Bi-directional Task-Guided Network for Few-Shot Fine-Grained Image Classification

Authors: zhen-xiang  ma
li-jun  zhao
zi-chao  zhang
tai  zheng
zhen-duo  chen
Venue: acmmm_org_ACMMM_2024_Conference
Published: 2024-03-28 06:55:47

Abstract: In recent years, the Few-Shot Fine-Grained Image Classification (FS-FGIC) problem has gained widespread attention. A number of effective methods have been proposed that focus on extracting discriminative information within high-level features in a single episode/task. However, this is insufficient for addressing the cross-task challenges of FS-FGIC, which is represented in two aspects. On the one hand, from the perspective of the Fine-Grained Image Classification (FGIC) task, there is a need to supplement the model with mid-level features containing rich fine-grained information. On the other hand, from the perspective of the Few-Shot Learning (FSL) task, explicit modeling of cross-task general knowledge is required. In this paper, we propose a novel Bi-directional Task-Guided Network (BTG-Net) to tackle these issues. Specifically, from the FGIC task perspective, we design the Semantic-Guided Noise Filtering (SGNF) module to filter noise on mid-level features rich in detailed information. Further, from the FSL task perspective, the General Knowledge Prompt Modeling (GKPM) module is proposed to retain the cross-task general knowledge by utilizing the prompting mechanism, thereby enhancing the model's generalization performance on novel classes. We have conducted extensive experiments on five fine-grained benchmark datasets, and the results demonstrate that BTG-Net outperforms state-of-the-art methods comprehensively.

