Long-tailed Object Detection Pretraining: Dynamic Rebalancing Contrastive Learning with Dual Reconstruction
Authors: Chen-Long Duan
Yong Li
Xiu-Shen Wei
Lin Zhao
Venue: NeurIPS 2024 poster (2024)
Keywords: Long-tailed object detection
pretraining
Primary area: machine_vision

Abstract: Pre-training plays a vital role in various vision tasks, such as object recognition and detection. Commonly used pre-training methods, which typically rely on randomized approaches like uniform or Gaussian distributions to initialize model parameters, often fall short when confronted with long-tailed distributions, especially in detection tasks. This is largely due to extreme data imbalance and the issue of simplicity bias. In this paper, we introduce a novel pre-training framework for object detection, called Dynamic Rebalancing Contrastive Learning with Dual Reconstruction (2DRCL). Our method builds on a Holistic-Local Contrastive Learning mechanism, which aligns pre-training with object detection by capturing both global contextual semantics and detailed local patterns. To tackle the imbalance inherent in long-tailed data, we design a dynamic rebalancing strategy that adjusts the sampling of underrepresented instances throughout the pre-training process, ensuring better representation of tail classes. Moreover, Dual Reconstruction addresses simplicity bias by enforcing a reconstruction task aligned with the self-consistency principle, specifically benefiting underrepresented tail classes. Experiments on COCO and LVIS v1.0 datasets demonstrate the effectiveness of our method, particularly in improving the mAP/AP scores for tail classes.

Long-Tailed Object Detection Pre-training: Dynamic Rebalancing Contrastive Learning with Dual Reconstruction
Chen-Long Duan', Yong Li', Xiu-Shen Wei Lin Zhao?
Nanjing University of Science and Technology
Schooo of Computer Science and Engineering, and Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, Southeast University
Abstract
Pre-training plays a vital role in various vision tasks, such as object recognition and detection. Commonly used pre-training methods, which typically rely on randomized approaches like uniform or Gaussian distributions to initialize model parameters, often fall short when confronted with long-tailed distributions, espe- cially in detection tasks. This is largely due to extreme data imbalance and the issue of simplicity bias. In this paper, we introduce novel pre-training frame- work for object detection, called Dynamic Rebalancing Contrastive Learning with Dual Reconstruction (2DRCL). Our method builds on a Holistic-Local Contrastive Learning mechanism, which aligns pre-training with object detection by captur- ing both global contextual semanties and detailed local patterns. To tackle the imbalance inherent in long-tailed data, we design dynamic rebalancing strategy that adjusts the sampling underrepresented instances throughout the pre-training process, ensuring better representation of tail classes. Moreover, Dual Recon- struction addresses simplicity bias by enforcing a reconstruction task aligned with the self-consistency principle, specifically benefiting underrepresented tail classes. Experiments on COCO and LVIS v1.0 datasets demonstrate the effectiveness our method, particularly in improving the mAP/AP scores for tail classes.
Introduction
With the advancement of deep learning, computer vision has seen significant progress, particularly in the development of large-scale pre-training and fine-tuning optimization paradigms [55] 59 63
65]. Numerous pre-training methods capture domain specific or task-relevant concepts, boosting downstream performance [7] [4
In the field of object detection, current methods typically leverage ImageNet [0 and COCO 35 for pre-training, allowing partial model components, such the backbone, achieve satisfactory pre-training. However, these pre-training paradigms leave some key detection components randomly initialized and tend to overlook the suboptimal performance issues caused by long-tailes distributions during pre-trate pre-training process.
[5]
27
30 48
52
In the traditional supervised pre-training paradigm, models are constrained by the distribution of labeled data, making it difficult for them to perform well in long-tailed settings, for example, in
"Corresponding author. The first authors contribute equally to this work. This work was supported by National Key R&D Program of China Y002IFFAOON 100), National Natural Science Foundation of China under Grant (62272231, 621 021222,,,, the Fundamental Research Funds for the Central Universities (4009002401), and the Big Data Computing Center of Southeast University.
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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tasks of pipeline failure detection [ and face recognition [3]. While self-supervised learning has demonstrated potential in enabling models to learn richer and more effective feature representations 52 significant challenges remain. An often-
without relying on labeled data [2 15 22
27
overlooked but crucial challenge in long- tailed object detection is simplicity bias 23 43
46 53 54],
where deep neural networks tend to rely on simpler predictive patterns while overlooking complex features that are crucial for model generalization. This bias is especially problematic for tail classes, as their limited examples make them more likely to be ignored by models that prioritize simpler patterns. To address these challenges, this work aims not only to develop a pre-training strategy that aligns with the unique demands of object detection but also to ensure its effectiveness across both balanced and long-tale tailed data distributions.
Motivated by this, we propose a novel pre-training framework called Dynamic Rebalancing Con- trastive Learning with Dual Reconstruction (2DRCL), specifically designed for long-tailed object detection pre-training. Our method incorporates Holistic-LLocl Contrastive Learning, which com- bines holistic and local feature learning to better align the pre-training process with the fine-tuning phase. To address the issues of long-tailed distributions during pre-training, 2DRCL integrates dynamic rebalancing strategy that improves the accuracy of tail classes. Unlike traditional resampling methods, our dynamic rebalancing sampler considers instance-level imbalance, offering more precise control over class distribution and ensuring that tail classes are adequately represented. Additionally, by introducing Dual Reconstruction, our method effectively mitigates simplicity bias, enabling the model to capture both complex patterns and nuanced features that are essential for long-taii object detection. This dual mechanism ensures that the model not only retains detailed visual information but also grasps deeper semantic relationships, which is particularly crucial for accurately recognizing and distinguishing tail classes with limited examples.
To evaluate the effectiveness of our method, we conduct extensive experiments on two benchmark datasets, i.e., COCO (35] and LVIS v1.0 [13, Experiments on these datasets from both quantitative and qualitative perspectives validate the effectiveness of our proposed method.
Related Work
Pre-training for Object Detection. Pre-training is a critical step in object detection, often involving the use of large-scale datasets to learn transferable representations. Commonly, CNNs pre-traine trainedd on image classification datasets like ImageNet [10) are fine-tuned for object detection tasks. Self- supervised pre-training methods [4] 6 (71 have gained traction in recent years. These methods do not require labeled data and aim to learn useful representations through contrastive learning. To bridge
the gap between pre-training and fine-tuning, dense-level contrastive learning methods 8 20 28 51
52 57]
explored local feature similarities between views, enhancing target perception and feature learning. Recognizing the insufficiency of pre-training solely the backbone, SoCo (52) advocated pre-training additional modules like FPN to process intricate scene-level information. In object detection, methods like UP-DETR [9] and DETReg (2] pre-trained entire DETR-like detectors with region matching and feature reconstruction tasks, while AlignDet (27 froze a pre-trained backbone during detection pre- training, achieving satisfactory results with fewer epochs. Nonetheless, these approaches still struggled with effectively addressing long- tailed distribution challenges.
Long-tailed Object Detection. In the literature
repeat factor sampling [3 58 aims to balance the data distribution by sampling tail classes more frequently. In object detection and segmentation tasks, achieving sample balance solely through straightforward resampling strategies is challenging due to the complexity of the scenes. Special loss functions represent another technical direction for tackling the long-tailed problem. EQL 45 protected tail classes from being over- suppressed by ignoring negative gradients from head samples, while EQL v2 [44] balanced gradients from head and tail classes. Scesaw loss (4] rebalanced the positive and negative gradients of each class using two reweighting factors. ECM loss [24] provided a theoretical understanding
55
of the long-tailed tracking detection problem and introduced a novel alternative objective that optimized the basged binary classification error. Beyond these loss functions, methods such as supervised contrastive learning 48 66), , decoupled training [| 40 and expert-based classifier training (56) 62 64 have also demonstrated effectiveness under long-tailed settings. While these
methods often implicitly reshape decision boundaries to protect tail classes, their indirect nature may limit their effectiveness in more complex long-tailed scenarios.

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Duall Reconstraction
Probability
Holistic Contrastive L Leaming
Decoder
SR Loss
X
Local Contrastive L Learinng
Upiato
HCL Leas
Detection
T.C..Tos
X
Encoder
Reconstructdd Image
Figure 1: Illustration of the proposed Dynamic Rebalancing Contrastive Learning with Dual Recon- struction (2DRCL) method, which consists of the Holistic Contrastive Learning (Section 3.1.I), the Local Contrastive Learning (Section (31.2), and the Dual Reconstruction (Section 3.3). The whole
network can be trained in an end-to-end manner.
3
Methodology
Our goal to develop pre-training approach tailored specific requirements of object detection, while maintaining robustness across both balanced and long-tailed data distributions. To this end, we introduce a novel method called Dynamic Rebalancing Contrastive Learning with Dual Reconstruction (2DRCL), specifically designed for pre-training in long-tailed object detection scenarios. In 2DRCL, we exploit a Holistic-Local Contrastive Learning (HLCL) paradigm to coordinate holistic and local feature learning to better align the pre-training with the mie-ttnnng phase. Building on this, dynamic rebalancing strategy is incorporated, which emphasizes tail classes at both the image and instance (object proposal) levels to address data imbalance during pre- tre-training. By integrating HLCL with this dynamic rebalancing strategy, we introduce Dual Reconstruction component aimed at mitigating simplicity bias, enabling the model concurrently capture both complex and subtle feature patterns essential for long-tailed object detection. Below, we present details of the three parts in 2DRCL.
3.1 Holistic-Local Contrastive Learning
In 2DRCL, the HLCL mechanism serves as the foundation for pre-training object detection models. The HLCL framework encompasses two key components: Holistic Contrastive Learning (HCL) and Local Contrastive Learning (LCLL). HCL focuses on learning generic visual representations, enabling the backbone model to capture comprehensive image patterns and general semantic abstractions effectively. To integrate object-level representations into the pre-training process, LCL is introduced to guide both the backbone and the detection head toward object-level details within the image. By pre-training all network components used in object detectors, LCL ensures that the model is more precisely aligned with object detection tasks, while also enhancing its ability capture fine- mn-ggrained object-level features.
3.1.1 Holistic Contrastive Learning
present HCL mechanism in Fig. [ As illustrated, we follow the typical CL framework, i.e., MoCo 15. to realize the holistic CL. in our proposed 2DRCL framework. Typically, for image I, we apply different image views to obtain æ and x4 as inputs for the encoder and momentum encoder in HCL. Each view is randomly and independently augmented. Notice that the scale and location of

