST SET
VAL SET - TEST SET
VAL SET
TEST SET
16
16
16
15
15
15
14
14
14
13
13
13
0.1
0.15
0.2
0.25
0.3
3
6
80
90
100
110
120
(a) 8
(b) K
(c) ch
Figure 4: MAE on the FSC-147 validation and test sets when varying (a) the ratio in D*T, (b)
the K in CRCO, and (c) the dense threshold c in GLCE.
Input image
MILM--Zero
WS-COC-Base
ws-coC
Input image
MILM-Zerr
WS-COC-Base WS-COC
Figure 5: Attention map comparisons for MLL.M-err, WS-COC-Base and WS-COC.
Next, we denote WS-COC w/ SCRCO variant, where we crop central regions of the image with sizes corresponding 4 4. and of the original dimensions. This like in Matryoshka doll, similar to the idea used in CrowdCLIP (Liang et al. 2023). Together with the original image, these crops form an image Z of size K = 4, which is inherently ranked by the retained area. As shown in Tab. [S this variant performs worse than our WS-COC and is even close the WS-COC w/o CRCO. This is likely because such ranking task (SCRCO) is much easier for MLLMS compared to the ranking task across different images (CRCO).
Furthermore, we vary K in this strategy from to 7. As shown Fig. b). we set = 4 by default since it yields the best performance.
Last, we investigate the effectiveness of our proposed sampling strategy. We denote WS-COC w/ CRCO,nd variant, where images in I are obtained by randomly sampling from all images of a given category. As shown in Tab, 5) WS-COC w/ CRCOrnd produces certain performance gains compared WS-COC w/o CRCO, yet the results are still inferior that of default setting. highlighting the benefit of ensuring wide coverage of object counts during sample selection.
Global-and-local counting enhancement (GLCE). In Tab. we denote WS-COC w/ GLCE (c*/c)) as a variant where GLCE is removed from WS-COC but only global prediction/aggregatee local prediction utilized. For the global-only variant (c*), the results show a clear increase in MAE, rising by +1.87 on the validation set and +1.81 on the test set, indicating the effectiveness of GLCE; Similar observations can found for the local-only variant (c)).
Next, we denote WS-COC w/ GLCE(L = i) as a variant where the input image is divided into i x grids for local enhancement. As shown in Tab. [ our default T. performs the best on the validation set, and concurrently also the best in testing.
We further conduct detailed analysis of the global and local predicted counts in GLCE. We find that approximately 81.2% of global predictions d underestimate object counts, while local aggregated predictions c². affected by the edge effect during aggregation over {ca 1 tend overestimate

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Published as a conference paper at ICLR 2026
Table Ablation study on the D CRCO, and GLCE.
VAL SET MAE RMSE4
TEST SET
Method
MAEL
RMSE
WS-COC w/o D
18.13
71.35
17.12
109.82
WS-COC w/ T-T
28.90
80.58
37.07
123.70
WS-COC w/o CRCO
17.39
65.23
16.75
107.45
WS-COC w/ SCRCO
17.24
64.83
16.63
103.57
WS-COC w/ CRCOrrd
16.77
69.58
16.04
105.23
WS-COC w/ GLCE (c#)
16.64
65.08
15.72
105.25
WS-COC w/ GLCE (c)
17.35
58.16
16.52
99.34
WS-COC w/ GLCE(L = 3)
15.28
57.64
14.34
99.93
WS-COC w/ GLCE(L = 4)
15.99
59.70
15.49
101.85
WS-COC
14.77
54.24
13.91
97.28
Table 6: Ablation study on different MLLM backbones of WS-COC.
