Toward Re-Identifying Any Animal
Authors: Bingliang Jiao
Lingqiao Liu
Liying Gao
Ruiqi Wu
Guosheng Lin
PENG WANG
Yanning Zhang
Venue: NeurIPS 2023 poster (2023)
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
Bingliang Jiao 1,2,3,5 Lingqiao Liu Liying Gao 1,2.3 G.
Ruiqi Wu 1,2,3
Guosheng Lin Peng Wang Yanning Zhang1.2.3
School of Computer Science, Northwestern Polytechnical University, China
Ningbo Institute, Northwestern Polytechnical University, China
National Engineering Laboratory for Integrated Aero-Space Space-Ground Ground-Ocent China
4 The University of Adelaide, Australia
Nanyang Technological University, Singapore
Abstract
The current state re-identification (ReID) models poses limitations to their applicability in the open world, they are primarily designed and trained for specific categories like person or vehicle. In light of the importance of RelD technology for tracking wildlife populations and migration patterns, propose a new task called "Re-identify Any Animal the Wild" (RelD-AW). This task aims to develop RelD model capable of handling any unseen wildlife category it encounters. To address this challenge, we have created 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, model named UniRelD specifically for the RelD-AW task. To enhance the model's s adaptability the target category, we employ dynamic prompting mechanism using -ptegory-specific visual prompts. These prompts are generated based on knowledge gained from set of pre- pre-given images within the target category. Ad- ditionally, we leverage explicit semantic knowledge derived from the large-scale pre-trained language model, GPT-4. This allows UniRelD focus on regions that are particularly useful for distinguishing individuals within the target cate- gory. Extensive experiments have demonstrated the remarkable generalization capability of our UniRelD model. It showcases promising performance in han- dling arbitrary wildlife categories, offering significant advancements in the field of ReID for wildlife conservation and research purposes. Our work is available in https: https://github.. wtt.iihhb.ccmmmaaaaiiii333 http:///miiiii./////iiii...
have developed universal re-identification
Introduction
Object Re-identification (ReID) aims to match identical target objects across non-overlapping camera views. Although existing RelD methods 77] 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 [ [24] 31). However, in open-world scenarios, the categories Rell technologies widely used toward animal objects for the purposes of analyzing population counts and migration patterns. To adapt these RelD methods to new animal categories, users have compile large-scale datasets first. This process is time-consuming and poses significant hurdle to the open-world applications of ReID methods. This limitation motivates us to propose novel and practical task named "Re-identify Any Animal in the Wild", which aims to construct universal
target objects could be diverse. For example,
tCorresponding outhor, bingliang.jiet jiao@ mail. www.ueew.uu..om beng.wange nwpu.edu.cn
37th Conference on Neural Information Processing Systems (NcurIPS 2023).

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Training data Wildlife-1
Support data
Unseen categories in open world Zebra
Koala
Buzzard
Zebra
Seall
Medusa
Belgianhare
Seal
Train
Inference
Universal RelD Model
Figure 1:
The sketch of the ReID-AW task, which aims to train a RelD model able to handle any animal category under the guidance of triplet of images within the target category.
Rell) model to break the category boundaries and handle unseen animal categories in the open world, as shown in Figure D
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 77] 24 31, which is insufficient for training 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 clue is ineffective or even counterproductive for differentiating chameleons.
In this work, we present the first attempt to construct a universal RelD model. To ensure that the RelD 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 [5), extracting target bounding boxes from a large-scale tracking dataset GOT- 10k [. and crawling web videos to extract target bounding boxes using a tracking algorithm 332. To the best of our knowledge, Wildlife-71 is the first ReID dataset encompassing multiple object categories. Moreover, in Wildlife-71, 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. 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.
provide triplet of images (two from the
In addition to our dataset, we construct a novel universal ReID framework named UniRelD. One of the major challenges faced is equipping UniRelD 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 UniRelD 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 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
2

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in prestion-ansseenen manner. GPT-4 is a large-scale pre-trained language model with extensive knowledge and remarkable zero-shot question-ansee 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 RelD tasks. Leveraging such dual-modal guidance, our model could rapidly adapt to unseen categories in the open world.
2 Related Work
Tasks Related 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 Rell works
are specifically designed for specific category, such as person 0 27] and vehicle 22 14 which
limits their generalization capability in real-world applications. For instance, many part- 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- 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- Re-identification. To facilitate the application of Rell) models, Song er al. (26 propose the domain generalizable ReID (DGRelI) task, which aims to train a RelD model that can work well on unseen environments (domains). Many recent works (26] 12 have been dedicated to addressing the influence caused by style divergence, e.g., viewpoint, illumination, and season variation, across domains to help RelD 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.
aims to train a robust classifier capable of 3) Few-shot Classification. Few-shot classification 25 17
achieving sufficient performance with 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-leve distinctions, e.g., distinguishing between elephant and mouse, where inter- class divergence is considerably large. 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 abundant training data to endow deep models with comprehensive knowledge and robust represen- tation capabilities. These models have shown their superiority in various vision-language tasks, such as zero-shot classification
have focused on leveraging
(23 and visual question answering [19] For example, CLIP 23 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 -answornngg capabilities. In this work, we aim to derive knowledge from
and remarkable these pre-trained models to facilitate the adaptation of ReID models toward novel categories.
Prompt Tuning. As large-scale pre- pre-trained models have shown their superiority in many tasks, B3 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 drestion-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 dategory-specifii prompts to adapt ReID models to various target categories dynamically.
numerous works 3
I0
3

