GOT-Edit: Geometry-Aware Generic Object Tracking via Online Model Editing
Authors: Shih-Fang Chen
Jun-Cheng Chen
I-hong Jhuo
Yen-Yu Lin
Venue: ICLR 2026 Poster (2026)
Keywords: Generic Object Tracking
Model Editing
Model Prediction
Model Adaptation
Visual Geometry
Null-Space
Primary area: transfer learning, meta learning, and lifelong learning
TL;DR: Online cross-modality model-editing for for a generic object tracker adaptation that adaptively integrates geometric cues while preserving semantic information with streaming inputs

Abstract: Human perception for effective object tracking in 2D video streams arises from the implicit use of prior 3D knowledge and semantic reasoning. In contrast, most generic object tracking (GOT) methods primarily rely on 2D features of the target and its surroundings, while neglecting 3D geometric cues, making them susceptible to partial occlusion, distractors, and variations in geometry and appearance.
To address this limitation, we introduce GOT-Edit, an online cross-modality model editing approach that integrates geometry-aware cues into a generic object tracker from a 2D video stream. Our approach leverages features from a pre-trained Visual Geometry Grounded Transformer to infer geometric cues from only a few 2D images. To address the challenge of seamlessly combining geometry and semantics, GOT-Edit performs online model editing. By leveraging null-space constraints during model updates, it incorporates geometric information while preserving semantic discrimination, yielding consistently better performance across diverse scenarios. Extensive experiments on multiple GOT benchmarks demonstrate that GOT-Edit achieves superior robustness and accuracy, particularly under occlusion and clutter, establishing a new paradigm for combining 2D semantics with 3D geometric reasoning for generic object tracking. The project page is available at https://chenshihfang.github.io/GOT-EDIT.

Published as a conference paper at ICLR 2026
GOT-EDIT: GEOMETRY-AWARE GENERIC OBJECT TRACKING VIA ONLINE MODEL EDITING
Shih-Fang Chen' Jun-Cheng Chen?
I-Hong Jhuo?
Yen-Yu Lin'
Department of Computer Science, National Yang Ming Chiao Tung University
2 Academia Sinica Microsoft Al
ABSTRACT
Human perception for effective object tracking in 2D video streams arises from the implicit use of prior 3D knowledge and semantic reasoning. In contrast, most generic object tracking (GOT) methods primarily rely on 2D features of the target and its surroundings, while neglecting 3D geometric cues, making them suscepti- ble to partial occlusion, distractors, and variations in geometry and appearance. To address this limitation, we introduce GOT-Edit, an online cross-modality model editing approach that integrates geometry-awand cues into generic object tracker from a 2D video stream. Our approach leverages features from a pre-trained Vi- sual Geometry Grounded Transformer to infer geometric cues from only a few 2D images. To address the challenge of seamlessly combining geometry and se- mantics, GOT-Edit performs online model editing. By leveraging null-space con- straints during model updates, it incorporates geometric information while pre- serving semantic discrimination, yielding consistently better performance across diverse scenarios. Extensive experiments on multiple GOT benchmarks demon- strate that GOT-Edit achieves superior robustness and accuracy, particularly under occlusion and clutter, establishing new paradigm for combining 2D semantics with 3D geometric reasoning for generic object tracking. The project page is available hhttpt:: https///hhehshhhahee qithub....... 1o/ GOT-EDIT
INTRODUCTION
Generic object tracking (GOT) (Bhat et al. 2019: Li et al., 2019 Javed et al.] 2022) aims to track an arbitrary user-specified target object, identified by its initially bounding box in the first frame, and to predict the locations of this target in subsequent frames. However, learning a robust tracker from limited visual information remains a significant challenge, especially in adverse conditions like partial occlusion, cluttered scenes with distractors, and significant object deformations.
Most contemporary GOT trackers trained on 2D datasets, e.g., (Muller et al.) 2018 Fan al. 2019 Huang et al. 2019 Peng et al. 2024). As a result, their 2D-based representations limited their ability to reason about contextual relationships between target and its surroundings, such dis- tinguishing target under partial occlusion or separating it from background distractors. In contrast, incorporating 3D information provides geometric cues for object boundaries, enabling more precise reasoning to mitigate challenges such as partial occlusion and inter-object discrimination.
Although several studies (Tan et al. 2025a Chen al., 2025b Feng al. 2025 Hu al. 2025
Xu et al. 2025b Zhang et al.) 2024a) have attempted to leverage 3D information for enhanced track- ing, they often rely on additional 3D data, such as objects represented in RGB-D or backgrounds in point clouds. This reliance is impractical, as GOT is primarily performed on 2D video streams. Hu- mans, by contrast, can track targets from the background, near or far, even when observing only 2D videos or single images. This is because prior 3D knowledge allows for perception that extends beyond the flat image plane (Koch et al. 2018 Gregory 1997).
Emerging techniques in geometric 3D vision (Wang et al. 2024 2025a Zhang et al. 2025 Wang et al. 2025b; Yang et al. 2025) offer a promising direction for advancing GOT. Among these, adopt the Visual Geometry Grounded Transformer (VGGT) (Wang et al. 2025a) for its strong performance and generalization, in alignment with the GOT objectives. Given one or a few 2D images as input, VGGT learns features for camera pose, point map, and depth estimation. While

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Knowledge Updated
Streaming
Geometry
Ref Frames
VGGT
Predictions
Transformer Encoder
Geometry Model Predictor
Null Space Constraint
Feature Align and Fusc
Cur Frame
Ref Labels
Transformer Encoder
Semantic Model Predictor
Localization Head
DINO
Semantic
Knowledge to be Preserved
Figure 1: The GOT-Edit Framework. GOT-Edit facilitates the understanding of 3D geometry to aid generic object tracking from 2D streaming inputs. It predicts semantic and geometric model weights concurrently incrementally adapt the tracking model. Through online model editing, it ensures geometry-aware, semantic-preserve marks the ground-trute -trth target in the input reference frames. The dashed red boxes indicate these same annotations utilized for the online knowledge update within the geometry branch. The green box represents the final predicted tracking result.
updates to the tracking model. The solid red box
VGGT has shown effectiveness in point tracking (Wang et al. 2025a Karaev et al. 2024), perception from 2D semantics remains essential for GOT. This is because point tracking operates at the pixel level and does not require an understanding of object semantics, whereas a robust GOT tracker benefits from both geometric and semantic information.
While geometric information is potentially beneficial for GOT, effectively balancing its contribution with erucial our later experiment, a naive fusion strategy improves geometry attributes tracking but degrades semantic attributes. To address this issue, propose an online model editing technique that better integrates 3D geometric features (Wang et al.] 2025a) with 2D semantic features (Oquab et al., 2023) from only 2D streaming inputs. Our approach inspired by the null-space model editing of AlphaEdit (Fang et al. (025). which integrates new knowledge into trained model while preserving existing knowledge through null-space constraints. However, AlphaEdit performs offline editing, whereas GOT requires online updates to handle dynamically varying targets and backgrounds in seen and unseen scenarios. To bridge this gap, we develop an online editing technique that enables a tracker to adaptively complement 2D semantics with 3D geometric features.
dominant semantic information remains a key challenge. As evidenced by
As illustrated in Figure [ our system begins by extracting both semantic and geometric features from the current and reference frames. These features are then aligned and fused to create en- riched representation, which serves as new knowledge for online tracker adaptation. Built upon the ToMP (Mayer et al. 2022), our approach employs two model predictors: one for the seman- tic branch and one for the geometric branch. These predictors generate the model weights for the localization head. During tracking, the reference labels, which provide correspondences between the reference frames and serve as few-shot examples of previously predicted and observed infor- mation, are dynamically updated to guide the tracker toward the target object. This process guides the model predictors to forecast model weights for the current frame in an online manner. Namely, the semantic model predictor estimates the semantic weights, while the geometry predictor gener- ates complementary weights. A null-space constraint is applied before combining these two sets of weights preserve the semantic information. Finally, the combined model weights are used by the localization head to localize the target in the current frame.
