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Difference Feature Map Distillation: Transferring Inter-Sample Relational Knowledge Towards Efficient Transformer-Based Tracking

Zhicheng Ding, Xinyu Chu, Qing Tian

arXiv:2610.05707v1cs.CV

TL;DR

Transformer-based trackers face computational and memory costs that hinder real-time deployment, while existing tracking distillation largely focuses on per-sample feature imitation. DFM-KD transfers inter-sample differences to align relational structure, and experiments report improvements across Transformer trackers without increasing student parameters or FLOPs.

  • Problem

    Transformer-based trackers’ computation and memory demands hinder deployment on real-time, resource-constrained platforms, while existing tracking distillation primarily mimics features per sample.

    Source: §I · p. 1

  • Method

    DFM-KD aligns teacher and student inter-sample difference feature maps to transfer batch-wise relational structure rather than only matching absolute activations.

    Source: §I · p. 2

  • Results

    DFM-KD improves student performance on MixFormerV2 and STARK without increasing student parameters or FLOPs; on MixFormerV2, NP is +0.98 over the vanilla student.

    Source: B. Quantitative Results · p. 5

  • Takeaways & Limitations

    Experiments on LaSOT-RoadUsers report consistent tracking improvements over state-of-the-art distillation approaches, emphasizing batch-wise manifold preservation in efficient visual tracking.

    Source: §V · p. 6

Abstract

from arXiv · show

In autonomous driving perception, visual object tracking systems must satisfy stringent latency and power constraints while remaining robust in complex and dynamic environments. Although transformer-based trackers achieve state-of-the-art accuracy, their substantial computational and memory overheads hinder deployment on real-time, resource-constrained platforms. To move toward this goal, we propose Difference Feature Map Knowledge Distillation (DFM-KD), a novel relational distillation framework tailored for transformer-based visual object tracking. Unlike conventional feature distillation methods that minimize point-wise discrepancies (e.g., mean squared error) between teacher and student feature representations, DFM-KD transfers knowledge through inter-sample feature differences, explicitly aligning the relational structure of the feature space. By distilling how the teacher models appearance variation and consistency across samples, rather than enforcing similarity in absolute activations, DFM-KD enables the student to better capture the structural dynamics of visual changes within a batch. As a result, the distilled model exhibits enhanced feature robustness and improved tracking performance. Extensive experiments demonstrate that DFM-KD consistently outperforms conventional feature-level distillation methods in both tracking precision and success rates.

I. INTRODUCTION

Transformer-based trackers offer strong performance but face computational and memory constraints that hinder real-time deployment. DFM-KD addresses the limits of instance-wise distillation by transferring inter-sample feature relationships.

I. INTRODUCTION

  • Transformer-based trackers’ substantial computation and memory use limit their applicability on real-time, resource-constrained platforms.Autonomous-vehicle tracking must also operate within strict computational, thermal, and latency budgets.Source: §I · p. 1
  • Existing tracking distillation methods mimic teacher feature maps independently for each sample, overlooking the teacher’s batch-wise organization of samples.The teacher’s relative arrangements can encode similarity between tracking pairs and distinctions between different targets.Source: §I · pp. 1–2
  • DFM-KD aligns teacher and student inter-sample difference feature maps to transfer relational structure rather than only reconstructing absolute features.These differences encode appearance variation, contextual similarity, and structural relationships across samples.Source: §I · p. 2
  • DFM-KD can be selectively applied to arbitrary Transformer layers, targeting informative layers while minimizing overhead.The framework is also described as architecture-agnostic and integrable with Transformer-based trackers.Source: §I · p. 2
  • DFM-KD integrates with STARK and MixFormerV2 and improves tracking precision and success rate with minimal computational cost.The contribution statement reports these gains without specifying numerical values.Source: §I · p. 2

II. RELATED WORK

The related-work discussion traces visual tracking from traditional methods to increasingly capable Transformer trackers, while noting their computational expense and the open challenge of transferring their interaction-layer knowledge to lightweight students.

A. Visual Object Tracking

  • Traditional tracking methods include correlation-filter and Siamese-based approaches, while Transformers have gained prominence for modeling long-range dependencies and expressive representations.STARK and MixFormerV2 are cited as Transformer trackers using attention to model spatiotemporal relations.Source: A. Visual Object Tracking · p. 2
  • Recent Transformer trackers strengthen interaction modeling through temporal cues, hierarchical fusion, language–vision alignment, distractor-aware memory, and specialized attention experts.The passage identifies these mechanisms as part of a broader trend toward stronger interaction layers and deeper feature fusion.Source: A. Visual Object Tracking · p. 2
  • Transformer designs are computationally expensive, and transferring their rich interaction-layer knowledge into lightweight student models remains largely unaddressed.Source: A. Visual Object Tracking · p. 2

B. Knowledge Distillation for Efficient Tracking

Prior tracking distillation mostly aligns teacher and student features per instance, while relational distillation preserves relationships among samples. DFM-KD uses lightweight inter-sample differences as complementary guidance for Transformer tracking.

