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TL;DR#
Point tracking, crucial for applications like video editing and augmented reality, faces challenges in long videos due to variations in target appearance and potential occlusions. Existing methods either require computationally expensive cost-volume processing or struggle to maintain tracking accuracy in the presence of significant changes or scene cuts.
TAPTRv3 tackles these issues by incorporating a novel Context-aware Cross-Attention mechanism that leverages contextual information for improved spatial feature representation and a Visibility-aware Long-Temporal Attention mechanism that handles occlusions and temporal drift effectively. These improvements, combined with a global matching module for scene cut handling, enable TAPTRv3 to robustly and accurately track any point across long videos, achieving state-of-the-art performance on several benchmark datasets.
Key Takeaways#
Why does it matter?#
This paper is crucial for researchers in computer vision and related fields due to its significant advancements in point tracking, a fundamental task with broad applications. The robust and accurate long-video point tracking method presented is a major step forward, exceeding state-of-the-art results and offering a novel approach applicable to diverse scenarios. The work opens new avenues for improving related technologies like video editing, SLAM, and robotic manipulation, and further research could explore more efficient attention mechanisms and extensions to 3D tracking.
Visual Insights#
🔼 This figure compares the performance of TAPTRv3 with several other point tracking methods across four challenging datasets. It highlights the impact of training data (real vs synthetic) on model accuracy and demonstrates that TAPTRv3, even with limited synthetic training data (11K), outperforms other methods and remains competitive with methods trained on significantly more real data (15M). The average video length for each dataset is indicated. Circle size represents the amount of training data used for each model, with larger circles denoting more data. The color of the circle represents the type of data, showing whether it is real-world or synthetic.
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Figure 1: Comparison of TAPTRv3 with prior methods. Evaluation on four challenging test sets, where ‘Avg. L.’ denotes average video length. Circle colors represent training data composition, ‘Real T. Data’ and ‘Syn. T. Data’ denote the real and synthetic data, larger circle indicates more data. Results show TAPTRv3 achieves the best performance on most datasets. Even with only 11K synthetic training data, TAPTRv3 remains competitive against BootsTAPIR that is trained on 15M additional real data.
| Method | Training Data | Kinetics AJ | Kinetics OA | RGB-Stacking AJ | RGB-Stacking OA | RoboTAP AJ | RoboTAP OA | DAVIS AJ | DAVIS OA | |
|---|---|---|---|---|---|---|---|---|---|---|
| PIPs [12] | FT† | 31.7 | 53.7 | 72.9 | – | 59.1 | – | – | 42.2 | 64.8 |
| PIPs++ [54] | PO† | – | 63.5 | – | – | 58.5 | – | – | 63.0 | – |
| TAP-Net [6] | Kub24 | 38.5 | 54.4 | 80.6 | 53.5 | 68.1 | 86.3 | 45.1 | 62.1 | 82.9 |
| TAPIR [7] | Kub24 | 49.6 | 64.2 | 85.0 | 55.5 | 69.7 | 88.0 | 59.6 | 73.4 | 87.0 |
| CoTracker [20] | Kub24 | 49.6 | 64.3 | 83.3 | 67.4 | 78.9 | 85.2 | 58.6 | 70.6 | 87.0 |
| TAPTR [26] | Kub24 | 49.0 | 64.4 | 85.2 | 60.8 | 76.2 | 87.0 | 60.1 | 75.3 | 86.9 |
| TAPTRv2 [25] | Kub24 | 49.7 | 64.2 | 85.7 | 53.4 | 70.5 | 81.2 | 60.9 | 74.6 | 87.7 |
| LocoTrack [5] | Kub24 | 52.9 | 66.8 | 85.3 | 69.7 | 83.2 | 89.5 | 62.3 | 76.2 | 87.1 |
| BootsTAPIR [8] | Kub24+15M | 54.6 | 68.4 | 86.5 | 70.8 | 83.0 | 89.9 | 64.9 | 80.1 | 86.3 |
| CoTracker3 (online) [19] | Kub64 | 54.1 | 66.6 | 87.1 | 71.1 | 81.9 | 90.3 | 60.8 | 73.7 | 87.1 |
| CoTracker3 (online) [19] | Kub64+15K | 55.8 | 68.5 | 88.3 | 71.7 | 83.6 | 91.1 | 66.4 | 78.8 | 90.8 |
| TAPTRv3 (Ours) | Kub24 | 54.5 | 67.5 | 88.2 | 73.0 | 86.2 | 90.0 | 64.6 | 77.2 | 90.1 |
🔼 This table compares the performance of TAPTRv3 against other point tracking methods on four benchmark datasets: Kinetics, RGB-Stacking, RoboTAP, and DAVIS. It highlights TAPTRv3’s state-of-the-art performance, even when compared to methods that used significantly more training data (CoTracker3 and BootsTAPIR used 15K and 15M additional real-world videos respectively, compared to TAPTRv3’s 11K synthetic training videos). The table also details the training data used by each method, specifying whether Kubric videos (24 frames or 64 frames per video), PointOdyssey, or FlyingThings++ were used.