VAL SET MAE RMSE]
TEST SET
Method
MAEJ
RMSE
WS-COC (DeepSeek-VL.22 VV2-333)
16.47
59.23
16.26
103.23
WS-COC DeeppSek-VLL2-1
14.45
56.71
13.67
95.51
WS-COC (Qwen3-VL-4BB
16.68
58.75
15.51
99.21
WS-COC (Qwen3-VL.-8B)
15.18
55.90
14.13
97.23
WS-COC WS-COC (LLaaVA-OneVersion-77)
17.05
60.87
17.32
105.89
14.77
54.24
13.91
97.28
object counts (about 79.4%). This complementary characteristic enables our solution to average them to improve the final counts.
Last, vary the threshold c² in this strategy, from 80 120. As shown in Fig. 4 set ch 100 by default, given performs the best on both validation and test sets.
Choice of MILMs. Tab. 6 presents the performance of WS-COC over different MLLM back- bones and model sizes, including Liavo-Qnneisioon-77 (Li et al. 2024). LLaVA-One Vision- 0.5B, Qwen3-VL-4B (Yang et al. 2025), Qwen3-VL-SB. DeepSeek- VL2-3B (Wu et all 2024) and DeepSeek-VL2-161. The results show that WS-COC generalizes well across different MLLM backbones: compared with our default LLaVA-One Verssonnn., Qwen3-VL-SB with similar model size yields comparable performance; on the other hand, the larger-scale DeepSeek-VLL VL2- 16B achieves better performance. Considering the efficiency and accuracy trade-off, we choose LLaVA-OneVision
by default.
Visualization of attention maps. better understand the counting behavior of different models, we visualize the attention maps between the count-related text tokens and the image patch tokens for MLLM-Zero. WS-COC-Base and WS-COC. As shown in Fig. 5 the models progressively refine their focus towards objects that are be counted, leading to more precise count predictions from MLLM-Zero to our WS-COC.
6 CONCLUSION
In this work, we present WS-COC, the first MLLM-driven weakly-supervised framework for class- agnostic object counting. The framework addresses the degradation of MLLM's counting perfor- mance in dense scenes through three strategies. First, the divide-and-discem dialogue tuning strat- egy reformulates count prediction as series of count judgments, progressively breaking down the prediction range from coarse to fine through multi-round dialogue. Second, the compare-and-rant count optimization strategy trains the model to rank images with diverse object counts. Third, the alobal-and-local counting enhancement strategy combines the global prediction with aggregated local predictions from partitioned sub-images to mitigate counting bias dense scenes during in- ference. Experiments across four benchmarks demonstrate the strong performance of WS-COC.
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7 ACKNOWLEDGMENT
This work was supported by the Fundamental Research Funds for the Central Universities.
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APPENDIX
This appendix provides 1) additional ablation study regarding the design choices of WS-COC com- ponents, the MLLM backbone selection, etc. and 2) additional analysis regarding cross-dataset eval- uations and failure cases analysis.
A ADDITIONAL ABLATION STUDY
This section provides additional results, unless otherwise specified, on the validation and test sets of FSC-147.
Multi-round dialogue in divide-and-discern dialogue tuning (D*T), Our proposed D&T strat- egy guides the MLLM determine whether the object count falls within specific range and progressively break down the range through multi-round dialogue in a curriculum manner, mov- ing from easy hard. We propose variant that replaces the multi-round dialogue with single-round dialogue, i.e. the initial range ([1. 2000]) is directly divided into multiple sub- ranges with interval A. For instance, for Á 200, the sub-ranges are [1, 200), [201, 400). [1801, 2000]. The MILM is then queried to predict the correct sub-range for given images using the text instruction "Given the image, please determine into which range the number of [obi] falls: {[1, 200). [201, 400)..... [1801, 2000]).". We vary A and report the performance in Fig. A The best performance is obtained with A = 300, which yields only a slight improvement over COC w/o D (see Tab. 5) and still remains clearly inferior to our D These results demonstrate the effectiveness of our multi-round dialogue in D
Image sampling mechanism in compare-and-rant count optimization (CRCO). In CRCO strategy, the images used for count comparison are sampled from the same object category to ensure consistent visual semantics during ranking. We further propose variant, denoted WS-COC w/ CRCO where we follow the similar strategy in CRCO but sample images from all categories in the training set. As shown in Tab. A this variant achieves certain gains over WS-COC w/o CRCO, but its performance is still inferior to our CRCO. This is because our CRCO preserves semantic consistency, which guides the model to focus on learning count difference among images. In contrast, cross- cross-category sampling introduces semantic variance that degrades performance.
