Causality Compensated Attention for Contextual Biased Visual Recognition
Authors: Ruyang Liu
Jingjia Huang
Thomas H. Li
Ge Li
Venue: ICLR 2023 poster (2023)
Keywords: Causal Inference
Object Recognition
Attention Mechanism
Confounding Context
Interventional Dual Attention

Abstract: Visual attention does not always capture the essential object representation desired for robust predictions. Attention modules tend to underline not only the target object but also the common co-occurring context that the module thinks helpful in the training. The problem is rooted in the confounding effect of the context leading to incorrect causalities between objects and predictions, which is further exacerbated by visual attention. In this paper, to learn causal object features robust for contextual bias, we propose a novel attention module named Interventional Dual Attention (IDA) for visual recognition. Specifically, IDA adopts two attention layers with multiple sampling intervention, which compensates the attention against the confounder context. Note that our method is model-agnostic and thus can be implemented on various backbones. Extensive experiments show our model obtains significant improvements in classification and detection with lower computation. In particular, we achieve the state-of-the-art results in multi-label classification on MS-COCO and PASCAL-VOC. 

Published as a conference paper at ICLR 2023
CAUSALITY COMPENSATED ATTENTION FOR CON- TEXTUAL BIASED VISUAL RECOGNITION
Ruyang Liu
Jingjia Huang" Thomas H. Li'
Li
"choool of Electronic and Computer Engineering, Peking University "ByteDance Inc (ruyangestu, ge1 8ece, thomas@] pku. edu.en huangjing ingji albytedance.com
ABSTRACT
Visual attention does not always capture the essential object representation desired for robust predictions. Attention modules tend to underline not only the target ob- ject but also the common co-oceurring context that the module thinks helpful in the training. The problem is rooted in the confounding effect of the context leading incorrect causalities between objects and predictions, which is further exacerbated by visual attention. In this paper, learn causal object features robust for con- textual bias, we propose a novel attention module named Interventional Dual At- tention (IDA) for visual recognition. Specifically, IDA adopts two attention layers with multiple sampling intervention, which compensates the attention against the confounder context. Note that our method is model-agnostic and thus can be im- plemented on various backbones. Extensive experiments show our model obtains significant improvements in classification and detection with lower computation. In particular, we achieve the state-of of-the-art results in multi-label classification on MS-COCO and PASCAL-VOC.
INTRODUCTION
The last several years have witnessed the huge success of attention mechanisms in computer vision. The key insight behind different attention mechanisms (Wang et al., 2018 Hu et al. 2018 Woo et al.) 2018 Chen et al., 2017: Zhu & Wu 2021: Dosovitskiy et al. [2200] Liu et al., (021) is the same, Le., emphasizing key facts in inputs, while different aspects of information such as feature map and token query are considered. The impressive performances of these works show that attention
proficient in fitting training data and exploiting valuable features.
However, whether the information deemed valuable by attention is always helpful real application scenarios? In Fig aa), the Spatial Class-Aware Attention (SCA) considers the context dining table as the cause of spoon, which helps the model make correct predictions even if the stressed region is wrong, but it fails in cases where the object is absent in the familiar context. [ib[[b) illustrates the problem in a quantitative view: we calculate the mIOU between the attention map (or the activation map for baseline) and the ground-truth mask. As is shown in the figure, although the model with attention gains some improvement measured by common evaluation metrics, the mIOU does not see progress, which means attention does not capture the more accurate regions of targets than the baseline. Moreover, Fig [[c) illustrates the attention mechanism more easily attends to the wrong context regions when the training samples are insufficient. All above evidence reveals that attention mechanisms may not always improve the performance of the model by accessing meaningful factors and could be harmful, especially in the case of -iit----iitibutiion scenarios.
Fortunately. the causality provides us with a theoretical perspective for this problem (Pearl et al., 2000 Neuberg, 2003). The familiar context of an object confounder (Yue et al., 2020: /Zhng et al. 2020: Wang et al.] (020) confusing the causality between the object and its prediction. Even though the scene of the dining table is not the root cause for the spoon, the model is fooled into setting up a spurious correlation between them. On the other hand, the problem is tricky because context is naturally biased in the real world, and common datasets annotated by experts (e.g.. MS- COCO (Lin et al. 2014)) also suffer from severe contextual bias. As is well known, networks equipped with attention can leam better representations of the datasets. However, the contextual bias in datasets can be also exacerbated when using the attention mechanism.

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Published as a conference paper at ICLR 2023
base
SCA MS-SCA
0.45
SCA
MS-SCA
0.365
83.5
prob of "ork"" 0.783
82.5
0.965
0.215
0.345
0.315
80.5
0.255
prob of "fork": 0.694 (a]Two qualitative results
mAp
mIOU of attention map
head
tail
(b]Comparison of mAP and miou
(c)mIOU from head to tail classes
Figure I: (a) The attention maps of two examples in MS-COCO (Lin et al. 2014) using ResNet101 + SCA (our baseline attention). (b) The mAP and attention map mIOU of ResNet101 baseline, SCA, and the MS-SCA (our de-confounded model). (c) Attention map mIOU of the three models from head classes tail classes.
To tackle the confounders in visual tasks, common method is the causal intervention Pearl et al. 2000 Neuberg 2003). The interventions in most existing methods (Wang al. 202T] Yang al., 2021b Yue et al. 2020 Zhang et al. 2020) share maturity pipeline: defining the causality among given elements, implementing the confounders and ultimately implementing the backdoor adjust- ment (Pearl] 2014). Most of these methods, however, are difficult to implement anism and migrate among different tasks. this paper, we prove that a simple weighted multi- sampling operation
attention mech-
attention can be viewed the intervention for the attention and confounding context. Based on that, we develop a novel causal attention module: Interventional Dual Attention (IDA). We first employ spatial class-aware attention (SCA) extract the class-specifie information in different positions of the feature map. Then, a multiple-sampling operation with Dot-Product Attention (DPA) re-weighting is implemented upon the SCA, which is essentially the causal inter- vention and builds more robust attention map insensitive to the contextual bias. To receive better trade-off between the performance and computation, we have two versions of IDA: the light one (pure two layers of attention) achieves huge improvements with limited parameter increment; the heavy one (the DPA extended a transformer) obtains the state-of-the-arte results with lower computation compared with the popular mansformer-based models on the multi-label classification task. Furthermore, improvements both classification and detection demonstrate the potential of method to be applied in general visual tasks.
Our main contributions can be summarized as follows:
We uncover that the attention mechanism may aggravate the existing contextual bias. Qual- itative analysis from the viewpoint of causality as well as experimental results is presented to guarantee our arguments.
We propose an effective and model-agnostic attention module Interventional Dual Atten- tion (IDA). Combining two different layers of attention and the multi-sampling interven- tion, the module is robust to the contextual bias, and can be extended to the popular trans- former to obtain further enhancement.
