Ground Truth Annotations: Seeking Clarification

Hello! I'm reaching out with a few questions concerning the ground truth annotations within the HallusionBench dataset. I've been reviewing the dataset and have encountered a couple of instances where the provided ground truth seems to differ from my understanding of the visual information. I'm hoping to get some clarification on these cases, to ensure I'm correctly interpreting the intended perspective and direction of the annotations. Your insights would be invaluable in helping me to fully utilize this excellent benchmark. The following sections detail the specific examples I'm referring to. Let's dive deeper and clear up any confusion together! It's all about ensuring the accuracy and reliability of the data for better research and understanding, right?

Case 1: Distance Perception in Animated Sequences

Let's start with the first point of discussion. This involves an animated sequence, and the question is designed to check our ability to perceive changes in distance. Here’s the specific example I’m referring to, extracted from the dataset:

{
    "category": "VD",
    "subcategory": "video",
    "visual_input": "2",
    "set_id": "17",
    "figure_id": "1",
    "sample_note": "animation",
    "question_id": "1",
    "question": "According to the positive sequence of the images, is this cartoon character getting far away? Answer in one sentence.",
    "gt_answer_details": "The cartoon character is getting far away.",
    "gt_answer": "1",
    "filename": "./VD/video/17_1.png"
}

The core of the question is whether a cartoon character is moving away from the viewer in the animated sequence. The ground truth (gt_answer) is marked as "1", which presumably means 'True'—that the character is moving away. Now, when I look at the image sequence, I'm not entirely convinced. My initial impression is that the character's distance doesn't significantly increase or decrease, or the change is subtle enough that it's hard to perceive clearly. If you have the same doubt, don't worry, let's explore this together! Perhaps there's a specific visual cue or context that I'm missing. Could it be a matter of subtle perspective changes, or perhaps the character's size might be giving off a different impression than intended? Maybe the animation style subtly influences our perception of depth, and what I'm perceiving as a stationary character is, in fact, moving away. Understanding the nuances of these visual cues is crucial. It would be great to clarify if the intended interpretation of the animation aligns with my perception or if there's a labeling error. It's also possible that the annotation intends for a minor change in distance, which, when analyzed closely, might be more apparent. My goal here is to make sure I am correctly aligned with how the dataset is intended to be used. I look forward to your insights!

Case 2: Direction of Movement in Skating Sequences

Let's move on to the second case, this time involving a sequence focused on skating. Here's a look at the data entry:

{
    "category": "VD",
    "subcategory": "video",
    "visual_input": "2",
    "set_id": "14",
    "figure_id": "1",
    "sample_note": "skating meme",
    "question_id": "3",
    "question": "They are skating to right. According to the positive sequence of the images, are they in the correct order? Answer in one sentence.",
    "gt_answer_details": "They are skating to the right",
    "gt_answer": "1",
    "filename": "./VD/video/14_1.png"
}

Here, the question focuses on the direction in which the characters are skating and whether the image sequence accurately depicts this direction. The ground truth states they are skating towards the right and the gt_answer is 1. The challenge for me is that based on the image sequence, the characters seem to be moving toward the left. The visual cues might be misleading and the characters are actually going to the right from a certain perspective. This can be a challenge. In this scenario, my understanding of the skating direction conflicts with the dataset's ground truth. Is this because of a difference in interpretation or maybe a labeling discrepancy? It's essential to understand the intended perspective here, to correctly analyze the relationship between the visuals and their annotations. I am hoping to get more clarification from the creators or experts in this area. It's also important to consider potential issues that could have caused the conflict, which might involve a misunderstanding of the image sequence, or maybe some unforeseen changes during the annotation process. This information is critical for anyone using this dataset. Could this be a labeling issue, or am I missing something? Understanding the intended perspective is crucial for evaluating the visual data. This understanding is going to impact how future machine learning models are designed and trained.

Seeking Clarity and Dataset Improvement

My primary aim here is to get clarification on these two specific examples from the HallusionBench dataset. My questions are centered around whether these ground truths are definitively correct, or if there might be potential annotation errors present. This clarification is very crucial for anyone using this dataset. If the ground truths are indeed correct, I’m eager to understand the intended interpretation, perhaps through a more detailed explanation of the visual cues or the rationale behind the annotations. If, however, there are any annotation errors, identifying them helps in improving the dataset's reliability and precision. This kind of clarification will benefit not only me, but also the broader research community that relies on the HallusionBench dataset. Having accurate and well-understood annotations is paramount for conducting meaningful research and developing robust models. It leads to the creation of more trustworthy and effective AI systems. Any insights or explanations you can provide are greatly appreciated! This will help improve the quality of future research that uses HallusionBench.

Conclusion

In essence, I'm looking for a deeper understanding of the ground truth annotations within the HallusionBench dataset, particularly regarding cases where the visual evidence seems to contradict the annotations. Are the ground truths accurate and correctly understood? Or is there room for clarification or potential correction? Understanding the intention of the annotations is key to accurately using the dataset for research. Your response will clarify the cases, which will ensure that the dataset is used most effectively.

For further information about computer vision and annotation, I recommend visiting Labelbox.