Title: Toward Re-Identifying Any Animal
Venue: NeurIPS 2023
Accepted: yes
Keywords: Re-identification

Category-generalizable
Abstract: The current state of re-identification (ReID) models poses limitations to their applicability in the open world, as they are primarily designed and trained for specific categories like person or vehicle. In light of the importance of ReID technology for tracking wildlife populations and migration patterns, we propose a new task called ``Re-identify Any Animal in the Wild'' (ReID-AW). This task aims to develop a ReID model capable of handling any unseen wildlife category it encounters. To address this challenge, we have created a comprehensive dataset called Wildlife-71, which includes ReID data from 71 different wildlife categories. This dataset is the first of its kind to encompass multiple object categories in the realm of ReID. Furthermore, we have developed a universal re-identification model named UniReID specifically for the ReID-AW task. To enhance the model's adaptability to the target category, we employ a dynamic prompting mechanism using category-specific visual prompts. These prompts are generated based on knowledge gained from a set of pre-selected images within the target category. Additionally, we leverage explicit semantic knowledge derived from the large-scale pre-trained language model, GPT-4. This allows UniReID to focus on regions that are particularly useful for distinguishing individuals within the target category. Extensive experiments have demonstrated the remarkable generalization capability of our UniReID model. It showcases promising performance in handling arbitrary wildlife categories, offering significant advancements in the field of ReID for wildlife conservation and research purposes.

Toward Re-Identifying Any Animal

Abstract

The current state of re-identification (ReID) models poses limitations to their applicability in the open world, as they are primarily designed and trained for specific categories like person or vehicle. In light of the importance of ReID technology for tracking wildlife populations and migration patterns, we propose a new task called 'Re-identify Any Animal in the Wild' (ReID-AW). This task aims to develop a ReID model capable of handling any unseen wildlife category it encounters. To address this challenge, we have created a comprehensive dataset called Wildlife-71, which includes ReID data from 71 different wildlife categories. This dataset is the first of its kind to encompass multiple object categories in the realm of ReID. Furthermore, we have developed a universal re-identification model named UniReID specifically for the ReID-AW task. To enhance the model's adaptability to the target category, we employ a dynamic prompting mechanism using category-specific visual prompts. These prompts are generated based on knowledge gained from a set of pre-given images within the target category. Additionally, we leverage explicit semantic knowledge derived from the large-scale pre-trained language model, GPT-4. This allows UniReID to focus on regions that are particularly useful for distinguishing individuals within the target category. Extensive experiments have demonstrated the remarkable generalization capability of our UniReID model. It showcases promising performance in handling arbitrary wildlife categories, offering significant advancements in the field of ReID for wildlife conservation and research purposes. Our work is available in https://github.com/JiaoBL1234/wildlife .

1 Introduction

Object Re-identification (ReID) aims to match identical target objects across non-overlapping camera views. Although existing ReID methods [7, 22, 24, 31, 33] have made the remarkable process, their identification capabilities heavily rely on their training benchmarks. This reliance often results in overfitting to base categories in the training set, e . g ., vehicle [22, 33] and person [7, 24, 31]. However, in open-world scenarios, the categories of target objects could be diverse. For example, ReID technologies are widely used toward animal objects for the purposes of analyzing population counts and migration patterns. To adapt these ReID methods to new animal categories, users have to compile large-scale datasets first. This process is time-consuming and poses a significant hurdle to the open-world applications of ReID methods. This limitation motivates us to propose a novel and practical task named 'Re-identify Any Animal in the Wild', which aims to construct a universal

†

Train

Unseen categories in open world

Inference

Training data in Wildlife-71

Support data

...

Probe

Retrieval

Zebra

Probe

Retrieval

Seal

...

Buzzard

Belgianhare

...

...

...

...

...

...

...

...

...

...

Medusa

...

...

...

id1

id2

id2

id1

id1

id2

id1

id2

Seal

Zebra

ReID model to break the category boundaries and handle unseen animal categories in the open world, as shown in Figure 1.

An effective universal ReID model must fulfill two essential criteria. Firstly, it should have sufficient knowledge learned from an extensive range of seen categories. Secondly, the model should possess the capability to transfer valuable knowledge from seen categories for distinguishing individuals within novel categories. The first criterion necessitates a training dataset that covers diverse object categories, multiple viewpoints, and abundant identities for each category. This ensures that the ReID model can acquire the necessary knowledge for generalization. Unfortunately, existing ReID benchmarks primarily focus on two categories, namely vehicle [22, 33] and person [7, 24, 31], which is insufficient for training a universal ReID model. The second criterion requires the ReID models to be able to adaptively transfer the knowledge learned from the training categories to new categories. It is crucial to recognize that not all the knowledge acquired from the base categories is applicable to the novel ones. For instance, the ReID model might be trained to consider color as a significant attribute for distinguishing many animals, such as dogs and cats. However, such a clue is ineffective or even counterproductive for differentiating chameleons.

In this work, we present the first attempt to construct a universal ReID model. To ensure that the ReID model meets the two aforementioned criteria, we first propose the Wildlife-71 dataset, which contains ReID data from 67 training (base) animal categories and 4 test (novel) categories. In our Wildlife-71 dataset, each training category contains an average of 29 identities, with each identity including approximately 56 images. Our Wildlife-71 dataset is collected from three sources: integrating existing datasets [21, 18, 16, 30, 15], extracting target bounding boxes from a large-scale tracking dataset GOT-10k [9], and crawling web videos to extract target bounding boxes using a tracking algorithm [32]. To the best of our knowledge, Wildlife-71 is the first ReID dataset encompassing multiple object categories. Moreover, in Wildlife-71, we provide a triplet of images (two from the same identity and another from a different identity) as support data for each category in the test set. These support data are pre-accessible for the ReID model to help it adapt to the novel categories. It is worth noting that a triplet image is the smallest unit for learning knowledge to distinguish between individuals, and it is readily available in real-world applications.