We also introduce another variant of CRCO that adopts a semi-cross semi-cross-categor categoyy sampling strategy. Specifically, we use GPT-5 to partition the object categories in the training set into semantically similar groups using the following prompt: "Divide these categories into different groups, ensuring that categories within each group share semantic similarities: [categories|". The grouping results are reported in Tab. We then sample images for CRCO from each group. We denote this variant by WS-COC w/ CRCO semi-crose and its performance is reported in Tab. A We find that it performs slightly better than the fully cross-category sampling variant WS-COC w/ CRCO but it is still inferior to the single-category sampling mechanism used by default in CRCO. Although categories within the same group are semantically related, they still exhibit substantial appearance differences (e.g., apples, pears, and oranges differ greatly in color), which can distract the MILM from focusing on learning to count.
Last, we illustrate the model performance on each individual category in the test of FSC-147, as shown in Fig. B we observe that both WS-COC w/ CRCO and WS-COC w/ CRCO semi-cross underperform WS-COC in the vast majority of categories.
Image partition mechanism in global-and-loca counting enhancement (GLCE). We have talked about the edge effect caused by local predictions in GLCE: the simple grid partition may split ob- jects at the boundaries, potentially causing overestimation for objects located at the grid edges. In contrast, the global predictions tend to underestimate objects in dense scenes. Based on complemen- tary nature of these two, we introduce GLCE to take advantage of both global and local predictions for accurate object counting. One way to alleviate this edge effect is to leverage segmentation masks generated by SAM (Kirillov et al. 2023) to construct partitions, hoping to ensure that each object is contained within only a single partition. We denote this variant by WS-COC w/ GLCEsAM- In ad- dition, LVLM-COUNT (Qharabagh et al. 2024) adopts a divide-and-conques approach to alleviate this effect. It relies on additional pretrained models (GroundingDINO (Liu et al.) (024b) and SAM) to generate region proposals and object masks divide the image for counting. This results in
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VAL. SET
TEST SET
18
17
16
15
100
200
300
400
500
A
Figure A: MAE on the FSC-147 validation and test sets with different in D
Table More ablation studies.
VAL SET RMSE
TEST SET
Method
MAEJ
MAE,
RMSEJ
WS-COC w/o CRCO
17.39
65.23
16.75
107.45
WS-COC w/ CRCO cross
16.74
63.58
16.33
104.56
WS-COC w/ CRCO,
semi--rra
16.86
65.34
16.25
102.76
WS-COC w/o GLCE
16.64
65.08
15.42
105.25
WS-COC w/ GLCESAM
16.59
70.18
15.34
103.14
WS-COC w/ GLCELVLM
16.28
63.17
15.19
104.30
MLLM-Zero
38.92
119.26
38.19
145.42
MLLM-Zero w/ GLCE
33.49
102.83
32.65
128.70
WS-COC-Base
21.70
87.53
21.08
122.18
WS-COC-Base w/ GLCE
19.36
75.96
18.85
111.53
WS-COC
14.77
54.24
13.91
97.28
more complex pipeline and strong dependency on the detection and segmentation quality, where errors can directly propagate the final counting result. We denote this variant by WS-COC w/ GLCE2VLM: As shown in Tab, A both variants achieve trivial gains over WS-COC w/o GLCE but remain inferior to WS-COC. Our GLCE is overall simple, efficient, and effective.