Our main contributions are threefold. First, we integrate semantic and geometric knowledge into generic object tracking without relying on additional 3D input data. This integration enriches 2D tracking with gcometry-aware reasoning, strengthening target discrimination in complex environ- ments. Second, we propose online model editing method with null-space constraint, which adaptively incorporates additional 3D geometric knowledge into GOT without degrading the dom- inant semantic features. Finally, extensive experiments multiple benchmarks validate the effec- tiveness of our approach, demonstratigg that it unlocks most of the geometric knowledge lacking in existing 2D trackers, resulting in superior performance.
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2 RELATED WORK
Generic Object Tracking. Existing methods for Generic Object Tracking (GOT) task are typ- ically derived from two pipelines (Javed et al. 2022): matching- trackers and tracking-by-
detection trackers. The matching based paradigm formulates tracking as a similarity learning task
followed by matching (Bertinetto et al. 2016; Li et al. 2018 Guo al. 2020; Xu et al.,
2020
Voigtlaender et al., 2020 Yu et al. 2020; Zhang et al. 2020 Yan et al. 2021a| Cheng et al.
2021
Chen et al. 2021 Ye et 2022
Guo et al. [022 Cai et al. 2023 Gao et 2022 He al.
2023
Zhou et al. 2023; Li et al. 2023 Chen et al. 2023 Jinxia et al. 2024 Bai et al. 2024; Shi et al.
2024
Cai et al. [2224] Song et 2023 Wu al. 2023: Zhao al. 2023 Zhang al. 2024b: Xie
et al. 2024 Zhu et al. 2025). These methods focus on training deep network to learn a function
that can distinguish and match a template of the object to a search region in the current frame. This
trained network is then used for tracking. Recent aatching-based trackers (Guo et al., 2025 Xu
ct al. 2025a Li et al. 2025 Kang et al. 2025 Xie et al. 2025) further improve their robustness by
propagating chronological contextual information from predicted hidden states.
Another paradigm, bookkin-by-detectioon frames generic object tracking as an online detection
task (Bolme et al. 2010 Henriques et al. | Nam & Han 2016 Kiani Galoogahi et al. [2017] Yao
iet [018 Lukezic et al.) [017]] Danelljan et al., 2017| 200111201111 Nai & Chen [023] Via et al.) 2024).
Recent trackers under this paradigm employ model predictor that generates target-specific track- ing model from paired reference images and labels, allowing more accurate object localization in
the current frame (Bhat et al., 2019 Mayer et al.] 2022 Chen al. (025a).. The model predictor is dynamically updated for each incoming frame by referring to previous tracking results and hence enhances the tracker's robustness and adaptivity. A separate localization head then uses this updated model to pinpoint the target.
Despite progress in the above two paradigms, they remain limited by their reliance solely on two- dimensional spatial and structural knowledge. To overcome this, our method integrates 2D semantic information with 3D geometric features, enabling a 2D tracker exploit 3D geometry information through online tracking model editing.
3D Features for Tracking. Existing trackers that utilize 3D features fall into two primary cate- gories: those that augment RGB images with additional modalities (RGB+X) Yan et al. 20216 Yang et al.] [222] Zhu et al. 2023 Hou et al. 2024 Cao et al.) 2024 Tan et al. 2025a b Chen al.
2025b Feng et al. 2025 Hu et al. (225)) and those that operate directly on point cloud data (Wu
et al., 2024 Nie al.] 2024 Liu et al. 2024a Zhang et al. 2024a Seidenschwarz
al., 2024 Xu et al. 2025b). These approaches require auxiliary inputs during tracking, such as pre-computed depth maps or scene point clouds, which are generally unavailable in real-world scenarios where
scenes and objects may be arbitrary and even previously unseen. Another line of research (Doersch
al.
2022 Harley et al. 2022, Doersch et al. 2023 Wang et al. 2023 Karaev al. 2024] Kim
et al.
2025). , known as point tracking, explores tracking any pixel.
Recent extensions
(iao et al.p
2025
Lai & Vedaldi 2025 Rajič et al. 2025] Wang et al., 2025a Harley et al.. 2025) incorporate
3D information for point tracking.
Unlike these methods, our tracker adaptively integrates 3D geometric knowledge with 2D semantic knowledge for GOT through online model editing. Specifically, embed VGGT (Wang et al., 2025a) into a 2D tracker, where a sequence of RGB frames is used to derive complementary 3D information. While geometric features from VGGT have proved effective for point tracking (Wang et al. tures into a 2D GOT tracker via model editing, thereby establishing direct connection between 3D geometric representations and object-level semantics for tracking. In this way, our formulation op- erates directly on RGB streams and extracts geometric cues from them, yielding geometry-avee and cemantics-preserveeee GOT formulation that matches the intrinsic nature of the task and aligns with the way human observers infer scene structure from wwo-dimensional imagery.
2025a Karaev et al. 2024), our method departs from this line of work by embedding these fea-
METHOD
Geometry inferred from 2D visual streams benefits GOT by enabling tracker to move beyond flat representations, but it must be balanced with semantic knowledge. Driven by this insight, we aim to enhance tracking with geometry- reasoning while preserving semantic discrimination.
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In the following sections, introduce null-space model editing in AlphaEdit and explain how it links geometry and semantics (Section 3.1.I) We then justify the track-by by-detectionn paradigm as a
natural fit for model editing (Section B.1.2). Finally, provide step-by-step description of our pipeline, highlighting our online model editing approach and objective function (Section 3.2
3.1
PRELIMINARY
3.1.1
NULL-SPACE CONSTRAINED KNOWLEDGE EDITING
Model editing updates the knowledge stored in model by adjusting its learned weights. Among existing model editing algorithms, we adopt the AlphaEdit (Fang et al. (025) because it excels at fusing unbalanced features while avoiding catastrophic forgetting. AlphaFdit treats the feed-forward
network (FFN) as a linear associative memory. where input features serve as keys and are mapped output features through model parameters W Id.xda;
V = WK, where K = | k2 .... ku] E 18 xu and V [v -v
V2 I... . v.l E Rddx X (I)
In Eq. D is the number of features to be updated, du and de are the dimensions of the respective FFN layers, and k; E Fdd and v, Re jointly represent the i-th key-value pair.
One representative optimization objective for model editing is defined by:
- -v|'+=//
[[w
AjKo Vol Voll).
A arg min A
where Ko and Vo represent originally learned knowledge, while K2 and V1 encode newly intro- duced knowledge. This objective seeks an optimal perturbation A, obtained by optimizing over candidate perturbations Á, edit the model to account for both original and new knowledge.
In practice, new edits often degrade performance on the learned knowledge, as original associations are disrupted. AlphaEdit addresses this by introducing a null-space constraint: the perturbation A is required to lie in the null space of Kg, i.e., AKo = 0. It follows that
W + A) WK2 = Vo-
This additional constraint ensures preservation of the learned knowledge when adapting the model to new knowledge. Thus, AlphaEdit is highly suitable for our proposed GOT-Edit, where dominant 2D semantic features serve as the knowledge to be preserved, while auxiliary 3D geometric features represent the newly introduced knowledge. Specifically, the tracker predicts the semantic model weights online and the perturbation weights from 3D features concurrently. These geometry-aware perturbation weights are projected into the null space the semantic knowledge to preserve seman- tics. The semantic weights and the projected perturbation weights are then combined, enabling dedicated integration of both semantic and geometric information for object tracking.