Main paper: 
 introduction: 
 Benefiting from the rapid advancement of deep learning [10, 14] , significant progress has been achieved in the computer vision task of image classification [2, 47] . These models rely on large-scale, label-rich training samples, however, the annotation process for large-scale image datasets is extremely time-consuming and costconsuming. In contrast, humans can effortlessly learn new concepts from one or a few samples. To mitigate the model's excessive dependence on data volume, researchers are focusing on Few-Shot Learning (FSL) [21, 29, 38] .FSL aims to use extremely limited labeled support samples based on transferable knowledge from base classes, enabling models to perform well on unlabeled query samples from novel classes. A number of successful FSL methods, based on meta-learning and metric learning, have been proposed. Inspired by this, researchers have extended FSL to more challenging fine-grained datasets, introducing the novel task of Few-Shot Fine-Grained Image Classification (FS-FGIC) [44, 54] .However, as a cross-task between Fine-Grained Image Classification (FGIC) and FSL, existing methods for FS-FGIC still have shortcomings that need further improvement, this is evident in two aspects. On the one hand, as a FGIC task [11] , effective fine-grained feature extraction constitutes the foundation for models to achieve good performance. However, constrained by the issue of overfitting under the FSL setting, most existing methods [40, 46] primarily rely on the last layer features of the backbone for classification, lacking sufficient utilization of the network's feature learning capabilities. Specifically, the high-level features outputted by the last layer of the network often contain rich global semantic information, but this unavoidably results in the loss of local detailed information. Solely relying on high-level semantic features for classification is often insufficient to distinguish fine-grained images with subtle interclass differences, thus impacting the performance of the method. Therefore, addressing the FS-FGIC task requires adequate assistance from local detailed features. Experience shows that mid-level features often contain more fine-grained information, but at the same time, they also include a significant amount of background and other trivial noise as shown in Figure 1 . Directly using them will exacerbate overfitting issues under the FSL setting. On the other hand, as a FSL task, the essence of the model training process lies in learning the commonalities between different tasks (i.e., episodes) and based on this foundation, focusing on the unique characteristics of each task. The high degree of similarity between different classes in fine-grained data dictates the presence of abundant shared knowledge across various tasks, and the differences between tasks are mainly reflected in the details. Existing methods primarily confine their learning to within a single task, lacking explicit modeling of cross-task general knowledge. Clearly, under the FSL setting, especially for fine-grained datasets, general knowledge facilitates transfer learning between tasks. If the model only focuses on intra-task feature extraction, it will cause the model to overfit on the trainable base classes, preventing effective learning of task-transferable knowledge for generalization to unseen novel classes, thus affecting the performance of FS-FGIC.To address the cross-task challenge of FS-FGIC, we propose a novel Bi-directional Task-Guided Network (BTG-Net), which contains two modules to solve the feature requirements of FS-FGIC from the perspective of two tasks respectively. From the perspective of the FGIC task, it is essential to introduce rich local detail features to assist classification, while reducing the overfitting issue as much as possible. Specifically, instead of simply concatenating high-level and mid-level features, we propose the Semantic-Guided Noise Filtering (SGNF) module to generate filters for mid-level features through high-level features with powerful target object localization capability. These filters are then employed to re-weight and adjust the mid-level features, effectively eliminating background and other trivial noises as shown in Figure 1 . This process provides the model with improved fine-grained information to assist in classification. From the perspective of the FSL task, it is necessary to learn cross-task general knowledge. We design the General Knowledge Prompt Modeling (GKPM) module, which utilizes high-level features to learn cross-task general knowledge, as these semantically rich high-level features often represent general attributes of categories. Inspired by the prompting mechanism in computer vision [18] , a learnable Task-General Prompt is designed to learn shared general knowledge across all tasks. By introducing crosstask general knowledge and using the self-attention mechanism of Transformer [9] , the query features are further optimized to reduce the distance between query features and prototypes of the same class while distancing them from prototypes of different classes, with limited support samples. This process enables the model to maintain robust generalization capabilities on the novel classes.In general, BTG-Net is dedicated to meeting the feature needs of both fine-grained task and few-shot learning task, and thereby achieving state-of-the-art results on five fine-grained benchmarks. Our contributions can be summarized as follows:• We propose a novel BTG-Net that addresses the feature requirements of the FS-FGIC from the perspectives of both FGIC and FSL tasks. • From the perspective of the FGIC task, we propose the SGNF module, which effectively filters