DAM4SAM: Multi-Domain Technical Systems

• DAM4SAM is a multifaceted term defining distinct systems in visual tracking, neural-symbolic reasoning, and wireless communications.
• It employs techniques like memory partitioning, depth fusion, and fractional delay alignment to enhance tracking, VQA, and MIMO performance.
• Empirical results demonstrate significant improvements in robustness, spectral efficiency, and low-PAPR performance across various benchmarks.

DAM4SAM refers to multiple distinct but unrelated concepts and systems in contemporary research literature. Below is an authoritative account of each meaning in its mathematical, architectural, and empirical detail, cross-referenced by specific application domain.

1. DAM4SAM in Visual Object Tracking: Distractor-Aware Memory for SAM2

System Overview and Motivation

The DAM4SAM system in visual tracking, introduced in (Videnovic et al., 17 Sep 2025), extends the memory module of the zero-training video-segmentation model SAM2.1 to significantly increase tracking robustness in the presence of distractors. The core idea is to bifurcate the memory bank of SAM2.1—traditionally a FIFO buffer of per-frame (image, mask, timestamp) triplets—into Recent Appearance Memory (RAM) for maintaining mask accuracy, and Distractor-Resolving Memory (DRM) for resilience to distractor-induced drift and improved redetection. The pipeline executes the same encoders and mask decoder as standard SAM2.1, with all architecture modifications isolated to the memory management code.

Mathematical Formulation

At each timestep $t$, the tracker predicts $K$ mask hypotheses $M_t^k$ and their IoU scores $s_t^k$, selecting $M_t = M_t^{k^*}$ with $k^* = \arg\max_k s_t^k$. DAM4SAM maintains

• $RAM_t = \{ (I_{t_i}, M_{t_i}, t_i) \}_{i=1}^{N_R}$ (with $N_R=3$)
• $$DRM_t = \{ M_0 \} \cup \{ (I_{d_j}, M_{d_j}) \}_{j=1}^{N_D}$$ (with $N_D=3$), $M_0$ being the initial annotated frame.

RAM is updated every $\Delta=5$ frames iff $|M_t| > 0$. DRM is updated on RAM-update frames satisfying all:

1. $s_t > \theta_{IoU}=0.8$
2. $\delta_t < \theta_{area}=0.2$, with $\delta_t$ the relative deviation of $|M_t|$ from a running median over prior $N_M=10$ frames
3. $r_t < \theta_{anc}=0.7$, where $r_t$ is the ratio between the union bounding box area of $M_t$ and the largest connected component of the alternative mask $Alt_t$ to the area of $B(M_t)$.

Implementation and Ablations

All code operates within the original SAM2.1-L (Hiera-L backbone), with no retraining required. The design was validated with a new benchmark, DiDi, comprising 180 long videos dominated by distractor-ambiguous frames. Ablations revealed that: blocking memory updates on empty masks, sparse RAM updates, and introspection-driven DRM collectively yield the highest robustness (measured by DiDi VOTS Q/A/R) and avoid memory corruption from spurious hypotheses.

Empirical Results

DAM4SAM surpasses vanilla SAM2.1-L in robustness and tracking quality across 13 established tracking and segmentation benchmarks. Typical gains:

• DiDi Q: $0.649 \to 0.694$, Robustness: $0.887 \to 0.944$
• VOT2020 EAO: $0.681 \to 0.729$
• LaSoT AUC: $70.0 \to 75.1$ On VOTS2024, robustness increases from $0.790$ to $0.864$. Integration as a drop-in module into EfficientTAM and EdgeTAM confers 4–11% gains without model retraining.

Significance

DAM4SAM demonstrates that explicit memory partitioning, introspection criteria, and on-line updating strategies are necessary to maintain state-of-the-art tracking accuracy and re-detection in challenging distractor-rich scenarios (Videnovic et al., 17 Sep 2025).

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2. DAM4SAM in Neural-Symbolic Multimodal Compositional Reasoning

Architecture and Workflow

In vision-language reasoning, DAM4SAM denotes a system uniting Segment Anything (SAM), Depth Anything Model (DAM), and GPT-4V in a zero-shot, train-free neural-symbolic pipeline (Huo et al., 2024). The method processes a single RGB image $I$ (optionally with prompt $\psi$).