3.1.2 TRACK-BY-DETECTON PARADIGM
The track-by-dee -eetecttnn paradigm (Henriques et al.) 2012 Javed et al.) 2022) forms the foundational framework for our GOT-Edit tracker. In this paradigm, a tracker predicts a target-specific tracking model (or filters), updates it dynamically online, and employs this model to localize the target in the current frame, thereby performing tracking by detection in an online manner.
Recent trackers (Bhat al. 2019 Mayer al. 2022 Chen al. 2025a) in this paradigm employ model predictor to generate the weights W for the localization head of the tracker. The weights are applied to the current frame features Zeur through convolution matrix multiplication to produce classification score map which highlights the target's 's location in the current frame at the feature resolution:
W Zeur-
Our GOT-Edit framework aims to adapt the W with the new knowledge through online model editing. As the formulation in Eq. 4 shares similar form to the linear equation of AlphaEdit, it allows GOT-Edit with AlphaEdit-like online model editing to make the fused knowledge semantics- preserved and geometry-aware thereby improving the generalization of the tracker.
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3.2
By combining 2D semantic understanding with 3D geometric reasoning, GOT-Edit enables trackers preserve semantic knowledge while adaptively incorporating geometric cues. In the following, we first present the pipeline that fuses semantics and geometry for GOT, and then describe the model-editing mechanism that regulates their interaction and ensures coherent cooperation between semantic and geometric modalities.
Feature Extraction. Given the reference frames (from previous frames) and the current frame (to be localized), we extract their semantic features (Oquab et al. 2023). ) Vee RCCHxM and U E RCCHXW and geometric features (Wang et al. 2025a). Frey E RRR H'''X'>>W'' and vg E R.C' 'H' xW" Note that two reference frames are used, but only one is shown here for brevity.
Alignment and Fusion. The geometric features are aligned to match the dimensionality and res- olution of semantic features using convolutional network Align(-) and then fused with semantic features via a gating mechanism:
F Frof Fiee + mref @ Align(ugee) and
F cur = v + Mear O Align(ug ug
our
(5)
where @ denotes point-wise multiplication; myyf E [0, 1J, and Meur E [0. 1][x are spatial gating masks predicted from the paired semantic and geometric features via a lightweight convolution and sigmoid function, for both of the reference and current frames, respectively.
Model Predictor. After fusing the semantic and geometrie features, they are spatially concatenated with positional encodings and fed into the model predictor, Transformer encoder (Mayer al. 2022 Carion et al. 2020p. The encoder Tene performs feature interaction, i.e.,
(Zref Zeur) Tenc ((Fee(FFrr+ Feurl), where
Free = Fref + (Lref egg)
In Eq. 6 Lief denotes the reference labels from past predictions, which indicate the correspondence of the target coordinates to the reference frame. Eig is a learned foreground embedding (Mayer et al., 2022 and the operator denotes point-wiss wise multiplication with broadcasting.
The resulting features from Eq. together with the learned foreground embedding Efg serving as the query, are fed into Transformer decoder (Mayer et al. 2022] Carion al.) (0200) Te which generates the weights A E CRC of the localization head via:
A Taee ([Zref Zeurl, efg)
Localization Head. The fused features of the current frame are then passed to the updated localiza- tion head for target localization:
p A Zour.
It is important to note that F'e, in Eq. 6 provides important information to differentiate the spatial and geometric properties of the target from the background in the reference frames and can serve as few-shot examples guide target prediction in the current frame.
Online Model Editing, Integrating 3D features enhances GOT by enabling geometric reasoning. However, their influence must carefully balanced with semantic information, as naive fusion can degrade semantic discrimination, as shown in 3 Semantic cues remain the primary signal for distinguishing the target from distractors, whereas geometric cues provide complementary robust- GOT-Edit therefore performs online model editing that projects geometry-in -induced perturba- tions into the null space of semantic features, resulting in an asymmetric interaction that preserves semantic knowledge while still leveraging geometric information.
Specifically, we develop a mechanism that preserves semantic knowledge while incorporating geo- metric cues by reformulating Eq. [ as follows:
p (W sem + A') Zewr:
where W sem C denotes the semantic weights, obtained by passing semantic features through the semantic model predictor. This process is analogous to those described in Eqs.[6aand] but uses only semantic features as input. Zeur CxW represents the fused semantic-geometric features
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of the current frame, as defined in Eq. 6 The perturbation weights A complement the semantic weights with geometric information and are defined as:
A
(10)
where A is obtained from Eq. using the geometry model predictor, and Pnalt E RCCC is the null-space projection matrix computed from the semantic features.
Inspired by AlphaEdit, use Singular Value Decomposition (SVD) to compute the null space pro- jector mull for semantic features. Rank deficiency frequently arises in feature representations in the GOT setting, which leads to ill conditioning and must be handled carefully. To ensure stability prior to SVD, we first apply whitening (Kessy et al., 2018) to the semantic features to obtain normalized features and then compute the regularized correlation matrix M:
M ZZ + AI,
(11)
where À is ridge regularization term (Hoerl & Kennard 1970).
We then construct the raw projector =
Unul Unalt by selecting the eigenvectors Unalt of M corresponding to low-energy cigenvalues (identifying the subspace with minimal semantic informa-
tion). To mitigate numerical drift during online inference, we explicitly symmetrize (Ammari et al. 2012a 2a b) the projector:
Paull 1 -PP+PT))
(12)
This projector then utilized Eq. to compute the geometry-aware perturbation weights.
Unlike AlphaEdit, which performs offline model editing by collecting all preserved knowledge as in [ our GOT-Edit predicts both preserved weights and perturbation weights in an online manner, enabling adaptive integration of geometric knowledge into the semantic model.
Box Regression. A regression decoder RegDee takes the comantic-geomeeet enriched classifica- tion score map and the current frame features as input to predict regression score map that provides the target bounding box in image resolution:
RegDee (p Zcar):
(13)
where the operator denotes channel- -wiee broadcasting multiplication, and the regression decoder RegDec, as used in (Mayer et al.) 2022; Chen et al.) (2025a), employs four convolutional layers to produce four feature maps et al. 2019). The coordinates with the highest classification score in are mapped onto the regres- sion score map for final bounding box prediction.
the lrrb (left, top, right, bottom) bounding box representation (Tian)
Objective Function. The training objective is identical to that of previous work (Mayer et al. 2022
Bhat al. 2019), i.e.,
C Aats Lots (p.p) + Agion Legion (d, d),
(14)
where ý and d are the ground-truth labels. The target classification loss Lats is compound hinge loss as described in (Bhat et al. 2019). while the GloU loss (Rezatofighi et al. 2019 Lioow is used supervise bounding box regression. Aets and Agion, are scalar weights that control the contribution of each loss, and these hyperparameters are identical to those in (Mayer et al. 2022).
4 EXPERIMENTAL RESULTS
4.1 EXPERIMENTAL SETTING
Modern trackers are trained on large-scale datasets comprising tens of millions of training samples and millions of test samples. We detail this as follows.