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F1
Altentive Macide
CLIP Image Encoder
GMP,
Triplet Loss Inference
CategnrySpeefft
Self-t21
Tettal Tokens el
Textual Template
Transformer
Transformer Leyer
Please give short phrases describing visual clues beneficial for distinguishing [cls] individuals
Embbaddings
CLIP Text Encoder
Visad Gairlene
Geneator
Cony.
GPT-4
Zebra [aadda]
Uniqque markings
Varied body siz Disonctive car shapes Individual for patterns
foo0
Dog
G Concatenation
LayerNorm
GMP: Global pooling
Figure 2: The sketch of our UniRelD model. We use pre-trained CLIP the backbone model. In our UniRelD, the visual guidance generator is responsible for utilizing a triplet of images to construct category-specifie visual prompts. The textual guidance generator is designed to derive semantic knowledge about the target category from GPT-4 model. The obtained semantic knowledge is then used to guide our UniRelD focus salient visual content via the text-guided attentive module.
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 after being trained on multiple seen classes C in our Wildlife-71 71 dataset. In RelD-AW, RelD) models are trained with the training data D {D*)C, D* { } is the labeled training data of the k-th category, where N₂ is the number of training images in this category. After being trained on Dt. the RelD model expected to effectively identify individuals belonging to arbitrary unseen class C Notably, C and C* is non-overlapped, termed ( n = Ø. 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, provide a triplet of labeled images for each test category, serving as support data. The image triplet is denoted as {La, In, J. where I. and Ip are images of the same identity, distinct from I,. RelD models are permitted to pre-access these support data for adaptation before commencing inference on the target categories.
Overall Framework
The overall framework of our UniRelD is depicted in Figure 2 As illustrated in this figure, our UniRelD model receives dual-modal guidance, i.e., visual and textual guidance, to facilitate adaptation 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 UniRelD: 1) visual guidance generator, designed to transfer knowledge from triplet of images into a group of category-specd speciiic visual prompts, which is used to adapt our model to the target category; 2) textual guidance generator, employed derive semantic knowledge about the target category from the large-scale scale language model GPT-4; 3) text- text- suidgg attentive module, which uses the obtained semantic knowledge to adapt our model to target category through an attention mechanism. These three modules will be introduced sequentially in the following subsections. Additionally, in our UniRelD, 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

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is extensively trained on large number of images containing various object categories, thereby endowing 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-speciffe Sampling Strategy. During the training stage, we employ category-specific sampling strategy. Specifically, in each mini-batch, we only sample instances from 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 surse-grained clues while ignoring fine-graing fine-grinedd clues that are actually beneficial for intra-category identification.
3.3
Visual Guidance Generator: Category-speciffe Visual Prompts
The first component of UniRelD is the visual guidance generator, designed to use a triplet of images to help our model adapt target categories. In this step, 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 of category-specific visual prompts to help our model dynamically adapt to various target categories. Specifically, in our UniRelD, we keep all the parameters of the CLIP model frozen (except for normalization layers) and inject it with a set of category-specinf 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 (()) 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-specing 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 -apegory--secific 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 {Ta, Ip, In], we first send them into a convolu- tional encoder to extract their features (fa- Jp: fn) with f. E C.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 firs. E ( x W which is then used to construct
visual prompts. In this work, we adopt deep prompt tuning [TO]. This method requires to construct visual prompt vector for each layer of the CLIPP's visual encoder, i.e., ViT [5). To do so, we use a transformer layer to transfer the knowledge from ftri. into group of learnable vectors L (1' 12, "i.,....,"", 1"), where I E RC220 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,
Ip' [ = Trans([1', ferr.).
(1)
where p¹ is the output visual prompts; Trans is the transformer layer: is the output of feri, which is discarded; [.] indicates concatenation operation.
By this means, we could obtain category-specific visual prompts P {p' p², ..., p" where p' E 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 s visual encoder adapt to the current category. Formally, we represent the input features of the i-th layer of encoder as {g' hf. hy.... h'y), ), where g is the class token, H = [hi, Mi,....,1,,, h } denote hidden features, and N is the length of image tokens. In this step, we first insert p' into the above feature sequence as [g': p², hi, h22 ..., h }. Then we send them into the i-th transformer layer of the visual encoder, which could be written as,
Ig 1+1
H '1 = Layer'( ,)'"
(2)
where Layer' is the i-th layer of visual encoder; is the output of p², 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-traine treitrained language models to adapt our model to the target categories. To achieve this, we
5