B. Knowledge Distillation for Efficient Tracking

  • Existing visual-tracking distillation generally aligns teacher and student representations per instance, overlooking inter-sample topology described as a source of robustness in dynamic tracking.Earlier approaches mimic intermediate features or final bounding-box predictions.Source: B. Knowledge Distillation for… · p. 2
  • DFM-KD transfers inter-sample difference feature maps to align student and teacher feature manifolds, offering a parameter-efficient alternative to dynamic layer skipping.The cited dynamic approach can retain full model parameters and introduce potential latency fluctuations on complex frames.Source: B. Knowledge Distillation for… · p. 2

C. Relational and Sample-Relation Knowledge Distillation

  • Relational distillation methods preserve sample relationships using pairwise distances, angles, correlations, similarities, or learned relation encoders.Examples include RKD [17], CCKD [18], SPKD [19], PRD [1], and VRM [28].Source: C. Relational and Sample-Relation… · pp. 2–3
  • Unlike full pairwise relational matrices, DFM-KD computes inter-sample difference maps with cost linear in batch size, complementing feature regression for Transformer tracking.The differences encode appearance changes, contextual similarity, and structural relationships.Source: C. Relational and Sample-Relation… · p. 3

III. METHODOLOGY

The methodology describes conventional feature-level distillation as per-sample teacher-to-student feature regression. Because it treats batch samples independently, it does not preserve the teacher’s latent organization across samples.

A. Baseline Feature-Level Distillation

  • Conventional feature-level distillation regresses student feature maps toward teacher maps on a per-sample basis, for example by minimizing mean squared error at selected layers.The feature tensors are described in terms of batch size, token count, and channel dimension.Source: A. Baseline Feature-Level Distillation · p. 3
  • Each batch element can represent a tracking pair or triplet, and the feature tensors span combined template and search tokens across transformer channels.Source: A. Baseline Feature-Level Distillation · p. 3
  • Sample-wise alignment treats batch elements independently and does not preserve the teacher’s latent organization, which can limit robustness under challenging tracking conditions.Source: A. Baseline Feature-Level Distillation · p. 3

B. Difference Feature Map Distillation

DFM-KD addresses instance-wise feature matching by transferring the teacher’s batch-wise topology through inter-sample differences. It constructs adjacent-sample difference maps along the mini-batch dimension, offering a linear-cost approximation to all-pairs differences.

B. Difference Feature Map Distillation

  • DFM-KD transfers the teacher’s batch-wise topology by distilling how representations change across samples, rather than supervising only each sample’s absolute features.This provides structural guidance about appearance variation, contextual similarity, and relationships among samples.Source: B. Difference Feature Map Distillation · p. 3

1) Construction of difference feature maps:

  • Teacher and student difference maps are formed from token embeddings by subtracting each sample’s features from the next sample’s features across tokens and channels.The resulting maps represent neighboring-sample changes within the mini-batch.Source: 1) Construction of difference feature… · p. 3, Figure 2 · p. 4
  • Because mini-batches use randomly shuffled samples, adjacent differences encode generic inter-sample relationships rather than a specific temporal sequence order.The figure’s right panel details construction along the mini-batch dimension.Source: 1) Construction of difference feature… · p. 3, Figure 2 · p. 4
  • DFM maps can be computed from intermediate token embeddings in either encoder–decoder architectures or pure transformer stacks.The method is described as applicable regardless of whether the tracker has an explicit encoder–decoder structure.Source: 1) Construction of difference feature… · p. 4
  • Adjacent-sample differences produce B − 1 maps with O(B) complexity, instead of the O(B^2) maps required for all sample pairs.The authors describe this as a lightweight approximation that still captures meaningful inter-sample topology.Source: 1) Construction of difference feature… · p. 4

2) DFM loss:

The DFM loss matches teacher and student difference feature maps with mean squared error. The method keeps these maps unnormalized to retain variation magnitudes and can aggregate losses across selected layers.

2) DFM loss:

  • The DFM loss is the mean squared error between the teacher and student difference feature maps.It is defined for a given layer.Source: 2) DFM loss · p. 4
  • The experiments use raw difference feature maps without normalization, retaining both the direction and relative strength of inter-sample feature changes.The authors state that preserving magnitude is important for retaining the teacher’s batch-wise topology.Source: 2) DFM loss · p. 4
  • When multiple transformer layers are distilled, their per-layer DFM losses are aggregated using selected layers and non-negative layer weights.The passage introduces the selected-layer index set and a weighting factor for each layer.Source: 2) DFM loss · p. 4

3) Overall Training Objective:

The overall training objective combines the original tracking supervision with feature-level and DFM distillation losses. Scalar weights control the contributions of the two distillation terms.