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the same object proposal are different across the augmented image views, which enables the model to learn cunslatioo-invariinn and scale-invariant object-level representations in the following LCL part. which we will elaborate next.
Subsequently, x and 24 are transformed via separate projectors, generating holistic-level representa- tions, z and 2+, which are then lg fg-normalized. Subsequently, we employ the InfoNCE loss 15 38 to drive the network training, formally:
exp (a =+/T)
Luci. = log
exp (a =+/T) + Σ exp (2-22//T)
where T is temperature hyper- parameter usually set as 0.2. For each input image, we use one positive and K negative samples for HCL, where is fixed as 65,536. For the update of momentum encoder in Fig. 1 we use the same Exponential Moving Average (EMA) strategy as that in MoCo 17 [5]. Through HCL,, the model is trained to effectively learn generic visual representations and capture comprehensive image patterns. However, solely relying on image-level pre-training may lead to an overemphasis on holistic representations, potentially neglecting features that are critical for object detection tasks.
3.1.2 Local Contrastive Learning
To introduce object-level representations into pre-training, we incorporate the LCL mechanism to bridge the gap between pre-training process and fine-tuning phase w.r.t object detection, illustrated in Fig. [ Specially, we employ a class-agnostic detector (26) to generate a series of proposals as bounding boxes B (b1,b,,... b2...., b. where by denotes the i-th bounding box within the augmented input image x. The object-level representation of a proposal is then obtained via object detection heads
(e.g., Rol (17), denoted 266-
The LCL loss for the local-level representation can be formulated as:
exp (206 2bb,//T)
LLCL= -log
exp (265- 206, /T) + Σ exp (Zss 2b5,/T)
where 266, means corresponding positive object proposal within another augmented input image x... The K negative proposals means any potential proposals within other unrelated images during training. To construct a dictionary comprising a large number of object proposals from different input images, we utilize a queue-based structure. Sequences from the current mini-batch are enqueued, while the oldest mini-batch sequences are dequeued, ensuring that the dictionary size is independent of the mini-batch size. LCL mechanism maximizes the similarity between object proposals across augmented views, enabling the model to learn comprehensive representations for diverse object proposals, thus enhancing its robustness in object detection tasks.
Finally, the objective w.r.t the HLCL mechanism can be formulated as:
LHICI. = a.Cnner + BeLtCr,
(3)
where 02 and Be are the weights of HCL and LCL loss, respectively.
3.2
Dynamic Rebalancing
To precisely control class distribution and ensure adequate representation of tail classes, we propose dynamic resampling method that considers both images and object proposals. Unlike traditional resampling strategies, such as Repeat Factor Sampling (RFS) [33, which primarily emphasize class- balanced sampling, our approach aims to prioritize tail classes more effectively through resampling level. Given that object detection requires the
at both the image level and the object-propose identification and localization of specific objects, addressing instance-level imbalance in addition to image-level imbalance is expected to achieve a more balanced representation, particularly benefiting tail classes.
The proposed resampling method incorporates dynamic adjustment mechanism, enabling the model to initially learn the overall distribution of the dataset and progressively shift its focus towards tail classes as pre-training advances. Specifically, for each category e, we calculate image-level and instance-level scores, denoted as f and f. respectively. Here, frm indicates the proportion of images belonging to the e-th category in the entire dataset, while f represents the proportion of object proposals associated with the e-th category across the dataset. These two scores reflect the

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imbalance ratio for category following the approach used in RFS [13]. The combined score for each category, far is then defined as the harmonic mean of these two scores:
fc
adJe fim (1 aa) fc
where the hyper-paramene parameter changes dynamically throughout pre-training, defined as ad Tmoe where I" is the current epoch and I mea is the total number of pre-training epochs. As pre-training progresses, the value of ag increases, gradually shifting the focus from image-level balancing to instance-level balancing, enabling the model to increasingly emphasize tail classes.
To achieve balanced sampling, define the category-level repeat factor Te based the score fc using the formula r. max ft. where is a fixed hyper-paramenn set at 0.001. This repeat factor ensures that categories with lower scores (typically tail classes) are sampled more frequently during training. The dynamic resampling strategy effectively addresses data imbalance at both the image and instance levels, enhancing focus on tail classes while mitigating the risk of overfitting due to excessive repetition of rare instances.
3.3 Dual Reconstruction
Building on the HLCL and dynamic resampling mechanisms, which provide the foundation for pre-training object detection and mitigate instance imbalance, respectively, our proposed 2DRCL framework introduces a Dual Reconstruction component to address simplicity bias. This component enables the model to concurrently capture both complex and subtle feature patterns, which are vital for effective long-tailed object detection.
3.3.1 Simplicity Bias
Simplicity bias [23 43 46) is a phenomenon where models tend to favor simpler predictive patterns, often neglecting complex features that critical for effective generalization. This issue is particularly prevalent in long-tailed distributions, where significantly affects the performance on tail classes. In such scenarios, models struggle learn the intricate and unique characteristics of these tail classes, further exacerbating the class imbalance problem.
To bridge this gap, propose Dual Reconstruction (DRC) component aimed mitigating simplicity bias by enhancing feature discrimination for both head and tail classes. As shown Fig. [ DRC comprises two key elements: Appearance Reconstruction (AR) and Semantic Reconstruction (SR). The AR component enforces pixel-level reconstruction, compelling the model to capture as many subtle details as possible for each input image. In contrast, the SR component ensures semantic consistency between the features of the original input image and those of a corresponding randomly occluded image. We hypothesize that the effective implementation of DRC will enable the model to retain fine-griined visual information while also capturing deeper semantic relationships. This capability is particularly important for accurately recognizing and distinguishing tail classes, which often have limited training examples. This strategy ensures accurate visual representation while promoting deeper semantic focus, enabling the model better handle tail classes in long-tailed object detection.
3.3.2 Appearance Reconstruction
To enforce appearance consistency, we utilize an auto-cncoding structure specifically designed for high-fidelity reconstruction of input images. The encoder f. parameterized by 0, maps an input image æ into a dense feature space, represented as z = f (x). A generator g. parameterized by n, then attempts invert this mapping, producing reconstructed version: å = g (f (x)). Through pixel-wise image reconstruction, the Appearance Reconstruction (AR) component compels the latent features, of (x), to capture many subtle details possible for each input image.
AR not merely replicating the input image; rather, acts as an auxiliary regularization mechanism that focuses on distilling discriminative visual features relevant to the primary object detection task for each input image. By enforcing image reconstruction, AR enables the model to effectively capture both prominent and nuanced details present in the input data. The AR loss is formulated as
5

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pixel-wise mean-squee squared error (MSE), expressed as:
LAR = 1| g
(5)
3.3.3 Semantic Reconstruction
While AR ensures that the model captures fine-grained visual details essential for accurately rep- resenting and distinguishing between different objects, especially in cases with limited examples of tail classes, it is equally important to maintain semantic integrity in the reconstructed images. This semantic consistency allows the model to focus on the underlying meaning and context of the image, rather than merely surface-level details, thereby promoting more robust and generalized understanding of each input.
To address this need, we introduce Semantic Reconstruction (SR), which incorporates controlled perturbations during the reconstruction process. SR is designed to preserve the semantic content of the original image while allowing the model to learn to recognize and reconstruct meaningful features even when certain parts of the image are altered or obscured. This approach ensures that the model develops deeper understanding of each input's inherent structure and context.
Specifically, we apply mask fixed percentage (e.g., 25%) of an object proposal within the reconstructed image (f (z)), resulting in masked version, denoted M (g (f (z))), where M means image masking operation. This masked image is then re-encoded by the encoder to generate the corresponding latent features, = f (M (g (ʃ (z)))). The Semantic Reconstruction (SR) loss is computed by measuring the congruence between the feature representations of the vanilla images and those of the masked reconstructed images, evaluated across multiple layers of the network. This approach ensures that the model maintains semantic consistency while learning to recognize and reconstruct meaningful features.
CSR [(((a))((M((((0))
(6)
where P represents the number of feature layers considered, and the SR loss, Lsn. is defined as the Euclidean distance between the original (vanilla) features and the reconstructed features across these layers. The SR component ensures that even in the presence of visual disruptions, the essential semantic features are preserved, allowing the model to learn robust, invariant features that go beyond superficial visual similarities. This approach enhances the model'ss ability to generalize by focusing on meaningful semantic information rather than just appearance.
Conclusively, our proposed DRC leverages both appearance and semantic consistency to address simplicity bias, encouraging the model learn rich and complex feature representations essential for accurate and robust detection of tail classes. The interplay between the two reconstruction losses enhances the model's sensitivity to both fundamental visual details and higher-Ievel semantic features, leading to more versatile and effective detection paradigm. This combined approach ensures that the model not only captures detailed visual information but also grasps abstract semantic relationships. improving its overall performance in taiget object detection tasks.
The total loss for the Dual Reconstruction combines the AR and SR losses can be formulated as: (7)
LDRC = a, + (1 - a,) LSR.
where a, balances the trade-off between visual fidelity and semantic accuracy. This dual-focus strategy force the model to reconstruct the image/features for both the head and the tail classes. This, DRC enhances the model's ability represent and detect tail classes effectively.
Overall, the final loss function of our method is optimized by:
L LHICL + LDRC + Laet
(8)
where Ldee denotes the loss of object detection that makes the pre-training consistent with the task. For simplicity, the weights of all losses in L are set to 1.
Experiments
In this section, we outline the experimental settings, implementation details, and main results. Additionally, comprehensive description of the experimental settings provided in Section of the Appendix.

