6.7

/10

Poster3 位审稿人

最低6最高7标准差0.5

4.3

置信度

正确性3.7

贡献度3.3

表达3.0

NeurIPS 2024

DINTR: Tracking via Diffusion-based Interpolation

Pha Nguyen,Ngan Hoang Le,Jackson Cothren,Alper Yilmaz,Khoa Luu

OpenReview PDF

提交: 2024-05-03更新: 2024-11-06

TL;DR

This work proposes a generative methodology to formulate the object tracking task and an interpolation operation as a faster approach to the diffusion mechanics.

摘要

关键词

Visual TrackingDiffusionUnification

评审与讨论

审稿意见

评分: 7置信度: 42024-07-05

This paper applies the diffusion mechanism to the image interpolation process, which can realize object tracking tasks. Five object representation object tracking tasks, such as bbox, point, and text, are applied using diffusion-based interpolation. The benchmark experiments show promising results.

优点

The diffusion mechanism applied in tracking tasks is novel, which may inspire others to apply generative methods to tracking.
Five object representations in tracking tasks are applied, which shows the application ability of the method.
Experiments are good and extensive, and results are promising.

缺点

The tracking process, including the training and inference process, needs to be explained more clearly.
Some results are still lagging behind the diffusion-based methods. For example, DiffPose in posetrack. DiffMOT in mot benchmark.
More ablation is needed. It lacks enough analysis of the diffusion scheme.

问题

See weakness

局限性

Yes

作者回复

2024-08-05

We greatly appreciate the reviewer's feedback regarding our details and experimentation.

1. Process Details

We encourage the reviewer find our discussion about the Implementation Details with Reviewer oiCF and Section F in our Appendices. The training and inference processes are outlined in Algorithms F.4 and F.5. These details will be organized to include in the revision.

2. Generalization vs Specialization

Our work focus is a comprehensive generalization across five representations and seven tracking benchmarks, as highlighted in your Strengths section. This level of grand unification is unprecedented, surpassing not only existing diffusion-based methods (DiffPose, DiffMOT), but also methods in other video understanding tasks. Other than that, we included DiffPose and DiffMOT because of the similarity in approach, but technically DiffPose is for Pose Estimation task (not tracking as ours) and DiffMOT models the motion on coordinate space.

3. Ablation Study

We have significantly expanded our ablation studies, as detailed in the global response. This comprehensive analysis provides a deeper understanding of DINTR's behavior under various configurations. Key aspects of our expanded study include:

A systematic evaluation of diffusion steps and their impact on image reconstruction quality and computational efficiency.
A comparative analysis of different interpolation techniques, including our novel offset learning approach.
An exploration of various temporal modeling strategies and their relative merits. This thorough investigation enhances the robustness of our findings and provides a solid foundation for further advancements in the field.

2024-08-08

Thanks for the response. It addresses my concerns. I maintain the ratings as accept.

2024-08-09

Thank you for reading our rebuttal and getting back to us with the positive rating. We appreciate the valuable feedbacks you have provided and we will revise our paper accordingly in the final revision.

审稿意见

评分: 7置信度: 52024-07-09

The paper introduces DINTR (Diffusion-based INterpolation Tracker), an object-tracking framework that uses diffusion models to perform tracking in the visual domain. It proposes a new "Tracking-by-Diffusion" paradigm that reformulates tracking based on visual iterative diffusion models. DINTR uses an interpolation approach instead of the standard denoising process, which is claimed to be faster and more stable. The method can handle multiple types of object representations (points, poses, bounding boxes, segments, text) in a unified manner. Experiments show competitive or state-of-the-art performance across several tracking benchmarks.

优点

DINTR can handle multiple types of object representations in a unified framework, which is flexible.
The method achieves competitive or state-of-the-art results on several benchmarks across different tracking tasks.

缺点

While faster than standard diffusion approaches, the method may still be computationally expensive compared to some traditional tracking methods. A more detailed description of the model size and speed should be provided.
The authors need to provide more experimental details. How is the training? What datasets are used during training? How are multiple tasks unified during training?
The paper lacks an overall framework, making it difficult to understand.