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Table 1: Comparison of TAPTRv3 with prior methods. Note that, CoTracker3† is a concurrent work. CoTracker3† and BootsTAPIR† both introduced extra data for training. Specifically, CoTracker3† re-rendered synthetic Kubric videos with length of 64 frames and additionally incorporated 15K real videos. In contrast, BootsTAPIR† trained on an additional 15M real videos. TAPTRv3 obtains state-of-the-art performance on most datasets and remains competitive with methods trained on extra internal data. Training data: (Kub24) Kubric [11] with 24 frames per video, (Kub64) Kubric with 64 frames per video, (PO) PointOdyssey [54], (FT) FlyingThings++ [29].
In-depth insights#
Long-Video Tracking#
Long-video tracking presents significant challenges compared to short-video tracking due to increased accumulation of errors over time and greater variations in appearance and motion. Methods designed for short videos often fail to generalize well, highlighting the need for specialized approaches. Robust long-video tracking demands algorithms that can effectively handle occlusions, appearance changes, and scene cuts. Strategies such as incorporating temporal context through mechanisms like recurrent networks or attention mechanisms are crucial, but must be carefully designed to avoid issues like feature drift. Effectively managing memory and computational resources is also key, especially for very long videos. The integration of global matching or re-initialization modules can be highly beneficial when scene changes occur, allowing the tracker to recover from significant tracking failures. Finally, evaluating long-video tracking performance requires benchmarks with videos of varying lengths and diverse challenges, making careful dataset selection essential for developing and comparing robust algorithms.
Context-Aware Attention#
Context-aware attention mechanisms represent a significant advancement in attention-based models by incorporating contextual information to improve the quality of attention weights. Unlike standard attention, which focuses solely on individual elements, context-aware attention considers the surrounding elements to enhance the relevance and accuracy of the attention scores. This approach is particularly beneficial when dealing with complex or ambiguous data where local information alone is insufficient to determine true relationships. By integrating spatial or temporal context, context-aware attention can effectively reduce noise and improve robustness against distractions, leading to more reliable and accurate results. Different implementations of context-aware attention exist; these variations stem from how contextual information is integrated into the attention mechanism and what type of context is used. Spatial context may incorporate neighboring elements within an image, while temporal context may use preceding or succeeding elements in a sequence. The choice of context depends on the specific application, and the method of integration can significantly impact the model’s performance and computational cost. Ultimately, context-aware attention enhances the ability of attention-based models to discern patterns and capture complex relationships in a way that is robust and efficient.
Global Matching Module#
The Global Matching Module, as described in the paper, addresses a critical limitation of point tracking in long videos: the handling of scene cuts. Scene cuts introduce abrupt changes in visual context, which traditional tracking methods struggle to recover from. The module acts as a re-initialization mechanism, triggered when a scene cut is detected. Instead of relying solely on local feature matching, which can easily fail after a scene cut, it leverages a global perspective. This is done by computing a similarity map between the target point and the entire frame’s feature map, facilitating a more robust re-localization. The key innovation lies in using patch-level context features, rather than just point-level features, to improve the accuracy and reliability of the similarity map. This context-aware approach enhances the model’s resilience to ambiguity and distractors, inherent in scene cut scenarios, leading to more successful re-acquisitions of the tracking point after a scene cut. The selective use of global matching (only when scene cuts are identified) balances efficiency with robustness, ensuring the system remains fast and accurate even in long, complex videos.