Last, to examine the generality of GLCE, we integrater into both MLLM-Zero and WS-COC-Base As show in Tab. A GLCE consistently improves the performance of these models, demonstrating its effectiveness as general strategy.
Dataset sensitivity of global-and-local counting enhancement (GLCE), GLCE was proposed based consistent observation across datasets: MLLMs tend to underestimate object counts in dense scenes. This is owing to the nature that MILMs have mostly been pre-trained on images with sparse crowds and small number of objects. Therefore, GLCE can be regarded empirically dataset-insensitive. To verify this, we conduct detailed ablation study of GLCE on various datasets in Tab C and observe clear improvement
each of them. We further vary the partition grid size L in GLCE. Although GLCE with L = 3 performs slightly better than L = 2 on SHA and PUCPR+ the overall gains are marginal and accompanied by notable increase computational cost. In contrast, GLCE with L. = 2 consistently performs the best on FSC-147, SHB, and CARPK (results on FSC-147 are reported Tab. 5 of the paper), achieving favorable balance between efficiency and effectiveness.
B ADDITIONAL ANALYSIS
Additional cross-dataset evaluation. Beyond the cross-dataset evaluation reported in Tab. 2 and Tab. further conduct experiments 1OCfis5SK (Sun et al., 2023) and PASCAL VOC
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30
WS-COC
WS-COC w/ CRCO semi-cross
WS-COC w/ CRCO cross
25
20
15
10
5
cashew nut
strawherry
apple
sungbas
stamp
green pea
finger food elephant
shrep
tree log
comfe book
key marble
egt
watch
sticky anl
hot air balloun
deer
marker
aex shell
red bean
akis
carruni pieres
logo
saucs bottle
cands piece
potato chip
crah cake
nail polish
Figure B: Per-catygory MAE performance comparison of WS-COC under different sampling strate- gies in CRCO.
Table B: Grouping results of 89 categories in the training set of FSC-147.
Group
Categories
Staple Foods and Pastries
buns, bread rolls, biscuits, cupcake tray, macarons, cupcakes, naan bread, croissants, baguette rolls, instant noodles, spring rolls
Fruits and Vegetables
oranges, potatoes, tomatoes, peppers, watermelon,
bananas
Snacks and Prepared Food
ice cream, goldfish snack, chewing gum pieces, m&m pieces, meat skewers, onion rings, calamari rings
Beans and Grains
kidney beans, coffee beans, cereals, rice bags, nuts
Animals and Living Beings
pigeons, fishes, crows, geese, penguins, goats, swans, buffaloes, people, cows, bees, zebras, clams
Home and Building Materials
windows, mini blinds, roof tiles, bricks, cement bags, stairs, polka dot tiles, mosaic tiles, supermarket shelf
Transportation and Machinery
cars, boats, cranes
Tools and Consumables/Satiioneyy
stapler pins, cartridges, matches, lighters, nails, screws, pencils, pens, crayons, coins, cassettes
Personal Items and Accessories/Daily Goods
jeans, shoes, lipstick, caps, kitchen towels, pearls, jade stones, gemstones, beads, candles, birthday candles, cotton balls, balls
Tableware, Containers and Miscellancous Leisure
cups, plates, bowls, spoon, bottles, cans, boxes, alcohol bottles, chopstick, straws, go game
2007 (Everingham et al.) 2015) datasets, which involve visually ambiguous and heavily occluded objects. As shown in Tab, DJ WS-COC achieves superior performance on both datasets, demonstrat- ing its strong generalizability in handling visual ambiguity and occlusion. In particular, WS-COC significantly outperforms other fully-supervised methods on the PASCAL VOC 2007 dataset. This advantage may be attributed the presence of similar data which have already been incorporated into the pre-training corpus of LLaVA-One Vision.
Failure case analysis. In Fig. (a) and (b), we present some representative failure cases on FSC- 147. The model may struggle in extremely dense scenes (see Cig/(((a)) and under severe occlusion (see Fig. C (b)), where individual instances are difficult to distinguish.