问题

Please refer to the weakness.

局限性

NO.

作者回复

2024-08-05

We sincerely appreciate your time and effort in reviewing our paper. We hope to address your concerns by directing your attention to relevant sections where these details were already discussed in our original submission.

1. Model Size and Speed

We have included speed metrics in the rebuttal PDF for your reference. The model size is similar to LDM [13] and ADM [116] checkpoints as we initialize our weights from their public models. Please refer to our Feasibility discussion with Reviewer ddR4.

2. Experimental Details

We provided comprehensive experimental details in Section F of Appendices. This includes Algorithm F.4, which outlines our online training for the Reconstruction process, and Algorithm F.5, which details our tracker's operation.

Our method diverges from traditional tracking approaches in its training and operation. We encourage viewing this work through a novel generative perspective, not similar to traditional trackers (ddR4 - 5. Questions). Our network captures and models video content, allowing conditioned instances in different modalities to be extracted seamlessly from this modeling process. Key points include:

No explicit object location training: Unlike conventional trackers, our approach doesn't require training on specific datasets or modalities to predict object locations.
Frame reconstruction focus: The model learns to reconstruct actual frames of the testing video through an autoregressive process (essentially, next frame prediction).
Flexible training options:
- Online fine-tuning: The model can be adjusted as a new frame of the testing video is received.
- Offline training: Similar to offline tracking methods like SUSHI [B], our model can be trained to capture the complete visual distribution of a video.
- Moreover, our model can also be distilled to operate single-step diffusion, as mentioned in our global response.
Generalization to different modalities (point, pose, box, segment, and text)

Unification of multiple tasks: We utilize a unified diffusion approach similar to ControlNet [C]. Specific operations are used for each representation, such as Gaussian kernel for points, masking operation for bounding boxes and segments, word embedding model for text. Extracted representation will be passed to a unified attention layer to compute feature correspondence. We encourage the Reviewer refer to DIFT [87] or ControlNet [C] implementation for this operation.

3. Overall Framework

We illustrated our overall autoregressive framework in Figure B.4 in our original submission, which builds upon the conditional Diffusion process shown in Figure B.3. For your convenience, we have included these figures in the rebuttal PDF with detailed captions that describe the process thoroughly.

4. Limitations

We have addressed the limitations of our approach in the paper, as noted by Reviewers ddR4 and kgMr.

[B] Cetintas, Orcun, Guillem Brasó, and Laura Leal-Taixé. Unifying short and long-term tracking with graph hierarchies. In CVPR 2023.

[C] Zhang, Lvmin, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In CVPR 2023.

2024-08-12

Thank you for your responses. It addressed most of my concerns. I will raise the rating to accept. I hope the authors will improve the structure and clarity of the paper in the final version.

2024-08-12

Thank you for reading our rebuttal and getting back to us with the positive rating! We appreciate the valuable feedbacks you have provided and we will revise our paper accordingly in the final revision.

审稿意见

评分: 6置信度: 42024-07-15

The paper "DINTR: Tracking via Diffusion-based Interpolation" introduces a novel approach for object tracking using diffusion models. The proposed methodology, Diffusion-based INterpolation TrackeR (DINTR), leverages diffusion mechanics to model temporal correspondences and reconstruct actual frames in video sequences. The authors claim that their interpolation mechanism offers a more interpretable, stable, and faster approach tailored specifically for the object tracking task, outperforming existing methods on several benchmarks.

优点

The use of diffusion models for object tracking is a novel idea that has the potential to advance the field. The authors' proposal to replace the extensive mapping to a Gaussian noise domain with a more efficient interpolation process is compelling.
DINTR supports multiple types of indication representations, including points, bounding boxes, segments, and textual prompts, making it versatile for different tracking tasks.
The experimental results show that DINTR achieves superior performance on seven benchmarks across five different indication types. The method's ability to handle both single-target and multiple-target tracking tasks is impressive.
The paper provides a thorough explanation of the proposed interpolation mechanism and its advantages over existing diffusion-based approaches. The inclusion of algorithm descriptions and detailed equations enhances the reproducibility of the work.
The authors benchmark their method against several state-of-the-art tracking methods, demonstrating the superiority of DINTR in various scenarios. The use of multiple metrics (e.g., MOTA, IDF1, HOTA) provides a comprehensive evaluation of the method's performance.

