Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: The paper proposes a new multi-faceted training objective that tries to enforce models to make predictions driven by known "right image regions" while being invariant to features outside of them (The paper names this FI explanation). The first is achieved via enforcing sufficiency (model should make a prediction given limited but sufficient input), and plausibility (produced importance map should align with known importance map); The latter is achieved via uncertainty (enforcing a max-entropy prediction when predictions are made based on "not right" regions), and invariance (when inputs change in the "not right" regions, the predictions should remain the same). The paper also proposes new metrics to measure whether a model is "right for the right reasons (RRR)". The experiments show that the proposed whole pipeline is indeed more successful than a randomly supervised counterpart. However, the authors find that simply improving RRR metrics does not translate to better OOD performance. 
strengths_and_weaknesses: # Strengths
- **[S1]:  The proposed objectives are well-defined, sensible, and implemented satisfactorily:** All four objectives used to train the model (+1 for measuring faithfulness) do truly reflect the desiderata from a model that satisfy the RRR property. While portions of this are found in the literature, this work uses a conjunction of all of them and also does a comprehensive study of which of them are more successful, and which translate to better accuracy or a more faithful model. 

- **[S2]: The experiments are thorough and well-presented:** The paper (+ appendix) shows a thorough and well-selected range of experiments that are presented clearly. Especially, the ablations with regard to each of the components in the loss function is well-appreciated by this reviewer. 


# Weaknesses

- **[W1]: Originality and Significance**: My major concern with this submission is the originality and significance of the conclusions reached by the paper. While the paper does a thorough and sensible job of presenting various aspects of enforcing correct grounding to improve OOD performance, the major parts of it are already studied by prior work (This is accurately and adequately reflected in the related section of the submission). The conclusions and results show only a moderate amounts of improvment over existing work and furthermore offer no explanation about why the "random" baselines are still so successful. 
Finally, the conclusions about lack of correlation of RRR and OOD results are not as significant as they might appear at a first glance and they do not add significantly to information already known previously (W3)

- **[W2]: RRR metrics that very closely track actual objectives are not super useful:** The paper reports a greatly improved RRR metrics by use of the objectives described. However, the training objectives are identical to what the RRR measures, which makes it unsurprising that they would do better than the baselines. While this reviewer agrees that the RRR metrics are sensible, there is a good chance that the metrics are not being measured correctly due to noise in the data in case of GQA and VQA-HAT; In VQA-HAT, the "scratch-off" style data collection still allows information from the image to be seen at a lower resolution; Furthermore, humans can make assumptions and the "scrached-off" section might not be a suitable source to determine the sufficiency (the scratched off regions might not be the only region that is enough to answer) or plausibility. Similarly for GQA, the paper uses

>... obtain bounding boxes of relevant objects identified through the question program, which we use as FI supervision.

which can also be noisy in cases of relationships with other objects (e.g., person throwing frisbee). In this case, the same types of correlations present in the "FI" supervision during training, will also exist during measurement. 

In short, RRR metrics themselves are not immune from the types of issues present in data that plague "traditional" in-domain evaluation. A stronger impact could be reached if the paper discussed how the proposed metrics worked in an absence of a strong and clean supervision about and whether the objectives can learn effectively in presence of noise/sparse training signal. This is not adequately addressed. 

- **[W3]: Conclusions reached by the paper are stronger than the degree to which they are actually supported by the paper:** Here are the conclusions copied directly from the paper. 

> In this paper, we show that (1) FI supervision can improve VQA model accuracy via our VISFIS method, (2) accuracy improvements appear to stem from improving explanation plausibility specifically for faithfully explained data, and (3) FI supervision can improve RRR metric performance, and (4) RRR metrics do not actually correlate well with OOD accuracy.

For 1, and 2; The paper correctly acknowledges that 1 alone is not a strong indicator of success as shown by prior work (e.g., [41], [51], since a random baseline can also do almost as well); If #2, was more thoroughly explored, this conclusion would be better supported. However, the paper only presents that the proposed method is *slightly* better than the random baseline, which is still a **very strong** contender (E.g.,2nd best for CLEVR and VQA-HAT among 7 tested). The paper claims that the VQA-HAT is an outlier in some regards, but does not fully explore or offer grounded explanations about why this might be the case.

For 3, It is unsurprising that this is the case (see W2)

For 4,  While the full range of RRR metrics as proposed by this work is not studied by prior work; a combination of prior work already establishes (at least a part of) this conclusion. E.g., [40] shows both improved alignment and improved OOD accuracy; [41] shows that it is possible to have one without having the other. While a thorough approach presented here is welcome, it is not a highly novel or significant observation (See W1 for more information). 
questions: Please take a look at the weaknesses above. Specifically, I would like answers to these questions

- Q1: Why does Suf-Random work so well? 