Training Data. Like most trackers Mayer et al. 2022 Chen et al. 2025a 2023 Lin et al. 2024)
the training splits of LaSOT, GOTTOK, TrackingNet, and COCO for model training. Some

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trackers (Kang al. 2025 Liang al. 2025 include VastTrack (Peng al. 2024) for training;
provide a variant of our tracker trained with this new dataset. The training data for GOT-Edit nigor-
ously follows the VOT2022 Kristan al. 2022 challenge protocol and the GOT-1OK guidelines.
Test Data. We use the following datasets for tracker performance evaluation:
AVisT (Noman et al. 2022): Designed for testing without a training set, it encompasses 120 short
and long sequences, each averaging 664 frames under adverse visibility conditions.
NIS (Galoogahi et al. (017) and OTB (Wu et al. 2015): Used for testing without training set, each dataset contains 100 sequences, with an average of 534 frames per sequence.
GOT-10k (Huang et al. 2019): It has 420 short sequences with an average of 149 frames per sequence, featuring non-overlapping object classes in the training and test sets.
LaSOT (Fan et al. 2019) and TrackingNet (Fan et al. 2019;: : They provide training data where test classes fully overlap with training classes. LaSOT has 280 long sequences with an average of 2k frames per sequence, and TrackingNet offers 511 short sequences, averaging 471 frames each.
VOT2020 (Kristan et al., 2020) and VOT2022 (Kristan et al. 2022): These the 2020 and 2022 editions of the Visual Object Tracking challenge (VOT-ST2020 and VOT-STb2022)..
Evaluation Metrics.
We evaluate trackers using the following metrics:
SUC (success rate): The percentage of frames in which the predicted bounding box overlaps the ground truth by at least an IoU threshold or the average of all thresholds.
SR75: It refers SUC with an IoU threshold of 75%.
OP50: The percentage of frames where the predicted and ground truth IoU exceed 50%.
Pr (precision): It measures the percentage of frames where the predicted target center is within T pixels of the ground-truth center. T is set to 20 in this work.
NPr (normalized precision): is the percentage of frames where the center location error, nor- malized by the target's box diagonal, is less than the threshold of 0.2.
AO (average overlap): The mean IoU between the predicted and ground-truth bounding boxes.
Implementation Details. Our method is implemented using PyTorch 2.0.0 and CUDA 11.7. We train the model on eight NVIDIA RTX 4090 GPUs (24 GB each). DeepSpeed (Rasley al.) 2020) is integrated to accelerate training. We also verify that applying activation checkpointing to the tracker further reduces memory consumption, enabling training of the tracker at high resolution (378 378) on four 24 GB GPUs. Inference is performed on a single NVIDIA RTX 4090 GPU and consumes approximately 9 GB of GPU memory during evaluation.
Following PiVOT (Chen et al., 2025a) and LORAT (Lin et al. 2024), we use the DINOv2 (Oquab) al. 2023 VIT-L backbone for image feature extraction. We initialize the model predictors and localization head with pretrained ToMP-L weights, a ToMP variant with a DINOv2-L backbone. The backbone remains frozen during tracker training. For integrating geometric information, extract intermediate features from the DPT head of VGGT (Wang et al. 2025a), which is kept frozen during training. We also evaluate an alternative geometry backbone using Depth Anything 3 (I.in et al. 2026), in Table and Stream VGGT (Zhuo et al. 2026), as in the Appendix.
For an efficient design, the dual model predictors share the same architecture and weights, but two independent lightweight convolutional layers are appended in parallel to the predictors, serving as task-specific heads for semantic weight prediction and perturbation weight prediction, respectively.
We sample 200K subsequences per epoch and train for 25 epochs. Each subsequence consists of two reference frames and one current frame, randomly selected from a 200-frame window within a video sequence. The frames to VGGT are concatenated spatially, which allows better geometric features through multi-frame interaction. Following ToMP (Mayer et al., 2022). PiVOT (Chen et al. (025a),, set the search area scale factor to 5.0 and perform data augmentation. The initial learning rate set to 10 with a StepLR scheduler that decays it by a factor of 0.2 at epochs 10, 15, and 20. AdamW W (Loshchilov & Hutter 2019) is used as the optimizer.
To mitigate the computational cost of higher image resolutions, as in recent works (Xie et al. 2025 Li al. 2025 Chen et al. 2023 Lin al. 2024), we use smaller resolutions for most ablations

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Table 1: Comparison with state-of-the-art methods. Each tracker is followed by its input reso- lution. The term 'Base' in the column "Training Data of Tracker' refers to trackers trained on the classical four datasets. 'Frames' denotes the number of frames a tracker uses on each frame during evaluation, denotes tracker trained solely the specific GOT-10K set (Huang et al. 2019).
Trining Checee
Low
Not Dxtrap
Full Cwettey
Dunsee
NSS
OTB
Lasor
TrokingNer
Tracker
Semantic
Feature
Goomeny
Featare
Transing Data
of Tracker
Framcs
Transtll Panureters
SUC
SUCC
51.0
A0
51275
NPv
14
51.
NI
SEC
Gi1--dii--5 (Chin)
GOT-Edh--35 (Ours)
DESO<2
DEN0334
VOGT
VOGT
RaswsSberlins Flack
Bese
3
5334
5338
64.5
63.7
11.1
00.3
25.0
73.0
80.2
70.5
85.2
84.8
63.2
62.9
15.0
75.3
$1.0
90.5
85.4
$5.T
GOT-Edd 378 (Ouns)
DESO<2 L
DA3-
Base VisTTock
3
2834
64.7
70.5
74.5
79.5
79.6
85.2
$3.4
75.5
91.0
#6.5
GET--Ed 25 [Ohre)
DENO<2
DM3-
Base- NestTickk
513
63:0
70.7
72.6
70.4
75.8
84.5
51.7
73.9
90.1
45.5
PVV01:377
Cbo
1005
DEN0KM4
Base
14M
02.3
08.2
71.3
70.9
75.5
84.7
63.1
$0.0
45.3
LoRA773775
Basc
3234
77.5
78.1
94.1
12.0
89.7
35.5
TPMP-375
DENOV2L
Base
253
61.5
936
90.8
DENOV2
NaaTTack
77.5
83.7
50.
PARIIPNNL
287M
96.1
$5.0
ARPTode
WIEMAE-L
Basc
GB3--130
MTTME-L
SAltack-35
SANT
Base
DrToock-144 164
Denolong VIT
Bast
60.3
73.0 04.
810
79.1
93.9
160
Success plct
Success pist
100
Sucoess plot
Oroita Proccson (N4)
GST TeBL EEN (I
40
1001 INEUTT T1
80-
10-
LB4 DSE
ROMTNOLLR ITLD)
20-1
1370 (918)
(M 41
Orutlap Bresnuldd
Ovel up Shreshuld
Overlap threstold
Figure 2: From left to to right, success plots of competing methods on OTB, AVisT, NiS are shown.
and higher resolutions for comparison with the state of the art: 1) GOT-Edit-252, where the frame resolution and the patch token size are 252 x 252 and 18 x 18, respectively; 2) GOT-Edit-378, where the frame resolution and the token size are 378 x 378 and 27 x 27, respectively. We also employ mixed-precision training with 16 and Float32 (or TFloat32) for efficiency.