缺点

In the Ablation Study (Section 5.3), the impact of different configurations is briefly mentioned, but detailed analysis and discussions on parameter sensitivity are lacking. Recommendation: Conduct comprehensive ablation studies, systematically varying key parameters such as the number of diffusion steps, noise levels, and interpolation techniques. Discuss how changes in these parameters affect the overall performance and stability of DINTR.
While the authors claim near real-time performance, the actual feasibility of deploying DINTR in real-time applications is not thoroughly explored. More experiments focusing on real-time constraints and efficiency improvements could strengthen the paper.
The paper does not mention the real-time image processing speed (i.e., frame rate) of the proposed tracking model, which is crucial for practical applications of tracking models.
While the authors connect multiple tracking tasks using diffusion models, there is a lack of comprehensive comparison with the latest state-of-the-art models.

问题

In Section 5.2, the model is described as being fine-tuned online. Is this a fair comparison with models that are trained offline? The paper mentions the use of 4 NVIDIA Tesla A100 GPUs for training, which may not be accessible to all researchers. Discussing the computational requirements and potential optimizations for less powerful hardware would be useful. Include more qualitative results, such as side-by-side comparisons of tracked objects using DINTR and other methods. Adding error analysis tables to show where and why DINTR performs better or worse would be useful. Include a table that explicitly states the experimental setup for each method being compared. This should cover the datasets used, hardware specifications, and evaluation metrics.

局限性

The limitations have been briefly addressed in the paper

作者回复

2024-08-05

We greatly appreciate the reviewer's feedback regarding our ablation study, practicality, and comparison.

1. Ablation Study

A systematic evaluation of diffusion steps and their impact on image reconstruction quality and computational efficiency.
A comparative analysis of different interpolation techniques, including our novel offset learning approach.
An exploration of various temporal modeling strategies and their relative merits. This thorough investigation enhances the robustness of our findings and provides a solid foundation for further advancements in the field.

2. Feasibility

We have expanded our analysis to include Table I (subtables C and D) and Table II, which provide comprehensive data on processing speed and FPS for DINTR under various configurations. Specifically, our model can be flexibly trained offline (similar to Tune-A-Video [114]) for known video lengths or applied online for variable-length videos (as detailed in Implementation Details in the main paper). We have added a new scenario in Table I.C and I.D where our offline-trained model is distilled to a single-step inferencing diffusion model (T = 1).

Please note that, offline trackers, such as SUSHI [B], continue to play a vital role in scenarios where comprehensive analysis (multimodality in our case) is needed. Additionally, as demonstrated in our Supplementary video, our interpolation process is adaptable to any framerate (e.g., sampled 10x skip frame in the video), showcasing the flexibility of the diffusion process.

3. Further Practical Applications

While real-time deployment is achievable as discussed above, we emphasize that DINTR's primary contribution lies in its robust theoretical framework for autoregressive video modeling, focusing on flexible instance-based understanding. This method offers significant potential beyond just tracking, opening new avenues for video applications based on these aspects:

Generative point and pose regression
Generative bounding box and segmentation prediction
Generative textual referring

Additionally, in Future Work, we elaborated on potential extensions of our framework to other instance-based understanding tasks, including visual content manipulation, visual motion modeling, temporal displacement analysis. DINTR is groundbreaking in its ability to bridge the gap between generative methods and fine-grained instance-based understanding tasks.

4. Comprehensive Comparison

We would like to emphasize the comprehensive nature of our comparisons in the main paper. Table 1 provides a feature and modality comparison with State-of-the-Art models across all paradigm categories, including tracking-by-regression, -detection, -segmentation, -attention, -unification.