- Q2: Why is the fact that the proposed objectives improve RRR important if 1) they are identical to what was optimized during training and 2) the RRR metrics proposed do not really correlate with OOD performance. 

- Q3: Please (re)highlight what new and significant conclusions the readers can take from this work that is not found (via a combination) of prior work.


limitations: N/A
review_title: null

final_score: rating: 4: Borderline reject: Technically solid paper where reasons to reject, e.g., limited evaluation, outweigh reasons to accept, e.g., good evaluation. Please use sparingly.
review_content: summary: The paper proposes VISFIS, which aims to improve the performance with the annotated important feature. The model is trained with multiple losses to meet the different restrictions, like with/without important feature and penalization of unimportant features. The paper also proposed an RRR metrics considering RRR-Suffiency, RRR-Uncertainty, RRR-Invariance, Explanation Plausibility, and Explanation Faithfulness. The method is evaluated on CLEVR-XAI, GQA-100k, and VQA-HAT.
strengths_and_weaknesses: Strength:

The idea is straight forward, and the approach is clear and easy to follow. 

ample experiments to show the capability of the proposed methods.

Weakness:

Some descriptions in this paper are a little confusing. 

Missing some necessary citation, like the claim of "early experiments suggest ...". 

The improvement of the method is limited.
questions: Line 186: Please cite the paper claimed that experiment result of inefficiency of the attention-based method.

In the Table. 3, compare with Selvaraju et al, the improvement of proposed method is limited. Also, in Table. 2, the method fails in VQA-HAT and it is not explained well. It is not clear what is the meaning of "this seems to be an artifact of the VQA dataset". And if there are "statistical biases" in the VQA, we can expect to see a larger gap between OOD ACCs than ID ACCs since the OOD ACCs may have different "statistical biases". However, I cannot observe such phenomenon in Table. 2. Can you explain it more detailly?

Bold the Suff-Random OOD result on VQA-HAT in Table. 2.

Please briefly introduce the Suff-human. It is not mentioned until Sec 7.3.

In Table. 4, it is interesting that the entire model cannot beat the ablated ones in the proposed RRR metrics, but no explanation is given.
limitations: Yes. The author discussed about Ethics and Limitations in the paper. I agree that te proposed method does not raise the negative societal impact.
review_title: null

final_score: rating: 8: Strong Accept: Technically strong paper, with novel ideas, excellent impact on at least one area, or high-to-excellent impact on multiple areas, with excellent evaluation, resources, and reproducibility, and no unaddressed ethical considerations.
review_content: summary: The present an approach to utilize human explanations to train the VQA models to be right for the right reason. In particular, the paper proposed five desiderates to help the VQA model by ensuring (1) important objects sufficiently contribute to the right answer; (2) irrelevant objects produce uniform answer scores; (3) model explanations are similar to human explanations, and (4) model explanations for unimportant objects are close to zero. The paper verifies the contribution of these four objectives and demonstrates a clear advantage over the strong random shuffle baseline. 
strengths_and_weaknesses: Strengths:
The paper presents the four desiderates for training the VQA model to be right for the right reasons and proposes the four additional objectives to instantiate each desideratum.  Strong experiment designs verify the value of each objective. The comparison with the baseline model (random shuffle ) and previous work is convincing. 

Weakness:
Not much to say. A minor point is that in section 7.2, the authors try to prove the faithful plausible explanation helps improve accuracy by showing that the accuracy positively correlated to the plausibility. As the goal is to show the improvement, it could be better to compare with a random shuffle baseline by showing the accuracy-plausibility curve for the three categories of faithfulness.

The paper is original, of good quality, and well-written.
questions: See the weakness part.
limitations: The authors have adequately addressed the limitations and potential negative societal impact of their work.
review_title: null

Meta-review: metareview: 2 out of 3 reviewers are positive about the paper and recommend accept after the author feedback. 

Importantly the paper contributes and the reviewers value 
- the idea and design to make VQA models right for the right reasons
- the experimental validation, including comparison to the recent work Singla et al. [47].
- new RRR-metrics
- analysis of the metrics including exposing limitation its use to estimate for OOD performance

The authors have addressed several of the concerns of the authors, and I recommend acceptance under the expectation that the authors will revise the paper as promised in the author response, including
- adding a plot for a random shuffle model to address reviewer JZyx's concern.
- adding clarifications provided in the author response and discussion with the reviewers to the paper/supplement wherever possible
confidence: Less certain
Decision: Accept