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Table 1: Comparisons with state-of-the-eee methods on COCO (Mask R-CNN with R50-FPN).
Backbone Initialization
Methods
AD
AP
AP
APmA
AD3
AP
DenscCL
51
39.6
59.3
43.3
Self-EMD
36
40.4
61.1
43.7
37.4
56.5
39.7
From scratch
SoCo 52
40.6
61.1
44.4
SlotCon 57
41.0
61.1
45,0
Surpervised
38.3
58.0
42.1
34.3
54.9
36.6
ImageNel pre-trained backbone
AlignDet
27
39.4
59.2
43.2
35.3
56.1
37.7
Ours
41.4
61.3
45.8
37.4
57.2
39.4
4.1 Experimental Configurations
We conduct experiments on two representative datasets: COCO 35 and LVIS v1.0 [13] The COCO dataset a standard benchmark for object detection, segmentation, and captioning tasks, comprising 80 classes with relatively balanced distribution, including 118k training images and 5k validation images. Given the balanced nature the class distribution in COCO, we use this dataset to evaluate the performance of the proposed 2DRCL under balanced settings. In addition, we utilize the LVIS v1.0 dataset benchmark long-tailed object detection scenarios. LVIS features 1,203 classes with highly imbalanced distribution, containing 100k training images and 19. 9.8k validation images. The classes LVIS are categorized into three groups based on their frequency of occurrence [3]: rare (I~10 10 instances), common (1 (11-100 instances), and frequent (> 100 instances). This categorization data distributions.
Datasets.
allows for a comprehensive assessment of 2DRCL's performance under
Implementation Details. Experiments are conducted with both Faster R-CNN and Mask R-CNN
frameworks. For comprehensive comparison, use both ResNet-50 and ResNet- 101 backbones. All models are implemented using the MMDetection toolbox [5]. We pre-train the models on 8 RTX3090 GPUs with batch size of 16. Unless otherwise specified, pre-training follows the 1 x schedule (12 epochs), starting with an initial learning rate of 0.02, which is reduced by a factor of 10 after the 8th and 11th epochs. For 2 schedule, models are trained with 24 epochs, and the learning rate decays at the end of epoch 16 and 22. In our experiments, the typer-parametent are set as follows: ar to 0.1, 3, is to 0.05, or set to 0.1. When conducting experimental comparisons on the LVIS v1.0 dataset in Table we first use our 2DRCL for mre-trainin, followed by the application of existing long-taile tailed methods for fine-tuning further enhance performance. Finally, we select 'ECM [24]] +2DRCL' as 'Ours' for comparison with state-of-the of-the-art methods.
4.2 Quantitative Results
Mask R-CNN with R50-FPN COCO dataset. Table presents the comparison, where all methods pre-trained on the COCO training dataset and eval- uated on the COCO validation dataset. Typically, the backbone can be initialized either from scratch ing an ImageNet pre-trained model. Methods such as DenseCL [1), Self-EMD [36), and SoCo [52]. which are initialized from scratch, achieve an A AP ranging from 39.6% to 41.0%. Notably, these methods rely 200-800 epochs for training. As comparison, meth- ods that utilize an ImageNet pre-trained style, including AlignDet and our proposed 2DRCL, require only 12 epochs for pre-training. Among the compared methods in Table I our 2DRCL demonstrates superior object detection performance, achieving the highest Aphh of 41.4% and Apmk of 37.3%, significantly outperforming both AlignDet and the supervised baseline. This improvement can be attributed to 2DRCL's capability to narrow the gap between pre-training and fine-tuning. By effectively bridging this gap, 2DRCL is able leverage the benefits of the pre-trained model more efficiently for object detection tasks.
Table 2: Comparisons with pre-trained
methods on LVIS v1.0 with a x sched- uler using Mask R-CNN.
Method
Ap
Apd
ApM
AP
MoCo v2 7
14,5
3.9
12.4
21.6
SimCLR
19.9
8.0
18.1
27.1
BYOL 12
15.3
5.4
13.2
21.9
SoCo 52
17.6
5.3
15.9
24.9
AlignDet
27
22.6
10.3
20.8
29.9
Ours
23.9
11.9
22.3
31.0

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Table 3: Comparisons with state-of-the-arr methods on LVIS v1.0 with 2x schedule.
(a) Faster R-CNN with R50-FPN.
(b) Mask R-CNN with NesNet-50/101.
Method
Ap
AP
AP!
API
Backhone
Method
AP
AP,
AP,
Ap
BCE
42
19.5
1.6
16.6
30.6
CE
18.7
0.4
16.5
29.3
19.7
RFS 13
24.2
14.2
22.3
30.6
RFS 13
23.7
14.2
22.9
29.3
24.7
DropLoss
21.8
5.2
21.8
29.1
EQLv2
25.2
17.4
24.1
29.9
26.0
19
6.2
R50-FPN
LOCK
30.7
27.4
FQL.2
25.4
15.8
23.5
31.7
SecSaw
47
26.9
19.6
26.8
30.5
27.3
Seesaw
47
26.4
16.8
25.1
32.2
ECM
24
27.4
19.7
27.0
31.1
27.9
BAGS
29
23.7
14.2
22.2
29.6
Ours
27.7
20.4
27.1
31.4
28.3
ACSL
50
22.2
9.9
21.3
28.5
CB
25.5
16.6
24.5
30.6
26.6
LOCE
11
25.1
EQLv2
27.2
20.6
25.9
31,4
27.9
BACL
40
26.1
25.7
30.9
RI01-FPN
SeeSaw 47
28.2
20.3
28.1
31.8
29.0
ECM
24
26.7
17.5
25.7
32.2
ECM
24
28.7
21.9
28.4
32.2
29.4
Ours
27.3
18.6
25.8
32.6
Ours
28.8
21.1
28.7
32.3
29.6
Comparisons with Pre-trained Methods on LVIS v1.0. In Table 2 we present a comparison of our method with several state-of-the- pre-trained methods on the LVIS v1.0 dataset using the Mask R-CNN framework with a 1 x scheduler. The results obviously illustrate the limitations of existing pre-training approaches in addressing the challenges posed by long-tailed distributions w.r.t the object detection tasks. Specifically, traditional pre-training methods consistently demonstrate inferior performance on tail classes, as evidenced by their relatively low AP scores. For instance, MoCo v2 7 and BYL 12 achieve AP scores of 3.9% and 5.3%, respectively, indicating a obvious deficiency in precisely detect the target objects w.r.t the long-tailed classes. Our proposed 2DRCL is specially designed for long-tailed object detection pre-training and shows consistent superiority in Table 2 By dynamically rebalancing the data distribution and incorporating Dual Reconstruction mechanisms, 2DRCL effectively captures object-level characteristies for both head and tail classes. The superior performance of the proposed 2DRCL highlights its efficacy in addressing long-tailed object detection challenges, demonstrating strong capability to focus on tail classes and alleviate the inherent imbalance issues in such datasets.
Comparisons with State-of-ther Long-tailed Object Detection Methods LVIS v1.0. To evaluate the effectiveness our method for long-tailed object detection, we compare 2DRCL with state-of-the-art techniques across different object detection frameworks (Faster R-CNN and Mask R-CNN) and backbone networks (ResNet-50 and ResNet- 101) on the LVIS v1.0 dataset. As shown in Table 3 our method achieves the highest accuracy both Ap and AP. Specifically, for the Faster R-CNN framework, our pre-training technique outperforms all the competitors, particularly in AP and AP This advantage consistently observed with the Mask R-CNN framework as well. We attribute this improved performance, particularly for tail classes, to 2DRCL's dynamic rebalancing of the data distribution and the introduction of Dual Reconstruction mechanisms. The effectiveness of 2DRCL stems from its ability significantly mitigate extreme imbalance and simplicity bias for tail classes during the pre-training phase. We will investigate the contribution for each components in 2DRCL in Section
Discussions. To address concerns that the performance gains might be attributed to longer training durations, we evaluate the COCO dataset using various men-ttnnng schedules, with the results presented in Table 4a The findings demonstrate that even with 1 x fine-tuning (12 epochs), our method surpasses the baseline trained with 4x fine-tuning, indicating that the observed performance improvements of 2DRCL are not merely due to extended training epochs. Furthermore, Table 4b shows results on the LVIS v1.0 dataset, where all methods are compared under the same total number of training epochs for fair evaluation. Specifically, while mang-talldd methods are fine-tuned for 12 epochs, our 2DRCL employs epochs pre-training followed by epochs fine-tuning, maintaining an equivalent overall training duration. The results reveal an average improvement 0.5% in AP and 1.3% in A A with 2DRCL's pre-training strategy, underscoring the effectiveness of 2DRCL, in enhancing tailed object detection performance.

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Table 4: Comparisons w.r.t different training/fine-tung epochs.
(a) Comparisons under different fine-tuning epochs COCO. The preceding four methods exploit ImageNet pre-trained backbone.
(h) Results
LVIS v1.0 with same training epochs.
Methods
APM
AP
AP
AP/
Fine- Fine-tuning Schedule
A Apé
AP46
AP
75
RFS
22.7
9.1
21.5
30.0
+Ours
10.3
21.7
x
38.3
58.0
42.1
EQL
24.9
14.8
24.1
30.4
2x
38.8
58.4
42.4
+Ours
25.2
15.9
24.3
30.3
3x
39.0
58.7
42.9
Scesaw
24.7
14.7
23.6
4x
39.2
59.5
42.9
+Ours
25.2
15.6
24.2
30,5
1x (Ours)
41.4
61.3
45.8
ECM
26.5
17.0
25.4
31.7
+Ours
19.0
25.8
Ablation Analysis across 2DRCL Components. To investigate the contribution for each component in 2DRCL, we evaluate our 2DRCL on the LVIS v1.0 dataset. As shown in Table 5 incorporating HLCL, which combines HCL and LCL, results in a 1.7% improvement A AP over the baseline. HCL focuses learning generic visual representations, enabling the backbone to capture comprehensive image patterns and semantic abstractions, while LCL ensures precise alignment with object detec- tion tasks and enhances the capture of fine- ane-grained object-level features. Additionally, DRB dynami- cally rebalances the data distribution and helps to significantly boosts performance for tail classes, leading to a 3.5% improvement in Ab, The Dual Reconstruction (DRC) mechanism, comprising AR and SR, brings the total improvement to 6. %. AR enforces pixel-level reconstruction, compelling the model to capture subtle visual details, while SR ensures semantic consistency between the original and occluded images. This combination allows the model retain intricate visual information while capturing deeper semantic relationships, resulting in enriched and coherent feature representations.
Table 5: Ablations for various components in our 2DRCL on LVIS v1.0.
HCL
LCL
DRB
AR
SR
APM
AP
AP
APP
x
X
X
22.7
9.1
21.5
30,0
V
X
X
22.5
10.5
21.0
29.3
21.9
9.8
20.8
28.7
X
22.4
10.8
21.1
29.0
X
23.8
14.3
22.3
30.1
X
24.2
14.9
22.6
30.3
ER
JOES
Bussine
GJ5
4.%
20RCL (Ous)
EIEA
2.%%
32%
Ear
Ouret
Loatan
Eakgnedd
ober
Figure 2: Error analyses comparisons. 2DRCL achieves superior performance on tail classes without significantly compromising accuracy for the more frequent classes.
4.3 Further Analysis
In this section, we conduct a thorough analysis of our proposed 2DRCI,, emphasizing its role in mitigating simplicity bias and enhancing feature representation through DRC mechanism.
Error Analyses. To determine which error types our 2DRCL method effectively mitigates, we conducted error analysis experiment. Following the error categorization paradigm from YOLO 41, we classify the top N predictions for each class into five error types. The pie charts in Figure 2 show the distribution of these errors for rare, common, and frequent classes on the LVIS v1.0 validation set. As shown in Figure 2 our 2DRCL shows noticeable improvements for rare object classes,