out noise such as background in mid-level features with the assistance of semantic guidance of high-level features. This process introduces rich fine-grained information to assist FS-FGIC, while minimizing overfitting problem associated with the addition of features as much as possible. • From the perspective of the FSL task, we propose the GKPM module. We design the Task-General Prompt to retain the cross-task general knowledge, enabling the model to exhibit strong generalization capabilities on novel tasks and classes. [22, 30] try to discover a suitable gradient-based optimization strategy to quickly adapt to novel tasks with few gradient updates. MAML [12] aims to learn a way to initialize parameters so that the model can easily adapt to new tasks.Data-augmentation based methods [3, 13, 35] seek to train a generator from base classes and leverage it to generate novel samples. Metric-learning based methods [33, 34, 38] focus on learning a fixed embedding function to map input images into a low-dimensional embedding space. FRN [43] uses support sample features to reconstruct query sample features for better similarity measurement. However, traditional FSL methods, which usually pay less attention to feature learning, may not always perform well on finegrained datasets with subtle inter-class differences. Therefore, FS-FGIC has attracted increasing attention in recent years. 
few-shot fine-grained image classification: 
 Expanding general FSL methods directly to fine-grained datasets does not yield satisfactory performance, therefore, Wei et al. [41] first define the FS-FGIC task and use bilinear features to train a classifier with piecewise mapping. BSNet [26] utilizes two metric functions for the joint optimization of the model. PaCL [40] employs contrastive learning to extract more discriminative features from various object regions. TDM [23] generates task-specific channel weights to localize the discriminative regions for each class. BiFRN [44] introduces a bi-reconstruction mechanism to simultaneously accommodate for inter-class and intra-class variations.The existing method AGPF [36] attempts to aggregate multi-scale information from different layers, but it neglects that introducing mid-level features with background noise exacerbates the overfitting issue under the FSL setting. BSFA [49] proposes a two-stage background suppression and foreground alignment framework to capture subtle differences and align support and query samples. This is an operation within high-level features, neglecting the rich detailed information in mid-level features. Additionally, BFSA directly crops the background of high-level features, not simply eliminating non-generalizable background noise. In contrast, we use high-level features to filter out background noise in mid-level features, thereby providing fine-grained information to assist classification in the FS-FGIC task. Furthermore, all the mentioned methods are confined to feature learning within a single task, overlooking the necessity for exploring commonalities across FSL tasks. In contrast, our proposed method explicitly models cross-task general knowledge using the prompting mechanism, enhancing the model's generalization capability on novel classes.Inspired by prompting mechanism in the field of natural language processing [1], VPT [18] is the first to apply prompting mechanism to computer visual tasks. Existing prompt-based methods [52, 53] typically require freezing large-scale pre-trained models, designing learnable prompts for parameter-efficient fine-tuning to adapt to downstream tasks. In contrast, our proposed BTG-Net does not require pre-trained models nor parameter freezing; the entire model is trained end-to-end. 
method 3.1 problem definition: 
 Under the standard FSL settings, the dataset𝐷 = {(𝑥 𝑖 , 𝑦 𝑖 ), 𝑦 𝑖 ∈ 𝑌 )} is divided into the training dataset 𝐷 𝑏𝑎𝑠𝑒 = {(𝑥 𝑖 , 𝑦 𝑖 ), 𝑦 𝑖 ∈ 𝑌 𝑏𝑎𝑠𝑒 )}, the validation dataset 𝐷 𝑣𝑎𝑙 = {(𝑥 𝑖 , 𝑦 𝑖 ), 𝑦 𝑖 ∈ 𝑌 𝑣𝑎𝑙 )},and the test dataset 𝐷 𝑛𝑜𝑣𝑒𝑙 = {(𝑥 𝑖 , 𝑦 𝑖 ), 𝑦 𝑖 ∈ 𝑌 𝑛𝑜𝑣𝑒𝑙 )}, where 𝑥 𝑖 is the original sample, 𝑦 𝑖 is the 𝑖-th class label, and 𝑌 𝑏𝑎𝑠𝑒 ∩ 𝑌 𝑣𝑎𝑙 ∩ 𝑌 𝑛𝑜𝑣𝑒𝑙 = ∅. We adopt the episodic training mechanism. Specifically, during the training phase, each episode represents an 𝑁 -way 𝐾-shot classification problem. 𝑁 classes are randomly selected from 𝐷 𝑏𝑎𝑠𝑒 , each class having 𝐾 labeled images in the support set and 𝑈 unlabeled images in the query set. During the testing phase, the model is evaluated on the test dataset 𝐷 𝑛𝑜𝑣𝑒𝑙 . Without loss of generality, the following discussion assumes consideration within an episode/task.To address the cross-task problem of FS-FGIC from the perspectives of both FGIC and FSL tasks, we propose the novel BTG-Net, and the overall framework is shown in Figure 2 .The BTG-Net consists of four components: The first component is the backbone network, which is used to extract deep features from the support and query samples. The second component is the SGNF Module, which takes the high-level features (output from the last layer of the backbone network) and mid-level features (from the second-to-last and third-to-last layers) as inputs. It utilizes the high-level features to perform noise filtering on the mid-level features. The third component is the GKPM module, which takes high-level features as input and utilizes the prompting mechanism to model task-general knowledge, so as to adjust query features and enhance the generalization ability of the model. The last component is the Similarity module, responsible for computing the cosine similarity between the query features and prototypes at each layer. The weighted sum of the three similarities is employed for the classification of the query samples.Through the