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design a textual guidance generator to derive explicit textual descriptions toward distinguishable clues of the target category from the GPT-4 model [9]. The GPT-4 is a large-scale pre-trained language model that possesses a wealth of semantic knowledge and boasts impressive zero-shot performance.
question-answert
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 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 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). Once we obtain these phrases, we feed them into the textual encoder of the treitrained 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', x2, 13, {²), where 1 E 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-base module.
3.5 Text-guided Attentive Module
In this subsection, we aim to use the acquired textual features T = (t,,,,, as guidance to assist 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 & of the j-th textual phrase and patch-level image features = h2,,..,,yy extracted from CLIP, we first apply linear projection to both of these features as,
Q'=FC K (H), V= (H),
where FC?, FC* and FC are fully connected layers used to embed textual and visual features; Q², K, and V represent Query, Key, and Value. Then we use these vectors to calculate attention as,
Q²KT KTT
Affinity: S Softmax(
Vd )
Aggregation: Fi F VV,
where d is the channel dimension of K; F 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 H/// in the channel dimension into multiple groups and applying attention to each group independently according to Equation 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/, in which all the Q. K, and V are embedded from B. Finally, we use a global max pooling to extract the final feature Fi IRC, which is used for loss computation and inference.
Through this attention operation, we could use explicit
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,
L L=
Σ Lir(Fi)y))
(5)
where L is the overall objective function of our UniRelD; Lez: indicates triplet loss; y is the ground truth. During the test phase, we concatenate these 4 refined visual features together for inference.
Experiments
4.1 Datasets and Implementation Details
Datasets. Wildlife-71. In this work, we evaluate the generalization capability of our UniRelD model on our constructed Wildlife-71 dataset. Briefly, our Wildlife-71 consists of 67 training wildlife

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Table 1: Comparison results of our UniRelD and other ReID methods under the RelD-AW setting.
"ReID" and "DGReID" indicate models designed for general and domain generalization RelD settings.
Type
Method
Reference
Zehra
CMC-1
mAP
Scal
CMC-L
mAP
Ginflle
CMC-1
mAP
Tiger
CMC-1
AVG
CMC-1
TransKelD 6
ICCV22II
26.3
15.3
19.7
50.3
12.2
67.1
62.8
02.0
31.3
50.2
ICCV2021
41.0
AGW +1
TPAM12021
24.0
40.1
20.8
55.9
42.7
68.8
58.5
59.5
37.3
56.0
IRN-Nel 20
HOCV2018
26.2
42.6
25.2
68.0
44.0
67.5
60.5
61.5
39.0
57.4
DGRaID
SNR
12
CVPR2020
25.5
42.4
24.1
55.0
42.5
67.0
61.5
63.4
38.4
57.0
MetaBIN
CVPR2021
26.4
45.6
23.0
56.7
42.6
69.3
61.2
62.0
38.5
D8.4
DTTIN-Ner 11
POCV2022
26.0
43.2
25.2
57.3
43.5
60.8
61.9
61.8
30.2
58.0
KelD.AW
UniKell
29.0
48.7"
28.5"
62.6"1
46.5"
73.9"
66.7
65.2"
42.8
62.6
categories and 4 test wildlife categories, namely zebra [21]. seal [18), giraffe [21), and tiger [[5] 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 RelD benchmark MSMT17 [30] and vehicle RelD benchmark VehiclelID [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 x 224. In the training phase, we adopt the enegory-specifie sampling strategy, as mentioned in Section 3.2 Our model trained for 20 epochs. The learning rate is initialized to 1.0 x 10 and divided by 10 at the 15th 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 meanstd std results are reported.
4.2 Comparison between Our UniRelD with Other State-of-the-art Algorithms
For fair comparisons, all methods (exclude our UniRelD) compared on our Wildlife-71 are trained on base categories and then fine-tuned with the support data triplet of images) of each test category for 10 iterations.
Comparison Wildlife-71. To evaluate the category generalization capability of our UniRelID 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 fixed environment, while DGReID aims to design robust models that perform well in unseen environments. The results are presented in Table [] 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 RelD) models to adaptively accommodate the unique characteristics of novel categories rather than solely addressing style divergence across domains [12]. Secondly, can find that our UniRelD model considerably surpasses all compared methods, achieving an average 3.6% better performance in mAP than the best competitor, DTIN-Net (T]] 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 mod- ules, i.e., the category- -ppecffc visual prompts and the text- gexteg 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 RelD model to handle unseen categories. In comparison to the "Baseline" model, our category-specific visual result in an average improvement of 1.1% in mAP. This enhancement sug-
prompts
7