• SAM outputs $N$ instance masks $\{M_i\}$, with semantic class labels $\{c_i\}$.
• DAM generates a dense depth map $D$.
• Symbolic fusion: For each mask $M_i$, average-pool the depth map over the mask pixels (i.e., $$\text{depth}_i = \frac{1}{|M_i|} \sum_{p \in M_i} D(p)$$), compute bounding-box centers $(x_i, y_i)$, sizes $(w_i, h_i)$, and assemble symbolic instances $S_i = (c_i, x_i, y_i, w_i, h_i, \text{depth}_i)$.
• Composition Reasoning: Compute pairwise spatial predicates such as $\text{left\_of}$, $\text{above}$, $\text{in\_front\_of}$, and advanced ones like $\text{inside}$ (mask containment), using thresholded offsets in $(x, y, \text{depth})$ or high mask IoU.
• Prompt assembly: Synthesize a GPT-4V prompt fusing the natural language query, per-instance properties, and extracted relations.

Zero-Shot Tasks and Quantitative Benchmarking

DAM4SAM enables two principal tasks:

• Compositional Reasoning: On 50 in-the-wild images, predicate classification achieves 94% (X), 91% (Y), 89% (Z), and 85% (“advanced”) accuracy.
• Zero-shot Symbolic VQA: On 100 GQA/VisualCOMET questions, accuracy is: | Method | VQA Acc. | | -------------- | -------- | | GPT-4V | 67.2% | | w/o Symbol | 70.4% | | DAM4SAM | 82.1% |

Ablation and Empirical Analysis

When isolating modules:

• “SAM-only” (no depth): strong lateral (X/Y) but weak Z (front/back) relations.
• “DAM-only” (no masks): only Z (depth) meaningful; X/Y meaningless.
• Full pipeline: best overall, X=94%, Y=91%, Z=89%.

Advanced relations improve from 78% (one cue) to 85% (fusion). Qualitative outputs demonstrate nuanced scene descriptions, outperforming plain GPT-4V for spatial reasoning.

Implementation

Publicly released at [https://github.com/AnthonyHuo/SAM-DAM-for-Compositional-Reasoning]. Installation uses standard Python virtual environments. At runtime, fed an image and prompt, the system outputs symbolic objects, pairwise relations, and enriched VQA answers.

Context and Future Directions

DAM4SAM enables systematic neural-symbolic fusion for VQA and scene understanding, bypassing any retraining or dataset-specific fine-tuning (Huo et al., 2024).

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3. DAM4SAM in Wireless Communications: Fractional Delay Alignment Modulation

Underlying Principle

Here DAM4SAM designates Fractional Delay Alignment Modulation as described in (Zhou et al., 2024). Standard iDAM achieves delay alignment under integer-multiple path delays. Real-world multipath, however, exhibits arbitrary (fractional) delays, leading to residual ISI unless compensated via precise fractional-delay filtering.

Algorithmic Structure

• Transmitter: Data symbols are upsampled by factor $Q$; per-path signals are delayed via fractional-delay Farrow filters (order $M=3$–$5$).
• Farrow filtering: The filter coefficients realize desired shifts $\Delta_l$ at sub-sample accuracy via online polynomial evaluation, sidestepping runtime tap recomputation.
• Joint ZF beamforming: For an $L$-path MISO channel, per-path ZF is enforced ($h_l^H f_{l'}=0$ for $l\neq l'$), maximizing SNR under total transmit power.

Performance Summary

Simulations with $M=64$ antennas, $L=3$ paths, $Q=2$ upsampling, and $M=3$ polynomial order demonstrate:

• Symbol Error Rate (SER) at 10 dB SNR:
  • iDAM (integer delays): $10^{-1}$–$10^{-2}$
  • fDAM (fractional delays): $10^{-4}$
  • OFDM: $\sim 10^{-3}$ (with $16.3\%$ CP overhead)
• Spectral Efficiency: $4.9$ b/s/Hz for fDAM, exceeding OFDM ($4.2$ b/s/Hz including CP).
• PAPR: fDAM retains low PAPR similar to iDAM, substantially outperforming OFDM (7.8 dB vs. 10.2 dB at $10^{-3}$ CCDF).

Comparison Table

Scheme	SER @10dB	Spec. Eff. (b/s/Hz)	PAPR (dB)
iDAM	$10^{-1}$–$10^{-2}$	4.0	7.5
fDAM	$10^{-4}$	4.9	7.8
OFDM	$\sim10^{-3}$	4.2 (with CP)	10.2

Implementation Notes

Upsampling and Farrow-based filtering all occur in baseband DSP, requiring no analog true-time-delay lines (Zhou et al., 2024).