We conduct extensive experiments to evaluate the superiority our method. Rich quanti- tative and ablation results show that our method can bring about significant improvements on datasets MS-COCO (Lin et al. (2144) and PASCAL-VOCC (Everingham al. (010)) for various computer vision tasks with both CNN-based and transformer-bau backbones.
RELATED WORK
Attention Mechanism. Attention mechanism (Itti et al. 1998 Rensink 2000 Corbetta & Shulman, 2002) aims at imitating the perception system of humans, which adopts sequence signals and selects to focus salient parts. Hence, matter in what forms, attention is expected to bias the weight towards the most informative parts of an input signal, and the signal can be varied from a sequence, feature map, token queries in different tasks.
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Over the past several years, attention mechanism has won huge success in wide range of tasks in computer vision. At the early stage, most of the works apply the attention to sequence-bat based models and tasks (Bluche, 2016; (Stollenga et al., 2014). Then, here comes the age of attention is all your need (Vaswani et al. (017), and many classic attention structures in computer vision arise such as SENET (Hu et al., 2018), Non-local (Wang et al. 2018) and CBAM (Woo et al. 2018). Finally is the popularity of self-attention (Dosovitskiy et al., 2020; Liu et al., [021] Yuan et al. (202I) in these years, and it can be concluded as the operation among the query, key and value, which even totally replaces the CNN in pure visual tasks. However, as mentioned above, the usage of attention usually not explicitly supervised. Consequently, the weight of attention easily biased towards the bias in the dataset.
Causalities in Computer Vision. Causality is one of the most important research areas in machine
learning including causal discovery (Yhhezkel Rohekar et al. 2021 Ng et al. (20TT) causal structure
learning (Kivva et al., [202T] AAkari et al. (022) and causal inference (Zhang et al. 2021: Kaddour
et al. 2021). Recent years have also witnessed growing applications of causalities in visual tasks such as long-tail classification (Tang et al. 2020 Zhu et al. (022), few/zero-shot learning (Yue et al. 2020 2021), and Visual Question Answering (Chen et al. [2020] Niu et al., 2021; Yang a
2021a). Significantly, many of these works are about conquering the contextual bias in their tasks.
Yue et al. (2020) considered pre-training information the culprit for the bias and debiased by implementing the backdoor adjustment. NNu et al.] (2021) clarified the "good" and "bad" language context and eliminated the bias by pursuing counterfactual total causal effect.
Most similar to our work, Yang et al. (2021b) and Wang et al. (2021) also visited the issue of attention aggravating the bias. Yang dealt with the confounding effect in vision-language tasks, and Wang resolved to build a more robust attention in OOD settings. However, our IDA is inherently
different, especially with Wang et al.] 202T) in two respects: 1) Different methods: we steer by the implementation of the confounder and other components when approximating the intervention, while Wang et al. (202T) focusing on the partitionn and annotation of each confounder explicitly. 2) Different applications: [Wang et al. (202T) was aimed at OOD settings, which is a direct task for contextual debiasing, but lacks flexibility due to the requirement of pre-defining or clustering each context. In contrast, our model does not have as many limitations, and thus has the potential to be applied in more areas. Furthermore, Waang et al. 2021) has two defects: being evaluated in limited foregrounds and backgrounds (only animals in 10 contexts in NICO) and performing poorly when there are multiple objects. The two points are particularly attended to in our method because our model works well in full large-scale datasets of multi-label classification and detection.
PRELIMINARIES: CAUSALITIES IN CONTEXTUAL BIAS
In this section, we first demonstrate a causal view of contextual bias in visual recognition. As is conveyed in Fig a)), we set up a simple Structural Causal Model (SCM), which clarifies the causal- ity among the context (C'), image content (X) and the prediction (Y). In SCM, each link denotes causalities between
entities, e-g., X - Y means effect Y is generated from X. Although an extra element (C) respectively points to a pair of the cause (X) and effect (Y) where we want to build mapping. Next, we will explore the rationale behind the SCM in detail.
the appearance of SCM describing the contextual bias may be different in different tasks (Yue et al.) 2020 Zhang et al. 2020 Yang al., 2021b Wang et al. [202I). their essence is depicted in Fig a):
X + Y denotes the predictions depend on the content in images, which is the desired causal effect: to learn a network mapping an image its prediction. The prediction is unbiased if the target in X is the only causal relation between X and Y. C -+4 X means the context prior determines how an image constructed by the contents. For example, if all spoons in a dataset appear on the dining table, the dining table may be regarded as a necessary context when picturing the image of spoon. C + exists because the contextual information has a considerable impact on the prediction, i.e. the object itself and its context both affect the recognition of it. X < C' Y together gives rise to a confounding effect: the network will be fooled into building a spurious causal relation e.g., dining table is taken as the cause for the prediction of spoon. 2) further illustrates the role of attention. The attention module can not identify the confounding effect, but focus on intensifying the causal effect X Y even if this causal link is wrong, causing the deterioration of context bias.
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Image (lork)
(scene dining
Prediction (fank)
X
relyforced sctentias
relfoorced ottention
Y
reieforced by attention
right causality
x wrong causality
reinforced by uttention
(a]Proposed causal graph
[b) examples of causal relation
(c) Causal intervention
Figure 2: (a) The proposed causal graph for the causalities contextual bias. (h) Two concrete causal examples. (c) The schematic diagram causal intervention.
The sole way to eliminate the confounder is by causal intervention. The operation preserves the unavoidable context prediction C - Y and cuts off the bad link that object relies certain con- text. Common approaches to realizing intervention include RCT, frontdoor adjustment and backdoor adjustment (Pearl 2014). while backdoor adjustment is the most frequently used in computer vision:
P(Y]do(XX) = Σ P (
(1)
where the do-operation denotes the causal intervention cutting off the edge C' X as illustrated in c). Free from the interference of confounding path, the network can always learn the unbiased causality between and Y. Here, X can be extended as attention features. Consequently, the reinforcement from the attention mechanism can lead to a better prediction.
4 METHODOLOGY
In this section, we present a novel framework that strengthens the robustness of attention on con- textual bias. The overview of our model is illustrated in Fig We first introduce our baseline attention: Spatial Class-Aware Attention (SCA), which can obtain class-specific representations but need guidance (Section 4.1). Then, to guarantee the attention module emphasizes the proper causality, we deduce and migrate the backdoor adjustment for the attention, where the intervention is approximated the multiple sampling with re-weighting upon SCA (Section 4.2). Finally, present the concrete implementation in our method, i.e., implementing three versions of multiple sampling on SCA (MS-SCA) and implementing the re-weighting as Dot-Product Attention (DPA) or the transformer (Section 4.3
4.1 CLASS-AWARE LAYER
The target of our Spatial Class-Aware Attention (SCA) to bias the spatial representations toward where the objects are the most likely to show up for each category. Given image, we can obtain its feature map X TR. xxWW WxX x from either CNN-based or transformer-
backbone, where D, H. W denotes the channel dimension, height, and width. Our purpose is to turn X into category- aware representations E
{e E IR KDD, where K the number of the classes. For each specific class k, its representation eş is computed from the weighted average of the spatial feature in X. Then, the feature for each category can be reformed according to its unique spatial information:
ek= -E
P(Y =|X== = Zi.j)
where P(Y k|X æiz) is obtained by feeding æıjj into a linear classifier pelf (.) followed with softmax(.) regularization.