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Table 2:
Ablation experiments of the designed components, The "Visual Prompt Mode" indicates which kind of prompt vectors are used in the current model. "Static" and "Adaptive" represent static prompts shared for all categories and our saiegory-specine prompts, respectively. We can find that both of our designed modules, namely category-specific prompts "Adaa.rrmpp"" and text- text-guided attentive module ("Textual Attention"), effectively help the RelD model to handle unseen categories.
Visual Prompt
Texmal Attention
Zébra
Seal
Girafe
Tiger
AVG
Mode
mAP
CMC-1
mAP
CMC-1
mAP
CMC-1
MAP
CMC-1
MAP
CMC-1
Baseline
Stutic
26.6
43.80.
26.204
58.2
13.8%
68.4
62.121
60.3%
39.7
57.7
Ada Prosupt UniReID
Adaptive
x
27.3%
15.4%
26.907
59.7%
39.79.
15.6
70.107
63.5
60.71
10.8
59.1
Adaptive
29.6
78.7
28.5
62.6
46.5%22
73.9"7
66.7
65,2
42.8
62.6
60.0
12.5
57.5
70.0
80.0
67.5
78.0
72.5
52.5
65.0
76.0
70.0
50.0
62.5
74.0
45.0
47.5
Nut Tuning
Nom tining
57.5
60.0
F.F Turing
Nomn Lanin
72.0
70.0
Full Turing
Korm Tining
65.0
62.5
Fut Tuning
torm Turing
42.
Prongt Turing
55.0
Prompt uninn
68.0
Prompe luning
Pronngt Tuming
Ada.hoomp
52.5
Adi.rommat
65.0
57.5
Ada Prompe
[A] CMC results on 7FBRA
(b) CMC on SEAL
16) on GIRAFFÈ
(d) CMC results TIGER
Figure 3: Ablation of our adaptive category-specific prompts Promp"", CMC-1 to CMC-5 results on 4 test categories are given. We can find that our category-specifi prompts tuning strategy outperforms all compared fine-tuning approaches.
gests that these adaptive visual prompts are indeed capable of extracting knowledge from support data and tailoring UniRelD 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 UniRelD, 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.Prompl 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 set of static prompts [TO shared for all categories on the basis of "Norm Tuning". The comparison results are given in Figure 3 from which 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 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 evident that our implemented "rompttompt" outperforms all other compared approaches. This could indicate that our adaptive category-specilf prompts indeed learn and transfer crucial knowledge to our model, enabling dynamic adaptation to target categories.
we compare it with other tine-tuning approaches,
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, 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 set of learnable vectors, thereby converting it into a self-attention module. The results, presented in Table 3 reveal that the self- attention module ("Self-Attn") only brings a 1.0% CMC-1 improvement 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 our text-guided attentive module with mismatched ones ("Mismatchedd") e.g., providing the zebra with monkey guidance, or with a general one ("General") the format of "Clue one, Clue two...." ... Notably, for the mismatched guidance, randomly select guidance from three different mismatched categories and averaged the
the model that does
8

---

Table 3: Ablation experiments of textual guidance. The "Self-Attn" is the version that replaces our text-guided attentive module with a self-attention module. The "Mismatched" indicates the version replaces the textual guidance used in our text-guided attentive module with mismatched ones, e.g,, providing the zebra with monkey guidance. The "General" is the version that replaces the textual guidance with group of general phrases like "Salient clue one, ..... We can find that our UniRell) with matched guidance ("Matched") outperforms all variations, which indicates our model could make good use of semantic knowledge adapt novel categories.
Textual
Zebra
Seal
Girafte
Tiger
AVG
Guidance Mode
mAP
CMC-1
mAP
CMC-1
mAP
CMC-1
mAP
CMC-1
mAP
CMC-1
w/o Attn
27.363
45.40.3
26.9h.
59.70.4
15.6
70.4%.
63.5
60.714
40.8
59.1
SelF-Art
28.70.1
47.0%.
27.40.2
60.2%.4
45.5%.1
70.5%
65.2
62.6
41.7
60.1
Mismatched
29.1 u
47.40
27.8
61.2"a
46.0".
72.6
65.7u3
64.3"2
42.2
61.4
UniRelD
General
29.2
47.6%.1
28.20.4
61.49.
46,20.1
73.19.
65.994
64.399
42.4
61.6
Matched
29.6
48.7
28.3
62.6
46.3
73.9
06.7
65.2
42.8
62.6
Table 4: Comparison results of our UniRelD model trained on different datasets, namely vehicle RelD dataset VehicleID, person RelD dataset MSMT17, and our Wildlife-71 1 dataset. The results indicate that our Wildlife-71 dataset significantly enhances the performance compared to single-category
benchmarks.
Zebra
Seal
Giraffe
Tiger
AVG
Training Set
mAP
CMC-1 1
mAP
CMC-1
mAP
CMC-1 1
mAP
CMC-1
mAP
CMC-1
MSMT17 (M) [30
23.7th 706
40.5
19.8
45.9*.
39. 39.11-a 1
65.70.4
45.9
48.20.3
32.1
50.1
VchicleID (V) 16
21.11.
35.31.7
23.71.2
55.61.
36.40.
59.5%.1
44.6%.
47.00.3
31.5
49.4
M&V
24.0%.
39.9
21.8".7
51.40.1
39.51.2
63.4
47.40.4
49.9
33.2
51.2
Wildhife-1
29.6
48.7
28.5
62.6
46.5
73.9
66.7".
65.2
42.8
62.6
final results. The results, presented in the "Mismatched" and "General" rows of Table 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 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 the first RelD benchmark that encompasses multiple object categories. To evaluate the value of this diverse dataset, we compare the performance of our UniRelD 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 UniRelD 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 both VehicleID and MSMT17, the UniRelD 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 RelD models with sufficient knowledge for generalization. Therefore, we believe that our Wildlife-71 dataset is right step toward constructing universal wildlife RelD 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 Wildlife-71 dataset, which contains ReID data from 71 wildlife categories. To the best of our knowledge. Wildlife- 71 is the first ReID dataset encompassing multiple object categories. Furthermore, we also design UniRelD model, which utilizes dual-modal guidance to dynamically adapt to novel categories. We hope our proposed task, dataset, and approach could support future research category generalizable ReID methods.
Limitation. As pioneers in designing only includes limited wildlife categories. In our future work, we will continually enhance our Wildlife-7l dataset and incorporate other categories and more instances.
ReID models, our Wildlife-71 dataset

