SoftLoc: Robust Temporal Localization under Label Misalignment
This work addresses the long-standing problem of robust event localization in the presence of temporally of misaligned labels in the training data. We propose a novel versatile loss function that generalizes a number of training regimes from standard fully-supervised cross-entropy to count-based weakly-supervised learning. Unlike classical models which are constrained to strictly fit the annotations during training, our soft localization learning approach relaxes the reliance on the exact position of labels instead. Training with this new loss function exhibits strong robustness to temporal misalignment of labels, thus alleviating the burden of precise annotation of temporal sequences. We demonstrate state-of-the-art performance against standard benchmarks in a number of challenging experiments and further show that robustness to label noise is not achieved at the expense of raw performance.
This paper proposes a new loss for training models that predict where events occur in a sequence when the training sequence has noisy labels. The central idea is to smooth the label sequence and prediction sequence and compare these rather than to force the model to treat all errors as equally serious. The proposed problem seems sensible, and the method is a reasonable approach. The evaluations are carried out on a variety of different tasks (piano onset detection, drum detection, smoking detection, video action segmentation). Suggestions * While the authors do bring up lots of related work on learning from noisy labels, the insights from that work, and its relationship to this proposed technique could be more productively explored * The connections between the assumptions of the evaluation metric and the motivation for the smoothing methodology could be more productively elucidated * The task explored in this paper, and the task-specific problem, should be described more generally since ICLR has a generalist readership. For instance, the paper gets off on a rather strange footing discussing large data (indeed, but evident to the vast majority of ICLR readers), but little is said in terms of the specifics of the temporal localisation problem except via citations to other papers. In particular, the task set up, the problem posed by label misalignment could be described elegantly right in the introduction with a carefully designed figure. In section 2, the description is hard to follow, the mathematical notation is vague and hard to parse. For instance, the label sequence Y, is d-dimensional- the meaning of d should be clarified. Calling it “discretised” is also a bit strange, for most readers- it’s a label sequence. The evaluation objective needs to be clarified, at least qualitatively; ideally in the introduction. Introducing a new objective is meaningful not only in light of noisy labels (always a problem), but in light of how the evaluation is carried out.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
SoftLoc: Robust Temporal Localization under Label Misalignment
This work addresses the long-standing problem of robust event localization in the presence of temporally of misaligned labels in the training data. We propose a novel versatile loss function that generalizes a number of training regimes from standard fully-supervised cross-entropy to count-based weakly-supervised learning. Unlike classical models which are constrained to strictly fit the annotations during training, our soft localization learning approach relaxes the reliance on the exact position of labels instead. Training with this new loss function exhibits strong robustness to temporal misalignment of labels, thus alleviating the burden of precise annotation of temporal sequences. We demonstrate state-of-the-art performance against standard benchmarks in a number of challenging experiments and further show that robustness to label noise is not achieved at the expense of raw 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>



This paper proposes a new loss for training models that predict where events occur in a sequence when the training sequence has noisy labels. The central idea is to smooth the label sequence and prediction sequence and compare these rather than to force the model to treat all errors as equally serious. The proposed problem seems sensible, and the method is a reasonable approach. The evaluations are carried out on a variety of different tasks (piano onset detection, drum detection, smoking detection, video action segmentation). Suggestions * While the authors do bring up lots of related work on learning from noisy labels, the insights from that work, and its relationship to this proposed technique could be more productively explored * The connections between the assumptions of the evaluation metric and the motivation for the smoothing methodology could be more productively elucidated * The task explored in this paper, and the task-specific problem, should be described more generally since ICLR has a generalist readership. For instance, the paper gets off on a rather strange footing discussing large data (indeed, but evident to the vast majority of ICLR readers), but little is said in terms of the specifics of the temporal localisation problem except via citations to other papers. In particular, the task set up, the problem posed by label misalignment could be described elegantly right in the introduction with a carefully designed figure. In section 2, the description is hard to follow, the mathematical notation is vague and hard to parse. For instance, the label sequence Y, is d-dimensional- the meaning of d should be clarified. Calling it “discretised” is also a bit strange, for most readers- it’s a label sequence. The evaluation objective needs to be clarified, at least qualitatively; ideally in the introduction. Introducing a new objective is meaningful not only in light of noisy labels (always a problem), but in light of how the evaluation is carried out.
assistant
This paper proposes a new loss for training models that predict where events occur in a sequence when the training sequence has noisy labels. The central idea is to smooth the label sequence and prediction sequence and compare these rather than to force the model to treat all errors as equally serious. The proposed problem seems sensible, and the method is a reasonable approach. The evaluations are carried out on a variety of different tasks (piano onset detection, drum detection, smoking detection, video action segmentation). Suggestions * While the authors do bring up lots of related work on learning from noisy labels, the insights from that work, and its relationship to this proposed technique could be more productively explored * The connections between the assumptions of the evaluation metric and the motivation for the smoothing methodology could be more productively elucidated * The task explored in this paper, and the task-specific problem, should be described more generally since ICLR has a generalist readership. For instance, the paper gets off on a rather strange footing discussing large data (indeed, but evident to the vast majority of ICLR readers), but little is said in terms of the specifics of the temporal localisation problem except via citations to other papers. In particular, the task set up, the problem posed by label misalignment could be described elegantly right in the introduction with a carefully designed figure. In section 2, the description is hard to follow, the mathematical notation is vague and hard to parse. For instance, the label sequence Y, is d-dimensional- the meaning of d should be clarified. Calling it “discretised” is also a bit strange, for most readers- it’s a label sequence. The evaluation objective needs to be clarified, at least qualitatively; ideally in the introduction. Introducing a new objective is meaningful not only in light of noisy labels (always a problem), but in light of how the evaluation is carried out.