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Table C: Performance comparison of GLCE variants on SHA, SHB, CARPK and PUCPR+
SHA MAE
SHB MAE
CARPK MAEJ
PUCPR+
Method
RMSEJ
RMSE!
RMSEL
MAE,
RMSEJ
WS-COC w/o GLCE
147.1
263.9
41.7
64.3
13.78
20.99
52.76
67.32
WS-COC w/ GLCE(L- = 2)
128.9
232.9
34.2
57.0
10.39
15.83
42.30
54.06
WS-COC w/ GLCE(L = -3)
127.4
227.7
38.5
57.7
11.14
15.64
41.64
53.95
WS-COC w/ GLCE(L = 4)
138.2
258.9
38.4
59.5
13.12
17.15
46.35
58.14
Table D: Cross-datased evaluation on IOCfish5K and PASCAL VOC 2007.
Supervision
IOCfish5K MAEI
PASCAL VOC 2007
Method
RMSE
MAEL
RMSEJ
CLIP-Count (Jiang 2023)
point-level
82.1
155.2
12.5
32.7
VLCounter KKng el al.
2024)
point-level
78.0
154.9
11.3
28.9
CountGD Naienn et T2ICount (Qian et al.
2024
point-level
67.4
144.8
8.7
21.7
2025
point-level
63.3
136.1
9.9
23.5
WS-COC
image-level
48.1
112.7
0.9
1.4
"books"
"brids"
"strawberries"
I 1021
GT:
GT:2092
CT:95
Pred:732
Pred:963
Pred: 140
(b)
Figure C: (a) and (b) denote representative failure cases.
To further investigate the model's behavior dense scenes, we respectively extract "extremely dense scenes" (count > 300) from the validation and test sets of FSC-147. These two subsets are denoted as VAL EDS SET and TEST EDS SET Tab. Next, we re-evaluate WS-COC and several representative methods (Jiang al. 2023 Amini-Naieni et al. 2024 Qian et al. 2025) them. We can observe that all methods perform poorly on these subsets, indicating that the difficulty is not specific to method; rather, most existing methods (even sully-supervised ones) struggle with spatial understanding under such extremely dense conditions.
To shed light the underlying cause, analyze the correlation between object size and counting errors. When scenes contain hundreds of objects, each object normally occupies only a few pixels, making individual objects nearly indistinguishable from each other or from background textures. To quantitatively verify this observation, we compute the average object size for each image in the FSC-147 test set: each image, three objects were originally annotated with bounding boxes for potential usage. Since in most images perspectives change insignificantly, we simply estimate the average object size of each image by averaging the three bounding box areas (in pixels). As shown in Fig. D images with smaller average object sizes typically lead to larger counting errors.
Given above, we study whether finer-grained GLCE partitions (i.e. partitioning the input image into 3 x 3 or 4 4 grids) can improve the model performance on these two subsets. In Tab. E. we can see both variants (WS-COC w/ GLCE(L 3) and WS-COC w/ GLCC(L = 4)) show some performance improvement, especially WS-COC w/ GLCE(L 3). However, these improvements are only on these "extremely dense scenes" subsets, while on the full validation and test sets they do not add much benefit. Specifically, implement another variant by selecting different grid sizes according to the predicted global count: for images with predicted global counts falling within [0. 100), we directly the global counts; for [100,300) fuse the global count with local predictions using
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190
160
140
120
100
80
60
40
20
409
500
1200
1400
1600
Average object size
Figure D: Scatter plot of MAE versus average object size (in pixels) per image on the FSC-147 test set.
Table E: Quantitative evaluation in extremely dense scenes (more than 300 objects per image) of FSC-147.