---

Acknowledgements This work was supported by National Key R&D Program of China AN..2020AAA01069000 the National Natural Science Foundation of China (No.U19B2037), Shaanxi Provincial Key R&D Program (No.2021 and Natural Science Basic Research Program of Shaanxi (No.2021JCW-033.
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12


Reviews:
[{"review_id": "qgcMp3xVnq", "reviewer": "Reviewer_9xKB", "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.", "questions": "--- What is the difference between using GPT-4 and other LLMs such GPT-3 or llama?\n--- If some instances change along with time, will the model work?\n--- Authors use 4 test prompts in this method. Will using more prompts works better?\n--- Will the model trained on Wildlife-71 work well on person reid dataset?", "limitations": "See Weakness", "rating": 7, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "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.\n--- Compared with other methods, the proposed method may suffer from the high computation complexity.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "1. **Evaluate the performance on other categories.**\\\n   Thank you for your suggestion. To extend our test set, we have further incorporated new instances from two seen categories, *i.e.*, alligator and morgan, as well as from an unseen category named chameleon. In these three categories, our UniReID achieves CMC-1 accuracy of 71.2%, 55.8%, and 68.9%. Compared with the top-performing competitor, DTIN-Net [A], which respectively achieves 67.8%, 52.7%, and 63.3% CMC-1 accuracy, our UniReID markedly outperforms, which indicates its superiority.\n2. **Comparison of computation complexity with other methods.**\\\n    When compared with the TransReID [B] method, which uses the same ViT backbone as our UniReID, the floating-point operations in our model increase only by 1.4 GMACs (19.7 v.s. 18.3). Additionally, the time taken to process and infer a single query image (tested on the Zebra dataset) shows a minor increase of 0.002 seconds (0.044 v.s. 0.046), which we consider to be acceptable. It is important to highlight that for a specific target category, both visual and textual prompts are consistent across all input images of that category. This means their features could be calculated only once and can then be reused for all test images. Apart from this, the additional computational consumption of our UniReID model merely stems from the text-guided attentive module.\n3. **The difference between using GPT-4 and other LLMs such GPT-3 or llama.**\\\n   As one of the most advanced language models,  the GPT-4 is trained with more data than GPT-3 and llama. Nonetheless, it is crucial to note that the superiorities of our UniReID do not depend solely on the usage of GPT-4. In fact, replacing the GPT-4 to GPT-3, our UniReID only suffers average 0.3% mAP degradation (42.5 v.s. 42.2).\n4. **If some instances change along with time, will the model work?**\\\n   Our UniReID possesses the capability to adapt to varying inputs. Crucially, UniReID can adapt to instances from any specific target category via a fully forward process, guided by the provided visual and textual prompts, without any need for fine-tuning. Consequently, if target categories and instances change over time, a mere modification of the visual and textual prompts could adapt our model accordingly. This adaptability is a significant advantage of our UniReID model.\n5. **Will using more text prompts works better?**\\\n   To respond to this issue, we increase the number of textual prompts to 6 and 8, respectively, and utilize them to train our UniReID. From the results, we find that using 6 and 8 prompts could only bring an average 0.3% and 0.2% mAP improvement to our UniReID. Upon analyzing the generated prompts, we observed that compelling GPT-4 to produce an excessive number of prompts could potentially cause them to concentrate on less distinctive clues, which can hardly result in performance improvement.\n6. **Will the model trained on Wildlife-71 work well on person reid dataset?**\\\n   To address this concern, we evaluated our UniReID model, trained on the Wildlife-71 dataset, using the test set of the Market-1501 dataset. Compared to the UniReID model trained under the person ReID dataset (details are given in Section 2 of the Appendix), the model trained on Wildlife-71 shows a 29.2% decrease in CMC-1 performance (72.6 v.s. 43.4). This decline could be attributed to the significant appearance and semantic differences between wildlife and pedestrians. The knowledge accumulated in Wildlife-71, such as patterns of fur, feathers, or horns, could hardly generalize to pedestrian instances within Market-1501.\n\n\n[A] Jiao BL, et al. Dynamically transformed instance normalization network for generalizable person re-identification. ECCV, 2022.\\\n[B] He ST, et al. TransReID: Transformer-based object re-identification. ICCV, 2021.\n"}, {"review_id": "Ee1wxOdtCT", "reviewer": "Reviewer_LLxx", "summary": "This 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.", "questions": "Please 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.", "limitations": "The 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.", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1) 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. ", "weaknesses": "**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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "1.**Why techniques are necessary and why it is distinctive for animal**\\\nSorry for causing confusion. Here, we need to clarify that the techniques encompassed within our model are not designed to fit animal objects but address the primary challenge of the ReID-AW task, i.e., identify instances within unseen categories. It is worth noting that existing ReID methods are primarily designed and trained to handle instances within a specific category. For instance, a tiger ReID model [B] could only identify tiger instances. In this work, we attempt to transcend this category boundary and train a universal ReID model on limited seen animal categories that can generalize to unseen animal categories in the wild. To the best of our knowledge, research on such cross-category ReID remains an unexplored domain and presents a considerable challenge. Besides, we need to clarify that while our evaluation primarily centers on animal ReID, the method we present holds inherent potential for extension to a broader array of cross-category instance-level retrieval (CCIR) challenges. The foundational elements established in our approach offer two pivotal insights that can be harnessed for analogous CCIR scenarios: first, the utilization of category-adaptive representations, and second, the incorporation of text-guided representations to integrate pre-existing category-related knowledge from large language models (LLMs). \n\n2.**Combination of popular techniques**\\\nPlease note that our model is not merely a naive combination of existing techniques; instead, it is carefully constructed based on our two key insights. Specifically, our first insight is to incorporate textual guidance from LLMs into the ReID model, empowering it with adequate knowledge to facilitate generalization. As R@9xKB also notes, this solution could inspire future research using LLMs. The second insight is to empower the ReID model with sufficient adaptability to efficiently adapt accumulated knowledge to handle unseen target categories. We believe that these two insights could not only address the ReID-AW but also be harnessed for analogous CCIR scenarios. \\\nRegarding some specific concerns, we address them below.