Ablation Study Analysis#
An ablation study systematically removes components of a model to understand their individual contributions. In this context, the ablation study on the point tracking model likely involved removing or deactivating key elements like the context-aware cross-attention (CCA), visibility-aware long-temporal attention (VLTA), and the global matching module. By analyzing the performance drop after each removal, researchers could quantify the importance of each component for overall accuracy and robustness. Significant performance degradation after removing CCA would highlight the crucial role of spatial context in disambiguation and accurate feature selection. Similarly, a large drop after removing VLTA would confirm the effectiveness of the temporal context integration for handling long-term drifts and occlusions. Finally, assessing the performance impact of removing global matching would illuminate its efficacy in handling scene cuts and abrupt motions. The results likely revealed a synergistic effect where the combination of these components significantly outperforms using any single component alone, demonstrating the power of the holistic approach.
Future Work#
Future work for TAPTRv3 could involve several key areas. Improving robustness to extreme motion blur and severe occlusions remains crucial. The current method shows resilience, but further enhancements are needed for challenging real-world conditions. Exploring more sophisticated temporal modeling techniques beyond the proposed VLTA, perhaps incorporating transformer architectures with longer memory, could enhance long-range tracking accuracy. Investigating alternative spatial context aggregation methods would also prove beneficial. While patch-level features improve over point-level, other techniques, like graph neural networks, might further enhance the representation of contextual information. Extending the framework to handle multiple points simultaneously is another significant direction. The current framework tackles a single point, which limits its application to more complex scenarios with multiple interacting objects. Finally, thorough evaluation on a wider range of datasets is necessary. While the existing datasets are challenging, expanding to videos with highly diverse characteristics (e.g., different camera types, lighting, and object types) would provide a more comprehensive assessment of TAPTRv3’s performance and generalizability.
More visual insights#
More on figures
🔼 Figure 2 illustrates the key improvements of TAPTRv3 over TAPTRv2. (a) shows how TAPTRv2’s point-level feature representation leads to ambiguous attention weights due to variations and distractions in the image. The red point represents the target, and green points are the considered sampling points for attention. (b) demonstrates TAPTRv3’s use of patch-level spatial context features, effectively mitigating distractions. (c) highlights TAPTRv2’s limitation to short-term temporal attention. (d) shows TAPTRv3’s improved long-term temporal attention mechanism that considers visibility (darker rectangle means higher visibility) for more accurate weighting.
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Figure 2: Illustration of our key contributions. The darker arrow indicates larger attention weight. (a) Point-level feature in TAPTRv2 causes ambiguous attention in cross-attention because of the large variation and distractions. The red point is the tracking point, the green points are sampling points in cross-attention. (b) Patch-level spatial context features help eliminate distractions in TAPTRv3. (c) TAPTRv2 limits temporal attention in a small window. (d) TAPTRv3 extends temporal attention to arbitrary length while utilizing visibility to further adjust the temporal attention weights. Rectangles indicate visibility predictions, the darker the larger.
🔼 Figure 3 illustrates the architecture of TAPTRv3, a point tracking model. It consists of two main stages: Point Query Preparation and Sequential Point Tracking. In the preparation stage, when a user specifies a point to track in the initial frame, the model extracts the point’s feature (content) and surrounding contextual features. This combined information forms the point query. The sequential tracking stage then iteratively processes new frames. For each new frame, the point query (content feature and location) and the frame’s feature map (keys and values) are fed into a multi-layer transformer decoder. The decoder refines the point’s location prediction which is then used to update the point query for the next frame, ensuring more accurate tracking. Note that the global matching module, used to handle scene cuts, is not shown in this simplified diagram.