We adopt SCA SCA easier to be affected by contextual bias (Zhu et al. [2017a] Ye et al. 2020 Zhao et al.) 2021 l. Quite a few works adopt similar class-specific representations, due the insight and interpretability that they empower the models to capture object-aware features in different areas of different images, which significant for multi-instance tasks. However, pure SCA works badly because it needs guidance to capture causal locations. SCA is designed to underline crucial positions for each class, while the "crucial position" could be the familiar background due the bias in the dataset. To tackle this problem, other works mainly employ complicated structures (e.g., GCN or Transformer) to further process the representations. By contrast, we argue that simple intervention (Sec 4 is enough to inspire the potentiality of class-aware attention. In the appendix A.4, we show our framework also gains improvement on other classic attention structures.
baseline attention for reasons: 1) SCA is useful for multi-object tasks. 2)
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MS-SCA
Intervention
Logits
person: 0.99
E
R
[ggtionall
0.87
tampling
Dot Product Attention
Transformer Encoder
RR
backbone CNN/Transformer
E2
Binary Classifier
Spatial Class-Aware Attention
X IR HXWXD
E
parton
Figure 3: Overview of our proposed model. could be either image feature from visual backbone or ROI feature from detection backbone. The model is composed of the baseline attention (SCA), the multiple sampling on SCA (MS-SCA), and the second attention layer (DPA or transformer).
4.2 CAUSAL INTERVENTION
P(Y]do(X = x)) calculates the probability of Y when all X turns to which is infeasible. Thereby, backdoor adjustment indeed uses realistic statistics (without do) to attain the effect equiva- lent to do-operation. However, is still challenging that
required stratify and sample every possible for rigorous backdoor adjustment in Eq. T In practice, it's difficult to quantificat every possible context, and consequently the P(C c) not explicitly observable.
Thanks to the perspective of Inverse Probability Weighting (IPW) (Pearl 2009), which further re- forms the adjustment and simplifies the implementation, we can approximate the sampling on e via the sampling on the observed data, i.e., (ki, æ). Firstly, we rewrite Eq. too get the equivalent formula:
P(Y k = = c)P(C c
P(Y k//o(X = x))
(3)
P(X =(('=
P(Y = X
(4)
P(X==(C=c)
where 1/P(X- = z|C c) is the so-called inverse weight. Although it's hard to sample c, in Eq. 4 there is only one (k:, x) given one thereby, the number of that Eq. 4 would encounter equals the number of samples (k,x) we observe. As a result, the observed P(Y, X. C*) can be used to approximate P(Y k/do(X x)), i.e., the essence of IPW lies in "assign the Inverse Weight 1/P(X = r = c) to every observed P(Y, X, C), and act as though they were drawn from the post-intervention P(Y k)do(X =))" PPear 2009). Hence, Eq. can be further approximated
as:
N P(Y k[Y=k|do(X- xx=))) P(Y k, = C=
(5)
P(X = x.' =
which transforms the summation of C into the sampling of X, and N is sampling times. Here, C' in the numerator can be omitted following the common practice of IPW. Then, we model terms in the summation the sigmoid activated classification probability of the class-aware attention features
x P(Y = = x" P(X x
Sigmoid((nee))P(e]
(6)
= C
where wP is the classifier weight for class k and e is the feature of class k in Sec. 4.1 Meanwhile, the denominator, i.e., the inverse weight, can the Propensity Score (Austin 2011 in the classi-
fication model following Rubin's theory, where the normalization effect is divided into the treated
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class-specific group (1ww=lle-lee 12)) and untreated class-agnostic group (y- (eg])))) Moreover, the confounder (context) in our model is countable, thereby the sampled og is finite and we simplify the ||eg ll 1. Finally, compute the ultimate intervention effect by assembling them follows:
N Sigmoid ( P(()),
P(Y P(Y=x)do(X=x))
[ +
where the implementations of en and P(ež) unfolded in the next section. In 5.3 will show that the SCA with multiple sampling intervention makes up a "I +1+12 > 2" effect.
4.3 SAMPLING AND RE- NE-WEIGHTING LAYER
In Eq. 7 the multiple sampling on eş is crucial to the intervention on the attention. Next, given fixed feature dimension and sampling dimension (e.g., 2048 and 512), we describe several versions of multiple sampling on SCA (MS-SCA): 1) Random sampling: complete randomness is of no mean assign random starting points and random intervals
and unfriendly to backpropagation. hence, for each sample, and the interval for a single sample is fixed. 2) Multi-head (aswwani et al.) (017): sample. 3) Channel-shufit considering the success of PixelShuffle (Shi et al. 2016 Liu et al. 2021), the shuffle on the chan- nel may also be meaningful. In fact, the multi-head operation is Channel-shufile with interval 1. Moreover, channel-shuffle can boost the sampling times with different assigned intervals. Interest- ingly, we will show by experiments that our model not sensitive these choices, indicating that the generic multi multi-sampling behavior is the main reason for the observed improvements.
equally divide the channel into N groups and take each group
Finally is the implementation of P(ef). The simplest way is to assign P(cğ) 1/N, which assumes a uniform prior of each sample. However, the status of each sample is unequal. Different positions in channel-wise dimension attend different objects (Zhu et al. 2017b), hence, to achieve better class-aware features, it is necessary to bias the weight into the more crucial samples rather than the average. Another alternative to introduce the learnable re-weight parameters, nonetheless, the learning capacity is limited and it is hard to scale up the model.
In consequence, we adopt another attention to operate the re- re-weighting. After the multiple sampling, where denotes the batch size and D' is sampling dimension. Then, we resize E into the sequence E E (N+((B)) Now, we implement the scaled Dot-Product Attention (Vaswani al. 2017
we get sample-specific aware feature E Bx K D
(DPA) re-weight among
different samples, for each class-aware representation:
(W,E* E* (WWEE" OW,E""
E = softmax
D
where Wg: Wi, W. are the linear projection mapping the sequence into common subspace for sim- ilarity measure. Thus far, we have explained our lightweight version of IDA. Our method increases almost no parameter except the shared KQV projection, and it outperforms all other models with comparable computation. Furthermore, to scale up the model, Eq. g can be naturally extended as the transformer, providing opportunities to trade-off between computation and performance:
(9) Eq. g and Eq. 99 can continue iterating to build a stronger class-specific representation, which becomes our heavyweight version of IDA. Finally, the class-aware representations fed into binary classifier followed by a sigmoid activation, and the average logit all samples is taken as the final logit for that class, where the classifier shares the same learnable weight with folf (.) in Sed4.1
E ma E W1 + b,) bg.