\n\n1) GPT-4 and multi-modal CLIP: Leveraging textual knowledge from GPT-4 is non-trivial. Unlike the visual guidance widely used in existing ReID models, e.g., keypoints [B], this textual guidance cannot be directly applied to image features due to the inherent modality gap.\nTo alleviate this gap and ensure that textual prompts can accurately associate with visual clues without explicit semantic annotations (e.g. segmentation maps), we propose to employ the CLIP model, adeptly trained to bridge visual and textual elements, embedding them into a shared hidden space for the subsequent attention operation.\n\n2) Animal ReID dataset: Existing animal ReID datasets focus solely on a few categories. The limited knowledge contained in these datasets is insufficient to support the training of a universal ReID model that could generalize to unseen animal categories. Therefore, we construct a more comprehensive dataset Wildlife-71 which contains 71 different animal categories to train the universal ReID model. For the existing animal datasets, i.e., Zebra, Seal, **Tiger**, and Giraffe, we use them as test sets to evaluate the category generalization capability of ReID models. \n\n3) Besides, our UniReID model does not overfit the animal objects. As we exhibited in Section 2 in Appendix, our UniReID also achieves state-of-the-art performance under the domain generalization person ReID task.\n\n3.**Prompts for unseen ids**\\\nDuring the inference phase, when deploying our UniReID model to address a specific target (unseen) category, such as the tiger, we initially provide a triplet of tiger images as support. Acquiring such minimal support data is feasible in real-world scenarios. Leveraging these support images, our visual guidance generator (elaborated in Section 3.3) can produce category-specific visual prompts shared for all target tiger images through a one-time, **fully forward process**. Armed with these category-specific prompts, our UniReID model can efficiently adapt to the tiger category without any training or tuning.\n\n4.**CLIP-ReID**\\\nWe compare our UniReID with the CLIP-ReID model [C] under ReID-AW setting. Across the four test sets, our UniReID surpasses the CLIP-ReID model by 6.3% CMC-1 on average (61.4 vs 55.1). The reason could be that the CLIP-ReID focuses on using knowledge embedded within CLIP, but falls short in adapting the accumulated knowledge to novel categories, which could be addressed by the visual and textual prompting strategy of our UniReID.\n\n5.**Relying on GPT-4**\\\nOur UniReID does not rely on the usage of GPT-4. In fact, replacing the GPT-4 to GPT-3, our UniReID still averagely outperforms the best competitor DTIN-Net [A] by 4.1% CMC-1 under ReID-AW.\n\n6.**Explanation of “we aim to identify...”**\\\nThis statement is intended to clarify the primary distinction between the few-shot classification (FSC) and our ReID-AW. Specifically, our ReID-AW represents a more fine-grained task compared to FSC. Compared with the FSC that seeks only to determine the category of a target instance (*e.g.*, distinguishing between an elephant and a mouse), our ReID-AW goes one step further, diving into each specific category to recognize the unique identity of a target instance (*e.g.*, identifying one particular elephant from other elephants). As a result, the inter-class (*i.e.*, inter-identity) divergence in ReID-AW is much smaller than the inter-class (*i.e.*, inter-category) divergence in FSC, which inherently makes ReID-AW a more challenging task.\n\n[A] Dynamically transformed instance normalization network for generalizable person re-identification. ECCV22.\\\n[B] Part-pose guided amur tiger re-identification. ICCVW19.\\\n[C] CLIP-ReID: Exploiting Vision-Language Model for Image Re-identification without Concrete Text Labels. AAAI23.\n"}, {"review_id": "yBW8XQUQFl", "reviewer": "Reviewer_5HdD", "summary": "This 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.", "questions": "1. 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\n", "limitations": "This seems to limit the use of the model because the model must use a triplet as the visual input.", "rating": 5, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. 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", "weaknesses": "1. 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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "1.**Performance comparisons in seen categories.**\\\nTo address this concern, we evaluated our UniReID model and the top-performing competitors, DTIN-Net [A], on two seen categories: alligator and morgan. Here, we need to clarify that we are continually augmenting our Wildlife-71 dataset and have amassed over 1 K new wildlife identities. These newly acquired data are used to construct the test set above. In these categories, our UniReID achieves 71.2% and 55.8% CMC-1 accuracy, respectively. Meanwhile, the DTIN-Net achieves  67.8% and 52.7%, demonstrating that our UniReID model averagely outperforms DTIN-Net by 3.3%, which indicates the superiority of our model. For a detailed comparison in the context of the person ReID task, please refer to Section 2 in the Appendix.\n\n2.**the authors do not list the contributions of this paper in an organized manner, and random erasing lacks corresponding citations [1].**\\\nThanks for your suggestions. We would refine our paper to list our contributions and include this citation. Specifically, the contributions would be summarised as follows:\n\n1) In this research, we propose a novel task named \"Re-identify Any Animal in the Wild\" (ReID-AW). The objective of ReID-AW is to develop a universal ReID model capable of handling any unseen wildlife category it encounters.\n2) To equip the universal ReID model with sufficient knowledge for generalization, we construct a diverse dataset named Wildlife-71 for training purposes, which includes ReID data from 71 different wildlife categories. To the best of our knowledge, Wildlife-71 is the first ReID dataset encompassing multiple object categories.\n3) In addition to the dataset, we also develop a novel ReID framework named UniReID for the ReID-AW task. Within our UniReID, a visual dynamic prompting mechanism and a textual attentive module are given, which are responsible for leveraging visual and textual guidance to adapt our model toward the target category.\n\n3.**The authors indicate that \"The rationale behind this strategy is that ...\".**\\\nFirstly, in terms of experimental results, removing our category-specific sampling strategy could result in a 2.7% CMC-1 degradation to our final model. The reason behind this might be that the knowledge employed by ReID models to distinguish instances from varied categories can be ineffective, or even detrimental when applied to identifying instances within the same category. Intuitively, if we concentrate the ReID model on differentiating instances between horse and cow during the training stage, it could regard coarse-grained clues such as the presence or absence of horns, as a key distinguishing clue. However, such coarse-grained clues are ineffective for fine-grained intra-category identification among horses. Additionally, it is worth mentioning that this observation has also been made by Jiao et.al. [A]. in the context of the domain generalization person ReID task. They discovered that when training a ReID model to differentiate samples from different domains, the model could be misled into considering coarse-grained cues like backgrounds and seasons as primary factors, which, however, are ineffective for person ReID. Consequently, they introduced a domain-specific sampling method, which aligns in principle with our approach.