3) Overall Training Objective:

  • The overall objective combines tracking supervision, conventional feature distillation, and DFM loss.The objective is expressed as Ltotal = Ltrack + λfeat Lfeat + λDFM LDFM.Source: 3) Overall Training Objective · p. 4
  • Ltrack denotes the original tracking supervision, while Lfeat denotes the standard feature-distillation loss.Both terms contribute to the overall training objective alongside DFM loss.Source: 3) Overall Training Objective · p. 4
  • λfeat and λDFM are scalar weights controlling the contributions of the feature-distillation and DFM terms.Source: 3) Overall Training Objective · p. 4

IV. EXPERIMENTS AND RESULTS

The experiments evaluate DFM-KD on STARK and MixFormerV2, which have different backbone designs but share transformer-layer sequences. Lightweight students are formed by reducing the number of transformer layers.

IV. EXPERIMENTS AND RESULTS

  • STARK and MixFormerV2 are the two evaluated trackers, representing CNN-backbone encoder–decoder and pure-transformer designs, respectively.Both share sequences of Transformer layers that progressively refine token embeddings.Source: 1) Models · p. 4
  • The lightweight students reduce transformer depth, from 12 to 6 layers for STARK and from 12 to 8 layers for MixFormerV2.Source: 1) Models · p. 4
  • Teacher and student layers are paired straightforwardly, such as by selecting evenly spaced teacher layers; the authors identify fixed pairing as future work.Distillation is performed on corresponding transformer outputs.Source: 1) Models · pp. 4–5

2) Data:

The evaluation uses a six-category LaSOT-RoadUsers subset and compares DFM-KD across MixFormerV2 and STARK. DFM-KD improves student tracking performance, with the combined distillation components performing best across three metrics.

2) Data:

  • LaSOT-RoadUsers comprises six road-user categories drawn from LaSOT2019’s 1,400 sequences; evaluations use AUC, Precision, and Normalized Precision.The six categories are bicycle, motorcycle, car, bus, truck, and person.Source: 2) Data · p. 5

3) Training Details:

  • STARK uses a 12-layer ResNet-50 teacher, while MixFormerV2 uses a 12-layer, 288-base teacher to distill an 8-layer student.Both settings use λfeat = 0.2; λDFM is 0.5 for STARK and 0.3 for MixFormerV2.Source: 3) Training Details · p. 5

B. Quantitative Results

  • +0.98 NP over the vanilla student and +0.51 over TDSC-KD on MixFormerV2; on STARK, DFM-KD narrows the teacher–student gap and surpasses the teacher in AUC.The reported improvements add no parameters or FLOPs over the student models.Source: Table I · p. 5, Table II · p. 5, B. Quantitative Results · p. 5
  • Combining DFM-only and vanilla feature KD achieves the strongest overall performance across all three metrics, with DFM-only providing a larger boost than vanilla feature KD alone.Table III ablates the two components on STARK.Source: Table III · p. 5, B. Quantitative Results · p. 5

C. Qualitative Results

In qualitative examples, DFM-KD tracks targets more stably than the STARK student baseline during occlusion and viewpoint changes. The conclusion presents DFM-KD as lightweight relational distillation that improves results across representative Transformer trackers.

V. CONCLUSION

  • DFM-KD transfers teacher inter-sample topology by aligning difference feature maps across training samples rather than relying only on point-wise feature regression.The conclusion characterizes the approach as computationally lightweight and architecture-agnostic.Source: §V · p. 5

C. Qualitative Results

  • DFM-KD maintains tighter localization through bicycle occlusion and viewpoint changes, while the STARK baseline’s boxes shrink or become unstable.Figure 3 maps DFM-KD predictions to green boxes and baseline predictions to red boxes across three frames after the initial frame.Source: C. Qualitative Results · p. 5, Figure 3 · p. 6

V. CONCLUSION

  • On LaSOT-RoadUsers, DFM-KD consistently improves tracking and outperforms TDSC-KD, with ablations confirming complementary benefits to standard feature-level distillation.The conclusion highlights batch-wise manifold preservation in efficient visual tracking.Source: §V · p. 6

VI. FUTURE DIRECTIONS

Future work would extend DFM-KD from batchwise relations to temporal feature changes and replace fixed teacher–student layer matches with adaptive correspondence.

Temporal relational distillation

  • Temporal difference maps between ordered frames are proposed to capture target motion, deformation, and gradual appearance changes that batchwise differences miss.Template-to-search differences use existing training data; a windowed version would require only a modified frame sampler.Source: §VI · p. 6

Adaptive layer correspondence

  • Learnable, student-aware correspondence could replace fixed even-spacing with weighted mixtures of teacher layers that evolve during distillation.The proposal fuses teacher and student hidden states, learns weights over teacher layers and their tokens, and uses a diversity regularizer to prevent collapse onto one layer.Source: §VI · p. 6
  • Adaptive teacher–student layer matching is proposed for distillation settings where a deep teacher supervises a shallower student.Source: §VI · p. 6

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