EXPERIMENTS
In this section, we conduct extensive experiments to demonstrate the superiority of the proposed method. We first introduce the general settings. Then, we present our quantitative results of multi- label classification and detection on three widely used datasets: MS-COCO (Lin et al.] 2014), VOC2007, and VOC2012 (Everingham et al, 2010). Finally, we perform various ablation stud- ies and analyses to show the effectiveness of different components in our model.

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Table Comparison of mAP (%) on the MS-COCO in multi-label classification. and H mean the light version and the heavy version respectively, and WH denotes the resolution.
All
Top3
Methods
Resolutions
mAP
CFI
OF1
CF1
OFI
ResNet- 101 (He et al. 2016
448
448
81.5
76.3
80.0
73.5
76.0
ML-GCN Chen et al. 2019C
448 448
83.0
78.0
80.3
74.2
76.3
MS-CMA You el al 2020
448 448
83.8
78.4
81.0
74.9
77.1
CSRA [Zhu & Wu
2021
448 448
83.5
77.9
80.3
74.4
76.5
IDA-RIO(L) IDA-RIO1(H)
448 448
84.3
78.5
81.1
73.6
77.3
448 448
84.8
78.7
80.9
73.9
77.4
SSGRL (Chen al. 2019b)
576 576
83.8
76.8
79.7
72.7
76.2
C- Trans hannhantin cl al. 2021
576 576
85.1
79.9
81.7
76.0
77.6
ADD-GCN Ye et al. 2020
576 576
85.2
80.1
82.0
75.8
77.9
CCD (Liu al. 2022
576 576
85.3
80.2
82.1
76.0
77.9
TDRG (Zhao et al. 2021
576 576
86.0
80.4
82.4
76.2
78.1
ID--RIOIL)
576 576
85.5
79.8
82.3
75.2
78.1
(DA-R101(H)
576 576
86.3
80.4
82.5
76.4
78.2
Swin-Base [Luu et al.
2021
384 384
88.4
82.2
84.0
77.2
79.9
Lwin-Large (Lu al. 202T
384 384
89.3
83.6
85.6
78.1
80.8
IDA-SwinB(H) IDA-SwinL(H)
384 384
89.3
83.7
85.1
78.0
80.4
384 384
90.3
84.7
85.9
79.0
81.1
Table 2: Comparison of MAP (56) between method and the baseline on the MS-COCO and
VOCO7 object detection.
MS-COCO
VOCO7
Methods
Detector
MAP@ 5
MAP@: bbox-mAP
MAP@.
Baseline
Boster-RCNN-ROIAIie
58.1
40.5
37.4
80.5
IDA (L)
Seeee-RCNN-OOAAlie
59.3
42.0
38.7
83.8
Baseline
RetinaNct-BR N5t
52.5
36.6
34.3
77.8
IDA (L)
RetinaNet-R50
55,8
38.6
36.4
80.5
Baseline
DeTR-DC5-RIOI
64.7
47.7
44.9
IDA (L.
DeTR-DC5-ROE
65.7
49.2
46.1
5.1
EXPERIMENTS SETTINGS
Implementation details. Unless otherwise stated, we use ResNet101 (He et al., 2016) pre-trained on ImageNet 1 (Deng et al. 2009) as our backbone. For the multiple-sampling module, we adopt channel-shuffle with start point = 0, intervals dimension), and thus we have N = 8. is set as 1/32. For the heavy version of IDA, we only have 2 layers transformer and do not implement the multi-head operation on dot-product attention. There is extra data preprocessing besides the standard data augmentation (Ridnik et al. 2021 Chen et al. 2019c). The lulti-label classification model and the detection model are both optimized by the Binary CrossEntropyLoss with sigmoid. We choose Adam
2, and sampling dimension 512 (1/4 of the full
optimizer with weight decay of le 4 and (31, { = (0.9, 0.9999). The learning rate is le 4 for the batch size of 128 with 1-cycle policy, All our codes were implemented in Pytorch (Paskke et al. 2017).
Evaluation metrics. For multi-label classification, we adopt the mean average precision (mAP) as our main evaluation metrie, and overall/per-clas Fl-measure (OF1/CFI) as supplements. Separate precision and recall are not employed because they are easily affected by the hyper-parameter. For detection, we employ the mAP@. .5, mAP@ .75 and mAP@ [.5, .95] for MS-COCO, and mAP@ for Pascal VOC. In Sec. [.3] we also evaluate our method on contextual bias benchmarks, The result of classification on Pascal VOC can be found in Appendix A.
5.2 QUANTITATIVE RESULTS.
Multi-label classification on MS-COCO. MS-COCO (Lin et al. 2014) is the most popular bench- mark for object detection, segmentation and caption, and has also become widely used in multi-label recognition recently. contains 122,218 images and covers 80 categories with 2.9 for each image average. Considering the result of multi-label classification is highly related the resolution and backbone, we perform experiments with different backbones and different resolutions to make the result more persuasive.
As is demonstrated in Table T for CNN backbone, we adopt ResNet101 pre-trained on ImageNet Ik with the resolutions of 448 x 448 and 576 576. The results of lightweight and heavyweight ver-

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Table 3: Ablation studies on components of our method with different resolutions. DPA means the dot-prot dot-product attention in the second layer and Trans means extending the DPA transformer.
Components
mAP
SCA
Multi-Sampling
DPA
Trans
448+448
576
81.4
82.7
81.9
83.0
82.1
83.0
83.6
84.8
82.2
83.1
84.3
85.5
84.8
86.3
sions are both present for fair comparisons. The first block shows that our light model has an obvious advantage over other methods with comparable computation (e.g., CSRA (Zhu & Wu 202T)). For heavyweight models, other models universally adopt 3 or more layers of transformer or GNN (e.g..
ADDGCN (Ye et al.] 2020), C-Trans (Lanchantin al. (021) and TDRG (Zhao al.) 2021 while ours only has two layers but outperforms all of them, confirming the trade-off between computation and performance in our method is more efficient. For transformer backbone, we use the recently popular Swin- Tein-TTrafformrr (Liu et al., 2021)) pre-trained on ImageNet 22k. The results in the last block convey our improvement. Based on such strong backbone, it very difficult to advance the performance, but our method still brings about considerable progress. In Appendix A.2 we further present the comparison of FL.OPs and parameters among some these methods.
Object Detection on MS-COCO and Pascal VOC. Our framework receives the feature map, which can also adopt the ROI features in bounding box as input. Therefore, our method can be seamlessly applied to the detection, including RCNN-like, YOLO-like and DETR-like methods whose neck output the feature maps. To validate that our method can gain improvement in a wider range of visual tasks, we also conduct experiments on object detection the two widely used datasets. We employ the Faster-RCNN (Ren et al. 2013), RetinaNet (Lin et al. 2017). and DETR (Carion et al. 2020) as basic detector. The IDA directly implemented on the Rol feature maps from the neck and the original bbox head is reserved as the residual path which is different from the classification. All the code is implemented in mmdetection (Chen et al. 2019a). The results are shown in Table We observe considerable improvements from the baseline on all detectors and datasets, revealing the generalization of IDA on other recognition tasks. Whereas, must be admitted that the enhancement is not as significant as that in classification, because the role of IDA partly overlaps with the location module in the detection model. Hence, our method obtains greater improvements in the one-stage detection whose object regions are not as accurate as those in two-stage detection.