\n\n4.**Evaluation on the chameleon category.**\\\nIn the previously submitted version of Wildlife-71, we have not included the chameleon category. Until now, as we continuously expand our dataset, we have amassed 134 distinct chameleon identities with various colors. Here, we utilized these newly acquired data as a test set and assessed the performance of our UniReID specifically for the chameleon category. In comparison with the top-performing competitor, DTIN-Net [A], our UniReID model demonstrated a 6.6% improvement in CMC-1 performance (68.9% v.s. 62.3%), which indicates the superiority of our model.\n\n[A] Jiao BL, et al. Dynamically transformed instance normalization network for generalizable person re-identification. ECCV, 2022.\n"}, {"review_id": "uUIwzI5ugO", "reviewer": "Reviewer_ABAL", "summary": "The 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.", "questions": "Some 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.", "limitations": "N/A", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "Personally, 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", "weaknesses": "Although 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", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "1.Data:\nFor the open-world problem, there are two mainstream solutions. One is using huge dataset to train a strong deep model, such as SAM. The other one is transfer-learning methods, e.g., UniDetetcor [A]. These methods employ a dataset with a certain volume to gather knowledge and increase the models' adaptability to adapt the accumulated knowledge to handle novel instances they encountered.\n\nIn this study, we chose the second, data-efficient, solution, since collecting a huge wildlife dataset could hardly be achieved in the foreseeable future. Since ReID dataset requires multiple frames for each instance, its collection could not be achieved by simply crawling web images as SAM. So, we collect wildlife ReID data from videos. But, due to the rarity of wildlife, we found that for each category, only an average of 50 available videos could be collected on Youtube. Thus, gathering your excepted dataset requires laborers to identify, track, and photograph over 260K animals in the wild. Given the limited economic value of wildlife, in the foreseeable future, there could hardly be any institution to afford such costs.\n\nAlthough Wildlife-71 may be not large enough to train a foundation model, its value should not be overlooked. As shown in Table 4, compared with existing datasets, wildlife-71 could provide ReID models with effective knowledge to identify animals. Besides, incorporating the textual knowledge derived from GPT-4 and our proposed category adaptation modules, the knowledge inside wildlife-71 could be further adapted to unseen animals, which indicates the value of wildlife-71 to support the second solution.\n\nFinally, our objective is toward (not achieved) re-identifying any animal. If you feel it's over-claimed, we are happy to replace it, e.g., \"Toward category-generalizable wildlife re-identification\".\n\n2.Novelty:\nIn terms of the overall design, our UniReID offers two pivotal insights. The first is deriving textual guidance from LLMs into the ReID model, empowering it with adequate knowledge for generalization. As R@9xKB also notes, this solution could inspire future research using LLMs. The second is empowering ReID models with sufficient adaptability to adapt accumulated knowledge to handle unseen categories. These insights could benefit both ReID-AW and analogous cross-category instance retrieval scenarios.\n\nFor technical details, **1)** Our visual prompt is not a tuning strategy. Unlike VPT methods that need to train a set of prompt vectors for each target task, our visual guidance generator (ViGG) could generate category-specific prompts through a fully forward process. As far as we know, this is the first work to construct visual prompts in such an adaptive and tuning-free manner. **2)** Rather than revise the attention module, our major consideration during incorporating textual prompts is to address the inherent modality gap between them and images and ensure they can accurately associate with visual cues without explicit semantic annotations (e.g. segmentation maps). For this issue, we propose to use the CLIP model, adeptly trained to bridge visual and textual elements, embedding them into a shared hidden space for the subsequent attention operation.\n\n\n3.Performance:\nGPT-4 and CLIP are large language and vision-language models. They suffer significant modal gaps with fine-grained visual tasks. Hence, their application to ReID-AW is not straightforward and does not necessitate improvement. As shown in Figure 3, a simple application of CLIP to ReID-AW results in unsatisfactory results. To address this, we propose the visual and textual prompting strategy, which helps to bridge this gap and make use of the knowledge in these models. The effort involved in this process should not be overlooked.\n\n4.Aggregation:\nWe replace our textual attentive module with these three aggregation modules and evaluate them on ReID-AW. [4] achieves 60.4% CMC-1 (v.s. UniReID's 61.4%). [5] replaces the attentive module with a transformer decoder, achieving a comparable performance, i.e., 61.1. [6] introduces a Gaussian filter to capture salient clues comprehensively, which slightly increases the CMC-1 of UniReID by 0.2%.\n\n5.Generators:\nWe replace ViGG with two other hyper-networks, namely, the fusion net[B] and the MLP net[C]. [B] uses 64 learnable vectors as templates and then uses features of support images to predict a set of fusion weights to aggregate these templates into visual prompts. [C] receives the features of support images and adaptively embeds them into visual prompts via an MLP. For ViGG, we were devoted to integrating the benefits of both methods above. In ViGG, we use a set of trainable vectors to accumulate knowledge and a transformer layer to calibrate them into category-specific prompts under the guidance of support images. Hence, UniReID suppresses the version using the above two methods by 1.0% and 0.8% CMC-1.\n\n6.Support images:\nFor each test set, we randomly select a triplet of images and designate them as the support data, which, once designated, remains consistent across all experiments.\n\n7.Speed:\nPlease refer to Answer 2 for R@9xKB.\n\n8.20:\n20 is the number of prompts used in each ViT layer.\n\n9.Duke:\nWe would remove experiments on Duke.\n\n10.Synthetic:\nYes. Although models like Diffusion can spawn diverse instances, they struggle to produce images of the same identity across varying viewpoints and poses. Recently, we discern that advancements in 3D generation methods may offer a solution. Such methods can create an array of 3D models, thus securing inter-identity diversity. 3D models can also be manipulated to generate images of the same identities.\n\n11.Tool:\nSince collecting a huge wildlife dataset could hardly be achieved in the near future, tools like GPT-4 are valuable.\n\n[A] Detecting everything in the open world: Towards universal object detection CVPR23\\\n[B] Dynamic convolution: Attention over convolution kernels CVPR20\\\n[C] Decoupled dynamic filter networks CVPR21"}]