Supervision
VAL EDS SET
TEST EDS SET MAE]
Method
MAEI RMSE
RMSEL
CLIP-Count (Jiang et al. 2023)
point-level
219.59
348.80
227.14
684.11
CountGD (Amini-Naten et al. 2024
point-level
174.37
283.62
269.87
745.52
T21Count (Qian et al. 2025
point-level
205.07
315.80
212.35
666.94
WS-COC WS-COC w/ GLCE(L = 3)
image-level
185.97
297.95
226.96
647.36
image-level
180.28
291.10
221.48
638.62
WS-COC w/ GLCE(L 4)
image-level
183.14
293.06
237.33
649.25
WS-COC w/ SynEds
image-level
181.57
295.81
224.35
651.81
2 x 2 grids (T. = 2); for global counts above 300, we adopt 3 x 3 grids (T. = 3). This variant achieves negligible performance gains compared GLCE, e.g. -0.13 MAE the full validation set of FSC-147. Since the performance benefit very marginal while the design introduces much additional complexity, we choose keep the current form GLCE.
Besides, we explore whether data augmentation can alleviate this limitation. We manually concate- nate multiple images of the same category from the FSC-147 training to generate new images, each containing more than 300 objects. This process generates a total of 472 images. After inte- grating these augmented images into the original training set and retraining WS-COC, we observe very slight performance gains on both the VAL EDS SET and the TEST EDS SET (see WS-COC w/ SynEds versus WS-COC Tab. Ep. However, this variant causes performance degradation com- pared with WS-COC on the full validation and test sets (i.e. +0.45 and +0.38 MAE respectively).
19


Reviews:
[{"review_id": "pUxbVV1cth", "reviewer": "Reviewer_NtbD", "summary": "The paper tackles the task of weakly supervised class-agnostic counting when only image-level counts are available. Proposed approach does not require point wise annotations. Instead of directly training an MLLM to emit a number, authors propose three strategies on top of Llava style MLLM: (1) Divide and Discern Dialogue Tuning (D3T): turn counting into a multi round binary search style judgement dialogue, then finalize the count once the range is narrow; (2) Compare and rank Count Optimization (CRCO): teach relative ordering by ranking 4 images sampled from different count bins per class; (3) Global and Local Counting Enhancement (GLCE): at inference, average the global prediction with a grid of local sub image predictions to balance global under counting and local over counting.", "questions": "1. As an ablation study, I would be curious to see MLLM-Zero + GLCE and WS-COC-Base + GLCE perform. Since GLCE can be applied at test time, it is compatible with both MLLM-Zero and WS-COC-Base. \n2. What are the main differences between the proposed approach and other contemporary works like https://arxiv.org/pdf/2412.00686v1 which also propose a divide and conquer approach for class agnostic counting, similar to GLCE.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "Proposed approach achieves reasonable performance without using any point wise annotations which are expensive to obtain.", "weaknesses": "1. Novelty of the proposed approach is somewhat limited. Each of the three proposed components rely on fairly standard heuristics. Novelty of the work is more in combining them for this setting than in any single algorithmic leap.\n2. GLCE uses a 2×2 grid, with simple averaging of global and local. While GLCE is effective, it’s a heuristic and can be dataset sensitive. \n3. Results are reported for only one MLLM backbone and model size. It's not clear if the approach will generalize to other MLLMs and model sizes.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "mzBbRPe3iu", "reviewer": "Reviewer_3dgp", "summary": "This paper proposes WS-COC, a weakly-supervised class-agnostic object counting framework that bootstraps multimodal large language models for object counting using only image-level supervision. Instead of directly fine-tuning MLLMs for regression, the authors introduce three strategies: (1) Divide-and-Discern Dialogue Tuning, which reformulates counting as iterative range judgment through multi-round dialogue; (2) Compare-and-Rank Count Optimization, which trains the MLLM to rank multiple images by their object counts; and (3) Global-and-Local Counting Enhancement, which fuses global and local predictions at inference to better handle dense scenes. Experiments show that WS-COC presents good performance while using only weak supervision.", "questions": "Please see the points listed in weakness section.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper is clearly written and well-structured, with effective figures that help illustrate the intuition behind each proposed component.\n- The three proposed strategies are conceptually simple yet effective, with clear motivations.