5.3 ANALYSIS.
Effects of different components in IDA. To evaluate the impact of our two layers of attention and the intervention, we split and reconstruct our model with different ablation components obeying the default setting. Results on MS-COCC with the resolutions of 448 and 576 are both given. For single multiple sampling, we adopt the channel-shuffle with the default setting and P(e)) = 1/N. As is illustrated in Table pure multi-sampling and pure SCA both get very limited improvement due to their respective limitation. However, when combining them together, the results see significant improvements, which confirms our arguments that the attention fits the dataset but suffers from con- textual bias, and the intervention mitigates the bias and activates the attention. All above tendencies are more obvious for the resolution of 576. We also implement the DPA on multiple samples without SCA, only to find the retrogress, indicating that the re-weighting in the second layer is meaningless there no attention information the first layer. Then, the dot-product attention and transformer further enhance the result at the cost of computation. The improvement of heavyweight IDA on resolution 576 more evident, meaning our heavyweight version has better model capacity. In Appendix II we will show our framework can gain improvement various attention structures.
Results on contextual bias benehmark. Contextual bias is widely seen in various visual tasks, meanwhile, there exist benchmarks particularly designed for contextual debiasing (or OOD setting). To directly evaluate the capacity of our method on contextual debiasing, we conduct experiments on two common benchmarks: ImageNet-A (Hendryeks al. 2021) and MetaShift (Liang & Zou, 2022). For MetaShift, we adopt the standard domain generalization setting with the subset "Cat Dog". For both datasets, we take the setting without human-annotated context, because there is
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Table Different implementations of multiple sampling (ubove) and P(ef) (below).
Table 5: Comparison among our method and other bias-
ing method on MetaShift and ImageNNet-A.
Sampling
P(et)
mAP
Methods
d=0.44
MetaShitt:
d=0.71
Car vs Dog
dm1.12
d=1.43
ImageNet-A
baseline SCA
81.9
ERM
84.4
00.
35.7
24.0
30.6
Random Sampling
Average
82.9
CaaM
81.9
63.8
39.2
36.1
35.6
Multi-Head (2)
Average
82.8
IDAL)
83.4
64.2
39.1
33.6
35.3
Multi-Head (4)
Average
83.2
Table Comparsion between our method and CaaM on
Multi-Heas (8)
Average
83.4
MS-COCO and contextual biased MS-COCO.
Channel-shuffle
Average
83.6
Method
OOD MS-COCO Original MS-COCO
Channei-shufile
Average
83.6
Bascline
50.0
81.4
Channel-shufile
Parameter
83.9
CaaM
53.7
81.0
Channel-shuffle
DPA
84.3
IDA-L
58.6
84.3
no design for utilizing such partitions in our method. We compare our method with the empirical risk minimization (ERM) and IRM-based CaaM (Wang et al. 202T). As is conveyed in Table on MetaShift, we achieve competitive results among the OOD methods. Specifically, ERM performs the best when the shifts are small; our method is best for moderate shifts: CaaM achieves the best when the the shifts are very large. CaaM obtains the best on ImageNet-A, while our methods have comparable result, and ERM performs much worse. It is also worth nothing that our method is more general for other tasks than the ERM-based and IRM-based methods.
Nonetheless, the benchmarks mentioned above are based on the single-label classification, where the context of single image is simple. While scenes in the wild are naturally multi-label, and the foreground of an object could be the context for other objects. Thereby, for fair comparison (because method has specific design for multi-label scene), we sample and build contextual biased subset from COCO to verify our effectiveness. Specifically, we first select six classes of worst performance (e.g., toothbrush) on the original test set. Then we rank the top5 co-exist classes with these target classes (e.g., toilet for toothbrush). Finally, we choose target classes arising in no frequently co-exist classes as positive samples, and the familiar context without target classes as negative samples. We sample half of the training set on MSCOCO in this way to form an OOD test set, and train on the rest training set. As illustrated by Table 6 our method gains much more significant improvement, while CaaM achieves modest advancement on OOD MS-COCO and even regresses under normal setting. The results prove our effectiveness under context bias, especially when the instances are multiple, where the tranditional debiasing method does not do well.
Implementation of multiple sampling and P(eş). In Sec. 4.3 we have introduced three multiple- sampling strategies: Random Sampling, Multi-Head, and Channel-Shuftle, as well three im- plementations of P(eç): Average, Parameter (learnable weight) and Dot Product Attention. For Random Sampling and Channel-Shuffle, we adopt sampling dimension = 512 and sampling number =8 (intervals = & 2 for Channel-Shuffle). For Multi-Head, the sampling number is fixed when the sampling dimension is chosen, thus we choose three sampling dimensions 1024, 512 and 256 with head numbers 2, 4 and 8 respectively. As is uncovered by Table all sampling strategies gain considerable improvement on the original attention, while Random Sampling and Multi-Head with head numbers 2 have lower performance relatively. For the former, random sampling may miss some of the channels and the potential is not fully developed; for the latter, the sample number is too little and the intervention is incomplete. Anyhow, the improvements are not so sensitive to these choices when we have adequate intervention, which indicates the generic multi-sampling behavior is the most important. What's more, the more sophisticated modeling of P(eç) gets better results based on the multiple sampling, and our two layers of attention achieve the best.
6 CONCLUSIONS
In this work, we propose an adaptive and effective Interventional Dual Attention (IDA) framework for visual recognition. We demonstrate that the attention mechanism may aggravate the contextual bias in visual tasks, which is blamed on the confounder in causal theory. Then, we define the inter- vention on the spatial class-aware attention (SCA) that guides the attention to reinforce the correct causal relation, where is intervention is implemented as multiple sampling with the dot-product at- tention re- weightting. Finally, extensive experiments on different datasets see improvement for both classification and detection, outperforming the -tate-of-the-arts the-arts multi-label classification with less computation and better performance, meanwhile, a variety of ablation analyses demonstrate the effectiveness of different components in our method.

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Acknowledgements. (GXWD20201231 China (No. 62172021).
This work was supported by Shenzhen Fundamental Research Program and National Natural Science Foundation of
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Table 9:
Comparison of computation between our method and the state-of-the-arr on the MS-
COCO. mAP, FLOPs, and parameters presented.
Methods
Param.
FLOPs(448)
mAP(448)
mAP(576)
10
44.7M
31.4
81.5
82.7
CSRA (Zhu & Wu 2021
45.5M
31.7
83.5
C-Trans (Lanchantin al 2021
45.0M
43.3
85.1
ADDGCN Ye et al. 2020
48.2M
32.6
85.2
CCD (Liu et al. 2022)
48.3M
32.0
84.0
85.3
TDRG (hao of al.