4.2 COMPARISONS WITH THE STATE-OF-THE-ARE METHODS
Table T compares our GOT-Edit with the SOTAs on several benchmark datasets. When compared with trackers that use semantic backbones based on DINOv2 (Oquab et al.] (023). our tracker demonstrates superior performance, generalizes well to out-of-disrribution targets, and achieves competitive results in-distribution targets. GdT-Edit shows performance gain of about 2-3% across datasets compared with ToMP-378, which is a DINOv2 variant of ToMP (Mayer et al. 2022) and serves the baseline tracker. Comparing against trackers that employ different semantic backbones, our tracker outperforms all trackers on nut-of-distribution targets, except MCITrack- 384 (Kang et al. 2025) in-distribution targets, which uses different semantic backbone and involves more trainable parameters and frames during training and evaluation. In addition to SUC, NPr, and Pr, we compare trackers using OP50 (Table 3), where all trackers share the same semantic backbone, and GOT-Edit uses VGGT as the geometry backbone. Our tracker outperforms others by clear margin on this metric. We also provide the success AUC curves in Figure 2 reporting the best-performing variants of our tracker alongside those of the compared methods. On OTB, our method consistently shows the best results. Our tracker outperforms all trackers when T > 0.2 on AViST, while outperforming MCITrack when T < 0.7 on NIS. Additionally, we provide an evalua- tion on the VOT challenge in Table
4.3 ABLATION STUDIES
Table 4 presents ablation studies on each GOT-Edit component under image resolution 252, trained using four classical datasets. Row (1) shows the baseline method trained using only semantic fea- tures. Row (2) shows that the GOT tracker takes features from the DPT head of VGGT. Even though these features are used to finetune the tracker with GOT data, performance still drops dra- matically due to the limited discriminative ability of the geometric information. Row (3) shows the fusion of semantic features from VGGT's DINO head and geometric features from VGGT's DPT head, which yields moderate improvement compared with using geometric features alone. Row
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Table 2: Comparisons among trackers on the VOT challenge using Robustness as the metric.
GOT-Edit
PiVOT 87.3
MixFormerL.
OSTrackSTB
TransT_ 84.9
ToMP 81.8
VOT-ST STb2022
89.8
85,9
86.7
VOT-ST2020
90.3
85,5
78.9
Table 3: Comparison with trackers using DINO features under OP50.
Table 4: Ablation studies GOT-Edit with several design choices compared across multiple datasets under SUC.
Datasel
Merric
AVIT
OP50
NIS
LaSOT
Semmatic
(MIN))
(VOGT DINO)
Scmunos
Geometry (VGGT)
Nuil Space
Constrin
Reguian zallen
AViST
NIS
Lasor
Ee--Edii--8.. Vast
14.4
89.3
85.9
12)
19
55.8
19.2
65.3
66.3
67.8
70.7
GOT-Edit-378
73.7
88.7
86.1
(3)
59.9
67.5
70.9
ToMP-378
72.6
85.7
84.8
(4)
60.2
68.55
71.3
LoRAT-378
72.4
(5)
61.5
69.3
72.7
(6)
62.0
70.2
73.5
Figure 3:
Attribute analysis of OTB, AVisT, and LaSOT from left to right, with average scores below.
(4) shows semantic features extracted from an independent DINO backbone, which perform better than semantic features from the DINO head of VGGT. This effect be largely attributed to the fine-tuning of the DINO backbone of VGGT with large-scale 3D data, which distorts the original semantic representations of the DINO backbone. Row (5) shows semantic-geometry fusion under the null-space constraint, which improves performance compared with fusion without the constraint. Row (6) shows that whitening and regularization applied input features before SVD further im- prove overall performance.
Overall, our online model editing strategy for geometry- combination improves the base- line by an average of 2.5%, while the null space constraint with regularization effectively enhances fusion, yielding notable gains across datasets: 1.8% on AVisT, 1.7% on NíS, and 2.5% on LaSOT. These results demonstrate the superiority of GOT-Edit.
Our method freezes semantic and geometry feature extractors and fuses them using the proposed knowledge-ediine approach during training, enabling seamless cooperation between the two modal- ities and further complements the semantic distortion in VGGT, where semantic features tend to be dominated by geometry, and complements existing GOT trackers, which lack geometric knowledge.
Table shows the ablation studies of GOT-Edit-252 components with regard to attributes. Row (1) presents the baseline performance. Row (2) reports the results of incorporating semantic and geometric information under a naive fusion method. For attributes related to 3D (c.g., occlusion, visibility, background clutter), the performance improves. However, for non-3D-related attributes (e.g., distractor, fast motion, illumination), the performance degrades. By addressing the fusion bal- ancing problem through the null-space constraint, adopted in GOT-Edit, the tracker achieves not only geometric benefits but also semantic consistency, as demonstrated in row (3).
4.4
COMPARISON OF ATTRIBUTES AMONG SOTA
We conduct an attribute- based analysis by comparing our GOT-Edit with several trackers like (Song ct al. 2022 Zheng et al., 2024 Ma et al. 2024 Cui et al., 2022 Wang et al. (02T) using large reso- lution input, as shown in Figure This analysis provides insights into the strengths and weaknesses of different methods and highlights potential areas for improvement. Note that attribute- plot- ting requires the results of a tracker. If the raw data of tracker is unavailable if datasets lack an attribute analysis protocol (e.g., those hosted on third party servers without attribute results), we exclude those trackers from the attribute analysis.

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Published as a conference paper at ICLR 2026
Table 5: Ablation studies of GOT-Edit components with regard the attributes.
Semanic (DINO)
Nall Caustrin
ANrel
T3501 Badigroaa
(VGGT)
Weatter Ceaditons (Tanges Vaubilys
Otstaction Effects (Qaddmaar))
Cunoulage [lockgoond Caaeery
Tuget Effecs (Ustmcca:
Paniil Occlasses
Full Occhneun
Carier
Motton
Fist
ilaetinaiin
10
64
57.84
42.21
48.35
65.97
62.93
54.15
60.19
72.02
121
19.83
47.18
7008
03.24
06.0
18.13
71.13
179
50.35
71.00
Annotation
PIVOT
LORAT
Figure 4: Visual comparisons of tracking results from GOT-Edit, PiVOT, and LoRAT across diverse video sequences under adverse scenarios shown. The three left columns illustrate object tracking evaluation on AVisT, while the three right columns present tracking results on LaSOT.
OTB: As the left column of Figure illustrates, our tracker achieves considerable performance gain on attributes such as background clutter, occlusion, and rotation, compared with the baseline ToMP-L378. These improvements result from the geometry information that aids the understanding of the scene and the object itself, while other attributes still outperform competing trackers.
AViST: As shown in the middle column of Figurel our tracker improves most attributes over other trackers. Although it trails PiVOT in Imaging Effects under low-light conditions, it still outperforms the baseline ToMP-L3378 across attributes, demonstrating effectiveness on unseen data.
The right column of Figure demonstrates that our tracker outperforms most attributes compared with other trackers; however, in viewpoint change and fast motion, performs similarly or slightly drops below some trackers. This is because visual geometry becomes less effective when the scene object moves rapidly or undergoes significant viewpoint changes.
LaSOT:
Limitations. While improved in most attributes, our tracker still requires enhancement in handling moving objects, as evidenced the LaSOT benchmark. The 'Target Effects' attribute AVisT benchmark, which contains distractors and fast-moving objects, provides evidence for improvement. Additionally, handling -iit----iittiuttion data, as AVisT, presents opportunities.
4.5
VISUALIZATION
We present visual comparisons among trackers in Figure 4 Our tracker more robust to occlusion and better discriminates distractors, enabled by semantic and geometric reasoning.