analysis from the perspective of the FGIC task in the introduction section, globally semantic-rich high-level features (i.e., the output of the network's last layer) cannot effectively handle fine-grained images with subtle differences between categories. Simply concatenating mid-level features will introduce trivial noise such as background, exacerbating the overfitting issue under the FSL setting. Therefore, we designed the Semantic-Guided Noise Filtering (SGNF) module, as illustrated in Figure 2 , to achieve a filtering effect of high-level features on mid-level features.Specifically, given a sample X, we obtain features through the backbone network 𝑓 as follows,EQUATIONwhereF 𝑙 ∈ R 𝐶 𝑙 ×𝐻 𝑙 ×𝑊 𝑙 , F 𝑠 ∈ R 𝐶 𝑠 ×𝐻 𝑠 ×𝑊 𝑠and F 𝑡 ∈ R 𝐶 𝑡 ×𝐻 𝑡 ×𝑊 𝑡 represent the feature maps obtained from the last layer, second-tolast layer, and third-to-last layer, respectively.Due to the rich global semantic information of the high-level feature F 𝑙 , which can effectively locate the global information of the target, we use the F 𝑙 to generate filters for mid-level features F 𝑠 and F 𝑡 . Considering the varying sizes of feature maps at different layers, we use Bilinear interpolation 𝐵(•) to map the size of the feature map of F 𝑙 to match that of F 𝑠 and F 𝑡 . It is important to note that the use of bilinear interpolation does not alter the number of channels in the feature maps, thereby avoiding any loss of information. The filters can be formulated asEQUATIONEQUATIONwhere 𝑔 𝜃 𝑠 and 𝑔 𝜃 𝑡 are sub-networks that include two convolutional layers each, and 𝜎 is the sigmoid activation function.Based on M 𝑠 and M 𝑡 , we can reweight and adjust the mid-level features to eliminate trivial noise such as background, which can be formulated asEQUATIONEQUATIONwhere ⊙ denotes element-wise product operation with the broadcasting mechanism. Next, we design the feature compression block to compress the feature spaces of F𝑠 and F𝑡 , mitigating potential overfitting issues. Features f 𝑠 ∈ R 𝐶 𝑠 and f 𝑡 ∈ R 𝐶 𝑡 can be obtained as follows,EQUATIONEQUATIONwhere 𝐶𝑜𝑛𝑣 𝑠 ) and support features (f 𝑆 𝑠 , f 𝑆 𝑡 ) can be separated from f 𝑠 and f 𝑡 , respectively. Following the ProtoNet [33] , we generate prototypes (f 𝑃 𝑠 , f 𝑃 𝑡 ) for each class. The computation for the prototype for the 𝑖-th class is as follows,EQUATIONEQUATIONIn this module, we extensively explored the network's feature extraction capabilities. By filtering and eliminating noise from midlevel features, we provide rich local details to assist in classification for the FS-FGIC task. This procedure also alleviates overfitting issues under the FSL setting.After addressing the demand for fine-grained features from the FGIC task perspective, we further analyzed the FSL task. If the model is confined to feature learning within a single task, it can not explicitly learn transferable knowledge, resulting in overfitting on the trainable base classes and poor generalization performance on novel classes. In light of this, we designed the General Knowledge Prompt Modeling (GKPM) module as illustrated in Figure 2 , employing the prompting mechanism to explicitly model cross-task general knowledge, and then introducing it into query features to ensure the model possesses robust generalization capabilities on novel classes. Thereafter, inspired by the prompting mechanism, we design the learnable Task-General (TG) Prompt (G ∈ R 𝐶 𝑙 ×𝐻 𝑙 ×𝑊 𝑙 ) to retain cross-task general knowledge. It is important to note that the TG Prompt is shared across all tasks during the training, validation, and testing phases. To incorporate cross-task general knowledge into the specific task, we add G with each prototype to obtain the task generalization information, which is formulated asEQUATIONwe omit the subscript 𝑖 for clarity in expression. Based on T 𝑃 𝑙 , we dynamically adjust the query features F 𝑄 𝑙 using the self-attention mechanism of the Transformer. Specifically, we flatten F 𝑄 𝑙 and T 𝑃 𝑙 into (𝐻 𝑙 × 𝑊 𝑙 ) tokens, each token is 𝐶 𝑙 -dimensional. Then, we use weighted parameters 𝑊 𝑞𝑘𝑣 to map each token to the query vector q, key vector k, and value vector v, as follows,EQUATION)EQUATION)Afterward, to adequately model the relationship between query features F 𝑄 𝑙 and the task generalization information T 𝑃 𝑙 , we concatenate vectors of the same type as follows,EQUATIONwhere [•, •] is the concatenation operation, then we use standard self-attention mechanism in Transformer to obtain the task generalization query features F𝑄 𝑙 . This process can be represented asEQUATION)To maintain task generalization query features with the same feature map size as the original query features, we slice F𝑄 𝑙 , retaining only the first 𝐻𝑊 vectors, and then reshape it back to the size of𝐶 𝑙 × 𝐻 𝑙 × 𝑊 𝑙 , i.e. F𝑄 𝑙 ∈ R 𝐶 𝑙 ×𝐻 𝑙 ×𝑊 𝑙 .Table 2 : The accuracy (%) of 5-way 1-shot and 5-shot on CUB (using raw images) dataset. The best results are shown in bold. The original feature F 𝑙 compresses the feature space in the same way as the previous module, asEQUATION)Based on f 𝑙 ∈ R 𝐶 𝑙 , we can obtain query features f 𝑄 𝑙 and support features f 𝑆 𝑙 , and then acquire prototype f 𝑃 𝑙 using the same approach as in Eq (8) . Finally, we integrate task generalization query features F𝑄 𝑙 into the query features to obtain a comprehensive representation that includes cross-task information, as follows,EQUATIONwhere 𝜆 is the learnable weight parameter and 𝐺𝑀𝑃 (•) is Global Max Pooling. Through the above procedure, we store cross-task general knowledge with the TG prompt and introduce it to optimize the query features, bringing the query closer to the prototype of the same class, further enhancing intra-class consistency feature learning with limited support samples. This aids in the FS-FGIC learning of well-transferable meta-knowledge, enabling the model to exhibit After the SGNF module and GKPM module, we obtained the outputs of three layers: ( f𝑄 𝑙 , f 𝑃 𝑙 ), (f 𝑄 𝑠 , f 𝑃 𝑠 ), (f 𝑄 𝑡 , f 𝑃 𝑡 ),EQUATIONwhere 𝜏 𝑚 are learnable scaling parameters and S(•) represents the Cosine similarity to measure the similarity between query features and the prototype. The overall loss is calculated asEQUATION 