2021
68.3M
73.7
84.6
86.0
(DA(L)
45.6M
31.7
84.3
85.5
(DA(H)
55.1M
33.8
84.8
86.3
Table 7: Comparison of mAP (%) between
Table 8: Comparison of (%) between our
method and the state-of-the-arts on VOCO7 multi-label classification. mAP" means the result of pretraining on MS-COCO Methods
method and the tf-te-rrs-s- on the VOC12 in
multi-label classification. All results are tested on official evaluation server.
mAP
mAp
Methods
MAP
mAp
ResNet101
92.9
ResNet101
92.5
MI.GCN al.
2019c
94.0
HCP 2015
90.5
ASId/Rimmk
2021
94.6
95.8
RCP Wang el al.
2016
92.2
CSRAiZhu Wu
2021
CChn cf al.
2019b>
93.9
94.8
ADDGCN Yc al.
2020
93.6
96.0
CSRA Zhu & Wu
2021
94.1
95.2
TDRG Zhao et al.
2021
95.0
ADDGCN(YC et
2020
95.5
IDA-RIOILL)
94.5
96.1
IDA-RIOI(L)
94.6
95.9
IDA-R101(1D)
95.0
96.4
IDA-R101(I))
95.0
96.3
A APPENDIX
In Appendix, we will include more quantitative results, ablation analyses, and qualitative results.
A.1 MULTI-LABEL CLASSIFICATION ON PASCAL VOC.
Smaller but more delicate, PASCAL-VOC (Everingham al. 2010 is another widely used dataset in detection and lulti-label classification. VOC 2007 has 9,963 images in the dataset with 20 categories, and VOC 2012 has 22,531 images with the same categories. We implement both light and heavy versions of IDA obeying the default setting. Note that some works train the model from scratch (Chen et al. 2019c Zhao et al. (20TT, while others load the COCO-pretrained model (Ridnik) et al., [021]] Ye et al. 2020). Hence, for fair comparisons, we implement both of them. 'Table reports the results on VOC 2007. can be seen that IDA outperforms previous methods, even when some of those models have stronger backbones (e.g., ASL (Ridnik al. 202 1) or resolutions (e.g., ADDGCN (Ye et al.] ). The results of VOC 2012 are conveyed in Table Different from VOC 2007, results VOC 2012 constrained to be tested the official evaluation server to guarantee fairness. As is shown, our method also outperforms other -ate-of-theeart methods by larger margin.
A.2 THE FLOPS AND PARAMETERS.
In the main body, we claim that the proposed method can obtain performance improvement with low computations. To verify the argument, we choose the most competitive method recently. We compute the FLOPs and parameters of these methods (of our implementation) using ptflops, and compare them with the baseline and our model. The results are presented in Table 9 Among lightweight models, our model achieves the best performance with similar FLOPs and parameters. For heavyweight models (TDRG), our model achieves better results with half of the computation, indicating method finds better way utilize the increment of parameters.
A.3 DOES IDA ACHIEVE ACCURATE ATTENTION?
In Sec[ we uncover that pure attention can not obtain precise attention map. To confirm it ex- perimentally and to validate our method achieves more robust attention map, calculate the
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Table 10: The class-specific (C-S) and image-specific (I-S) attention mIOU (%) different models.
Methods
C-S mIOU
I-S mIOU
baseline
16.8
22.2
SCA
17.0
22.3
MS-SCA
19.2
25.1
IDA
20.5
26.5
Table 11: IDA on different attention structures. Results of mAP on MS-COCO are presented.
SENET
CBAM
Non-local
GCNET
SCA
Vanilla
81.6
81.8
82.0 83.0
82.4
81.9
+IDA(L)
82.5
83.0
83.5
84.3
mIOU between the attention map and the ground truth segmentation mask MS-COCO, which provides an exact evaluation of whether attention emphasizes the right position. In fact, computing Pseudo-Masks from CAM (Selvaraju et al. 2017) of the classification model a common approach in
use CAM from the last layers before the classifier as mask, for bare SCA, we use the attention map, and for interventional SCA, weighted average (IDA) of the attention map from different heads. We compute both image-specific and class-specific mIOU evaluate the model different aspects. As is reported in Table [T] pure attention brings about very limited improvement in the attention IOU compared with the baseline, indicating the attention does not help the model find better location of instances. While our full model outperforms the pure attention and the baseline in both metrics, which proves quantitatively that our method obtains more accurate and robust attention map.
Semantic Segmentation (Li et al. 2018). For the bascline,
the average (Multi-head sampling SCA with P(c 1/N and the
A.4 IDA FOR OTHER ATTENTION MODULES.
Recent years have witnessed numerous attention structures for various visual tasks. Considering our method has the potential be flexibly migrated to different attention modules, we also implement the IDA on four typical visual attention: Non-local, SENET, GCNET, and CBAM on COCO classi- fication. As is shown in Table IDA can give rise to improvement in different attention modules, though the SCA the best result and the most progress, because the SCA is the most applicable for the multi-target tasks, whereas it is also the most likely to be affected by the confounding context.
mAP
FLOPs.
84.8
84
MAP (19
84.6
83.5
84.4
33.3
83
84,2
32.3
84
82
31.3
layer num.
1
6 head num.
12
16
Figure 5: Ablation studies on the number of trans-
Figure 4: The influence of head number in IDA. We the IDA with multi-head sampling.
former layers in IDA (H).
A.5 EFFECTS SAMPLING NUMBER.
A larger sampling dimension and more sampling numbers mean more fine-grained sampling and better results, however, the computation also rises up rapidly for self-attention when the sequence is longer. Hence, it is important to carefully choose a rational sampling number. In Fig we study the impact of sampling numbers. For fair comparison, we adopt the multi-head sampling, and project different head dimensions (of different head numbers) into the same 512. As is shown, more sampling heads bring about more improvement, especially in the early stage, where one more sampling means much more fine-grained approximation for the intervention. The improvement converges when head numbers become large, while the computation keeps rising. As result, 4-8 heads achieve better trade-offs between performance and cost.
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Input
Head1
Head2
Head3
Head4
Average
label:
weight: 0.65
weight: 0.1
weight: 0.15
weight: 0.1
label: car
weight: 0.8
weight: 0.1
weight: 0.02
weight: 0.08
weight:
weight:
weight:
weight:
label: phone
weight: 0,12
weight:
weight:
weight:
label:
weight:
weight:
weight: weight:
label: toothbrush
weight: 0,39
weight: 0.11
weight: 0.42
weight: 0,08
Visualization baseline SCA and our IDA (L) using CAM, which comes from the spatial attention weight cach class. For our method, we adopt multi-head sampling and use the attention map diffcrent sampling heads and their weighted average, whose weight comes from DPA in the second layer.