---

with correct predictions increasing from 51.8% in the baseline to 53.5%, alongside a reduction in both backgroudd classification errors and background prediction errors. This suggests that our 2DRCI enhances the model's ability to accurately classify the rare objects and accurately distinguish them from the background. Although there a slight accuracy decrease for common and frequent classes, this trade-off is minimal, with the gains in rare class detection outweighing these minor losses. This demonstrates that our method effectively addresses long-tailed object detection challenges by improving performance on tail classes without obviously compromising accuracy for other frequent classes across the dataset.
Simplicity Bias Analyses. To explicitly illustrate how our method addresses simplicity bias, we present a visualization of the activations corresponding to randomly sampled test images from the LVIS v1.0 dataset in Figure 3 The results demonstrate that 2DRCL effectively mitigates simplicity bias in long-tailed object detection by learning more comprehensive patterns that encompass informative regions, particularly for images belonging to tail classes. In comparison, 2DRCL consistently identifies more critical regions than ECM (44, highlighting the superiority our approach in addressing simplicity bias. The comparisons presented in the fourth and fifth rows underscore the effectiveness of the proposed DRC mechanism, revealing that the introduction of the DRC mechanism significantly enhances feature attention on tail classes while reducing background interference. This finding further indicates that the DRC plays a crucial role in mitigating simplicity bias, enabling the model to retain intricate visual details and capture deeper semantic relationships, thereby producing enriched and coherent feature representations.
Money, Igniter
Whipped cream. Cripe
CromEs, Lettuce
Aztrona Maciiaa giz
Garbage
Tricyle
Traege Eogeot
Attantion Magituke (Red Indicalle high aiteciiti
Bosdibe
FIM
ZDRCE (wio DRC)
ZORCE
Figure 3: Attention map comparisons w.r.t Baseline [3) ECM 24,, 2DRCL (wlo DRC) and 2DRCL (our method) on LVIS dataset. The top row shows the corresponding class names of the input images. Best viewed in color.
5 Conclusions and Limitations
We proposed Dynamic Rebalancing Contrastive Learning with Dual Reconstruction (2DRCL) to address the challenges posed by long-tailed distributions in object detection pre-training. By inte- grating holistic and local contrastive learning with dynamic rebalancing and dual reconstruction. 2DRCL aligned the pre-training strategy with the specific demands of object detection, ensuring ef- fectiveness across both balanced and long-tailed data. It successfully mitigated simplicity bias for tail classes, enhancing their feature representations and overall performance. Experiments demonstrated significant improvements in attention to tail classes and reduced background errors, as confirmed by both quantitative and qualitative analyses. However, method had limitations, particularly in its relatively high computational costs. Future work will focus on optimizing computational efficiency.
10

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References
[1] Mohammad H. Alohaidi, Mohamed A. Meguid, and Tarek Zayed. Semi-supervised learning framework for and gas pipeline failure detection. Scientific Reports. 12(13758). 2022.
[2] Amir Bar, Xin Wang. Vadim Kantorov, Colorado Reed, Roci Herzig. Gal Chechik, Anna Rohrbach,
Trevor Darrell, and Amir Globerson. DETReg: Unsupervised pretraining with region priors for object detection. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pages
2022.
[3] Dong Cao, Xiangyu Zhu, Xingyu Huang. Jianzhu Guo, and Zhen Lei. Domain balancing: Face recognition on long-tailed domains. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pages 5670-5678, 2020. [4] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsu- pervised learning of visual features by contrasting cluster assignments. In Proc. Adv. Neural Inf. Process. Syst., pages 1912-9924. 2020.
[5] Kai Chen, Jiaqi Wang. Jiangmiao Pang. Yuhang Cao. Yu Xiong. Xiaoxiao Li, Shuyang Sun, Wansen Feng. Ziwei Liu, Jiarui Xu, Zheng Zhang, Dazhi Cheng, Chenchen Zhu, Tianheng Cheng. Qijie Zhao, Buyu Li. Xin Lu, Rui Zhu, Yue Wu, Jifeng Dai, Jingdong Wang, Jianping Shi, Wanli Ouyang, Chen Change Loy, and Dahua Lin. MMDetection: Open MML.ab ab detection toolbox and benchmark. arXiv preprint
arXiv: / 1906,077 07150 2019.
[6] Ting Chen, Simon Kornblith, Kevin Swersky. Mohammad Norouzi, and Geoffrey Hinton. Big self- supervised models are strong semi-supervised learners. arXiv preprint arXiv:2006. 10029, 2020.
[7] Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He. Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.0m 04097,.49...00 2020.
[8] Zeren Chen, Gengshi Huang. Wei Li. Jianing Teng, Kun Wang, Jing Shao, Chen Change Loy, and Lu Sheng. Siamese DETR, In Proc. IEEE Conf. Comput. Vis. Pattern Recognit. pages 15722-15733 15731, 2023. Zhigang Dai, Bolun Cai, Yugeng Lin, and Junying Chen. UP-DETR: Unsupervised pre-training for object detection with transformers. In Pruc. IEEE Conf. Comput. Vis. Pattern Recognit., pages 1601-1610, 2021. [10] Deng, Wei Dong. Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: large-scale hicrarchical image database. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pages 248- 248- 2009. [11] Chengjian Feng. Yujie Zhong. and Weilin Huang. Exploring classification equilibrium in long-tailed object
pages
detection. In Proc. JEEE Ini. Conf. Comput. Vis., pages 3417-3426 2021.
[12] Bran-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Docrsch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Remi Munos, and Michal Valko. Bootstrap your
latent- a new approach self-
supervised learning. In Pruc. Adv Neural Inf. Process. Syst., pages 21271-21284, 2020.
[13] Agrim Gupta, Piotr Dollar, and Ross Girshick. LVIS: A dataset for large vocabulary instance segmentation.
In Proc. IEEE Conf. Comput. Vis. Pattern Recognit. pages 5356 5356-5364, 2019.
[14] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders
are scalable vision learners. In Proc. TEFE Conf. Comput. Vis. Pattern Recognit, pages 15979-15988,
2022.
[15] Kaiming Hc, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit,, pages 2020.
[16] Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. Mask R-CNN. In Proc. IEEE Inf. Conf.
Comput. Vis., pages 2961-2969, 2017.
[17] Kaiming Hc, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell., 1904)999441111 2015. [18] Kaiming He, Xiangyu Zhang. Shaoging Ren, and Jian Sun. Deep residual learning for image recognition. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit. pages 770-778, 2016.
[19] Yin-Yin Hc, Peizhen Zhang, Xiu-Shen Wei, Xiangyu Zhang, and Jian Sun. Relieving long-tailed instance segmentation via pairwise class balance. In Proc. IEFE Conf. Comput. Vis. Pattern Recognit. pages
7000-7009, 2022.
[20] Olivier J Hénaff, Skanda Koppula, Jean- Caan-Baptiste Alayrac Aaron van den Oord, Oriol Vinyals, and João Carreira. Efficient visual pretraining with contrastive detection. In Proc. IEEE Int. Conf. Comput. Vis.,
pages 10086- 10096, 2021.
[21] Ting-I Hsieh, Esther Robb, Hwann-Tzong Chen, and Jia-Bin Huang. DropLoss for long-tail instance segmentation. In Proc. AAAI Conf. Artif. Intell., pages 1549-1557, 2021.
[22] Feiran Hu, Chenlin Zhang, Jiangliang Guo, Xiu-Shen Wei, Lin Zhao, Anqi Xu, and Lingyan Gao. An asymmetric augmented self-supervised lcarning method for unsupervised fine- fine-grained image hashing. In Proc. IEEE Conf. Compur. Vis. Pattern Recognit., pages 17648-17651 2024.
[23] Minyoung Huh. Hossein Mobahi, Richard Zhang. Brian Cheung. Pulkit Agrawal, and Phillip Isola. The low-rank simplicity bias in deep networks, arXiv preprins arXiv:2 10427, 2021.
[24] Jang Hyun
and Philipp Krähenbühh. Long-tail detection with effective class-margins. In Proc. Eur. Conf. Comput. Vis., pages 698-714, 2022.
[25] Sergey loffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proc. Int. Conf. Mach. Learn., pages 448-456, 2015.
[26] Dahun Kim, Tsung-Y, Yi Lin, Anelia Angelova, In So Kweon, and Weicheng Kuo. Learning open-world object proposals without learning to classify. IEEE Trans. Robot. Autom., 7(2):5453-5460, 2022.
[27] Ming Li, Jie Wu, Xionghui Wang. Chen Chen, Jie Qin, Xuefeng Xiao, Rui Wang. Min Zheng, and Xin Pan. AlignDet: Aligning pre- pre-training and tuning in object detection. In Proc. IEEE Ins. Conf. Comput. Vis..
11

---

pages 6866-68768 6876, 2023.
[28]
Yong Li and Shiguang Shan. Contrastive learning of indepeedent representations for facial action
unit detection. IEEE Trans. Image Process., 113212-32255 2023.
[29] Yu Li, Tao Wang. Bingyi Kang, Sheng Tang, Chunfeng Wang, Jintao Li, and Jiashi Feng. Overcoming
classifier imbalance for long-tail object detection with balanced group softmax. In Proc. IEEE Conf.
Comput. Vis, Pattern Recognit. pages 19988719977 2020.
[30] Yong Li, Jiabei Zeng, and Shiguang Shan. Learning representations for facial actions from unlabeled videos. IEEE Trans. Pattern Anal. Mach. Intell., 44(1):302-313 2022.
[31] Yong Li, Jiabei Zeng, Shiguang Shan, and Xilin Chen. Occlusion aware facial expression recognition using enn with attention mechanism. IEEE Trans. Image Process. 28(5):2439 24124444-40 2019.
[32] Yong Li, Jiabei Zeng. Shiguang Shan, and Xilin Chen. Self-supervised representation learning from videos for facial action unit detection. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pages 10916-10925, 2019.
[33] Tsung-Y Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. Feature pyramid networks for object detection. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit, pages
2117-2125, 2017.
[34] Tsung-Yi Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. Focal loss for dense object detection. Proc. IEEE Int. Conf. Comput. Vis., pages 2980-2988, 2017.
[35] Tsung-Yi Yi Lin, Michael Maire, Serge Belongie, James Hays, Pictro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. Microsoft COCO: Common objects in context. In Proc. Eur. Conf. Comput, Vis.,
pages 740-755, 2014.
[36] Songtao Liu, Zeming Li, and Jian Sun, Self-EMD Self-supe object detection without imagenet.
arXiv preprint arXiv:2011, 1.261111777,,. 13677, 2020.
[37] Vinad Nair and Geoffrey E. Hinton. Rectified linear units improve restricted boltzmann machines. In Proc.
Inr. Conf. Mach. Learn pages 807-814, 2010.
[38] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive
coding, arXiv preprint 107xv:1877.03744 2018.
[39] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Z. Yang. Zach Vito,o Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang. Junjie Bai, and Soumith Chintala. PyTorch: An imperative style, high sigh-performance deep learning library. In Proc. Adv. Neural Inf. Process. Syst., pages
2019.
[40] Tianhao Qi, Hongtao Xic, Pandeng Li, Jiannan Gc, and Yongdong Zhang. Balanced classification: A unified framework for long-tailed object detection. IELE Truns. Multimedia, 26:3088-3100 2024.
[41] Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time
object detection. In Proc. JEEE Conf. Comput. Vis. Pattern Recognit.. pages 788, 2016.
[42] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster R-CNN: Towards real-time object detection with region proposal networks. IEFE Trans. Pattern Anal. Mach. Intell., 19(6))1137-1149 2016. Seoopprrosss [43] Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, and Praneeth Netrapall. The pitfalls of simplicity bias in neural networks. In Proc. Adv. Neural Inf. Process. Syst., pages 9573-9585, 2020. [44] Jingru Tan, Xin Lu, Gang Zhang, Changqing Yin, and Quanquan Li. Equalization loss v2: A new gradient balance approach for long-tailed object detection. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit.. 2021.
pages
[45] Jingru Tan, Changbao Wang, Buyu Li, Quanquan Li, Wanli Ouyang, Changqing Yin, and Junjie Yan. Equalization loss for long-tailed object recognition. In Proc. IEFE Conf. Comput. Vis. Pattern Recognit., pages 11662-11671, 2020.
[46] Damien Teney, Ehsan Abbasnejad. Simon Lucey, and Anton Van den Hengel. Evading the simplicity bias: Training a diverse set of models discovers solutions with superior ood generalization. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit. pages 16761-16772, 2022.
[47] Jiaqi Wang. Wenwei Zhang, Yuhang Zang, Yuhang Cao,, Jiangmiao Pang. Tao Gong, Kai Chen, Záwei Liu, Chen Change Loy, and Dahua Lin. Seesaw loss for long-tailed instance segmentation. In Proc. JEEE Conf. Comput. Vis. Pattern Recognit.. pages 9695-9704, 2021.
[48] Peng Wang, Kai Han, Xiu-Shen Wei, Lei Zhang, and Lei Wang. Contrastive learning based hybrid networks for long-tailed image classification. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pages 943-952,
2021.
[49] Tao Wang, Yu Li, Bingyi Kang, Junnan Li, Junhao Liew, Sheng Tang, Steven Hoi, and Jiashi Feng. The devil is in classification: A simple framework for long-tail tail instance segmentation. In Proc. Eur. Conf. Comput. Vis., pages 728 744, 2020.
[50] Tong Wang, Yousong Zhu, Chaoyang 7hao, Wei Zeng, Jinqiao Wang, and Ming Tang. Adaptive class suppression loss for long-tail object detection. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pages
3103-3112, 2021.
[51] Xinlong Wang. Rufeng Zhang, Chunhua Shen, Tao Kong, and Lei Li. Dense contrastive learning for self-supervised visual pre-training In Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pages 3024-3033, 2021.
[52] Fangyun Wei, Yue Gao, Zhirong Wu, Han Hu, and Stephen Lin. Aligning pretraining for detection via object-level contrastive learning. In Proc. Adv. Neural Inf. Process. Syst., pages 22682-22694, 2021.
12