For numerical comparisons, we included a wide range of approaches, extending beyond traditional tracking methods to encompass recent advancements in related fields, including PoseTrack21: FAMI-Pose, DiffPose (Pose Estimation, not tracking - CVPR 2023); LaSOT: methods with and without textual prompt input; MOT: diffusion-based approaches (AAAI 2023 and CVPR 2024) and unification approaches. This comprehensive comparison allows for a thorough evaluation relative to the latest SOTA across various tasks and methodologies.

We welcome the reviewer's suggestions for additional recent methods to include in our revision, further enhancing the depth of our comparative analysis.

5. Questions:

5.1. Fairness of Comparison: Our fine-tuning approach is considerably fair to compare with offline-trained models, and may even be at a disadvantage for our model. Unlike existing methods that explicitly learn to predict object location, our model learns to reconstruct or interpolate real frames without learning to predict direct location from training data. Note that this work should be seen from a novel generative perspective, where apple-to-apple comparison to traditional methods could not be always fit.

5.2. Computational Requirements: The computational resources mentioned in our paper represent the hardware available to us for conducting this research. The minimum requirement for running our model is a GPU with at least 10GB of VRAM.

5.3. As we always strive to improve our paper quality, we will incorporate qualitative comparisons to strengthen the comparative analysis in the revision.

We believe these clarifications and additions will provide a more comprehensive understanding of our model's performance and requirements relative to existing approaches. We will include these clarifications to the revision.

[B] Cetintas, Orcun, Guillem Brasó, and Laura Leal-Taixé. Unifying short and long-term tracking with graph hierarchies. In CVPR 2023.

2024-08-12

Thank you for your response, which has addressed most of my concerns. I hope that the contents of the rebuttal can be incorporated into the paper. I will maintain my original score.

2024-08-12

Thank you for reading our rebuttal and getting back to us. We appreciate the valuable feedbacks you have provided and we will revise our paper accordingly in the final revision.

作者回复

2024-08-05

We sincerely appreciate the reviewers' insightful comments and suggestions. The feedback highlights our paper's strengths, including its novel generative approach, the method's impressive versatility, thorough explanation for reproducibility, and comprehensive evaluation. Reviewers ddR4 and kgMr lean towards acceptance (Weak Accept and Accept, respectively) due to the novelty and extensive experimentation. Reviewer 9rR3 assigned a Borderline Reject rating, primarily due to a perceived lack of specific details in our original submission. We will first address the common key point (KP) about additional Ablation Study, followed by individual responses to specific comments.

(KP) Additional Ablation Studies:

We appreciate the reviewers' feedback on our ablation study. In response, we have conducted more comprehensive ablation studies, as presented in Tables I and II of the rebuttal file:

1. Diffusion Steps

We systematically varied the number of diffusion steps (1, 50, 100, 150, 200, 250) and analyzed their impact on performance and efficiency. Results show that with a timestep bound T = 250 in the reconstruction process, we can reconstruct an image extremely close to the original (per-pixel MSE of 0.04). In Table I.C and I.D, we added a new scenario where our offline-trained model is distilled to a single-step inferencing diffusion model (T = 1).

2. Noise Scheduler

We maintained a linear noise scheduler across all experiments, as it is the default in all available implementations and directly dependent on the number of diffusion steps above.

3. Interpolation Techniques

We compared four interpolation methods: Linear (2a), Two learning methods (2b & 2c), Our proposed offset technique (DINTR). Results demonstrate that our offset learning approach, which uses two anchor latents to deterministically guide the start and destination points, yields the best performance. This method provides superior control over the interpolation process, resulting in more accurate and visually coherent output. The performance difference between methods 2b and 2c, which use a single anchor at either the start or destination point respectively, is minimal. However, we observed slightly higher effectiveness when controlling the destination point (2c) compared to the starting point (2b), suggesting that end-point guidance has a marginally stronger impact on overall interpolation quality.