\n- The method achieves better results compared to other methods under image-level supervision.", "weaknesses": "- The paper does not discuss computational efficiency. Since the framework relies on large multimodal language models and multi-round dialogues, a comparison of training and inference cost against other weakly- and fully-supervised baselines would help clarify its practical scalability and deployment feasibility.\n- The experiments are conducted on general object counting datasets; however, the model’s ability to generalize to other domains remains unclear. For example, it would be valuable to investigate how the proposed framework performs on indiscernible object counting tasks where objects are visually ambiguous or heavily occluded. The related references are listed below.\n- Missing related work. Several recent works on object counting are relevant and should be discussed:\n   - A. Distribution Matching for Crowd Counting\n   - B. Indiscernible object counting in underwater scenes\n   - C. Counting Everyday Objects in Everyday Scenes", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "79Pn0OdzdE", "reviewer": "Reviewer_GPEv", "summary": "This paper introduces WS-COC, a novel framework for weakly-supervised, class-agnostic object counting. This is the first work, to my knowledge, to successfully bootstrap a Multimodal Large Language Model (MLLM) for this task using only image-level count supervision. The core problem is that MLLMs, while capable of basic counting, fail significantly in dense scenes.", "questions": "see weakness", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper tackles a highly practical and important problem: class-agnostic counting without expensive point-level annotations. The approach of \"bootstrapping\" a generative MLLM (LLaVA) for this counting task, rather than building a discriminative VLM (like CLIP) with a specialized counting head, is a novel and promising direction.\n\nThe experimental validation is thorough. WS-COC's performance on the primary benchmark, FSC-147, is excellent, significantly reducing the MAE in dense scenes compared to baselines (Fig. 1(d)).", "weaknesses": "The paper's CRCO strategy is well-motivated as a way to handle the modality gap using relative ranking. However, the related work section overlooks prior art from the VLM (Vision-Language Model) domain that has explored similar concepts. A discussion in Section 2.1 or 2.2 situating WS-COC's CRCO strategy against VLM-based ranking methods like CrowdCLIP would significantly strengthen the paper's contribution and provide clearer context on its novelty.\n\n\nThe failure case analysis shows the model still struggles with \"extremely dense scenes\" (e.g., \"books\" GT: 1021, Pred: 732). Is this a fundamental limitation of the MLLM's spatial understanding and resolution, or could this be further mitigated by, for instance, a finer-grained GLCE partitioning (e.g., $L=3$ or $L=4$, which Table 4 shows performed worse) or a more sophisticated data augmentation strategy for dense scenes?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ANo8sbSZNp", "reviewer": "Reviewer_QMGG", "summary": "This paper introduces the use of Multimodal Large Language Models (MLLMs) for weakly-supervised, class-agnostic object counting. To enhance MLLMs’ performance on dense object scenes, the authors propose two training strategies and one inference strategy. Experimental results demonstrate that the proposed approach achieves state-of-the-art performance among weakly-supervised models and is only slightly inferior to the best fully-supervised counterparts. Ablation studies further validate the effectiveness of the proposed strategies.", "questions": "- MLLMs are known to incur high inference costs, making them unsuitable for deployment on resource-constrained devices. What potential application scenarios or deployment strategies do the authors envision for MLLM-based counting models?\n- Could the authors provide visual or attention-based analyses to better understand how the model perceives and counts objects? This would help clarify how the model interprets scenes and focuses on visual cues during the counting process.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper is one of the first attempts to explore the use of MLLMs for weakly-supervised, class-agnostic object counting. The results achieved by the proposed method are competitive with those of fully-supervised approaches, which is a notable contribution to the field.