---

[53] Xiu-Shen Wei, Yang Shen, Xuhao Sun. Peng Wang, and Yuxin Peng. Atrrbute-awan deep hashing with self-consistency for large-scale fine-grained image retrieval. IEEE Trans. Pattern Anal. Mach. Intell.,
45(1 19:1344--,3920, 2023.
[54]
Xiu-Shen Wei, Xuhao Sun, Yang Shen, Anqi Xu, Peng Wang. and Faen Zhang. Delving deep into simplicity bias for long-tailed image recognition, arXiv preprint arXiv:2302. 03224, 2023.
[55] Xiu-Shen Wei, He- Yang Xu, Zhiwen Yang, Chen-Long Duan, and Yuxin Peng. Negatives make a positive: An embarrassingy simple approach to supi-sspe few-shot learning. IEEE Trans. Puttern Anal. Mach. Intell., 46(4):2091-2100 2024.
[56] Xiu-Shen Wei, Siu-Lin Xu, Hao Chen, Liang Xiao, and Yuxin Peng. Prototype-based classifier learning for long-tailed visual recognition. Sei. China Inf. Sci., 1606), 2022.
[57] Xin Wen, Bingchen Zhao, Anlin Zheng, Xiangyu Zhang, and Xiaojuan Qi. Self-supervised visual representation learning with semantic grouping. In Proc. Adv. Neural Inf. Process. Syst., pages 16423- 16438, 2022.
[58] Burhaneddin Yaman, Tanvir Mahmud, and Chun-Hao Liu. Instance-aware repeat factor sampling for long-tailed object detection. arXiv preprint arXiv:2305.08000 2023.
[59] Lu Yang. He Jiang. Qing Song, and Jun Guo. survey on long-tailed visual recognition. Int. J. Comput. Vis., 130(7):1837-1872,, 2022.
[60] Shaoyu Zhang. Chen Chen, and Silong Peng. Reconciling object-level and global-level objectives for
long-tai detection. In Proc. IEEE Int. Conf. Comput. Vis., pages 18982-18992. 2023.
[61] Shifeng Zhang, Cheng Chi, Yongqiang Yao, Zhen Lei, and Stan Z. Li. Bridging the gap between anchor- based and anchor-free detection via adaptive training sample selection. In Proc. IEEE Conf. Comput. Vis. Puttern Recognit., pages 9759-9768, 2020.
[62] Yifan Zhang, Bryan Hooi, Lanqing Hong, and Jiashi Feng. -upervied aggregation of diverse experts for test-agnostic long- long-tailed recognition. In Proc. Adv. Neural Inf. Process. Syst., pages 34077-34090, 2022.
[63] Yifan Zhang, Bingyi Kang, Bryan Hooi, Shuicheng Yan, and Jiashi Feng. Deep loig-tailed learning: survey. IEEE Trans. Puttern Anal. Mach. Intell.. 45(9):10795-1088
2023.
[64] Boyan Zhou, Quan Cui, Xiu-Shen Wei, and Zhao-Min Chen. BBN: Bilateral- branch network with cumulative learning for long-tailed visual recognition. In Proc. IEEE Conf. Compur. Vis. Pattern Recognit, pages 97 179997288, 2020.
[65] Beier Zhu, Kaibua Tang. Qianru Sun, and Hanwang Zhang. Generalized logit adjustment: Calibrating fine-tuned models by removing label bias in foundation models. In Proc. Adv. Neural Inf. Process. Syst., pages
2023.
[66] Jianggang Zhu, Zheng Wang, Jingjing Chen, Yi-Ping Phoebe Chen, and Yu-Gang Jiang. Balanced contrastive learning for long-tailed visual recognition. In Proc. IEEE Conf. Comput. Vix. Patlern Recognit., pages 6898-6907, 2022.
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A Appendix supplemental material
In the supplementary materials, we present further information about the proposed 2DRCL pre- training framework, including: 1) More detailed experimental settings, including the specifics of pre-training and downstream fine-tuning, as well as the setup for error analysis; 2) Additional experimental results for further analysis.
A.1 Implementation Details
Pre-training Settings. First, we generate a series of high-quality bounding boxes using a class- agnostic detector [26]. Then, we randomly select 8 bounding boxes from this set for subsequent pre-training. Through the introduction of object proposals, the architectural discrepancy reduced be-
tween pre- pre-training and downstream detection fine-tuning, Faster R-CNN 42 and Mask R- R-CNN [[6]
are commonly adopted frameworks to evaluate transfer performance. We employ MMDetection as our detection framework to conduct our experiment. Both the projection network and prediction network are 2-layer MLPs which consist linear layer with output size 256 followed by batch normalization [25]. rectified linear units (ReL.) [37]. and final linear layer with output dimension 256. Once all views are constructed, we employ the data augmentation pipeline of MoCo (7, Our generator architecture consists of four deconvolutional layers with dimensions (2048, 512), (512, 256), (256, 64), and (64, 3), respectively, and each layer uses a kernel size of 4. ReLU is used for non-linear activation between the layers. Specifically, crop, color distortion, Gaussian blur, and the solarization operation. The models are trained with a total batch size of 16 8 GPUs (RTX3090 with 24 GB VRAM). Unless otherwise specified, all pre-training follows the default 1 x (12 epochs) schedule. In each stage, the learning rate starts at 0.02 and decreases by 0.1 after and 11 epochs, respectively. If not specified, the supervised pre-trained ResNet 18 in PyTorch 39 is used by default for both the pre-training and fine-tuning stages.
apply random horizontal flip, random
baselines, including end-to-end and decoupled methods, such as RFS [33. SeeSaw [47), ECM (24), ROG [60], LOCE [1]] and BACL (40). following their default experiment settings. In terms of the model architecture, we opt for the popular ResNet (18 with FPN (33] as the backbone and train detection models of RaNNer-RCNN and Mask-RCNN for 1x 2x scheduler. We trained the models using SGD with 0.9 momentum. The batch size and learning rate are set as 16 and 0.02, and the data augmentation strictly follows previous long-tailed detection methods [24 47). For 1x schedule with 12 training epochs, the learning rate is initialized as 0.02, and then decays by 0. at the end of epoch 8 and 11. For 2x schedule, models are trained with 24 epochs, and the learning rate decays at the end of epoch 16 and 22. We evaluated our models using both COCO and LVIS metrics. For COCO, we report object detection metrics including average precision for bounding boxes (AP"), AP with an IoU threshold of 50% (AP))) and AP with an IoU threshold of 75% (AP For instance segmentation, report Apmmk (AP for masks), AP and AP The LVIS evaluation includes mean average precision (mAP), AP at an IoU of 50% (APso), AP at an IoU of 75% (AP7s), as well as AP for rare (AP,). common (AP.). and frequent classes (AP,). For Mask R-CNN, we report AP for instance segmentation and Ap for object detection.
Training Details. We reproduce multiple methods with different paradigms
The Setting of Error Analyses. Following the settings of 41 we choose the top N predictions for each category during inference time. Each prediction is classified based the type of error:
Correct: correct class and IOU > 0.5
Location Error: correct class and 0.1 < IOU < 0.5
Background Error: IOU < 0. for any object
Classification Error: class is wrong and IOU > 0.5
Other: class is wrong and 0.1 IOU < 0.5
A.2 Additional Experiment
Consistent Improvements. We evaluate the effectiveness of our method on the LVIS v1.0 dataset by combing the proposed 2DRCL method with existing long-tailed object detection methods. As
14