Figure 6:
A.6 DEEPER, BETTER?
In theory, the transformer extended from DPA can iterate for many rounds to get a larger model capacity. Hence, show results MS-COCO with different layers of transformer for IDA (H). As is shown in Fig the performance improves when we add the FFN and another layer increase. of transformer for IDA (L), but sees invariability or drop when the layer continues The possible reason may lie in the function of the second layer that aims at re- re-weighting the class representations in different samples. Two layers of the transformer are enough to fit needed weights for different samples, and adding more transformer layers is of little use.
A.7 QUALITATIVE RESULTS.
To demonstrate the effect of our method intuitively, we visualize the parameters via GradCAM (elvarrjuu et al. 2017). For the baseline SCA, we use the spatial attention maps of each class, and for our method, we adopt the multi-head sampling of 4 heads and visualize the attention maps of SCA in different heads as well as their weighted average. The average weight comes from the result of softmax ( (D/N) in the DPA of IDA (L). The result is illustrated Fig In general, the attention in different samples attends different things: some for the target objects,
16

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Published as a conference paper at ICLR 2023
some for the whole background and some for the important context. Then, the weight from the second layer assembles the results, and the weight value is rational as shown. The comparisons between the baseline and our final result further reveal our effectiveness. For high-frequency classes (e.g., person and car), our method can locate the fine-grained or hard instances precisely. For the classes easily misled by the context fork and phone), and is not biased to the familiar context. Finally, for the rare or hard classes (e.g., toothbrush and handbag), our attention map is inevitably biased more to the context. Compared with the baseline, however, our method can still include the target in the active locations. In the future, we resolve to develop more fine- greegga method achieve accurate activation maps for these hard instances. Moreover, the context is not always bad for the predictions, e.g. when the training and test set have the same bias distributions. Therefore, may be better to develop a self-adapting method to judge if the task needs to mitigate contextual bias. As is shown in the visualization, different samples in our method indeed attend to different information, creating possibilities for the above assumptions, which will be studied in our future work.
attention still finds the target exactly
17


Reviews:
[{"review_id": "dUywx8w6Hb", "reviewer": "Reviewer_UmuF", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper proposes an attention mechanism that is more robust to misleading visual context. The motivation follows from the fact that current recognition models tend to mistake correlations (e.g. between background and objects) for causal factors, leading to mistaken predictions when e.g. observing the background without the foreground object.\n\nThe proposed attention mechanism comes in two variants: one that relies on two successive attention layers, and a more heavyweight version which involves a transformer-based encoder inserted between the aforementioned layer. These extra processing layers are combined with a form of sampling-based intervention, which should allow the model to ignore spurious context.\n\nThe method is evaluated on two multi-label classification tasks (MS-COCO and Pascal VOC), and compares favourably to competing methods. An ablation study shows how different components of the approach improve over the baseline.", "summary_of_the_review": "The paper presents a method that to me is only tenuously connected to the stated motivation. It is hard to connect the stated need for causal reasoning to overcome misleading context and the exact operations carried out by the proposed layer. As such, I believe this paper tells a story that is not quite supported by the method and the experiments. I am open to being corrected as this assessment may be the result of a misunderstanding.", "strength_and_weaknesses": "I struggled to understand the approach and in particular to connect the stated motivation with the method being proposed as well as the results. The paper starts by describing an often observed problem, namely that common recognition methods struggle when the combination of foreground and context violates expectations. Based on the presentation, it is neither clear how the proposed method would address this, nor why the (small) improvements over the baseline indicate that the problem is indeed being successfully addressed. \n\nThe presentation of \"causalities in contextual bias\" in section 3 is confusing. The presented structural causal models (see Fig 2) only model the problem at a very abstract level. Its also for example not clear what is being proposed in (2a), if the directions of the edges make sense, why a misleading relationship between X & Y might be reinforced specifically by attention, and what it means to break the connection between the context and the image (2c) both conceptually and in practice. The connection between what is presented here and the method is very tenuous. How is the \"multiple-sampling operation with Dot-Product Attention (DPA) re-weighting\" \"essentially [a] causal intervention\"? What happens in the case when the context is necessary to correctly identify e.g. a small/low-resolution object? \n\nThe paper argues that an attention map that tracks more closely with the ground truth mask of the object is a sign of the method's success. Such results are presented e.g. in Fig 1b and in the Appendix (A.4, see final paragraph in section 5). These results show modest improvements and are also based on the faulty assumption that the context is to be ignored lest it be faulty, when in fact one would rather prefer a model that can take context into account when needed.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "WEHO_lhHRcy", "reviewer": "Reviewer_sj7K", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The  article propose a method to learn casual image features for contextual bias via an attention mechanism called interventional dual attention (IDA). The mechanism consists of two attention layer with multiple sampling intervention that protects attention from cofounder context. The approach is evaluated on well-known datasets (COCO, VOC) and achieves SotA performance on multi-label classification. ", "summary_of_the_review": "The motivation of proposing interventional dual attention for visual recognition is very good but the novelty aspect on the proposed mechanism is not fully explored. The proposed approach is thoroughly evaluated and compared to the state-of-the-art (SotA). It lacks the description on the computational complexity and critical discussion on performance of the model in comparison to the SotA. Please refer to the sections 2/3 for the details. This is a good work I would like to see authors rebuttal to my posed questions.", "strength_and_weaknesses": "Strengths:\nThe paper explained the problem  of contextual biases in attention models for solving visual recognition problems.\n\nTo address this the proposed interventional dual attention (IDA) consisting attention and multi-sampling intervention.\n\nEvaluation of the proposed approach in well-known datasets COCO and VOC, and comparison to the state-of-the-art.\n\nAblation study justifying the components IDA and visualization using GRAD-CAM to show the attention map.\n\nWeakness:\nI have doubt about the novelty aspect of the approach. Although the paper claims it is novel but it is more re-packaging of attention mechanism to extract class-specific information. The causal intervention seems to be incremental. The final eqn 7 is more of standard weighted attention?\n\nThe approach is evaluated using heavy backbones (ResNet-101 or Swin-Base) with image resolution of 384, 448 or 576. Is there any justification to use heavier backbones or large resolution? Experiments should have been carried out with lighter model ResNet-50 or NAS-Net with input resolution of 224 which is widely used for various visual recognition problem.\n\nThe claim “attention does not capture the more accurate regions of targets than the baseline.” This could be for a specific image example. Generalizing this claim requires thorough examination which is not done in the evaluation process.\n\nThe spatial class-aware attention is sensitive to spatial augmentations?  The impact of spatial augmentation (scale, crop, rotation, etc.) on SCA is not discussed.