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Figure 3: Overview (a) and core components (b) (c) of TAPTRv3. After the user has specified the point to track, the point query preparation stage prepares the content feature and spatial context features for this point in the initial frame. When TAPTRv3 receives a new frame, the sequential point tracking stage will regard the content feature and the specified location as the point query, and regard the new frame’s corresponding image feature map as a set of keys and values. The points query, keys, and values will be sent to the multi-layer transformer decoder to detect the tracking point in the new frame. The location prediction will further be used to update the point query’s positional part, providing a better initial position for tracking in the next frame. For clarity, the global matching module is not plotted.
🔼 Figure 4 illustrates the calculation of patch-level similarity within the Context-aware Cross-Attention (CCA) module of TAPTRv3. The figure shows how, for each sampling point, a patch of surrounding context features (Km) is extracted from the image feature map. This patch is then compared with a corresponding patch of context features (C) centered at the query point. The comparison is done using an element-wise multiplication (producing Sm), which is then flattened and fed into an MLP to produce a similarity score (wm). These scores represent the similarity between the query point and each sampling point in its spatial context, guiding the attention mechanism.
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Figure 4: Illustration of patch-level similarity calculation.
🔼 This ablation study analyzes the impact of key components in TAPTRv3 on its performance. Specifically, it investigates the effects of Long-Temporal Attention (LTA), visibility information (‘Vis.’), the sliding window mechanism (‘No Window’), and supervision of invisible point positions (‘No Inv. Sup.’). The table quantifies the impact of removing or modifying each of these components on the Average Jaccard (AJ), the average occlusion accuracy (<δavg), and the overall accuracy (OA) metrics. The results highlight the contributions of each component to the model’s overall robustness and accuracy.
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Table 2: Ablations on key component. “LTA” refers to Long-Temporal Attention, “Vis.” is short for visibility, “No Window” indicates the removal of the sliding window, and “No Inv. Sup.” represents no supervision of the invisible points’ positions.
🔼 This table presents the ablation study results on different methods for calculating patch-level similarity used in the Context-aware Cross-Attention (CCA) module of TAPTRv3. It compares three approaches: the element-wise method, using every pair of points, and the average approach. The results show that considering all pairs of points within the patches leads to the best performance, outperforming simpler methods.
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Table 3: Ablation on patch-level similarity.
🔼 This table presents ablation study results on different methods for updating context features in the Context-aware Cross-Attention (CCA) module of TAPTRv3. It compares the performance using different update methods, focusing on the Average Jaccard index (AJ), the percentage of correctly classified occluded points (<daug), and the Overall Accuracy (OA). The goal is to determine the optimal approach for updating spatial context features within the CCA module to improve the model’s accuracy in tracking any point in a video.
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Table 4: Ablation on context features updating.
🔼 This figure visualizes a comparison of tracking performance between TAPTRv3 and TAPTRv2, along with the ground truth. The early frames show that both models accurately track the points. However, when the camera moves and the points are out of view for over 100 frames, TAPTRv2 fails to maintain the tracking, while TAPTRv3 consistently maintains stable tracking. This demonstrates TAPTRv3’s enhanced robustness in challenging tracking scenarios.
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Figure 5: Visualization of ground truth, TAPTRv3, and TAPTRv2. In the early stages of tracking, both TAPTRv2 and TAPTRv3 can produce accurate tracking results. As the camera shifts and the tracking points disappear from the video for an extended period (over 100 frames), TAPTRv2 completely loses the tracking, whereas TAPTRv3 maintains stable tracking. Best view in electronic version.
🔼 This figure provides a detailed visual explanation of the global matching process in TAPTRv3. It illustrates how the model uses the spatial context features to compute a more robust similarity map between the tracking point and the current frame’s feature map, thereby improving the accuracy of global matching.
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Figure 6: Detailed visual illustration of global matching.
🔼 This figure provides a detailed visual comparison of different methods for calculating patch-level similarity, a key component of the Context-aware Cross-Attention (CCA) module in TAPTRv3. It illustrates the difference between the ‘Every two points’ method used in TAPTRv3, which considers pairwise similarity between all points in two patches, and the ‘Element-wise’ method, which only considers similarities between points at the same position in two patches. The figure highlights the process of generating similarity scores from patch features, showing how these scores are used to weigh the contribution of different sampling points in the attention mechanism. This visual aids in understanding the choice of the ‘Every two points’ method in TAPTRv3 for achieving superior performance in handling complex spatial variation.