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Table A.1: Experiments on LVIS v1.0. We combine eight existing methods with our method 22RRCL'. The ResNet-50-FPN and 101-FF- are adopted as backbones for Mask R-CNN. We reproduced all methods using their official code and trained with a schedule, totaling epochs.
LVIS v1.0 ReesNet-50-FPP Apd
LVIS v1.0 (ResNet- 101
(FFN)
Strategy
Schedules
Methods
+Ours
AP
AP
AP
Ap
AP,
AP
AP
22.7
9.1
21.5
30.0
24.8
12.1
23.4
31,9
RFS
13
yes
23.9
11.9
22.3
31.0
25.1
12.7
23.5
32.4
24.4
14.3
22.6
30.8
26.3
16.5
24.5
32.5
IRFS
yes
14.3
22.9
26.5
16.7
24.6
32.8
BQLv2
no
24.9
14.8
24.1
30.4
26.3
17.7
24.4
31.2
End-to-end
12 opochs
yes
25.7
16.5
24.5
31.0
26.9
18.9
25.1
32.5
SeeSaw +7
120
24.7
14.7
23.6
30.4
26.3
15.1
25.4
26.2
17.5
25.0
31.5
27.0
17.6
25.6
32.6
25.4
31.7
27.9
26.5
BCM 24
yes
27.3
19.2
25,9
32.5
28.0
19.5
26.2
33.7
25.7
16.4
24.4
31.2
27.3
18.5
26.2
32.5
ROG
yes
26.2
16.9
24.8
27.6
26.3
27.2
18.7
25.7
32.6
28.5
19.0
27.0
34.3
24+6 epochs
LOCE
Decoupled
yes
18.9
26.5
33.0
28.7
20.2
26.8
34.4
epochs
BACT.
40
n0
26.1
16.0
25.7
30.9
27.2
16.7
26.8
32.3
yes
27.0
17.5
25.9
32.5
28.4
18.9
27.3
33.7
Table A.2: One-stage object detection results on LVIS v1.0 validation set. We compare different methods with ResNet-50 backbone on 2 schedule using ATSS.
Methods
Aph
Aph
AP
ApAs
Focal Loss
34
25.6
14.5
24.3
31.8
ECM 24
26.1
16.6
25.2
31.3
Ours
26.4
17.7
25.4
30.8
shown in Table A.I| using 2DRCL leads consistent Ap improvement over existing classification- based methods, surpassing all of them with large margins. Interestingly, combining our methods can be observed further growth in multiple paradigms. The method 'ECM+2DRCL' (which trained with 1 schedule) can almost achieve the same rare object detection accuracy as the LOCE [II] method, and surpasses BACL 44 for about 1.0% AP Therefore, we speculate that by using 2DRCL during training, the model can generate more balanced feature representations, allowing to achieve comparable results to the decoupled method with minimal training when combined with end-to-end approaches.
Comparison on ATSS Framework. Table A.2 presents the performance comparison of our method against the baseline Focal Loss (34] and ECM Loss 24 on the ATSS [6]] detection framework. Our method achieves the highest overall average precision (AP")) at 26.4%, outperforming both Focal Loss and ECM Loss. Notably, for rare classes, our method significantly improves performance with an A of 17.7%, compared to 14.5% for Focal Loss and 16.6% for ECM Loss. Our method also shows consistent improvement for common classes, surpassing both Focal Loss and ECM Loss. Although Focal Loss achieves the highest precision for frequent classes, our method maintains competitive performance across all categories.
Results on COCO-LT. To further verify the generalization ability of our 2DRCL, we construct long tailed distribution dataset COCO-LT by sampling images and annotations from COCO [35] train 2017 split. Following [49] we divide 80 classes into groups with 20, 20-400, 400-8000, and >= 8000 training instances and report the accuracy for each group as AP1, AP2, AP3, AP4. In Table A.3 we compare our 2DRCL method with the baseline model and several state-of-the-ar long- tailed detection methods on the COCO-LT dataset. The results demonstrate that 2DRCL consistently
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Table A.3: Results on COCO-LT dataset. All experiments were conducted using the Mask R-CNN framework with ResNet-50-FFN backbone and a x training schedule.
Methods
AP
AP1
AP₂
AP3
AP4
CE
18.7
0.0
8.2
24.4
26.0
BAGS
29
21.5
13.4
17.7
22.5
26.0
BQLv2
44
23.1
3.8
17.4
25.8
29.4
Scesaw 47
22.7
3.4
15.5
26.2
28.5
ECM
24
22.9
11.0
18.7
25.7
28.7
Ours
24.4
14.4
20.2
26.1
29.4
Table A.4: Efficiency evaluation of Mask R-CNN with ResNet50-FPN.
Methods
VRAM
Training Time
Aphs
APA
ECM (12 epochs)
16.1
26.5
17.0
+AlignDet (6+6 epochs)
103 GB
19.5 h
26.2
16.7
+Ours (6+6 epochs)
106 GB
20.6 h
27.0
19.0
outperforms the baseline model by a notable 5.7% in overall AP. Notably, 2DRCL achieves the highest AP across both group 1 and group outperforming the closest competitor, ECM, by 3.4% and 1.5% AP, respectively. We attribute these improvements to 2DRCL's ability to mitigate the simplicity bias toward tail classes during pre-training, which contrasts with methods such as Seesaw and ECM that primarily address the issue of unequal competition between foreground classes without sufficiently addressing the inherent bias in representation learning. By directly confronting these biases, our method demonstrates substantial gains in tail category performance while maintaining strong results across the full distribution.
Efficiency Evaluations. Table A.4 compares the VRAM usage, training time, and performance of different methods for long-taile object detection. Our method, which includes epochs of pre- training followed by 6 epochs of fine-tuning, utilizes slightly more VRAM and training time compared AlignDet and ECM. Despite the increased computational cost, our method delivers a notable performance boost, achieving the highest AP of 27. 37.0% and AP of 19.0%, demonstrating a clear performance-coe trade-off. These results indicate that while our pre-training strategy demands more resources, it does not negatively impact fine-tuning performance and offers significant improvements in long-tailed detection.
Qualitative results. We present additional visualization results on the LVIS dataset For simplicity, we use RFS [13] with Cross-Entrops (CE) loss as the baseline and combine it with our 2DRCL method. As shown in Figure both the baseline and our 2DRCL detect most objects in an image, but 2DRCL generally captures more details. For instance, 2DRCL helps discover missed boxes, such as the horse carriage in the center-left images. Additionally, 2DRCL generates more accurate bounding box predictions, like correctly identifying boats on the ground. While using basic method still results in numerous classification errors, this issue can be mitigated by carefully designing the loss function.
Bias Analyses. We visualize how 2DRCL mitigates the weight norm bias induced by long-tailed distributions. Figure A.2a shows the classifier weight norm distribution across classes for models trained with the LVIS v1.0 training split. The transparent lines represent actual weight norms, while the solid lines show polynomial fits, providing a smoothed interpretation of trends. Our 2DRCL method achieves weight norm performance comparable to ECM 24), indicating effective bias mitigation. Figures A.2b A.2c further illustrate that each component of 2DRCL contributes to reducing weight norm bias. Despite these gains, some advantages achieved at the feature level are slightly diminished during downstream fine-tuning, limiting overall progress in final outcomes.
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Figure A.I: Visualizations of detection results before (in the left of each group) and after (in the right) using our 2DRCL. We adopted RFS [13 as the baseline in LVIS and combined it with our 2DRCL pre- pre-training method. comparison, the proposed method is good at detecting missing objects and rectifying bounding box predictions. This figure needs to be viewed in color.
Drught camdoot
-0121
440 4740
Freqarncy
(a)
(b)
(c)
Figure A.2: (a) and (b) are classifiers' weight norm distribution across different classes in Mask R- CNN models trained with the LVIS v1.0 training split [13.. The X-axis represents the sorted category index based on category frequency. The Y-axis shows the weight norm. Transparent lines depict the actual weight norms for each category, providing raw look at the data distribution. The solid lines represent polynomial curves fitted to the transparent data, offering a smoothed interpretation trends across classes. (a) represents the comparison with the tfate-of-the-we art methods, while (b) represents the comparison with the proposed components in this paper. (c) represents the average weight norm of the classifiers for each frequency category.
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24