experiments 4.1 dataset: 
 We use five widely-used few-shot fine-grained benchmark datasets, including CUB-200-2011 [39] , Stanford Dogs [19] , Stanford Cars [20] , meta-iNat [15, 42] , and tiered meta-iNat [42] . We follow the commonly used standard dataset splitting way [43, 54] , as shown in Tab. 1.For a fair comparison with previous work, we adopt two commonly used Conv-4 and ResNet-12 as the backbone networks, and both accepted input image sizes of 84 × 84. Meta-testing Details: We apply the standard 5-way 1-shot and 5-way 5-shot settings. We save the best-performing model on the validation set to test. We report the 95% confidence interval results for 2,000 test episodes for all experiments.In this section, we compare our proposed method with a number of state-of-the-art methods, including general FSL methods (e.g. ProtoNet, DeepEMD, and FRN) and FS-FGIC methods (e.g. Helix-Former, AGPF, and BiFRN). The experimental results are shown in Table 2 -4. Based on the experimental results, it can be observed: Firstly, our proposed method outperforms state-of-the-art methods, achieving comprehensive performance leadership. This superiority is evident not only in typical fine-grained datasets (CUB, Stanford Cars, and Stanford Dogs) but also in more challenging finegrained datasets (meta-iNat and tiered meta-iNat). Furthermore, compared to AGPF based on multi-scale feature aggregation, our method consistently exhibits performance improvements across all experimental settings. This not only indicates the importance of filtering out background and other trivial noise while introducing mid-level features, but also validates the effectiveness of the SGNF module. Additionally, under the 1-shot setting with only one support sample, our method demonstrates a more significant leading advantage, indirectly proving the effectiveness of BTG-Net in preventing overfitting. It also confirms that, by introducing the TG prompt, based on cross-task general knowledge learned, the model can be well generalized to novel tasks with very limited task-specific support samples. Finally, BTG-Net exhibits excellent performance on both Conv-4 and ResNet-12, showing nonsignificant overfitting with an increase in feature extractor parameters.To further demonstrate the effectiveness of the two modules and the rationality of the detailed designs within the modules, we conduct additional experiments and analyses.In order to demonstrate the effectiveness of the two modules proposed, we conduct the modulewise ablation study as shown in Table 5 . The experimental results demonstrate that both SGNK and GKPM enhance baseline performance. When the two modules are combined, the performance sees a significant further improvement, validating the effectiveness of the proposed method in addressing FS-FGIC tasks from both FGIC and FSL perspectives.To demonstrate that the SGNK module is an ideal framework for leveraging the feature learning capability of the network in a multi-layer strategy, we conducted additional experiments, and the results are shown in Table 6 . The results show that the concatenation operation of multiple layers inevitably introduces noise, leading to overfitting issues under the FSL setting, thus, the performance improvement is not significant. Therefore, it is essential to filter out background noise while introducing mid-level features, and the SGNK module designed in this paper proves to be the most effective structure.4.4.3 Effectiveness analysis of TG Prompt in the GKPM. We conducted additional experiments with (w/) and without (w/o) TG Prompt, as shown in Table 7 . The results indicate that TG Prompt contributes to performance improvement, further confirming that learning cross-task general knowledge is effective for FS-FGIC, and can enhance the generalization ability of the model on novel classes. To fully demonstrate the effectiveness of the SGNF module, we present additional visual results as shown in Figure 4 . Based on the results, it can be observed that the proposed SGNF module effectively eliminates background noise in the mid-level features.To better understand the effectiveness of each module from the perspective of feature mapping results, we also show the t-SNE visualization results in Figure 5 . It can be observed that results generated by only a baseline model (Figure 5a ) are unsatisfactory, with blurry class boundaries and many points of different classes intermingled. According to Figure 5d , it can be observed that, after combining SGNF and GKPM, each class forms a compact cluster and the boundaries between different clusters are obvious. This further demonstrates the effectiveness of SGNF and GKPM, then verifies that our proposed model has strong generalization ability and can learn optimized fine-grained features. 
conclusion: 
 In this paper, we propose a novel Bi-directional Task-Guided Network (BTG-Net) for FS-FGIC. The BTG-Net consists of two modules. The Semantic-Guided Noise Filtering (SGNF) module effectively filters out noise such as background in mid-level features, and the General Knowledge Prompt Modeling (GKPM) module ensures robust generalization performance on novel classes by introducing cross-task general knowledge. Our proposed method achieves comprehensive leadership in performance on five benchmark datasets. 
 