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Figure 7: Detailed visual illustration of different methods for computing patch-level similarity.
🔼 Figure 8 illustrates the two different methods used in TAPTRv3 for updating spatial context features. Method (a) uses VLTA (Visibility-aware Long-Temporal Attention) where each spatial context feature (Cn) is treated as a query, and VLTA attends to the refined content features of past frames to update it. This method leverages long-term temporal information and visibility to improve the quality of the update. Method (b) uses a simpler MLP (multilayer perceptron) approach to update the spatial context features. This method concatenates the current frame’s spatial context features (Cn) with the point query’s content feature and then updates them via the MLP. The figure visually compares these two strategies for updating spatial context features within the TAPTRv3 framework.
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Figure 8: Detailed visual illustration of different methods for the updating of spatial context features.
🔼 Figure 9 presents a visual comparison of TAPTRv2 and TAPTRv3’s performance on a video of a turtle swimming underwater. Starting from frame 36, TAPTRv2 begins to lose track of fine details such as the turtle’s flippers. By the end of the video sequence, TAPTRv2 fails to track the shell of the turtle and a point marked on a fish beneath it. In contrast, TAPTRv3 maintains consistent and accurate tracking of all designated points throughout the entire video duration, demonstrating its improved robustness and accuracy.
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Figure 9: Visual comparison between TAPTRv2 and TAPTRv3. Best view in electronic version. From the third image in the first row (36th frame), TAPTRv2 loses tracking of the turtle’s flippers, and in the last few frames loses tracking of the turtle shell and the point on the fish below the turtle. TAPTRv3, on the other hand, maintains stable and accurate tracking throughout the video.
🔼 This figure compares the performance of TAPTRv2 and TAPTRv3 in tracking a goldfish. When the goldfish briefly leaves the frame, TAPTRv2 loses track of many points and incorrectly identifies occluded points as visible, even labeling them as being on a different fish. In contrast, TAPTRv3 maintains accurate tracking throughout the sequence. Even when the goldfish is later viewed from a different angle, obscuring the originally tracked points, TAPTRv3 successfully recovers these points, demonstrating its enhanced robustness in handling challenging viewing conditions and occlusions.
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Figure 10: Visual comparison between TAPTRv2 and TAPTRv3. Best view in electronic version. When the goldfish is about to swim out of the frame from right to left (119th frame), TAPTRv2 loses many target tracking points. Afterward, the goldfish swims back from left to right, and starting from the 358th frame, the video shows the other side of the goldfish, where the original target tracking points are occluded. However, TAPTRv2 incorrectly estimates them as visible or on another fish. TAPTRv3, on the other hand, maintains the correct estimation. Until the last dozens of frames, when the goldfish turns around again, TAPTRv3 successfully detects the initial target tracking points, estimates them as visible, and provides accurate positions.
🔼 This figure compares the performance of TAPTRv2 and TAPTRv3 in tracking points on a jellyfish over time. TAPTRv2’s estimations of point location and visibility become increasingly inaccurate as the video progresses, with errors accumulating. In contrast, TAPTRv3 maintains significantly more accurate tracking throughout the video sequence.
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Figure 11: Visual comparison between TAPTRv2 and TAPTRv3. Best view in electronic version. Over time, TAPTRv2 incorrectly estimates the location and visibility of points on jellyfish, and the error accumulates, while TAPTRv3’s results are more accurate.
More on tables
| Row | LTA | Vis. Aware | No Window | No Inv. Sup. | CCA | AJ | OA |
|---|---|---|---|---|---|---|---|
| 1 | ✗ | ✗ | ✗ | ✗ | ✗ | 46.0 | 62.8 |
| 2 | ✓ | ✗ | ✗ | ✗ | ✗ | 48.9 | 63.0 |
| 3 | ✓ | ✓ | ✗ | ✗ | ✗ | 50.1 | 63.1 |
| 4 | ✓ | ✓ | ✓ | ✗ | ✗ | 51.1 | 63.3 |
| 5 | ✓ | ✓ | ✓ | ✓ | ✗ | 51.9 | 64.8 |
| 6 | ✓ | ✓ | ✓ | ✓ | ✓ | 52.9 | 65.8 |
🔼 This table presents an ablation study on the impact of different decoder input positions on the model’s performance. It compares the model’s Average Jaccard (AJ), Average Occlusion Accuracy (<d_avg>), and Overall Accuracy (OA) when using different input positions for the decoder. These positions include predictions from the previous frame and the results from global matching when a scene cut is detected. The results help determine the optimal input strategy for robust point tracking.