In addition to our dataset, we construct a novel universal ReID framework named UniReID. One of the major challenges we faced is equipping UniReID with the ability to select and apply appropriate knowledge for adapting to unseen categories. This step is non-trivial, as it requires the ReID model to both learn the knowledge about the target categories promptly and possess sufficient adaptability to the obtained knowledge. To accomplish this, we train our UniReID to efficiently acquire knowledge about target categories from dual-modal guidance, i . e ., visual and textual guidance, and use the knowledge to dynamically adapt to various categories. For visual guidance, we use a triplet of images within the target category to guide the adaption of our UniReID. Particularly, for each training category, we randomly sample an image triplet in training set as guidance, and for test categories, we adopt the given support data. Inspired by visual prompt tuning [10], which efficiently adapts deep models into novel tasks by injecting task-relevant prompt vectors, we employ a transformer layer to transfer the knowledge from the support images into a group of learnable vectors. These vectors then act as visual prompts, adapting our backbone model to the target categories. In addition to the implicit guidance from these visual prompts, we also derive semantic knowledge from a large-scale language model as explicit textual guidance. In this step, we construct a natural language template in the form of 'Please give me 4 short phrases describing visual clues beneficial for distinguishing [cls] individuals', where [cls] is the category label, to derive knowledge from the GPT-4 model [19]

in a question-answering manner. GPT-4 is a large-scale pre-trained language model with extensive knowledge and remarkable zero-shot question-answering capability. By this means, we could obtain textual descriptions of crucial clues for distinguishing individuals within the target category. Then, we utilize text-guided attention to help our model focus on these clues. As far as we know, this is the first work introducing knowledge from large-scale language models into ReID tasks. Leveraging such dual-modal guidance, our model could rapidly adapt to unseen categories in the open world.

2 Related Work

Tasks Related to Our ReID-AW : 1) Re-identification. Re-identification (ReID) aims to match the same object across different camera views. In recent years, numerous studies [24, 6, 31, 27, 22, 14] have been proposed and made significant progress. However, most existing ReID works are specifically designed for a specific category, such as person [6, 27] and vehicle [22, 14], which limits their generalization capability in real-world applications. For instance, many part-based methods [27, 6] assume that pedestrian instances always stand vertically, and they consequently partition pedestrian images into several horizontal stripes to extract local features from head to feet. Although these methods enhance pedestrian re-identification performance, they fail to handle other object categories, such as reptiles, that do not meet the vertically standing assumption. To address this limitation, we propose the ReID-AW task, which aims to design universal re-identification models capable of handling unseen wildlife categories.

2) Domain Generalizable Re-identification. To facilitate the application of ReID models, Song et al . [26] propose the domain generalizable ReID (DGReID) task, which aims to train a ReID model that can work well on unseen environments (domains). Many recent works [26, 12, 4] have been dedicated to addressing the influence caused by style divergence, e . g ., viewpoint, illumination, and season variation, across domains to help ReID models generalize to unseen domains. However, DGReID models only consider style divergence and environment variation but fail to account for changes in the target category, which still limits their application in the open world.

3) Few-shot Classification. Few-shot classification [25, 17] aims to train a robust classifier capable of achieving sufficient performance with a limited amount of support (labeled) data. Notably, there are two major differences between our ReID-AW task and few-shot classification. First, classification focuses on category-level distinctions, e . g ., distinguishing between elephant and mouse, where interclass divergence is considerably large. In contrast, in our ReID-AW, we aim to identify instances within the same category where inter-class divergence can be very subtle. This necessitates ReID models to dynamically adapt to target categories and capture their unique discriminative clues. Second, classification models need only to classify input images into a limited number of categories provided by the support data. As a result, many few-shot classification models [25, 17, 1] can achieve classification by directly calculating the feature similarity between support data and input images without specifically understanding these categories. In contrast, ReID models need to learn sufficient knowledge about the target category to support image matching across unlimited unseen identities.

Large-Scale Pre-trained Models. Recently, many works [19, 3, 23] have focused on leveraging abundant training data to endow deep models with comprehensive knowledge and robust representation capabilities. These models have shown their superiority in various vision-language tasks, such as zero-shot classification [23] and visual question answering [19]. For example, CLIP [23] is a lightweight vision-language model trained on 400 million image-text pairs using a contrastive learning strategy, achieving promising zero-shot classification performance. Furthermore, GPT-4 [19] is a robust NLP model trained with 300 billion words, providing it with comprehensive knowledge and remarkable question-answering capabilities. In this work, we aim to derive knowledge from these pre-trained models to facilitate the adaptation of ReID models toward novel categories.

Prompt Tuning. As large-scale pre-trained models have shown their superiority in many tasks, numerous works [3, 10, 13, 2] focus on transferring these models to downstream tasks. For example, Brown et al . [3] propose to use manually designed natural language templates to derive knowledge from the pre-trained language model, GPT-3, and adapt it to different tasks, such as translation and question-answering. Similarly, Jia et al . [10] propose to use a group of task-specific visual prompts to adapt pre-trained vision models to the downstream tasks. In this work, we go one step further to construct category-specific prompts to adapt ReID models to various target categories dynamically.

Visual Tokens

CLIP Text

Encoder

Unique facial markings

Varied body size

Distinctive ear shapes

Individual fur patterns

Transformer Layer

CLIP Image

Encoder

Textual Template

Textual Tokens

Visual Embeddings

c

1

Category-Specific

Prompts

Visual Guidance

Generator

MatMul

Scale

Softmax

MatMul

Text-guided Attn.

Self-Attn.

Norm

Q

MatMul

Scale

Softmax

MatMul

K

V

Q

K

V

3

Text-Guided

Attentive Module

Triplet Loss /

Inference

GMP

2

Textual Guidance

Generator

Learnable Vectors

Class =

[ Panda]

[ Zebra ]

[  Dog   ]

...