\n- The proposed training and inference strategies are simple yet effective, and the experiments are meticulously structured.", "weaknesses": "- **The paper lacks analysis of inference speed and computational cost**. Since MLLMs are computationally expensive, it is important to quantify inference time, memory usage, and FLOPs, as these factors are critical for real-world deployment.\n- **Limited mechanism interpretability**. Although the proposed method is empirically effective, the underlying mechanisms that drive the improvement remain unclear. Visualizations of model attention or response patterns (e.g., Grad-CAM on the vision-language alignment) may help elucidate this.\n- **The paper lacks qualitative comparisons with other object counting models**. The visual results only compare different variants within the MLLM family, but omit comparisons with existing leading density-map based or detection-based weakly- or fully-supervised baselines. Including such comparisons could help clarify the strengths of the proposed method relative to other approaches.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "wrMuaGxUe7", "reviewer": "Reviewer_XCqq", "summary": "This paper proposes WS-COC, the first MLLM-driven weakly-supervised framework for class-agnostic object counting. Instead of directly fine-tuning MLLMs to predict object counts, WS-COC incorporates three key strategies: divide-and-discern dialogue tuning, compare-and-rank count optimization, and global-and-local counting enhancement. Extensive experiments are conducted on four benchmarks, showing that WS-COC matches or surpasses many fully-supervised methods while reducing annotation costs significantly.", "questions": "1. Regarding the failure cases in extremely dense scenes and severe occlusion (mentioned in the paper’s appendix), what specific characteristics of these scenes most strongly correlate with the model’s performance degradation? Is there a quantitative analysis to clarify the key factors limiting WS-COC’s performance in such scenarios?\n2. In the Compare-and-Rank Count Optimization (CRCO) strategy, cross-category sampling reduces performance due to semantic variance. However, the paper does not specify whether the performance decline is consistent across all category pairs.\n3. The Global-and-Local Counting Enhancement (GLCE) strategy uses a fixed threshold to distinguish dense and sparse scenes. How was this threshold determined?\n4. Extend CRCO to support semi-cross-category sampling: Instead of restricting sampling to the same category, test a \"semi-cross-category\" sampling strategy. For example, group semantically similar categories (e.g., \"apple\", \"pear\", \"orange\" as a \"fruit group\") and sample images within each group.\n5. Optimize the dense-scene threshold c^h adaptively.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This work innovatively proposes the first weakly supervised general category object counting framework WS-COC based on MLLM, filling the technical gap of \"MLLM+weakly supervised+general category\" object counting.\n2. It designs three core strategies. The Divide-and-Discern Dialogue Tuning converts counting into multi-round range judgment to reduce learning difficulty; the Compare-and-Rank Count Optimization establishes the connection between visual features and counting through relative ranking; the Global-and-Local Counting Enhancement fuses multi-scale predictions to correct biases. These strategies optimize counting capabilities throughout the entire process from training to inference.\n3. The experimental design is rigorous and comprehensive. It selects four representative benchmark datasets (FSC-147, CARPK, PUCPR+, and ShanghaiTech) that cover diverse scenarios and categories, ensuring the universality of results. Key implementation parameters such as MLLM backbone and LoRA settings are reported in detail to ensure experimental reproducibility.", "weaknesses": "1. Its performance is still insufficient in extremely dense scenes or under severe target occlusion. The model struggles to accurately distinguish individual instances, leading to cases of large counting errors.\n2. In the Compare-and-Rank Count Optimization strategy, cross-category sampling introduces semantic differences and leads to performance degradation. Currently, image sampling can only be conducted within the same category, which limits the flexibility and application scope of the strategy.\n3. The Global-and-Local Counting Enhancement strategy uses simple grid partitioning to split images into sub-images, which easily causes overestimation of local counts due to edge effects. Although this issue is alleviated by averaging with global counts, the fundamental problem caused by the partitioning method is not solved, leaving room for optimization.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)