Implications

fDAM uniquely restores perfect path alignment for channels with fractional path delays, supporting high spectral efficiency and low-PAPR single-carrier massive-MIMO transmission without recourse to OFDM or complex equalization.

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4. DAM4SAM in Multi-User Massive MIMO Transmission

Concept and System Model

In (Wang et al., 2022, Wang et al., 2023), DAM4SAM refers to applying (Fractional) Delay Alignment Modulation to multi-user mmWave massive-MIMO systems. The transmission leverages delay pre-compensation and per-path beamforming to convert a frequency-selective, multi-user channel into parallel, ISI- and IUI-free single-carrier links. Each symbol is pre-delayed such that all multipath components for a user arrive at the same tap ($n_{k,\max}$).

Beamforming Strategies

• MRT: Each path's beamformer matches the array response of that path. This achieves perfect alignment and ISI/IUI suppression as $M \gg L_{\rm tot}$.
• ZF: Pathwise ZF beamforming orthogonalizes all cross-paths, requiring $M \geq L_{\rm tot}$.
• RZF: Regularized ZF interpolates between MRT and ZF, with per-path power allocation solved via SCA.

The received signal at user $k$ after DAM is:

$$y_k[n] = \sum_{l=1}^{L_k} h_{k,l}^H f_{k,l} s_k[n-n_{k,\max}] + \text{ISI/IUI terms} + z_k[n]$$

with cross-terms vanishing under asymptotic orthogonality or ideal ZF.

Achievable Rate Region

The achievable rate region is parametrized via the rate-profile approach, solving (for each user $k$)

$$\max_{R,\{\mathbf{f}_{k,l}\}} R \quad \text{s.t.} \quad R_k\geq \alpha_k R, \quad \sum_{k,l}\|\mathbf{f}_{k,l}\|^2\leq P$$

with resulting $K$-dimensional Pareto-optimal rate tuples.

Empirical Results

• Spectral Efficiency: DAM-based MRT/ZF/RZF outperforms strongest-path and OFDM across SNRs.
• PAPR: Single-carrier DAM achieves lower PAPR than OFDM, marginally exceeding that of strongest-path.
• Robustness: DAM performance is robust to $L$ (number of paths), unlike strongest-path approaches that degrade rapidly.
• Guard overhead: For a block of $G_c$ symbols, DAM’s guard overhead is $2n_{\max}/G_c$, compared to OFDM’s $n_{\max}/(M+n_{\max})$ per symbol.

Computational Considerations

• Complexity: MRT requires $O(M L_{\rm tot})$ flops; ZF/RZF require $O(M L_{\rm tot}^2 + L_{\rm tot}^3)$ for pseudo-inverse computation and SCA power optimization.
• Realization: Practical limitations mostly concern accurate path delay and angle estimation, and high-rate baseband buffering.

Summary

DAM4SAM in this context designates the system-level application of (fractional) delay alignment modulation, merging pathwise pre-compensation with spatial beamforming to extract the full capacity of highly sparse, multi-user mmWave MIMO without multi-carrier modulation, delivering both high spectral efficiency and low PAPR (Wang et al., 2022, Wang et al., 2023).

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5. DAM4SAM: Nomenclature and Cross-Domain Collision

It is notable that "DAM4SAM" refers to distinct technical entities in very different domains:

• Distractor-Aware Memory for SAM2 in visual object tracking (Videnovic et al., 17 Sep 2025)
• Depth-Anything Model for Segment-Anything Model in neural-symbolic VQA (Huo et al., 2024)
• Fractional Delay Alignment Modulation for Sparse Antenna Massive-MIMO (Zhou et al., 2024, Wang et al., 2022, Wang et al., 2023)

This nomenclatural overlap is coincidental; care must be used to disambiguate the term based on research context.

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• (Videnovic et al., 17 Sep 2025): “Distractor-Aware Memory-Based Visual Object Tracking”
• (Huo et al., 2024): “Composition Vision-Language Understanding via Segment and Depth Anything Model”
• (Zhou et al., 2024): “Fractional Delay Alignment Modulation for Spatially Sparse Wireless Communications”
• (Wang et al., 2022): “Multi-User Delay Alignment Modulation for Millimeter Wave Massive MIMO”
• (Wang et al., 2023): “Achievable Rate Region and Path-Based Beamforming for Multi-User Single-Carrier Delay Alignment Modulation”