Please give me 4 short phrases

describing visual clues beneficial for

distinguishing [cls] individuals

Linear

Q

Linear

Linear

Linear

Linear

Linear

: Freeze

c

: Concatenation

GMP : Global max pooling

Attn.: Attention

Norm: LayerNorm

3 Approach

3.1 Problem Definition

In this work, we propose a novel task named 'Re-identify Any Animal in the Wild' (ReID-AW). This task seeks to develop a universal ReID model capable of re-identifying animals from unseen classes C u after being trained on multiple seen classes C s in our Wildlife-71 dataset. In ReID-AW, ReID models are trained with the training data D t = { D k } C s k =1 . D k = { ( x i , y i ) } N k i =1 is the labeled training data of the k -th category, where N k is the number of training images in this category. After being trained on D t , the ReID model is expected to effectively identify individuals belonging to arbitrary unseen class C u . Notably, C u and C s is non-overlapped, termed C u ∩ C s = ∅ . The statistics of our Wildlife-71 are given in Section 1 in the Appendix . In summary, Wildlife-71 includes 67 training (seen) categories and 4 test (unseen) categories. To help ReID models adapt to unseen categories, we provide a triplet of labeled images for each test category, serving as support data. The image triplet is denoted as { I a , I p , I n } , where I a and I p are images of the same identity, distinct from I n . ReID models are permitted to pre-access these support data for adaptation before commencing inference on the target categories.

3.2 Overall Framework

The overall framework of our UniReID is depicted in Figure 2. As illustrated in this figure, our UniReID model receives dual-modal guidance, i . e ., visual and textual guidance, to facilitate adaptation to the target category. The visual guidance consists of a triplet of images, from which our model could learn to identify and capture visual patterns that differentiate target individuals. For textual guidance, we directly derive explicit semantic knowledge from GPT-4 model to inform our model about the discriminative clues of the target category. There are three major components in our UniReID: 1) visual guidance generator , designed to transfer knowledge from a triplet of images into a group of category-specific visual prompts, which is used to adapt our model to the target category; 2) textual guidance generator , employed to derive semantic knowledge about the target category from the large-scale language model GPT-4; 3) text-guided attentive module , which uses the obtained semantic knowledge to adapt our model to the target category through an attention mechanism. These three modules will be introduced sequentially in the following subsections. Additionally, in our UniReID, we utilize the pre-trained CLIP [23] model as the backbone network for extracting features of both input images and textual guidance. This design is based on two key benefits. Firstly, CLIP

is extensively trained on a large number of images containing various object categories, thereby endowing it with robust pattern recognition capabilities that benefit generalization. Secondly, CLIP is trained on an image-text matching task, enabling its visual and textual encoders to naturally embed image and text inputs into a shared space, which greatly facilitates textual guidance over image features.

Category-specific Sampling Strategy. During the training stage, we employ a category-specific sampling strategy. Specifically, in each mini-batch, we only sample instances from a single randomly selected category for training. The rationale behind this strategy is that emphasizing cross-category distinguishing could guide ReID models to concentrate more on coarse-grained clues while ignoring fine-grained clues that are actually beneficial for intra-category identification.

3.3 Visual Guidance Generator: Category-specific Visual Prompts

The first component of UniReID is the visual guidance generator, designed to use a triplet of images to help our model adapt to target categories. In this step, we find that visual prompt tuning [10] could be an ideal solution, which could efficiently adapt deep models to the target task by introducing task-relevant prompt vectors. Inspired by this, we develop a set of category-specific visual prompts to help our model dynamically adapt to various target categories. Specifically, in our UniReID, we keep all the parameters of the CLIP model frozen (except for normalization layers) and inject it with a set of category-specific visual prompts, which are dynamically constructed based on a triplet of images. Here, for each training category, the image triplet is randomly sampled from the training set, and for test categories, we adopt the support data. As depicted in Figure 2 (1), in this module, we employ a transformer layer to transfer knowledge from the image triplet into a set of learnable vectors, which then serve as category-specific visual prompts. This design has two primary benefits. First, by keeping the parameters unchanged, we can prevent our model from overfitting to seen categories. Second, through the integration of category-specific visual prompts, we can transfer essential knowledge to our model, allowing it to adapt to target categories dynamically.

In practice, given the image triple of target category { I a , I p , I n } , we first send them into a convolutional encoder to extract their features { f a , f p , f n } with f ∗ ∈ R C,H,W . Here, C indicates channel dimension, and ( H,W ) represents spatial scale. Thereafter, we spatially flatten these features and concatenate them together in the flattened spatial dimension. The concatenated features are denoted as f tri. ∈ R C, (H × W × 3) , which is then used to construct category-specific visual prompts. In this work, we adopt deep prompt tuning [10]. This method requires us to construct a visual prompt vector for each layer of the CLIP's visual encoder, i . e ., ViT [5]. To do so, we use a transformer layer to transfer the knowledge from f tri. into a group of learnable vectors L = { l 1 , l 2 , ..., l n } , where l i ∈ R C, 20 is the prepared prompt vector for the i -th transformer layer, and n is the depth of visual encoder. This process could be written as,

where p i is the output visual prompts; Trans is the transformer layer; _ is the output of f tri. , which is discarded; [ , ] indicates concatenation operation.

By this means, we could obtain category-specific visual prompts P = { p 1 , p 2 , ..., p n } , where p i ∈ R C, 20 is the prompt vector for i -th layer of visual encoder. Then, we follow deep prompt tuning [10] to insert P into the CLIP's visual encoder to adapt it to the current category. Formally, we represent the input features of the i -th layer of encoder as { g i , h i 1 , h i 2 , ..., h i N } , where g i is the class token, H i = { h i 1 , h i 2 , ..., h i N } denote hidden features, and N is the length of image tokens. In this step, we first insert p i into the above feature sequence as { g i , p i , h i 1 , h i 2 , ..., h i N } . Then we send them into the i -th transformer layer of the visual encoder, which could be written as,

where Layer i is the i -th layer of visual encoder; _ is the output of p i , which is discarded. By this means, we could adapt our model toward the target category.

3.4 Textual Guidance Generator

In addition to employing implicit guidance from visual prompts, we acquire explicit textual knowledge from pre-trained language models to adapt our model to the target categories. To achieve this, we

design a textual guidance generator to derive explicit textual descriptions toward distinguishable clues of the target category from the GPT-4 model [19]. The GPT-4 is a large-scale pre-trained language model that possesses a wealth of semantic knowledge and boasts impressive zero-shot question-answering performance.