CONCLUSION
We present GOT-Edit, the first framework integrate 3D geometric cues into generic object tracking via online model editing, while using only 2D streaming inputs during tracking. By constraining up- dates to preserve semantics, GOT-Edit prevents semantic degradation while incorporating geometric cues that conventional 2D trackers overlook. Through online model editing with null-space con- straint, it retains semantic knowledge while adaptively integrating geometric information, achiev- ing robustness under occlusion, clutter, and visual ambiguity. The framework generalizes across datasets, targets, and environments while maintaining stability and robustness. Beyond surpassing ofate-of-the-ar trackers in generalization, the results demonstrate that principled model editing can bridge modality gaps and recover geometry information missed by purely 2D approaches. These advances chart path toward reliability, safety, and social responsibility in vision systems.
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ETHICS STATEMENT
The proposed GOT-Edit framework improves generic object tracking by adaptively integrating se- mantic and geometric reasoning through online model editing. This capability offers potential so- cietal benefits, including greater reliabiliity of autonomous and robotic systems and improved as- sistance in challenging visual environments. However, the method may be misused for intrusive surveillance or other applications that compromise privacy and security. Deployment must therefore comply with legal and ethical standards, particularly in contexts involving personal data or sensitive environments. The tracker is trained solely publicly available datasets, consistent with existing methods and accordance with established ethical standards. As a pioneering method that enhances tracking by integrating visual geometry, GOT-Edit highlights the emerging, potentially imperfect na- ture of 3D inference from 2D inputs as key direction for ongoing technical improvement. Respon- sible requires transparency, rigorous validation, and adherence established ethical guidelines.
REPRODUCIBILITY
To ensure reproducibility, detailed implementation instructions for GOT-Edit are provided in 4.1 The source code is publicly available at athttps://git thb.. com/ chenshi fang / /OT These measures are intended to facilitate the verification and replication of the results by other researchers.
ACKNOWLEDGEMENT
This work supported in part by the National Science and Technology Council (NSTC) un- der grants 112-2221-E- -E-A9-0000-MY3, 1 144-2221-EA449-03-MMM Academia Sinica under grant AS-IAIA-1 14MM10 and by MediaTek. This work was supported by H100 GPU computing resources donated by Wistron.
and 114-2634-F-002-0044 by
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A APPENDIX
This document supplements the main paper with details on GOT-Edit, including comparisons with state-of-the-ar trackers using NPr, Pr, and SUC plots, ablation studies on model complexity, and video appendix for qualitative visualization, available on the paper submission forum.
B COMPUTATIONAL COST ANALYSIS
Table 6: The analysis quantifies the computational costs of each component of the GOT-Edit in terms of runtime per frame (milliseconds, ms).
Backbone
Align and
Model Predictors
Reg/Cls Decoders
Frame Resolution
Total
VGGT
DINO
Fuse
252 252
65.6
8.7
2.3
6.8
0.7
84.1
378 378
91.9
17.6
2.7
14.5
0.7
127.4
The computational cost of each tracker component is reported in Table per-frame runtime (ms). The primary computational overhead is dominated by geometric feature extraction (VGGT), Our core contribution, the online model editing modules (Align and Fuse and Model Predictors), is highly efficient, with a runtime of only 9.1 ms at a 252 x 252 frame resolution or 17.2 ms at 378 x 378 resolution. The evaluation model uses BF for VGGT.
Table 7: Runtime and FLOPs breakdown for VGGT, DINO, and the tracker component.
Tracker Excluding
Frame Resolation
Metric
VGGT
VGGT & DINO
Runtime (ms)
65.6
8.7
9.8
252 252
FLOPs (G)
1000
105
Kuntime (ms)
91.9
17.6
17.9
378
FL.PPs (G)
2253
251
73
We also provide the model complexity in terms of FLOPs (Floating- Point Operations), as shown in Table 7 FLOPs agnostic to device and precision, and compute MACs coultpply-accumulle operations and multiply) and multiply the result by two to obtain FLOPs.
C MORE EXPERIMENTS
Analysis of Alternate Geometry Backbone Choices
To enhance speed performance, utilize VGrT Zhuo al. 2026) to replace VGGT for geometric feature extraction and report the results Table 8 In this table, 'GlobalAttn FineTuue' refers to using DoRA (Liu al. (2024b) fine-tune the linear layers of the global attention layer in the geometry model, where the global attention layer the key mechanism for handling cross- frame information. 'MemCache' refers the number of historical K/V caches used for tracking. 'Frqquecc'' denotes the frequency for geometric feature extraction. The DoRA rank is set to 16, and only 2.4 M parameters are fine-tuned for the geometry model. The experimental results in the table demonstrate that optimized geometric variants and selective feature application (we set the memory cache to and apply geometric information every 3 frames in the Stream VGGT variant) can significantly increase the speed (e.g., runtime is reduced by approximately 40% when Stream VGGT replaces VGGT, while competitive accuracy is maintained.
Analysis of Attribute- -Wise Performance under Semantic and Geometry Configurations
To explicitly evaluate the influence of both the geometric and semantic backbones, we conduct addi- tional experiments at the consistent resolution of 378 x 378. These extended experiments validate our method by varying both the semantic backbone (DINOv2 vs. MAE HH et al. 2022 ) and the geometric backbone (VGGT vs. SGGT) Experiments (1) and (3) in Table establish the baselines using only the semantic backbones MAE-L and DINOv2-L, respectively. Once additional geometric backbones, VGGT and Stream VGGT, are adopted, our GOT-Edit can leverage the geo- metric features and substantially improve performance across various challenging attributes, such as occlusion, background clutter, and distractors.
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Table 8: Efficiency in runtime (ms per frame) and accuracy (%) for VGGT and Stream VGGT with varying cache and update frequency.
Geometry Method
GlobalAttn Fune
Tracker
Frequency
Runtime
LaSOT
AVisT
NES
Cache
VGGT
Every Frame
84,1
73.8
62.0
70.2
Every Frame
72.5
72.8
61.4
69.7
Every Frame
72.9
6L.6
70.0
GOT-Edit-252
2
Every 2 Frames
59.4
72.3
61.8
69.5
VGGT
Every 3 Frames
53.9
72.7
62.0
69.2
Every Frames
73.1
62.7
Every 3 Frames
56.2
73.4
61.9
69.8
VGGT
Every Frame
127.4
75.0
64.5
71.1
Every 2 Frames
84.6
74.3
63.2
69.3
Every Frames
GOT-Edit-378
VGGT
2
Every 3 Frames
72.4
74.8
64.3
70.7
Every 2 Frames
92.1
74.8
63.2
71.2
Every Frames
78.4
75.2
63.3
71.4
Table Attribute-wise performance with different semantic and geometry configurations.
Semantic
Goometry
AVisT Obstraction Effects (Occlasion)
DINO
MAE
VGGI
StreamVGKGiT
Weather Conditions (Target Visibilizy)
Camoullage (Background Clutter)
Target Efficts (Distractor)
(1)
68.077
56.69
62.07
44.58
65.81
60.10
66.21
45.93
(4)
68.54
41
68.33
48.86
68.39
61.31
68,73
49.68
NPr, Pr, and Suc Plots
We report NPr, Pr, and SUC plots on four datasets: NIS, AVisT, LaSOT, and OTB. Other datasets, such TrackingNet and GOT-10K, are evaluated on online servers without plots and thus excluded.
Overview Guidelines for NPr, Pr, and Sue Plots:
In the Precision (Pr) and Normalized Precision (NPr) plots, the x-axis denotes pixel normalized distance thresholds, while the y-axis indicates the percentage of frames which the distance be- tween the predicted and ground-truth target centers falls within the specified threshold. A balance typically sought between higher precision and lower localization error. Trackers are commonly ranked by their performance at threshold of 0.2 in NPr or 20 pixels in Pr.