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Table 5: Ablation on decoder input positions.
| Patch-level Similarity | AJ | <δxavg | OA |
|---|---|---|---|
| Element-wise | 52.5 | 65.1 | 85.7 |
| Every two point | 52.9 | 65.8 | 87.2 |
🔼 This table presents an ablation study on the impact of the global matching trigger mechanism on the TAPTRv3 model’s performance. It compares the model’s performance on the TAP-Vid-Kinetics dataset with and without employing the global matching positions when scene cuts are detected. The results are presented in terms of Average Jaccard (AJ), < δαυα (average precision at thresholds of 1, 2, 4, 8, and 16 pixels), and overall accuracy (OA). A comparison is made for the entire dataset and specifically on subsets containing scene cuts. This helps to determine the effectiveness of triggering global matching only when necessary, avoiding unnecessary computation and potential negative impacts in videos without scene changes.
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Table 6: Ablation on global matching trigger. Whether to employ the global matching positions when detecting scene cuts. “Kin.” is short for TAP-Vid-Kinetics, and “S.C.” indicates scene cuts.
| OA | |||
|---|---|---|---|
| VLTA | 52.6 | 65.3 | 86.8 |
| MLP | 52.5 | 65.7 | 87.3 |
| No Updates | 52.9 | 65.8 | 87.2 |
🔼 This table presents the results of an ablation study on the impact of varying the number of context features used in the Context-aware Cross-Attention (CCA) module of the TAPTRv3 model. The number of context features is determined by N² where N is the grid size used for sampling the features. The table shows the Average Jaccard (AJ), the percentage of correctly classified occluded points (<δ_avg), and the overall Occlusion Accuracy (OA) for different values of N (N = 1, 3, 5). It demonstrates the effect of the number of context features on the model’s performance.
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Table 7: Ablation on number of context features N2superscript𝑁2N^{2}italic_N start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT.
| Input Positions of Decoder | AJ | <δxavg | OA |
|---|---|---|---|
| Global Matching Calculation | 51.0 | 63.3 | 84.1 |
| Previous Frame’s Prediction | 52.9 | 65.8 | 87.2 |
🔼 This table presents the ablation study results on the memory size used in the Visibility-aware Long-Temporal Attention (VLTA) module. The study tests different memory sizes (12, 24, 48 frames, and all past frames) to determine the impact of memory length on the model’s performance. The results show that increasing the memory size improves the model’s accuracy in tracking, as measured by Average Jaccard (AJ), occlusion accuracy (OA), and the average precision of visible points’ position (<δ_avg). This demonstrates the effectiveness of VLTA in capturing long-range temporal context for robust point tracking.
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Table 8: Ablation on memory size of VLTA.
| Input Positions of Decoder | Dataset | AJ | <$"\delta^{x}_{avg}$"< | OA |
|---|---|---|---|---|
| Previous Frame’s Prediction | Kin. | 54.5 | 67.5 | 88.2 |
| Global Matching if S.C. | Kin. | 54.6 | 67.8 | 88.4 |
| Previous Frame’s Prediction | Kin. w/ S.C. | 54.4 | 66.9 | 86.6 |
| Global Matching if S.C. | Kin. w/ S.C. | 55.1 | 68.1 | 87.4 |
🔼 This table presents an ablation study on how the similarity map for global matching is calculated in TAPTRv3. Two methods are compared: using point-level features and using spatial context features. The results show a small improvement when using spatial context features, suggesting that incorporating context information improves the global matching accuracy slightly. However, given the low computational cost and the infrequency of global matching (only triggered by scene cuts), the authors conclude that including spatial context features is beneficial despite the modest performance gain.
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Table 9: Ablation on calculation of similarity map
Full paper#