In this part, we follow the approach described in [3] to utilize a natural language template to extract knowledge from the GPT-4 model. Our template is constructed as 'Please give me 4 short phrases describing visual clues beneficial for distinguishing [cls] individuals,' where [cls] represents the class label of target categories, such as dog, cat, and panda. By populating this template and submitting it to the GPT-4 model, we obtain four textual phrases that outline visual clues useful for distinguishing individuals within the target category, as illustrated in Figure 2 (2). Once we obtain these phrases, we feed them into the textual encoder of the pre-trained CLIP model to extract their features. Notably, to preserve the knowledge of CLIP, we also keep the parameters of its textual encoder frozen. We then pass the extracted features through a trainable transformer layer to adapt them to the current task. The final output textual features are denoted as T = { t 1 , t 2 , t 3 , t 4 } , where t j ∈ R M,C represents the features of the j -th phrase and M is the sequence length. These textual features T are then employed as guidance to help our model focus on salient visual content through an attention-based module.

3.5 Text-guided Attentive Module

In this subsection, we aim to use the acquired textual features T = { t 1 , t 2 , t 3 , t 4 } as guidance to assist our model in focusing on visual content that is beneficial in distinguishing individuals within the target category. To accomplish this, we employ a text-guided attentive module to capture salient content over visual features. Formally, given the textual features t j of the j -th textual phrase and patch-level image features H = { h 1 , h 2 , ..., h N } extracted from CLIP, we first apply linear projection to both of these features as,

where FC Q , FC K , and FC V are fully connected layers used to embed textual and visual features; Q j , K , and V represent Query, Key, and Value. Then we use these vectors to calculate attention as,

where d is the channel dimension of K ; F j t is the enhanced feature under the guidance of the j -th textual phrase. Particularly, in this work, we follow the multi-head attention [28], partitioning the Q/K/V in the channel dimension into multiple groups and applying attention to each group independently according to Equation 4. Through this attention operation, we could use explicit textual guidance to help our model to focus on distinguishable visual clues. For intuitive examples of how textual guidance aids our model in capturing discriminative clues, please refer to Figure 2 in the Appendix . Thereafter, we replay Equation 3 and Equation 4 to apply a self-attention over the F j t , in which all the Q , K , and V are embedded from F j t . Finally, we use a global max pooling to extract the final feature F j ∈ R C , which is used for loss computation and inference.

Training and Test Procedure. During the training stage, we use these 4 phrase features to respectively guide the refinement of visual features and utilize refined visual features to calculate triplet loss independently. The objective function could be written as,

where L is the overall objective function of our UniReID; L tri indicates triplet loss; y is the ground truth. During the test phase, we concatenate these 4 refined visual features together for inference.

4 Experiments

4.1 Datasets and Implementation Details

Datasets. Wildlife-71 . In this work, we evaluate the generalization capability of our UniReID model on our constructed Wildlife-71 dataset. Briefly, our Wildlife-71 consists of 67 training wildlife

categories and 4 test wildlife categories, namely zebra [21], seal [18], giraffe [21], and tiger [15]. Each training category contains an average of 29 identities, with each identity including approximately 56 images. To further enrich the training data, we include a person ReID benchmark MSMT17 [30] and a vehicle ReID benchmark VehicleID [16] into the training set of Wildlife-71 as two additional object categories. During the evaluation phase, we evaluate our model on the four test (unseen) categories. For more details about Wildlife-71, please refer to Section 1 in the Appendix .

Implementation Details. We employ the pre-trained CLIP [23] as the backbone model. During the training and test stage, we resize all images into 224 × 224 . In the training phase, we adopt the category-specific sampling strategy, as mentioned in Section 3.2. Our model is trained for 20 epochs. The learning rate is initialized to 1 . 0 × 10 -4 and divided by 10 at the 15 th epochs. Random flipping, random erasing, and color jittering are employed for data augmentation. Two NVIDIA TITAN GPUs are used for model training. We employ the CMC [29] and mAP [34] metrics for evaluation. All ablation experiments in this section are repeated times, and the mean std results are reported.

4.2 Comparison between Our UniReID with Other State-of-the-art Algorithms

For fair comparisons, all methods (exclude our UniReID) compared on our Wildlife-71 are trained on base categories and then fine-tuned with the support data (a triplet of images) of each test category for 10 iterations.

Comparison on Wildlife-71. To evaluate the category generalization capability of our UniReID model, we compare it with other state-of-the-art ReID models on our Wildlife-71 dataset. In this part, we compare our UniReID with models designed for general ReID setting (ReID) and domain generalization ReID setting (DGReID), both of which concentrate on single-category object identification. Specifically, ReID aims to develop effective models that achieve optimal identification performance in a fixed environment, while DGReID aims to design robust models that perform well in unseen environments. The results are presented in Table 1, revealing two key findings. Firstly, on our Wildlife-71 dataset, DGReID methods only marginally outperform general ReID methods, with an average improvement of 1 . 1% in CMC-1. This observation suggests that re-identifying unseen object categories may be more complex than the DGReID task, requiring ReID models to adaptively accommodate the unique characteristics of novel categories rather than solely addressing style divergence across domains [12]. Secondly, we can find that our UniReID model considerably surpasses all compared methods, achieving an average 3 . 6% better performance in mAP than the best competitor, DTIN-Net [11]. This outcome implies that the dual-modal guidance employed in our model effectively enables dynamic adaptation to novel categories, leading to enhanced generalization capabilities.

4.3 Ablation Studies

To clarify, in the rest of this paper, the 'Baseline' indicates a CLIP model trained with visual prompt tuning [10] using static prompt vectors shared for all categories.