In the Success (SUC) plot, the x-axis represents the IOU thresholds (measuring the overlap between the predicted bounding box and the ground truth), while the y-axis indicates the percentage of frames in which the IoU meets or exceeds the corresponding threshold. Trackers are commonly ranked by their performance, measured the average precision across all thresholds.
We analyze the plots for each dataset as follows:
NGS: In Figure 5 our tracker outperforms others once the threshold exceeds 0.1 in NPr or 10 pixels in Pr. For SUC, it consistently surpasses all baselines across thresholds.
AVisT: As shown in Figure 6 AVisT, a training-free dataset with diverse adverse scenar- ios. Under conditions NPr with T' < 0.3, our tracker outperforms all baselines. For PR, our tracker outperforms competitors across thresholds. For SUC, our tracker outperforms competitors when T > 0.4.
OTB: In Figure 7 our tracker consistently outperforms competitors e.g., (Lin et al. 2024 Chen et al. 2025al Mayer al., 2021 Wang al. 2021) in SUC. For Pr and NPr, most trackers perform similarly, while our method remains sigmificantly competitive.
LaSOT: In Figure 8 on this in in-distribution dataset, our tracker outperforms SOTA meth-
ods, e.g., (Zheng al. 2024 Cai et al. 2023 Song et al. 2023 2022) when NPr 7 > 0.1. 7 > 10 pixels, and SUC < 0.7. LoRAT surpasses our tracker only under very strict con- ditions, such NPr 7 < 0.1, P 10 pixels, and SUC> > 0.8. Nevertheless, method consistently outperforms other trackers with the same backbone, including PiVOT-L378 and ToMP-L378.
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Normallazd Precision plet
Precision plet
Sucouss plot
Dizuine Procion (N)
60
GOT T4R
Diexrce
BOT TOKLY NOW (OCRT
DCT KARCETT
179 (P11)
28-
20-
bachacy 204 M13)
0100 123 (9.2)
633
Location threshdd
52
Localon tpreshoid (p
20
0.2
06 threshald
Figure 5: Comparison of methods using NPr, Pr, and SUC on NES, left to right.
110
Normalized Prectston plct
Pracision plct
Sucoess plct
Dusa 10 Noctaan [S]
68
(310101.11
FVOT 20.2
1220
L100
CaN (120
DICRON L1SL NAN
20.
(40 (0919971
2
50
Location onet thrcahold
Localios Jror Urrelio (paes)
Overlap threstod
Figure 6: Comparison of methods using NPr, Pr, and SUC on AViST, left to right.
Normalizad Preciaton plat
Precision plct
Sucoess plot
With (111)66))
PoRT LITH (.A
L9kr 178 19-
100/.A0 (1ua)
Yourr (864)
19621
12.
1531
6.3
Location threshold
92
34
18
Locaton threshord (paess
20
30
02
06
threstadd
Figure 7: Comparison of methods using NPr, Pr, and SUC OTB, left to right.
3.0
Normalized Precision plct
100
Pracision pict
200
Sucosss
Eigaroo Prctian [3])
Dieovos Precssann 194)
Betk (534)
GFT
LTT BA-7.
201
42
10.
(1M6 (79
g
20.
OIN Loniin 21
(16) 109/71AL
0101 200- 363
Location erio thrcchold
Locazios orior Uniesto di socs
Oveilap thrsstold
Figure& 8: Comparison of methods using NPr, Pr, and SUC on LaSOT, left to right.
D THE USE OF LARGE LANGUAGE MODELS
The research is original, and large language models were used only for polishing the writing.
21


Reviews:
[{"review_id": "cCJRSg8iG7", "reviewer": "Reviewer_z7fB", "summary": "This paper proposes GOT-EDIT, an innovative approach that integrates 3D geometric features into general object tracking tasks through online model editing. It primarily leverages the VGGT module, trained on large-scale 3D datasets, to extract 3D geometric information from a set of 2D images. By employing null-space constraints, it seamlessly combines 3D geometric information with 2D semantic information, enabling the model to adaptively fuse 3D geometric information while preserving the semantic knowledge learned by the tracker. Extensive experiments demonstrate GOT-EDIT's robustness and accuracy.", "questions": "1. One innovation of this paper is that seamlessly integrating 3D geometric information with 2D semantic information can enhance model performance. However, the mechanism by which 3D geometric information influences 2D semantic information remains undeclared. It is recommended to add relevant content.\n2. Why was the VGGT module chosen for extracting 3D geometric features? It is recommend to compare it with other commonly used modules for 3D geometric feature extraction and detailing its specific advantages.\n3. The paper employs null-space constraints but does not discuss whether these constraints may cause overfitting and performance degradation while preserving semantic integrity. The authors are advised to address this issue.\n4. The fonts in Equations 6 and 7 do not match the subsequent text, potentially causing confusion. A thorough review and correction of relevant content is recommended.\n5. Figure 1 lacks explanations for the annotations, including the red solid and dashed boxes as well as the green box.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "The authors propose an innovative method for integrating 2D semantic information with 3D geometric information, and conducts experimental validation using datasets from diverse scenarios.", "weaknesses": "It is recommended to incorporate additional visualization results across diverse scenarios to substantiate the method's generality. While online model editing has achieved performance gains, it may introduce increased algorithmic complexity and computational overhead, potentially impacting real-time performance—particularly with high-resolution video inputs. Furthermore, the section detailing the online model editing methodology lacks detailed formulaic steps for operations based on AlphaEdit, which may cause confusion.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "kS4nDlLsCw", "reviewer": "Reviewer_SbSc", "summary": "This paper proposes GOT-Edit, a geometry-aware framework for online model editing in 2D object tracking. The key innovation lies in dynamically updating the target model during tracking, guided by multi-geometry information. The framework integrates a geometry-aware representation module with an online editing mechanism that refines the target model incrementally based on geometric consistency.", "questions": "Please see the weaknesses.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "1. Introducing 3D geometric cues into online model editing for object tracking is both innovative and practical, providing a new perspective for improving robustness against shape and viewpoint variations.\n2. The paper is well-written, logically structured, and easy to follow.\n3. Demonstrates improvements on several benchmarks and provides qualitative examples showing better model adaptability during long-term tracking.", "weaknesses": "1. Since online model editing is computationally non-trivial, reporting runtime comparisons with baselines would help clarify practical deployment feasibility.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Xx2AmSCOjc", "reviewer": "Reviewer_EcpM", "summary": "This paper proposes GOT-Edit, the first framework that integrates geometry-grounded reasoning into generic object tracking without requiring explicit 3D inputs. By introducing constraints during online model editing to preserve learned semantic consistency, GOT-Edit prevents model degradation while incorporating geometric cues that are often overlooked by conventional 2D trackers. Through online model editing with a null-space constraint, the method adaptively fuses geometric information while retaining semantic knowledge, thereby achieving greater robustness under occlusion, clutter, and visual ambiguity. The proposed approach has been evaluated on multiple state-of-the-art (SOT) benchmarks and demonstrates competitive performance.", "questions": "See Weaknesses.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The introduction of a geometry-aware correspondence learning mechanism is interesting and effective in visual tracking.\n2. The proposed approach has been evaluated on multiple state-of-the-art (SOT) benchmarks and demonstrates competitive performance.", "weaknesses": "- Compared with the baseline, VGGT introduces additional computational overhead. It is recommended that the authors include a speed and FLOPs comparison to better illustrate efficiency.\n- The VGGT component continuously updates learned knowledge during training. How does the method address potential error accumulation in this iterative learning process?\n- While obtaining geometric features directly from 2D data without using 3D inputs can reduce data collection costs, it raises concerns about whether accurate geometric representations can be captured in this manner.\n- The experiments are conducted using only 3 frames, and the observed performance gains might primarily come from the multi-template setting rather than the proposed algorithmic innovation. Moreover, since the model aims to learn geometric knowledge from multiple frames, there is a concern that using only three frames may be insufficient to capture rich and reliable geometric representations.