\n\nI feel there are some recent work on spatial attention by exploring semantic regions for fine-grained visual recognition such as CAP (Behera et al., AAAI 2021), SR-GNN (Bera et al., TIP 2022) exploring GNN and Wharton et al. (BMVC 2021) exploring relationships between hierarchical structures via GCN and could be cited.  \n\nWhere is the table containing FLOPS and parameters, and comparison to the baselines and state-of-the-art models? In supplementary, it says Table A.2 which is pointing to the Table 8 that says M", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "LDd5jGLKJP", "reviewer": "Reviewer_GZYH", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper aims to reduce contextual bias in multi-label classification. To this end, the paper aims to remove the effect of unseen context $C$ from the prediction. Specifically, given a casual structure {$C \\to X, C \\to Y, X \\to Y$}, the usual classifier predicts $P(Y|X)$ which implicitly reflects the unseen $C$. Instead, the paper aims to predict $P(Y|do(X))$ to discard the contextual bias. This probability can be computed by backdoor adjustment (Eq. (1)) and inverse probability weighting (Eq. (4)). This term can be estimated by setting the context $c$ as a pair or class and image $(k,x)$, leading to the final estimator (Eq. (7)). The proposed method shows good performance on various multi-label classification benchmarks.", "summary_of_the_review": "I think the overall idea is interesting. However, I'm lean toward borderline reject for the current version of the paper. I hope the revised paper includes:\n- Polish presentation -- clarify the scope and clearly explain the proposed components (complex $\\neq$ novel)\n- Extend the scope more than multi-label classification and add empirical validations for those experiments", "strength_and_weaknesses": "### Strength\n\n- The motivation in Figure 2 is interesting. Addressing contextual bias via modifying the prediction from $P(Y|X)$ to $P(Y|do(X))$ is clear.\n- The proposed method outperforms the baselines for multi-label classification.\n\n### Weakness\n\n**Concerns in presentation**\n\nMy major concern is the overclaiming presentation. The proposed method is just an improved Transformer block for multi-label classification. However, I did not catch this point until carefully looking at the method. The current title, abstract, and introduction look like the proposed method can improve the robustness of ViT on the contextual bias, e.g., applied for general classification [1] or even object detection. The paper should clarify its scope.\n\nThe method is also overly complex and missing some important details. If I understand correctly, isn't spatial class-aware attention (SCA) just an average pooling of spatial class-wise features (which are widely used [2])? So the baseline SCA is just pooling the class-wise feature and applying a binary classifier for each class vector. Then, the proposed method is just an interaction layer of this class vector: convert a R^{KxD} vector to a refined R^{KxD} vector. Figure 3 and the explanation in Section 4 could be significantly improved. Currently, the overview of the method says the structure to be (1) SCA, (2) MS-SCA, (3) DPA. However, it does not match the title of the subsection, which confuses the readers. Also, neither subsection 4.2 nor 4.3 explains what the MS-SCA is exactly -- one can merely guess that Eq. (7) is MS-SCA.\n\nIn addition, the paper has lots of typos:\n- bare -> base in Figure 1 caption?\n- Sec.7 -> Eq. 7 in page 4?\n- number of c Eq. 4 -> number of c in Eq. 4?\n\n**Concerns in scope and evaluation**\n\nAnother main concern is the scope and evaluation. The proposed causal framework is general and may be applied beyond multi-label classification. Extending the method for general classification or even object detection would highly strengthen the paper. Also, I encourage the authors to include common benchmarks for measuring contextual bias such as MetaShift [1].\n\n[1] Liang & Zou. MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts. ICLR 2022.\\\n[2] Yun et al. Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels. CVPR 2021.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "9Bt1VrXTbyX", "reviewer": "Reviewer_JL6E", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper focuses on addressing the over-reliance on contextual biases, and proposes a new method based on the attention mechanism. It draws inspiration from the causal intervention, and leverages attention together with feature sampling/shuffling to realize the function. Experimental results show that the proposed method is able to outperform the corresponding baselines without considering causal relationships for multi-label classification.  ", "summary_of_the_review": "While this paper proposes an interesting method for tackling the issues of contextual biases, its experiments are somewhat insufficient to demonstrate the advantages of the method. There are also some intriguing results that are closely related to the overall objectives of the reduction of biases, but are not carefully studied in the main paper. I am leaning towards rejecting the paper given its current presentation, but I would consider updating the scores if more in-depth analyses/experiments are provided.", "strength_and_weaknesses": "As far as I am concerned, the paper has the following strengths:\n\n(1) Developing visual systems that make decisions by faithfully gathering key information instead of relying on spurious correlations is an important topic. \n\n(2) It provides an extensive ablation study on different components of the method, which facilitates understanding of their contributions.\n\n(3) The proposed method is applicable to a wide range of deep networks, and consistently improves their performance.\n\nHowever, there are also notable weaknesses:\n\n(1) It is not new to leverage the attention mechanism for addressing issues related to the causal intervention (Yang 2021b and Wang 2021). The paper does not provide a comparison with these closely related studies, which makes it difficult to understand its contribution. I am aware that the paper focuses on a different task (multi-label classification vs standard image classification). However, without a fair comparison, the experiments still fall short of demonstrating the advantages of the method. I would suggest that the authors consider following experimental paradigms in related studies. Note that Wang 2021 not only experiments with NICO, but also other datasets such as ImageNet-A (which makes the statement in Section 2 inaccurate).\n\n(2) Several studies [ref1, ref2, ref3] (see references below) point out that the attention mechanism may not be a good indicator of regions of interest for decision-making. As this paper emphasizes leveraging attention to address biases, I believe it would be interesting to investigate how attention truly benefits the reduction of biases. For instance, what will be the correlation between the permutation of attention and the change of prediction distributions (could be measured by JSD, see details in [ref1])?\n\n(3) Table 1 shows that the proposed method only achieves limited improvements over the compared methods, despite that all of them do not take into account causal relationships. In addition, these results are for standard evaluation and do not offer too many insights in terms of contextual biases. It would be good to move the results in the appendix (e.g., Table 9) to the main paper, which are more relevant to the overall goal. \n\n(4) Looking at the results in Table 9, it appears that the IoU scores are relatively low (i.e., around 0.2), does it imply that the model still largely relies on contextual biases? \n\n(5) In terms of the qualitative results shown in Figure 6, attention maps for most of the heads seem to attend to context while only a few (with higher weights) have a stronger focus on the correct objects. Any idea why? Is the observations somewhat connected to the redundancy of multi-head attention [ref4] or their diverse behaviors [ref5, ref6]?\n\n(6) (minor) Is there a reference for spatial class-aware attention? \n\n(7) (minor) It appears that the paper slightly exceeds the page limit for ICLR. \n\nReferences:\n\n[ref1] Attention is not Explanation. NAACL, 2019.\n\n[ref2] Attention is not not Explanation. EMNLP, 2019.\n\n[ref3] Is Attention Interpretable? ACL, 2019.\n\n[ref4] Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned. ACL, 2019.\n\n[ref5] Revealing the Dark Secrets of BERT. EMNLP, 2019.\n\n[ref6] What Does BERT Learn about the Structure of Language? ACL, 2019.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}]

Author rebuttal:

Decision: Accept: poster