The Effectiveness of Our Designed Modules. To investigate the effectiveness of our proposed modules, i . e ., the category-specific visual prompts and the text-guided attentive module, we incrementally incorporate them into the 'Baseline' model and compare the performance improvements. The results, as presented in Table 2, show that both of our designed modules effectively help the ReID model to handle unseen categories. In comparison to the 'Baseline' model, our category-specific visual prompts ('Ada.Prompt') result in an average improvement of 1 . 1% in mAP. This enhancement sug-

(a) CMC results on ZEBRA

(b) CMC results on SEAL

(c) CMC results on GIRAFFE

(d) CMC results on TIGER

42.5

40.0

45.0

47.5

50.0

52.5

55.0

57.5

60.0

52.5

55.0

57.5

60.0

62.5

65.0

67.5

70.0

72.5

66.0

68.0

70.0

72.0

74.0

76.0

78.0

80.0

57.5

60.0

62.5

65.0

67.5

70.0

72.5

75.0

Prompt Tuning

PromptTuning

Prompt Tuning

一

Prompt Tuning

Ada.Prompt

Ada.Prompt

Ada.Prompt

Ada.Prompt

1

5

1

5

1

1

gests that these adaptive visual prompts are indeed capable of extracting knowledge from support data and tailoring our UniReID to the target category. Moreover, the text-guided attentive module leads to a further increase of 2 . 0% in mAP on average. This indicates that using explicit semantic guidance from large-scale language models could indeed help ReID models to capture the discriminative clues of novel categories.

The Effectiveness of Adaptive Prompt Tuning. In our UniReID, we use a set of category-specific prompts to adapt our model to novel categories dynamically. To evaluate the effectiveness of our adaptive prompt tuning strategy ('Ada.Prompt'), we compare it with other fine-tuning approaches, namely 'Full Tuning' that fine-tunes all parameters of CLIP, 'Norm Tuning' [8] that fine-tunes only normalization layers inside CLIP, and 'Prompt Tuning' that introduces a set of static prompts [10] shared for all categories on the basis of 'Norm Tuning'. The comparison results are given in Figure 3, from which we can get three important findings. Firstly, the 'Full Tuning' approach demonstrates the poorest performance among all strategies compared. This could be attributed to the fact that modifying the original parameters of CLIP leads to overfitting the base categories, thereby failing to generalize. Secondly, the performances of 'Norm Tuning' and 'Prompt Tuning' are roughly comparable. These results are expected, as the prompt vectors used in 'Prompt Tuning' are especially learned for the base categories, potentially limiting its generalization capabilities towards novel categories. Thirdly, it is evident that our implemented 'Ada.Prompt' outperforms all other compared approaches. This could indicate that our adaptive category-specific prompts indeed learn and transfer crucial knowledge to our model, enabling dynamic adaptation to target categories.

The Effectiveness of Textual Guidance. From the results in Table 2, we can find that our text-guided attentive module plays a pivotal role, contributing a significant improvement of 2 . 0% in mAP on average. However, a question may arise: does the model actually learn to utilize the textual guidance, or is this improvement simply the consequence of introducing attention modules? In response to this question, we evaluate the effectiveness of textual guidance from two aspects. 1) Firstly, we replace the textual tokens in our text-guided attentive module with a set of learnable vectors, thereby converting it into a self-attention module. The results, presented in Table 3, reveal that the selfattention module ('Self-Attn') only brings a 1 . 0% CMC-1 improvement to the model that does not use attention modules ('w/o Attn'). This could be attributed to the learnable vectors inside the self-attention module potentially overfitting the base categories, leading to poor generalization. This finding indicates that the superiority of our model does not solely stem from the additional attention module. 2) Secondly, we replace the textual guidance used in our text-guided attentive module with mismatched ones ('Mismatched'), e . g ., providing the zebra with monkey guidance, or with a general one ('General') in the format of 'Clue one, Clue two, ...'. Notably, for the mismatched guidance, we randomly select guidance from three different mismatched categories and averaged the

final results. The results, presented in the 'Mismatched' and 'General' rows of Table 3, show that unsuitable textual guidance causes substantial performance degradation compared to using matched ones ('Matched'), i . e ., 1 . 1% CMC-1 on average. This finding indicates that our model indeed learns to utilize semantic knowledge effectively. For intuitive cases of how textual guidance aids our model in capturing salient clues, please refer to Figure 2 in the Appendix .

Effect of Our Wildlife-71 Dataset. In this work, we construct the Wildlife-71 dataset, which comprises 71 wildlife categories. To the best of our knowledge, our Wildlife-71 dataset is the first ReID benchmark that encompasses multiple object categories. To evaluate the value of this diverse dataset, we compare the performance of our UniReID trained on Wildlife-71 with versions trained on two mainstream ReID benchmarks, namely VehicleID [16] and MSMT17 [30], which only contain pedestrian or vehicle instances. As depicted in Table 4, the performance of the UniReID model trained on the Wildlife-71 dataset surpasses the models trained on single-category benchmarks by a significant margin, achieving an average 12 . 8% CMC-1 improvement. Furthermore, compared to the version trained on both VehicleID and MSMT17, the UniReID model trained on Wildlife-71 also displays superior performance, with an increase of 11 . 4% in CMC-1. These results indicate that the diverse categories and the plentiful images in the Wildlife-71 dataset are indeed effective in endowing ReID models with sufficient knowledge for generalization. Therefore, we believe that our Wildlife-71 dataset is a right step toward constructing universal wildlife ReID models for open-world applications.

5 Conclusion

In this work, we propose a ReID-AW task, which aims to construct universal ReID models to handle unseen animal categories in the open world. To support the ReID-AW task, we construct a Wildlife-71 dataset, which contains ReID data from 71 wildlife categories. To the best of our knowledge, Wildlife71 is the first ReID dataset encompassing multiple object categories. Furthermore, we also design a UniReID model, which utilizes dual-modal guidance to dynamically adapt to novel categories. We hope our proposed task, dataset, and approach could support future research on category generalizable ReID methods.

Limitation. As pioneers in designing category-generalizable ReID models, our Wildlife-71 dataset only includes limited wildlife categories. In our future work, we will continually enhance our Wildlife-71 dataset and incorporate other categories and more instances.

Acknowledgements This work was supported by National Key R & D Program of China (No.2020AAA0106900), the National Natural Science Foundation of China (No.U19B2037), Shaanxi Provincial Key R & D Program (No.2021KWZ-03), and Natural Science Basic Research Program of Shaanxi (No.2021JCW-03).

References

Kelsey Allen, Evan Shelhamer, Hanul Shin, and Joshua Tenenbaum. Infinite mixture prototypes for few-shot learning. In Proc. Int. Conf. Mach. Learn. , pages 232-241, 2019.