Reviews:
[{"review_id": "Lb7Np8GBFm", "reviewer": "Reviewer_kReB", "summary": "The authors proposed a pre-training method for long-tail object detection. Specifically, the authors integrated holistic and object-level contrast within a contrastive learning framework, used a dynamic rebalancing technique to transition from image-level resampling to instance-level resampling, and implemented a dual reconstruction strategy to maintain natural appearance and internal semantic consistency. Extensive experiments on LVIS demonstrated its effectiveness.", "questions": "see weakness", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1) The data in the world follows the long-tailed distribution and it is meaningful to solve the imbalanced object detection problem.\n2) The paper is well organized.\n3) The proposed method can be combined with different existing methods.", "weaknesses": "1) The long-tailed object detection problem has already been explored, the author should give more discussion about the existing works and the proposed method.\n2) The proposed method is too complex and involves different losses to supervise the learning. The author should give more analysis of these losses and discuss their relationship.\n3) The author should evaluate the method on more datasets, like COCO-LT.\n4) Too many hyper-parameters and how to set them is confusing.\n5) The author compares the proposed method to different self-supervised learning methods. However, I think traditional methods like MOCO,  SimCLR, etc are potential baselines and should also be compared.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## Thank you for the comments. Below please find our responses to some specific comments. \n\n*Comment_1: The author should give more discussion about the existing works and the proposed method.*\n\nResponse_1: Our proposed method addresses a significant gap in long-tailed object detection by introducing a pretraining strategy that specifically mitigates the simplicity bias in underrepresented tail classes through a component designed for this purpose (DRCL). This approach effectively addresses the insufficiency in tail class feature representation seen in current methods. By emphasizing tail class features, our method integrates seamlessly with existing long-tailed detection approaches, resulting in improved detection performance. We will further include a more detailed discussion of related work and the unique contributions of our method in the revised version of the paper.\n\n---\n\n*Comment_2: The author should give more analysis of these losses and discuss their relationship.*\n\nResponse_2: In the paper, we have indeed conducted a detailed ablation study of these losses in Table 4. Furthermore, we also supplement this with an ablation study specifically for the Holistic-Object Contrastive (HOC) component.\n\n| Holistic-level Contrast | Object-level Contrast | DRB | AR | SR | $\\mathrm{AP}^{b}$ | $\\mathrm{AP}_{r}^{b}$ | $\\mathrm{AP}_{c}^{b}$ | $\\mathrm{AP}_{f}^{b}$ |\n|---|---|---|---|---|---|---|---|---|\n| × | × | × | × | × | 22.7 | 9.1 | 21.5 | 30.0 |\n| √ | × | × | × | × | 22.5 | 10.5 | 21.0 | 29.3 |\n| × | √ | × | × | × | 21.9 | 9.8 | 20.8 | 28.7 |\n| √ | √ | × | × | × | 22.4 | 10.8 | 21.1 | 29.0 |\n| √ | √ | √ | × | × | 23.8 | 14.3 | 22.3 | 30.1 |\n| √ | √ | √ | √ | × | 24.2 | 14.9 | 22.6 | 30.3 |\n| √ | √ | √ | √ | √ | **24.4** | **15.2** | **22.7** | **30.3** |\n\nFrom the results presented in the table, it is evident that both the Holistic-level Contrast and Object-level Contrast individually contribute to improved performance on tail classes. The Holistic-level Contrast drives the model to learn global differences and similarities across images, enhancing overall feature learning. The Object-level Contrast focuses on capturing local differences and similarities, refining features crucial for object detection tasks.\n\nThe Dual Reconstruction approach aims to enrich the feature representation of tail classes. The Appearance Consistency Reconstruction focuses on pixel-level reconstruction. Furthermore, we introduce Semantic Consistency Reconstruction, which ensures that the model pays more attention to semantic consistency rather than just pixel-wise details. This enhances the model's robustness by promoting semantic-level understanding and reducing overfitting risks. We will include these results in the revised paper.\n\n---\n\n*Comment_3: The author should evaluate the method on more datasets, like COCO-LT.*\n\nResponse_3: We supplement our experiments with evaluations on the COCO-LT dataset. As shown in the table, our approach outperforms existing methods with an overall average AP improvement of 1.3%. It demonstrates a significant advantage, particularly in tail classes.\n\n| Method | $\\mathrm{AP}$ | $\\mathrm{AP}_{1}$ | $\\mathrm{AP}_{2}$ | $\\mathrm{AP}_{3}$ | $\\mathrm{AP}_{4}$ |\n|---|---|---|---|---|---|\n| Base | 18.7 | 0.0 | 8.2 | 24.4 | 26.0 |\n| EQLv2 | 23.1 | 3.8 | 17.4 | 25.8 | **29.4** |\n| Seesaw | 22.9 | 3.4 | 15.5 | **26.2** | 28.5 |\n| ECM | 22.7 | 11.0 | 18.7 | 25.7 | 28.7 |\n| BAGS | 21.5 | 13.4 | 17.7 | 22.5 | 26.0 |\n| Ours | **24.4** | **14.4** | **20.2** | 26.1 | **29.4** |\n\n---\n\n*Comment_4: Too many hyper-parameters and how to set them is confusing.*\n\nResponse_4: We used the default settings (cf. L113, L130, L183) as provided in the paper for all experiments, without extensive hyper-parameter tuning. This demonstrates the robustness of our method and its insensitivity to parameter changes. In fact, tuning the hyper-parameters is straightforward. It can be accomplished by dividing the dataset to include a validation set and systematically adjusting the parameters.\n\n---\n\n*Comment_5: The author compares the proposed method to different self-supervised learning methods. However, I think traditional methods like MOCO, SimCLR, etc are potential baselines and should also be compared.*\n\nResponse_5: We conduct additional experiments with traditional methods such as MOCO, SimCLR, and BYOL, following your suggestions for comparison. We will include these results and their analysis in our final version to provide a comprehensive comparison.\n\n| Method | $\\mathrm{AP}^{b}$ | $\\mathrm{AP}_{r}^{b}$ | $\\mathrm{AP}_{c}^{b}$ | $\\mathrm{AP}_{f}^{b}$ |\n|---|---|---|---|---|\n| MoCo v3 | 14.5 | 3.9 | 12.4 | 21.6 |\n| SimCLR | 19.9 | 8.0 | 18.1 | 27.1 |\n| BYOL | 15.3 | 5.4 | 13.2 | 21.9 |\n| Ours | **23.9** | **11.9** | **22.3** | **31.0** |", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "f2SgPZJIFS", "reviewer": "Reviewer_gQpU", "summary": "This paper tackles the underperformance of object detection on long-tailed datasets using a novel pretraining methodology called Dynamic Rebalancing Contrastive Learning with Dual Reconstruction (DRCL). DRCL addresses biases in classifier weight norms and feature representation by integrating holistic and object-level contrasts, employing dynamic rebalancing from image-level to instance-level resampling, and maintaining both natural appearance and semantic consistency. Combining self-supervised and supervised learning, DRCL reduces pretraining time and resources, achieving state-of-the-art performance on the LVIS dataset across multiple detection frameworks and backbone networks.", "questions": "What are the specific values of the trade-off parameters in Equation 9?", "limitations": "NA", "rating": 7, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "1. **Clear Presentation:** The paper is well-presented and easy to follow, ensuring that the research is accessible and comprehensible.\n\n2. **Practical and Valuable Research:** The focus on long-tailed object detection is highly practical and valuable, addressing a significant challenge in the field. The motivation behind the proposed methodology is logical and well-founded.\n\n3. **Innovative Methodology:** The approach demonstrates considerable innovation, and the findings, such as those illustrated in Figure 2, effectively support the motivation and design of the method.\n\n4. **Effective Experimental Validation:** The experimental results robustly validate the effectiveness of the proposed method, demonstrating its superiority in handling long-tailed distributions in object detection.", "weaknesses": "1. The paper lacks qualitative analysis, which could provide a more in-depth understanding of the model's performance and behavior.\n\n2. There is an absence of specific failure case analysis, making it difficult to understand the conditions under which the proposed method may not perform well.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## Thank you for the positive comments. Below please find our point-to-point responses.\n\n*Comment_1: The paper lacks qualitative analysis, which could provide a more in-depth understanding of the model's performance and behavior.*\n\nResponse_1: We have already included analyses in our paper with Figure 5, presenting visualized results of the detected bounding boxes, Figure 3, showcasing feature activation maps to highlight the advantages of our method, and Figure 4, presenting an error analysis. In the final version, we will conduct a deeper analysis and consider providing additional qualitative analyses, such as more detailed error analysis, class-specific performance examples. These additions will provide a comprehensive understanding of our model's strengths and areas for improvement.\n\n---\n\n*Comment_2: There is an absence of specific failure case analysis, making it difficult to understand the conditions under which the proposed method may not perform well.*\n\nResponse_2: We will address it by including a detailed analysis of failure cases in the final version of our paper. This will help in understanding the limitations of our method and provide insights for further improvements.\n\n---\n\n*Comment_3: What are the specific values of the trade-off parameters in Equation 9?*\n\nResponse_3: In our experiments, the trade-off parameters in Equation 9 for \\mathcal{L}_{HOC}, \\mathcal{L}_{DRC} and \\mathcal{L}_{det} were all set to 1. We will ensure this information is clearly stated in the final version of the paper.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "VgkPMObyih", "reviewer": "Reviewer_XbTc", "summary": "The paper proposes DRCL, an object detection pretraining methodology for datasets with long-tailed object class distributions. Their proposed framework consists of three losses: 1) image-level constrastive instance discrimination, 2) object-level contrastive instance discrimination, and 3) a reconstruction loss, with both pixel-wise and feature-wise components. Furthermore, the paper proposes a rebalancing sampling method that takes into account both image count and instance count when estimating the repeat factor per image. The results indicate that the proposed pretraining method is beneficial for long-tailed object detection.", "questions": "In my opinion, the most important issue is related to the experiments, based on my understanding that the method is not self-supervised and that the results in Table 1 compare \"12 ep. DRCL pretraining + 12 ep. long-tailed finetuning\" with \"12 ep. long-tailed finetuning\". If that is indeed the case, I believe different experiments are required to support the claims of the paper. Specifically, as mentioned in the weaknesses, I would suggest that the authors include in the paper: \n\\\n**a)** the computational cost (Time/epoch & VRAM requirements) of their method compared to the most significant alternative works (supervised long-tailed and self-supervised),\n\\\n**b)** the results of finetuning baseline long-tailed methods for the same number of epochs (or a computationally fair equivalent) as the proposed method (including the finetuning). \n\\\nOverall, as I mentioned in the weaknesses, given that the proposed method requires some label information, to demonstrate its utility the authors must, in my opinion, demonstrate that it is worth pretraining and then finetuning, rather than simply finetuning for longer (or even with a bigger model given the, I assume, increased VRAM requirements of DRCL).\n\nRegarding the novelty issue I raised in weakness 3, I would welcome the authors' highlighting aspects I might have missed or underestimated regarding the novelty in their proposed approach. \n\nI emphasize, however, that my main concern with the paper relates to the (in my opinion) insufficient evaluations, and that the writting issues and the novelty concern are secondary.", "limitations": "The authors adequately addressed the limitations.", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "**1)** The proposed framework is intuitive and clearly presented.\n\n**2)** Leveraging self-supervised methodologies for detection pretraining is a proven approach. The proposed method specifically is interesting in that it demonstrates that different self-supervised objectives (image-level contrast, object-level contrast, class-unaware detection and reconstruction) can be effectively incorporated in a single pipeline.\n\n**3)** The proposed pretraining is beneficial, as demonstrated by the paper's results.\n\n**4)** The problem tackled by the paper (i.e. tackling datasets with imbalanced classes) is significant in the object detection domain.", "weaknesses": "**1)** The proposed method is not self-supervised, as the dynamic rebalancing component implies and requires object label information for the images of the training set. Specifically, we need to know in advance how many instances of each class are present in each specific image. That should be clear in the paper, as there is phrasing that implies the proposed method is self-supervised (L35-36, L226-230).\n\n**2)** I believe there are several issues with the evaluations conducted and the results presented:\n\\\n**2A)** The comparisons of Table 1 are unfair. Compared to the baseline long-tailed frameworks the proposed method requires twice as much training (12 pretraining epochs on top of the 12 finetuning epochs). Therefore, that it leads to performance gains is to be expected. A fair comparison would require roughly equal computational budget. Succinctly: since pretraining and fine-tuning are done on the same dataset, the key question is not whether additional training with the proposed method is beneficial (which is what is shown in Table 1 and is trivial), but if the same computational budget is better spent pretraining and finetuning instead of simply finetuning for longer with a baseline long-tailed framework.