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "9rNya6QKj8", "reviewer": "Reviewer_HoKn", "summary": "This paper proposed GOT, which, by introducing the geometric knowledge of VGGT, achieves highly competitive experimental results with smaller trainable parameters.", "questions": "1.Apart from the limitations in comparison with related work, what improvement directions does the author think there are for GOT itself (e.g., the utilization of backbone and the future work of GOT)?\n2.Which of the author 's experiments completed the declaration of 'without degrading the dominant semantic features'(on line 100)?\n3.The author believes that the most core contribution is the creation of the engineering model or the proposal of an editing method that can effectively integrate the information of the two types of models?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.The motivation of this paper is quite clear, and the performance-related experiments have well demonstrated the contribution. It is reasonable that information from multiple views (2D + 3D) can be better represented.\n2.Sufficient visualization clearly demonstrates the improvement of GOT compared to the baseline. The dataset involved in the experiment is sufficient to demonstrate the generalization ability of GOT.\n3.The author's most significant contribution lies in demonstrating the existence of the missing geometric prior and the necessity of integration, and proposing an effective editing method to integrate multi-channel information.", "weaknesses": "1.The ablation experiment results in Table 5 seem unable to be compared horizontally with those in Table 1. There are also many models based on DiNOv2-L, and their performances vary. Therefore, the backbone part is still worth comparing.\n2.I have concerns about parameter and inference efficiency. Specifically, Table 6 shows the proportion of reasoning time for each part. The VGGT introduced as an innovation point accounts for the vast majority, and no horizontal comparison is made. The author only presented the trainable parameters and did not show the complete cost of model deployment. In fact, the results of some methods in specific scenarios (e.g., Table 1, Full Overlap column, PiVOT-378 row) do not differ much from GOT. Stating these differences clearly helps to clarify contributions.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "gSamN1PlkS", "reviewer": "Reviewer_ukrA", "summary": "This paper proposes the GOT-Edit method to integrate visual geometric knowledge into general object trackers through online model editing. First, the Visual Geometric Grounded Transformer (VGGT) trained on large-scale 3D annotated data is used to directly extract reliable geometric cues from 2D images. Secondly, to achieve seamless fusion of geometry and semantics, an online model editing strategy with null space constraints is designed. This adaptively fuses geometric information while retaining the semantic knowledge learned by the tracker, making its performance better than the simple fusion of geometric and semantic cues in a variety of scenarios. Finally, through extensive experiments on multi-class GOT benchmark datasets, GOT-Edit performs well in robustness and accuracy, especially in occluded and cluttered scenes. It has established a new paradigm for combining 2D semantics and 3D geometric reasoning for the field of general object tracking.", "questions": "1. Compared to existing work that \"extracts geometric information from 2D images to assist tracking without requiring additional 3D input\" (e.g., tracking methods based on monocular depth estimation), what is the original difference in the core mechanism (non-input method) of GOT-Edit? If the sole contribution is \"the introduction of VGGT + null-space constraints,\" why does this combination constitute an innovative contribution?\n\n2. Besides \"converting AlphaEdit from offline to online,\" are there any other original improvements (e.g., optimizing the calculation of the null-space projection matrix, dynamic constraint strength adjustment)? If the sole contribution is \"online,\" please justify the necessity of these adjustments for the tracking task—why are the dynamic update mechanisms of existing online tracking methods (e.g., ToMP) ineffective?\n\n3. Why does geometric information fail in LaSOT's \"fast motion\" and \"viewpoint changes\" scenarios? Is it because the geometric features extracted by VGGT are insufficiently accurate, or is the fusion mechanism not well-suited to dynamic scenes? Please provide additional quantitative analysis and ablation experiments (e.g., to separately verify the effectiveness of geometric features in dynamic scenes).", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "**1. Problem identification aligns with practical needs:** This paper accurately identifies the performance bottlenecks of current 2D general object tracking (GOT) under conditions of occlusion, background interference, and drastic changes in target geometry/appearance. It also proposes a targeted solution by integrating 3D geometric information, addressing the core pain point of \"lack of scene depth understanding\" in tracking tasks.\n\n**2. Technical adaptability is reasonable:** Addressing the limitations of existing 3D-assisted tracking methods, which rely on additional 3D inputs such as RGB-D and point clouds, this paper employs VGGT to extract geometric features from 2D images, addressing the unavailability of 3D data in real-world scenarios. Furthermore, this paper improves AlphaEdit's offline null space constraint to online model editing, meeting the real-time requirements of tracking dynamic targets and backgrounds.\n\n**3. Comprehensive experimental design:** Performance is verified on multiple benchmark datasets (such as AVisT, NfS, LaSOT, and GOT-10k), covering scenarios such as \"in-distribution/out-of-distribution objects\" and \"poor visibility.\" Detailed ablation experiments verify the effectiveness of components such as semantic/geometric feature sources, null space constraints, and input regularization, resulting in clear results (see Tables 4 and 5).", "weaknesses": "1. The paper's proposal to \"use 3D geometric features to compensate for the shortcomings of 2D semantics\" is not a new idea. Existing work (e.g., Tan et al., 2025a/b; Chen et al., 2025b) has attempted to use 3D information for tracking, but has relied on additional 3D input. This paper merely changes the \"source of 3D input\" to \"extracted from 2D images\" (using VGGT). This is essentially an \"adjustment of the input method\" rather than an \"original breakthrough in the tracking mechanism,\" and does not propose a completely new geometric-semantic fusion logic.\n\n2. Model Editing Module: This is directly based on the nullspace constraint of AlphaEdit (Fang et al., 2025), merely changing \"offline editing\" to \"online updating.\" It lacks theoretical innovation (such as nullspace calculation methods or constraint optimization), making it an \"engineering tweak\" rather than an \"algorithmic originality.\"\n\n3. Overall Framework: This is entirely based on the \"track-by-detection\" paradigm of ToMP (Mayer et al., 2022), adding only two components: \"VGGT geometric feature extraction\" and \"nullspace fusion.\" No new tracking architecture (such as feature interaction mechanisms or localization head design) has been designed. This essentially replaces and supplements components of the existing framework.\n\n4. The paper claims that \"nullspace constraints protect semantic features from degradation,\" but fails to explain why geometric features inevitably fall into the nullspace of semantic features.\n\n5. Some existing work (such as Wang et al., 2025, which uses VGGT for point tracking) has attempted to extract geometric information from 2D images. The paper does not clearly explain the fundamental difference between GOT-Edit and such work in terms of \"tracking task adaptation,\" emphasizing only that \"point tracking lacks semantics\" without demonstrating the uniqueness of its semantic-geometric fusion.\n\n6. The paper only notes that it is \"inadequate for scenes with rapid motion and viewpoint changes\" without further analyzing the underlying causes (e.g., estimation errors of geometric features in dynamic scenes? Improper adaptation of the dynamic weights of semantic-geometric fusion?), nor does it propose potential solutions, demonstrating a lack of understanding of the technical bottlenecks.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)