Hyojin Bahng, Ali Jahanian, Swami Sankaranarayanan, and Phillip Isola. Visual prompting: Modifying pixel space to adapt pre-trained models. arXiv preprint arXiv:2203.17274 , 2022.

Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. In Proc. Advances in Neural Inf. Process. Syst. , pages 1877-1901, 2020.

Seokeon Choi, Taekyung Kim, Minki Jeong, Hyoungseob Park, and Changick Kim. Meta batch-instance normalization for generalizable person re-identification. In Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , pages 3425-3435, 2021.

Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In Proc. Int. Conf. Learn. Representations , 2020.

Shuting He, Hao Luo, Pichao Wang, Fan Wang, Hao Li, and Wei Jiang. TransReID: Transformerbased object re-identification. In Proc. IEEE Int. Conf. Comp. Vis. , pages 15013-15022, 2021.

Tianyu He, Xu Shen, Jianqiang Huang, Zhibo Chen, and Xian-Sheng Hua. Partial person re-identification with part-part correspondence learning. In Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , pages 9105-9115, 2021.

Xuehai He, Chunyuan Li, Pengchuan Zhang, Jianwei Yang, and Xin Eric Wang. Parameterefficient fine-tuning for vision transformers. arXiv preprint arXiv:2203.16329 , 2022.

Lianghua Huang, Xin Zhao, and Kaiqi Huang. GOT-10k: A large high-diversity benchmark for generic object tracking in the wild. IEEE Trans. Pattern Anal. Mach. Intell. , 43(5):1562-1577, 2019.

Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, and Ser-Nam Lim. Visual prompt tuning. In Proc. Eur. Conf. Comp. Vis. , pages 709-727, 2022.

Bingliang Jiao, Lingqiao Liu, Liying Gao, Guosheng Lin, Lu Yang, Shizhou Zhang, Peng Wang, and Yanning Zhang. Dynamically transformed instance normalization network for generalizable person re-identification. In Proc. Eur. Conf. Comp. Vis. , pages 285-301, 2022.

Xin Jin, Cuiling Lan, Wenjun Zeng, Zhibo Chen, and Li Zhang. Style normalization and restitution for generalizable person re-identification. In Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , pages 3143-3152, 2020.

Rabeeh Karimi Mahabadi, James Henderson, and Sebastian Ruder. Compacter: Efficient low-rank hypercomplex adapter layers. In Proc. Advances in Neural Inf. Process. Syst. , pages 1022-1035, 2021.

Ming Li, Xinming Huang, and Ziming Zhang. Self-supervised geometric features discovery via interpretable attention for vehicle re-identification and beyond. In Proc. IEEE Int. Conf. Comp. Vis. , pages 194-204, 2021.

Shuyuan Li, Jianguo Li, Hanlin Tang, Rui Qian, and Weiyao Lin. ATRW: A benchmark for amur tiger re-identification in the wild. In ACM Int. Conf. Multimedia , pages 2590-2598, 2020.

Hongye Liu, Yonghong Tian, Yaowei Yang, Lu Pang, and Tiejun Huang. Deep relative distance learning: Tell the difference between similar vehicles. In Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , pages 2167-2175, 2016.

Jinlu Liu, Liang Song, and Yongqiang Qin. Prototype rectification for few-shot learning. In Proc. Eur. Conf. Comp. Vis. , pages 741-756, 2020.

Ekaterina Nepovinnykh, Tuomas Eerola, Vincent Biard, Piia Mutka, Marja Niemi, Mervi Kunnasranta, and Heikki Kälviäinen. SealID: Saimaa ringed seal re-identification dataset. Sensors , 22(19):7602, 2022.

OpenAI. Gpt-4 technical report. arXiv preprint arXiv::2303.08774v3 , 2023.

Xingang Pan, Ping Luo, Jianping Shi, and Xiaoou Tang. Two at once: Enhancing learning and generalization capacities via IBN-Net. In Proc. Eur. Conf. Comp. Vis. , pages 464-479, 2018.

Jason Parham, Jonathan Crall, Charles Stewart, Tanya Berger-Wolf, and Daniel Ian Rubenstein. Animal population censusing at scale with citizen science and photographic identification. In AAAI Spring Symposium , pages 37-44, 2017.

Wen Qian, Hao Luo, Silong Peng, Fan Wang, Chen Chen, and Hao Li. Unstructured feature decoupling for vehicle re-identification. In Proc. Eur. Conf. Comp. Vis. , pages 336-353, 2022.

Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In Proc. Int. Conf. Mach. Learn. , pages 8748-8763, 2021.

Yongming Rao, Guangyi Chen, Jiwen Lu, and Jie Zhou. Counterfactual attention learning for fine-grained visual categorization and re-identification. In Proc. IEEE Int. Conf. Comp. Vis. , pages 1025-1034, 2021.

Jake Snell, Kevin Swersky, and Richard Zemel. Prototypical networks for few-shot learning. In Proc. Advances in Neural Inf. Process. Syst. , pages 4080-4090, 2017.

Jifei Song, Yongxin Yang, Yi-Zhe Song, Tao Xiang, and Timothy M Hospedales. Generalizable person re-identification by domain-invariant mapping network. In Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , pages 719-728, 2019.

Yifan Sun, Liang Zheng, Yi Yang, Qi Tian, and Shengjin Wang. Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline). In Proc. Eur. Conf. Comp. Vis. , pages 480-496, 2018.

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Proc. Advances in Neural Inf. Process. Syst. , pages 6000-6010, 2017.

Xiaogang Wang, Gianfranco Doretto, Thomas Sebastian, Jens Rittscher, and Peter Tu. Shape and appearance context modeling. In Proc. IEEE Int. Conf. Comp. Vis. , pages 1-8, 2007.

Longhui Wei, Shiliang Zhang, Wen Gao, and Qi Tian. Person transfer GAN to bridge domain gap for person re-identification. In Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , pages 79-88, 2018.

Mang Ye, Jianbing Shen, Gaojie Lin, Tao Xiang, Ling Shao, and Steven CH Hoi. Deep learning for person re-identification: A survey and outlook. IEEE Trans. Pattern Anal. Mach. Intell. , 44(6):2872-2893, 2021.