 Appendix:  


Decision: The paper presents an approach to few-shot fine-grained image classification.
The approach is agreed by most reviewers to be new. The experiments and results are considered to be strong by all reviewers.
Authors rebuttal was deemed as effective by all except one reviewer. The lingering issues raised by the lone reviewer who voted for weak reject seem to be minor and fixable in the final paper. Hence this paper should be in the accepted batch.

Reviews: review: paper_summary: paper_summary: The paper addresses the Few-Shot Fine-Grained Image Classification (FS-FGIC) problem by proposing a novel Bi-directional Task-Guided Network (BTG-Net). This network aims to enhance performance by tackling the cross-task challenges in FS-FGIC. From the Fine-Grained Image Classification perspective, it introduces a Semantic-Guided Noise Filtering (SGNF) module to filter noise in mid-level features, which are rich for the task. From the Few-Shot Learning perspective, it employs a General Knowledge Prompt Modeling (GKPM) module to retain cross-task general knowledge, leveraging the prompting mechanism for better generalization to novel classes. Extensive experiments on five benchmark datasets demonstrate that BTG-Net outperforms state-of-the-art methods.
strength_weakness: strength_weakness: - **Clarity:** the paper and proposed method are easy to follow and easy to grasp.
- **Extensiveness of experiments:** the amount of datasets and baseline method prove the strength of the proposed method. Nevertheless, refer to the first limitation for a doubt about baseline evaluation.
limitations: limitations: - **Reported baseline results:** The reported numbers for BiFRN [44] in the quantitative evaluation do not match those in the original paper, in some of the cases they are off by more than 3%, and the paper does not state whether the results are a re-implementation by the authors. Given that some of the original results are higher than those obtained by the proposed method, a clarification on the matter is crucial.
- **Role of $\alpha$:** the paper does not provide an explanation of the role of the two merged branches and the interpolation through the use of $\alpha$. Since both of the branches are convolutional and act on the same input, how are they differentiated?
paper_summary: paper_summary: This proposes a novel Bi-directional Task-Guided Network (BTG-Net) for Few-shot Fine-Grained Image Classification. Especially, the Semantic-Guided Noise Filtering (SGNF) module to filter noise on mid-level features rich in detailed information. Moreover, the General Knowledge Prompt Modeling (GKPM) module is proposed to retain the cross-task general knowledge by utilizing the prompting mechanism, thereby enhancing the model’s generalization performance on novel classes. The extensive experiments on five few-shot fine-grained benchmark datasets demonstrate the effectively of the proposed method.
strength_weakness: strength_weakness: 1、The motivation is reasonable.