\n\\\n**2B)** The comparisons in Table 3 are, in my opinion, not meaningful. Regarding SoCo, it pretrains the backbone from scratch, whereas AlignDet and the proposed method use a pretrained (indeed supervised) backbone. More broadly, they are fully self-supervised methods whereas the proposed method leverages label information. For a fair comparison, the proposed method should be evaluated without the dynamic rebalancing component, with the same pretraining data, and taking into account the computational cost of pretraining.\n\\\n**2C)** The pretraining time and VRAM requirements of the method should be included in the paper. If I understand correctly, the proposed method requires (for each training step) 3 backbone forward passes (2x encoder + 1x momentum encoder), 2 passes from the detection head, and 1 decoder pass. The computational cost is likely much greater than the baseline long-tailed methods and self-supervised methods like AlignDet, a fact which should be taken into account when contrasting the results presented.\n\\\n**2D)** The proposed method is only applied to one dataset (for both pretraining and finetuning) and with only one type of detector (Faster/Mask R-CNN).\n\n**3)** The novelty of the proposed method is, in my opinion, somewhat limited. In effect, it is a combination of MoCo (image level instance discrimination), SoCo (object level instance discrimination), and masked image modelling methods (pixel/feature reconstruction).  The dynamic rebalancing method is a minor improvement over [11]. Overall, while the effective combination of these methods does represent a meaningful contribution, I am not sure it is up to the level of NeurIPS. I also consider important in this regard the framework's efficiency: if the authors demonstrated that they have combined these approaches while keeping the framework relatively efficient (compared to the original approaches) the impact of the proposed method would be significantly greater.\n\n**4)** The paper is, in places, poorly written and imprecise:\n\\\n**4A)** Abstract: \"By synergistically combining self12 supervised and supervised learning modalities, our approach substantially reduces pretraining time and resource demands.\" I think the term modality is used incorrectly in this context, and nowhere in the paper is it claimed and/or supported that the proposed method is more efficient that the alternatives. Indeed, the opposite is mentioned in the conclusion (L291).\n\\\n**4B)** Sec. 3.2 \"training\" should be replaced with \"pre-training\" to make clear that this applies to the pretraining stage, not the downstream task fine-tuning (training) stage, following the distinction the authors make in Appendix Sec. 1.\n\\\n**4C)** L189: \"We combine our DRCL with existing long-tailed methods\". I assume the authors mean they pretrain with their method and fine-tune with the corresponding long-tailed methods. This should be stated clearly to avoid confusion.\n\\\n**4D)** Pretraining/training epochs: In L199-202, L431, L437, and L447 references are made to pretraining and training epochs. This information should be condensed and made clearer to avoid confusion between training and pretraining.\n\\\n**4E)** It is not mentioned which class-agnostic object detector is used to extract object proposals for detection pretraining, what is the architecture of the generator, and the width of the projection/predictor MLPs.\n\\\n**4F)** L240 mentions that SoCo is pretrained for 530 epochs. This is misleading without the clarification that it pretrains the backbone from scratch, whereas the proposed method and AlignDet use pretrained backbones. Additionally, it is my understanding that AlignDet and the proposed method are pretrained for approximately the same number of steps (AlignDet pretrains for 12 epochs on COCO's train set). Therefore, the \"despite their extensive training epochs\" comment in L242 is misleading.\n\n**5F)** The related works section is very limited. It should properly reference at least the most established and relevant self-supervised methods for backbone (MoCo, SwAV, DINO, SimCLR etc. for object-centric and SoCo, SlotCon, Odin, DetCon etc. for scene-centric pretraining) and for detector pretraining (Up-DETR, DETReg, CutLER etc.). Furthermore, it is unclear what is meant by the fact that AlignDet \"decouples\" pretraining to avoid high costs. Similar to the proposed method, AlignDet uses a pretrained backbone, the only distinction being that the backbone is frozen during detection pretraining. If that is what is meant, it should be made clear.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## Thanks for the comments.\n\nComment_1: The proposed method is not self-supervised.\n\nResponse_1: The self-supervised components mentioned in our paper refer specifically to the Holistic-Object Contrastive Learning and Dual Reconstruction training modes. In the final version, we will eliminate any ambiguous statements and make it clear that our method involves supervised elements due to the dynamic rebalancing component requiring object label information.\n\nComment_2A: The comparisons of Table 1 are unfair.\n\nResponse_2A: To ensure fairness, we first employed AlignDet, one of the best current pretraining frameworks for object detection, to perform 12 epochs of pretraining on the LVIS dataset. Subsequently, we used a selection of representative long-tailed object detection methods for comparison. The results showed that our method provides significant improvements. \n\n| Methods | $\\mathrm{AP}^{b}$ | $\\mathrm{AP}_{r}^{b}$ | $\\mathrm{AP}_{c}^{b}$ | $\\mathrm{AP}_{f}^{b}$ |\n|---|---|---|---|---|\n| AlignDet+RFS | 22.8 | 10.1 | 21.2 | 30.2 |\n| Ours+RFS | **23.9** | **11.9** | **22.3** | **31.0** |\n|\n| AlignDet+Seesaw | 25.2 | 15.3 | 23.5 | 30.9 |\n| Ours+Seesaw | **26.2** | **17.5** | **25.0** | **31.5** |\n|\n| AlignDet+ECM | 26.5 | 17.9 | 25.2 | 31.6 |\n| Ours+ECM | **27.3** | **19.2** | **25.9** | **32.5** |\n|\n| AlignDet+ROG | 25.6 | 16.1 | 24.6 | 31.1 |\n| Ours+ROG | **26.2** | **16.9** | **24.8** | **31.8** |\n\nComment_2B: The comparisons in Table 3 are not meaningful.\n\nResponse_2B: Following your suggestion, we conduct additional experiments with a modified setup without the dynamic rebalancing component and with the same pretraining data. Specifically, we replaced the from-scratch backbone of SoCo with a pretrained backbone, denoted as SoCo*, and performed the same pretraining on the LVIS dataset. The experimental results show that AlignDet outperforms SoCo*, and our method surpasses AlignDet. This demonstrates the effectiveness of our approach.\n\n| Methods | $\\mathrm{AP}^{b}$ | $\\mathrm{AP}_{r}^{b}$ | $\\mathrm{AP}_{c}^{b}$ | $\\mathrm{AP}_{f}^{b}$ |\n|---|---|---|---|---|\n| SoCo* | 22.4 | 8.9 | 20.1 | 30.3 |\n| AlignDet | 22.8 | 10.1 | 21.2 | 30.2 |\n| Ours | **23.7** | **11.0** | **21.9** | **31.1** |\n\nAlignDet is one of the most advanced pretraining schemes for object detection and has been compared with SoCo, highlighting SoCo as a strong competitor. By comparing our method with them on the LVIS dataset, we aim to demonstrate that our method is more suitable for handling long-tailed data.\n\nComment_2C: The pretraining time and VRAM requirements of the method should be included in the paper. \n\nResponse_2C: We will include this information in the final version of the paper to ensure a comprehensive understanding of the computational resources involved.\n\nComment_2D: The proposed method is only applied to one dataset and with only one type of detector.\n\nResponse_2D: Following your suggestion, we further conduct experiments on the COCO-LT dataset, and report the results in the following table. As shown, the proposed method demonstrates a significant advantage.\n\n| Method | $\\mathrm{AP}$ | $\\mathrm{AP}_{1}$ | $\\mathrm{AP}_{2}$ | $\\mathrm{AP}_{3}$ | $\\mathrm{AP}_{4}$ |\n|---|---|---|---|---|---|\n| Base | 18.7 | 0.0 | 8.2 | 24.4 | 26.0 |\n| EQLv2 | 23.1 | 3.8 | 17.4 | 25.8 | **29.4** |\n| Seesaw | 22.9 | 3.4 | 15.5 | **26.2** | 28.5 |\n| ECM | 22.7 | 11.0 | 18.7 | 25.7 | 28.7 |\n| BAGS | 21.5 | 13.4 | 17.7 | 22.5 | 26.0 |\n| Ours | **24.4** | **14.4** | **20.2** | 26.1 | **29.4** |\n\nAdditionally, we conduct experiments with another type of detector, i.e., ATSS [Ref 1], and report the results in the following table. As shown, our method achieves the best results.\n\n| Methods | $\\mathrm{AP}^{b}$ | $\\mathrm{AP}_{r}^{b}$ | $\\mathrm{AP}_{c}^{b}$ | $\\mathrm{AP}_{f}^{b}$ |\n|---|---|---|---|---|\n| Focal Loss | 25.6 | 14.5 | 24.3 | **31.8** |\n| ECM | 26.1 | 16.6 | 25.2 | 31.3 |\n| Ours | **26.4** | **17.7** | **25.4** | 30.8 |\n\n[Ref 1] Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection, CVPR, 2020.\n\n\nComment_3: The novelty of the proposed method is limited. \n\nResponse_3: We would like to emphasize that we inspect long-tailed object detection through the lens of simplicity bias (SB). As described in L138-148 and observed in Figure 3, we found that tail classes in long-tailed object detection suffer significantly from SB, a problem that has not been adequately explored by existing methods. Based on this observation and mechanism, we specifically proposed Dual Reconstruction to tackle this problem. As illustrated in Figure 3 of the paper, the inclusion of Dual Reconstruction significantly enriches the feature representation of tail classes and alleviates the SB problem. This provides new insight into the field.\n\nAdditionally, the dynamic resampling strategy is designed explicitly for long-tailed object detection. These contributions highlight the novelty and targeted nature of our approach. As highlighted by Reviewer ZXbG in Strengths_2, the modules in our proposed method are specifically tailored for long-tailed object detection.\n\nSimply combining existing methods would result in significant resource and time consumption, with a pretraining speed of 2.37 s/iter. Through our efficient implementation of a unified framework, we have optimized GPU utilization by employing mixed-precision training and limiting the number of generated bounding boxes used for pretraining to a maximum of 8 per image. These optimizations have reduced the pretraining speed to 0.96 s/iter. It ensures the practicality of our method without requiring more powerful hardware resources.\n\nComment_4: The paper is, in places, poorly written and imprecise. The related works section is very limited.\n\nReponse_4: We will thoroughly review the entire paper to address these issues, making necessary corrections and additions in the final version. If needed, we can provide specific details during the discussion stage.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "0XU4drmI9W", "reviewer": "Reviewer_ZXbG", "summary": "This paper proposes Dynamic Rebalancing Contrastive Learning with Dual Reconstruction (DRCL) to tackle long-tailed object detection challenges. DRCL integrates dynamic rebalancing to address instance-level imbalance and a dual reconstruction strategy to enhance feature representation for tail categories. Experimental results on the LVIS v1.0 dataset demonstrate that DRCL achieves State-of-the-Art performance, significantly improving detection accuracy for rare categories across various detectors and backbones. The method shows an average improvement of 0.9% APb, with substantial gains in rare categories (+1.5% APbr), highlighting its effectiveness and competitiveness.", "questions": "Figure 4 brings more insightful information. However, more discussions are encouraged to be involved in the corresponding content.", "limitations": "NA", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "+ This paper is well-written and clearly structured, making it easy to follow the methodology and understand the contributions. The clarity in presentation enhances the readability and ensures that the proposed concepts and findings are effectively communicated.\n\n+ The motivation behind the proposed method is strong, focusing on the critical issue of long-tailed object detection. The design of the modules within the method is well-targeted and thoughtfully crafted to address this specific problem, showing a deep understanding of the challenges involved.\n\n+ The experimental results are robust, demonstrating significant improvements in performance. The method's versatility is highlighted by its applicability across various detectors and backbones, reinforcing its potential for widespread use in different settings.", "weaknesses": "One weakness of the paper is that some section titles could benefit from further refinement. For instance, the titles of sections 3.1.1, 3.1.2, and 4.3 may not fully capture the content they encompass, potentially causing confusion for readers. Improving the clarity and descriptiveness of these section names would enhance the overall structure and navigability of the paper.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## Thank you for the positive comments. Below please find our point-to-point responses.\n\n*Comment_1: One weakness of the paper is that some section titles could benefit from further refinement. For instance, the titles of sections 3.1.1, 3.1.2, and 4.3 may not fully capture the content they encompass, potentially causing confusion for readers. Improving the clarity and descriptiveness of these section names would enhance the overall structure and navigability of the paper.*\n\nResponse_1: In the final version of the paper, we will improve the clarity and descriptiveness of these section titles to enhance the overall structure and navigability of the paper. This will help ensure that the content is clearly communicated and easily understood.\n\n---\n\n*Comment_2: Figure 4 brings more insightful information. However, more discussions are encouraged to be involved in the corresponding content.*\n\nResponse_2: In the final version of the paper, we will include a deeper analysis of the various types of errors presented in Figure 4, combined with more extensive data. This will provide a more comprehensive discussion and insights into the conditions under which our method performs well and where it may need improvement.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: The paper explores long-tailed object detection, introducing Dynamic Rebalancing Contrastive Learning with Dual Reconstruction (DRCL) to mitigate biases. Initially, reviewers expressed concerns about the approach's complexity and the lack of sufficient experimental results. However, the authors successfully addressed these issues in their rebuttal, leading to unanimous positive feedback from the reviewers. After reviewing the comments and rebuttal, the AC recommends acceptance. The authors are encouraged to incorporate the feedback, particularly concerning the discussion and connection to prior work, into the final camera-ready version.
Decision: Accept (poster)