Yifu Zhang, Peize Sun, Yi Jiang, Dongdong Yu, Fucheng Weng, Zehuan Yuan, Ping Luo, Wenyu Liu, and Xinggang Wang. Bytetrack: Multi-object tracking by associating every detection box. In Proc. Eur. Conf. Comp. Vis. , pages 1-21, 2022.

Jiajian Zhao, Yifan Zhao, Jia Li, Ke Yan, and Yonghong Tian. Heterogeneous relational complement for vehicle re-identification. In Proc. IEEE Int. Conf. Comp. Vis. , pages 205-214, 2021.

Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, and Qi Tian. Scalable person re-identification: A benchmark. In Proc. IEEE Int. Conf. Comp. Vis. , pages 1116-1124, 2015.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper focuses on the ReID problem. Different from previous works, which mainly focus on persons and vehicles, the paper wants to reID any animals. To realize this, the authors construct a new dataset that contains many animals. Also, the authors propose a visual guidance generator, a textual guidance generator, and a Text-Guided attentive module. By integrating the power of CLIP and GPT4, authors claim that they achieve re-identifying \"ANY\" animals.\n\n20230808 Update: after checking the cons by other reviewers, I still lean to reject the paper. Kindly ask the fellow reviewers check what I concern, thanks.\n\nSTRENGTHS:\nPersonally, I appreciate the paper and spent a whole day on this paper as well as its supplementary. What makes me interested in this paper is the following:\n\n(1) The interesting idea: Re-Identifying Any Animal is very interesting and promising to me. It has more influence than Re-Identifying people and vehicles. I think it is a step to Re-Identifying Anything.\n\n(2) The good writing: I can fully understand what authors want to express by reading only once. The writing is smooth and excellent.\n\nWEAKNESSES:\nAlthough the paper is very interesting and the topic authors discussed is very promising, I feel there are some major concerns. Below please find my comments on these concerns. Please correct me if I’m wrong.\n\n(1) The very small dataset. Building a dataset is very admirable in the data-centric AI era. However, I think the significance of the dataset is limited. As you claim in the title, you want to reID any animals; however, in the Sup Table 1 and 2, there are only 2671 identities in your proposed dataset. The number of identities is even much smaller than Vehicle datasets (Vehicle) and Person datasets (MSMT17). In the ReID task, each identity is regarded as one class. To achieve \"ANY\", you should include a very large identity, for example, 267100. As expected, with such a small number, you can only freeze the CLIP backbone and use the GPT4 API (I see the fine-tuning results in worse performance). In conclusion, I doubt the value of the proposed Wildlife-71. What I expect is very large re-identifying datasets and the methods are ONLY trained on this to achieve \"ANY\". So here, my suggestion is to enlarge the dataset by 100 times to see what happens.\n\n(2) The proposed method lacks novelty. I understand that it is acceptable that the main contribution is a dataset, and the proposed method is just regarded as a strong baseline to benchmark the proposed dataset. However, as I stated in (1), I think the value of the dataset is limited. The method part is built on existing works: 1. The CLIP and VPT: you only regard the supported images as prompt to tune efficiently. It is not novel and easy to do. 2. The Text-Guided Attentive module: there are so many methods [1-3] used to aggregate the information from vision and languages. May I know what new insights are considering these prior works? So here, my suggestion is only to focus on the training method on your proposed dataset. The model pre-training on a huge dataset (assuming you have labeled one) is very attractive. I see you only have two TITAN GPUs; using at least 8*A100 is more suitable for the \"ANY\" task. Apologize in advance if you do not have this.\n\n(3) The insufficient and not convincing experiments. I know the paper contains a braving idea, however, I find: 1. With the help of the strongest GPT4 and CLIP, your method only archives about +3% over other famous ReID and DGReID methods (Table 1). In my opinion, the improvement is too marginal, even with the strongest tools. 2. I would suggest adding comparisons with existing aggregation modules [4-6], which is lacking in this paper. 3. The design of the visual guidance generator: I see that you compare the tuning method in your proposed methods and others. What I am concerned about is if there are any other kinds of visual generators. Do they perform well? 4. I am curious about how to select the supported images in the test process. Do you assume there are labels? Do you use any matching methods? Comparisons are needed. 5. During the test, you concatenate the 4 visual features. That incurs how much computing burden. Please show the inference speed compared with other methods. If the added computational cost is significant compared with the accuracy gain, it is hard to judge the advantage of this system. If the authors could add the suggested experiments it would be very good.\n\n[1] Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone.\n[2] Language As Queries for Referring Video Object Segmentation.\n[3] mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections.\n[4] Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models\n[5] Non-local Neural Networks (you may adapt it to utilize text)\n[6] A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolution\n\nQUESTIONS:\nSome small questions:\n\n1. What is the meaning of  \"20\" in Line 199?\n\n2. How many prompts are used in each ViT layer?\n\n3. Remove the Duke dataset due to its invasion of privacy.\n\n4. If you want to work on reID anything, how can you obtain the dataset? Have you considered the synthetic dataset?\n\n5. Do you think it is possible to reID any animals without any help from the tools like GPT4? I am charmed at Vision Foundation (Big) Models. I want you to focus on this.\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2023-06-13"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a new task called \"Re-identify Any Animal in the Wild\" (ReID-AW) which aims to develop a ReID model capable of handling any unseen wildlife category it encounters. To address this challenge, the authors created a comprehensive dataset called Wildlife-71, which includes ReID data from 71 different wildlife categories, and developed a universal re-identification model named UniReID specifically for the ReID-AW task. The authors employed a dynamic prompting mechanism using category-specific visual prompts to enhance model adaptability and leverage explicit semantic knowledge derived from GPT-4 to focus on regions useful for distinguishing individuals within the target category. UniReID showcases promising performance in handling arbitrary wildlife categories, offering significant advancements in the field of ReID for wildlife conservation and research purposes.\n\nSTRENGTHS:\n1. this paper presents a new generic task compared to object re-identification and provides a dataset including 71 categories.\n2. For this task, the authors propose a generic re-identification model that combines visual and textual features to re-identify categories that were not seen during model training.\n\nWEAKNESSES:\n1. The authors only evaluated performance comparisons in categories that had not been seen for the experimental evaluation. However, comparisons in seen categories, such as pedestrian versus other methods, are missing.\n2. Some minor issues： the authors do not list the contributions of this paper in an organized manner, and random erasing lacks corresponding citations [1].\n\n\n**Referecens**\n[1] Zhong Z, Zheng L, Kang G, et al. Random erasing data augmentation. Proceedings of the AAAI conference on artificial intelligence. 2020, 34(07): 13001-13008.\n\nQUESTIONS:\n1. The authors indicate that \" The rationale behind this strategy is that emphasizing cross-category distinguishing could guide ReID models to concentrate more on coarse-grained clues while ignoring fine-grained clues that are actually beneficial for intra-category identification\". The current understanding of intra-class and inter-instance differences in the ReID task is limited due to the previous studies' sole focus on the differences between classes. Hence, it remains unclear how these differences could affect the model's performance. Additionally, while the results indicate better performance on unseen classes, the accuracy of the model on the seen classes is questionable. Why not consider all variations within and between classes to gain a comprehensive understanding of the ReID task and improve the accuracy of the model? \n\n2. The authors give an interesting example in the introduction section using chameleons, why not use this as a test set or whether this dataset contains that category and how it performs in that category?\n\nThe authors have proposed an interesting task and I think the dataset is very important for re-identifying communities, however, there are some doubts about the construction of the dataset and the input on the method, I will raise the score if the authors have solved my problem well.\n\nLIMITATIONS:\nThis seems to limit the use of the model because the model must use a triplet as the visual input.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 4, "DATE": "2023-06-19"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a new task called “Re-identify Any Animal in the Wild” (ReID-AW) and created a comprehensive dataset called Wildlife-71 which is used to evaluate ReID-AW methods. Furthermore, the authors present a universal re-identification model named UniReID specifically for the ReID-AW task. This model receives dual-modal guidance, i.e., visual and textual guidance, to facilitate adaptation to the target category. Experimental results show that the UniReID model considerably surpasses all compared methods.\n\nSTRENGTHS:\n1) New task: The authors propose a practical task named “Re-identify Any Animal in the Wild” and first created a comprehensive dataset encompassing multiple object categories.\n\n2) Integration of new technologies: The method uses the large-scale pre-trained model and visual prompt tuning to improve model performance.\n\n3) comprehensive experiment: The method yields good performance under multiple settings.\n\nWEAKNESSES:\n**Major Concerns**\n1) My major concern is that the proposed framework is a good combination of many popular techniques:\n\n**a) GPT-4; b) multi-modal CLIP; c) Visual prompt tuning [10]; And focuses on a new dataset for d) animal re-identification.**\n\na) The using of GPT-4.0 seems just generate 4 fixed text phrases. This operation could be simply replaced by any other manual annotation or search engine including wiki.\nThe use of CLIP in b) and c) generation of visual prompts seems interesting while using the text guidance for visual features is quite common.\nFor d), there exist many datasets focusing on animals, including tigers, and fishes. Thus why not directly use these datasets considering they are relatively large? And I am still confused why this proposed method only focuses on animal re-identification, but not other objects including vehicles or human beings.  \n\n2) Some details are not clear to me. As a Re-identification task, how do the authors generate category-specific prompts for unseen ids? In my view, the unseen IDs during inference do not have learnable specific prompts for further tuning. This design seems unnatural in the re-identification task.\n\n3) There are also some other works using CLIP models for the same task of re-identification. This paper could incorporate it into a comparison or at least a discussion.\n\n[A] *Li, S., Sun, L., & Li, Q. (2023, June). CLIP-ReID: Exploiting Vision-Language Model for Image Re-identification without Concrete Text Labels. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 1, pp. 1405-1413).*\n \n**Other Minor Concerns**\n\n4) GPT-4 is not an open-source model. So the textual guidance generator relying on GPT-4 may be limited in its use.\n\n5)   The authors claim that“we aim to identify instances within the same category where inter-class divergence can be very subtle”. This statement is not clearly explained and the authors do not discuss this in detail.\n\nQUESTIONS:\nPlease refer to the weakness section. The authors could respond to why these techniques are necessary and why it is distinctive for only the animal re-identification task. Or the animal re-ID is just a newly-proposed setting to attract the research attention? I cannot see why this new setting is distinctive and should only be solved by these new techniques.\n\nDue to time limitations, the authors could focus on my major concerns as a matter of priority.\n\nLIMITATIONS:\nThe limitation is clearly stated after the conclusion. The authors claimed that they will incorporate other categories in future work, which is also one of  my major concern for this manuscript.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2023-07-02"}