2、The writing is good, and the proposed method is clear to understand.

3、Extensive evaluation verifies the effectiveness of the proposed method.
limitations: limitations: 1、Thanks to the high generalization of the Visual-Language Models (VLMs), the CLIP with the prompt-tuning methods shows impressive performance on zero-shot learning and few-shot learning. Therefore, what is the advantage of the proposed method compared to CLIP with prompt tuning for the few-shot fine-grained image classification?

2、It lacks the comparison on the backbone of ResNet-18. Moreover, why the comparison of meta-iNat and tiered meta-iNat is only using the Conv-4 backbone and without considering the ResNet12 similar to the previous comparison?

3、What is the core motivation of the General Knowledge Prompt Modeling, and how it can solve the problem in Few-shot Fine-Grained Image Classification?
paper_summary: paper_summary: This paper focus on few-shot fine-grained classification task and designs two modules from the perspectives of few-shot task and fine-grained classification task respectively. This paper proposes a Semantic-Guided Noise Filtering (SGNF) module to reduce the impact of noise on model performance during feature fusion. This paper also proposes a General Knowledge Prompt Modeling (GKPM) module to retain cross-task knowledge to improve model performance on novel classes. The proposed model achieves SOTA results on five few-shot fine-grained benchmark datasets.
strength_weakness: strength_weakness: This paper proposes two novel modules to simultaneously enhance model performance on few-shot and fine-grained classification task. 
In the SGNF module, using high-level features to filter out noise from mid-level features can effectively retain useful fine-grained information. 
In the GKPM module, a trainable prompt is used to extract general knowledge from episode training process, which improves model’s generalization capability on novel classes. 
Alation studies demonstrate the effectiveness of two modules and the whole model achieves SOTA results on five benchmarks.
limitations: limitations: The trainable prompt may have learned dataset-aware information, which is invalid for the new test dataset. 
This paper did not conduct cross-dataset test experiments. 
The iteration term $U$ in formulation 18 has not been explained.
paper_summary: paper_summary: This paper introduces a Bi-directional Task-Guided Network (BTG-Net) to tackle Few-Shot Fine-Grained Image Classification (FS-FGIC) task. Extensive experiment results on five few-shot fine-grained benchmark datasets demonstrate that the proposed network outperforms state-of-the-art methods.
strength_weakness: strength_weakness: 1. The proposed methods and experimental results are well presented, and the manuscript is well organized overall.
2. The performance gain is impressive when compared to state-of-the-art methods.
limitations: limitations: 1. The author should consider whether "Bi-directional" in the title is appropriate.

2. The author should discuss the performance impact of the feature compression block.

3. Figure 2 should be readjusted to avoid ambiguity.

4. The formulas and symbols in Chapter 3 need to be reorganized. For example: 
a) There is an error in formula (3).
b) The dimension of $F^{S}$ should be added to $R^{N \times K \times C \times H \times W}$, otherwise, formula (10) and $Avg$ in Figure 2 are not clear.
c) Formula (12) and formula (13) are misleading because $W_{qkv}$ is unclear.
d) Formula (2) and formula (3) are inconsistent with the description in Figure 2.
e) Many formulas and terms, such as $B(\cdot)$, need more explanations.

5. In ResNet-12, how is the middle layer set up?

6. There are many typos, such as BS-Net on line 216 and missing punctuation on line 906.

7. Consider adding the latest FS-FGIC articles and results for comparison, such as:

-  J. Wu et al., Bi-Directional Ensemble Feature Reconstruction Network for Few-Shot Fine-Grained Classification. TPAMI, 2024.

-  Z.-X. Ma et al., Cross-Layer and Cross-Sample Feature Optimization Network for Few-Shot Fine-Grained Image Classification. AAAI, 2024.