4. Temporal Modeling

We evaluated three additional diffusion-based temporal modeling approaches: Pseudo-noise Latents (i), Inflated Self-Attention (ii), Semi-online Processing (iii). Their formulation, relative strengths, and weaknesses are discussed as follows:

(i) Pseudo-noise Latents

The real image $\\mathbf{I}\_{t}$ itself does not come from the training distribution of the U-Net $\\epsilon\_\\theta$ . DIFT[87] proposed a straightforward approximation. Sampled noise respective to time step $k$ is directly added to the real image latent $\\mathbf{z}\_{k}$ . Without temporal modeling, this process approximately moves the image into the noise distribution that the U-Net was learned to reconstruct without fine-tuning, formally presented as follows:

\\tilde{\\mathbf{z}}\_{k} = \\epsilon\_\\theta(\\mathbf{z}\_{0} + \\epsilon, k), \\text{where } \\epsilon \\sim \\mathcal{N}(0, 1)

It enables extracting latent features even though the real image does not match the training distribution. However, this approach could only partially bridge the distribution shift. As a result, this approach performs the worst performance overall.

(ii) Inflated Self-Attention

Instead of the one-shot fine-tuning strategy, another approach can approximate this goal. To maintain the temporal coherence, VDMs [A] proposed to further extend the spatial 2D convolution layers and self-attention to the spatiotemporal domain. Specifically, the inflated self-attention is derived as:

\\text{from } Attn \\Big(\\epsilon\_\\theta(\\mathbf{z}\_k, k), \\epsilon\_\\theta(\\mathbf{z}\_k, k)\\Big) \\text{ to } Attn \\Big(\\epsilon\_\\theta(\\mathbf{z}\_k, k), \\epsilon\_\\theta\\big([\\mathbf{z}\_k \\| \\mathbf{x}\_k], k\\big)\\Big)

where $[\\cdot \\| \\cdot]$ is the concatenation operation, and the attention parameters only need to be reshaped without fine-tuning pre-trained weights. This solution is feasible for generating longer videos due to its flexibility. However, similar to (i), the actual distribution is not well captured, resulting in lower performance. Converting to this approach from our DINTR base leads to 5% - 8% performance drop as in Table I. This decrease is anticipated as target distributions cannot fully be incorporated into the reconstruction process.

(iii) Semi-online Processing

In addition to frame-by-frame operation, we extend (ii) to a clip-by-clip paradigm. Formally, given a video clip $v\_t \in \\mathbb{R}^{I \times H \times W \times 3}$ , where $I$ is the fixed clip length (e.g. $I$ = 16), we pass it into the conditioned diffusion model. This semi-online approach extracts multiple frame features $\\mathbf{z}^{v\_t}\_k$ via the U-Net $\\epsilon\_\\theta$ . Here, a sparse causal attention computes matrices between frame $\\mathbf{z}^{v\_t}\_k$ and two previous frames $\\mathbf{z}^{v\_0}\_k$ and $\\mathbf{z}^{v\_{t-1}}\_k$ as:

\text{from } Attn\\Big(\\epsilon\_\\theta(\\mathbf{z}^{v\_t}\_k, k), \\epsilon\_\\theta(\\mathbf{z}^{v\_t}\_k, k)\Big) \text{ to } Attn\Big(\\epsilon\_\\theta(\\mathbf{z}^{v\_t}\_k, k), \\epsilon\_\\theta\\big([\\mathbf{z}^{v\_0}\_k \| \mathbf{z}^{v\_{t-1}}\_k], k\big)\Big)

The outputs constitute $I$ trajectory predictions across the $I$ frames of the clip. This approach achieves mediocre performance, better than (i) but lower than (ii) because of the feature discrepancy between batches.

[A] Ho, Jonathan, et al. Video diffusion models. Advances in Neural Information Processing Systems 35, 2022.

最终决定Accept (poster)

2024-09-25

The paper introduces a novel method for object tracking using diffusion models. The proposed method, DINTR, replaces traditional Gaussian noise mapping with a more efficient interpolation process, enhancing tracking accuracy and stability. The method's versatility is evident as it handles various object representations, including points, bounding boxes, segments, and textual prompts. Overall, DINTR is a technically solid paper with innovative contributions and promising results. Despite some areas for improvement, such as detailed ablation studies and real-time performance evaluation, the paper’s strengths outweigh the weaknesses. Given its novel approach, versatile application, and strong experimental results, the ACs recommend accepting this paper.