comments: SUMMARY:
This paper extends common person or vehicle reid task to re-identifying any animal in the wild, called ReID-AW task. First, authors propose an animal reid dataset for the ReID-AW task that contains 71 different categories. Then, authors also propose a new method for this task to tackle specific challenges in this task. Specially, authors propose to use GPT-4 to generate prompts to guide attention learning on the visual embedding, which can inspire researchers in using GPT.

STRENGTHS:
This paper extends common person or vehicle reid task to re-identifying any animal in the wild, called ReID-AW task. First, authors propose an animal reid dataset for the ReID-AW task that contains 71 different categories. Then, authors also propose a new method for this task to tackle specific challenges in this task. Specially, authors propose to use GPT-4 to generate prompts to guide attention learning on the visual embedding, which can inspire researchers in using GPT.

WEAKNESSES:
--- In experiments, only “zebra”, “seal”, “giraffe”, and “tiger” are used as test set. It’s better to evaluate the performance by setting other categories as test sets.
--- Compared with other methods, the proposed method may suffer from the high computation complexity.

QUESTIONS:
--- What is the difference between using GPT-4 and other LLMs such GPT-3 or llama?
--- If some instances change along with time, will the model work?
--- Authors use 4 test prompts in this method. Will using more prompts works better?
--- Will the model trained on Wildlife-71 work well on person reid dataset?

LIMITATIONS:
See Weakness
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2023-07-07