Inducing Stronger Object Representations in Deep Visual Trackers
Fully convolutional deep correlation networks are integral components of state-of- the-art approaches to single object visual tracking. It is commonly assumed that these networks perform tracking by detection by matching features of the object instance with features of the entire frame. Strong architectural priors and conditioning on the object representation is thought to encourage this tracking strategy. Despite these strong priors, we show that deep trackers often default to “tracking- by-saliency” detection – without relying on the object instance representation. Our analysis shows that despite being a useful prior, salience detection can prevent the emergence of more robust tracking strategies in deep networks. This leads us to introduce an auxiliary detection task that encourages more discriminative object representations that improve tracking performance.
= Summary This paper proposes to learn a visual tracking network for an object detection loss as well as the ordinary tracking objective for enhancing the reliability of the tracking network. The main motivation is that, current state-of-the-art models based on the Siamese architecture often blindly predict the center of a search window as target location due to the bias in datasets and training strategies. This issue is alleviated in this paper by introducing an auxiliary task, target detection in the entire image space. The auxiliary task is conducted by another branch on top of the visual feature shared with the tracking branch. By learning to detect object in the entire image space, the shared feature extractor will be trained to capture discriminative and unique appearance features of target. = Decision Although the main motivation is convincing and the manuscript is well written, I would recommend to reject this submission mainly due to its limited contribution and weakness in experimental analysis. (1) In the experiments, the practical benefit of adding the auxiliary detection task is demonstrated, but the final scores of the proposed model are clearly below those of current state of the art in terms of both reliability and accuracy. Further, it is not explained why the proposed model is worse than the other models in performance and what can be claimed as an advantage of the proposed method even in this situation. Also, I do not understand why the proposed model is not based on the current state of the art like SiamRPN++ but is built upon a manually designed/low-performance model. (2) The experiments in Section 5 do not demonstrate the advantage of the proposed model at all. In Figure 6 and 7, the difference between the proposed model and its reduced version without the auxiliary task looks quite subtle, and it is hard to say which one is better than the others. In Figure 8, adding the auxiliary detection task results in even worse tracking performance. (3) More qualitatively and quantitatively analysis should be done on the other tracking benchmarks and be compared with other tracking models recently proposed too.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Inducing Stronger Object Representations in Deep Visual Trackers
Fully convolutional deep correlation networks are integral components of state-of- the-art approaches to single object visual tracking. It is commonly assumed that these networks perform tracking by detection by matching features of the object instance with features of the entire frame. Strong architectural priors and conditioning on the object representation is thought to encourage this tracking strategy. Despite these strong priors, we show that deep trackers often default to “tracking- by-saliency” detection – without relying on the object instance representation. Our analysis shows that despite being a useful prior, salience detection can prevent the emergence of more robust tracking strategies in deep networks. This leads us to introduce an auxiliary detection task that encourages more discriminative object representations that improve tracking performance.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



= Summary This paper proposes to learn a visual tracking network for an object detection loss as well as the ordinary tracking objective for enhancing the reliability of the tracking network. The main motivation is that, current state-of-the-art models based on the Siamese architecture often blindly predict the center of a search window as target location due to the bias in datasets and training strategies. This issue is alleviated in this paper by introducing an auxiliary task, target detection in the entire image space. The auxiliary task is conducted by another branch on top of the visual feature shared with the tracking branch. By learning to detect object in the entire image space, the shared feature extractor will be trained to capture discriminative and unique appearance features of target. = Decision Although the main motivation is convincing and the manuscript is well written, I would recommend to reject this submission mainly due to its limited contribution and weakness in experimental analysis. (1) In the experiments, the practical benefit of adding the auxiliary detection task is demonstrated, but the final scores of the proposed model are clearly below those of current state of the art in terms of both reliability and accuracy. Further, it is not explained why the proposed model is worse than the other models in performance and what can be claimed as an advantage of the proposed method even in this situation. Also, I do not understand why the proposed model is not based on the current state of the art like SiamRPN++ but is built upon a manually designed/low-performance model. (2) The experiments in Section 5 do not demonstrate the advantage of the proposed model at all. In Figure 6 and 7, the difference between the proposed model and its reduced version without the auxiliary task looks quite subtle, and it is hard to say which one is better than the others. In Figure 8, adding the auxiliary detection task results in even worse tracking performance. (3) More qualitatively and quantitatively analysis should be done on the other tracking benchmarks and be compared with other tracking models recently proposed too.
assistant
= Summary This paper proposes to learn a visual tracking network for an object detection loss as well as the ordinary tracking objective for enhancing the reliability of the tracking network. The main motivation is that, current state-of-the-art models based on the Siamese architecture often blindly predict the center of a search window as target location due to the bias in datasets and training strategies. This issue is alleviated in this paper by introducing an auxiliary task, target detection in the entire image space. The auxiliary task is conducted by another branch on top of the visual feature shared with the tracking branch. By learning to detect object in the entire image space, the shared feature extractor will be trained to capture discriminative and unique appearance features of target. = Decision Although the main motivation is convincing and the manuscript is well written, I would recommend to reject this submission mainly due to its limited contribution and weakness in experimental analysis. (1) In the experiments, the practical benefit of adding the auxiliary detection task is demonstrated, but the final scores of the proposed model are clearly below those of current state of the art in terms of both reliability and accuracy. Further, it is not explained why the proposed model is worse than the other models in performance and what can be claimed as an advantage of the proposed method even in this situation. Also, I do not understand why the proposed model is not based on the current state of the art like SiamRPN++ but is built upon a manually designed/low-performance model. (2) The experiments in Section 5 do not demonstrate the advantage of the proposed model at all. In Figure 6 and 7, the difference between the proposed model and its reduced version without the auxiliary task looks quite subtle, and it is hard to say which one is better than the others. In Figure 8, adding the auxiliary detection task results in even worse tracking performance. (3) More qualitatively and quantitatively analysis should be done on the other tracking benchmarks and be compared with other tracking models recently proposed too.