Author rebuttal:

Meta-review: This paper has 7 (accept) -4 (borderline reject)-5 (borderline accept)-2 (strong reject) ratings. Critiques from reviewers are mainly about the novelty and limited improvement. On the other hand, reviewers also acknowledge the contribution of the dataset, the proposal of a new re-id setting and that the proposed method is tailored for this problem. 

The AC read the paper and all author-reviewer correspondances and tends to agree that the pros outweight cons. The AC thinks the community would benefit from the research outcome of this paper not only in the new re-id application, but also in regards to how the proposed cross-species re-id is tentatively addressed. Regarding novelty, authors made a fair point:

*Our insights: (1) the utilization of category-adaptive features (2) the incorporation of text-guided representations to integrate pre-existing category-related knowledge from large language models, seem acknowledged.*

*These two insights we believe are novel in the ReID community and could be extended to analogous cross-category instance-level retrieval scenarios. The primary divergence in our perspectives seems to be the novel degree to these insights.*

The AC tends to agree with this argument. 

That said, in regard to limited improvement, authors are strongly suggested to provide confidence interval or statistical significance tests, to demosntrate that the improvement is statistically significant. Moreover, the authors are suggested to move the attention map figure and dataset statistics table from supplementary materials to the main paper, because they are quite important. 

In addition, the SAC found some papers that also released multi-species animal re-id datasets. So the authors are suggested to cite the relevant ones and make more fair claims around whether being the first to release a multi-species RE-ID dataset.

A Benchmark Database for Animal Re-Identification and Tracking (https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10052988)
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https://www.sciencedirect.com/science/article/pii/S1574013720303890
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https://sites.google.com/view/wacv2020animalreid/home

Authors are asked to search the network for multi-species RE-ID animal datasets and discuss briefly in the paper how their dataset differs from each existing dataset.

After extensive discussions between AC and SAC, this paper is recommended for accept.
Decision: Accept (poster)