Sensor Fusion

With the rise of large-scale foundation models, efficiently adapting them to downstream tasks remains a central challenge. Linear probing, which freezes the backbone and trains a lightweight head, is computationally efficient but often restricted to last-layer representations. We show that task-relevant information is distributed across the network hierarchy rather than encoded solely in the last layers. To leverage this distribution of information, we apply an attentive probing mechanism that dynamically fuses representations from all layers of a Vision Transformer. This attentive layer fusion (ALF) learns to identify the most relevant layers for a target task and combines low-level structural cues with high-level semantic abstractions. Across 20 diverse datasets and multiple pretrained foundation models, ALF achieves consistent, substantial gains over standard linear probes. Attention heatmaps further reveal that tasks different from the pre-training domain benefit most from intermediate representations. Overall, our findings underscore the value of intermediate layers and demonstrate a principled, task-aware approach for unlocking their potential for probing-based adaptation.

Causal inference in modern large-scale systems faces growing challenges, including high-dimensional covariates, multi-valued treatments, massive observational (OBS) data, and limited randomized controlled trial (RCT) samples due to cost constraints. We formalize treatment-induced structural non-overlap and show that, under this regime, commonly used weighted fusion methods provably fail to satisfy randomized identifying restrictions.To address this issue,we propose a constrained joint estimation framework that minimizes observational risk while enforcing causal validity through orthogonal experimental moment conditions. We further show that structural non-overlap creates a feasibility obstruction for moment enforcement in the original covariate space.We also derive a penalized primal–dual algorithm that jointly learns representations and predictors, and establish oracle inequalities decomposing error into overlap recovery, moment violation, and statistical terms.Extensive synthetic experiments demonstrate robust performance under varying degrees of non-overlap. A large-scale ride-hailing application shows that our method achieves substantial gains over existing baselines, matching the performance of models trained with significantly more RCT data.

Existing deep spatial–spectral fusion (SSF) methods typically learn the fusion mapping in the coordinate domain using convolutions and attentions, making it hard to scale across varying spatial resolutions and offering limited control over the frequency content of the reconstructions, which may further lead to severe spectral distortion. In this work, we propose Latent Spectral Operators (LSO), a SSF framework that learns fusion mappings between spectral functions through a structured operator parameterization. Specifically, LSO first applies a cross-attention projection, where learned latent tokens serve as spectral prompts, to compress high-dimensional observations into a compact latent representation, and then adopts a hierarchical, patch-based architecture to integrate rich multi-scale cues. Furthermore, to parameterize the latent fusion operator in a controllable manner, a Trigonometric Basis Solver is elaborated, which represents the mapping using a trigonometric basis expansion. This formulation naturally supports multi-frequency modeling, with a capacity-stability trade-off governed by the number of basis functions. Extensive experiments on the CAVE and Harvard benchmarks demonstrate that LSO achieves consistent state-of-the-art performance and exhibits strong transferability across different spatial scales.

Protein-protein interactions (PPIs) are essential for many biological processes. However, existing PPI prediction approaches suffer from two major limitations: they overlook the hierarchical organization of proteins, particularly meso-scale motifs that critically regulate PPIs, and fail to effectively integrate sequence, structure, and function modalities. To address these limitations, we propose MMM-PPI, a Hierarchical Motif-based Multi-Modal protein Encoder for PPI Prediction that constructs PPI embeddings in a bottom-up multi-modal manner across three scales. At the micro-scale, we encode three modal residue features; at the meso-scale, a novel multimodal motif encoder aggregates residues into spatially-informed motif embeddings; at the macro-scale, a multimodal protein encoder integrates motifs into protein embeddings by jointly modeling motif importance and inter-modal correlations. The pre-trained encoder can be used off-the-shelf for large-scale PPI prediction. Extensive experiments on multiple PPI datasets show that MMM-PPI outperforms state-of-the-art multi-label PPI prediction models, particularly under challenging data partitions and limited data scenarios. Codes are in https://github.com/yzf-code/MMM-PPI.

Multi-view classifiers typically fuse all observed views into a single representation, which becomes fragile when some views are missing or corrupted.We propose a prototype-anchored fusion module based on an entropically regularized unbalanced optimal transport (UOT) barycenter.Each view is summarized into a small set of learned atoms and is matched to a shared prototype support; fusion outputs a probability measure over prototypes with fixed dimension.By relaxing marginal constraints with a generalized KL penalty, the UOT objective can leave a fraction of view mass unmatched when matching is geometrically costly, yielding a simple differentiable trimming mechanism without hand-tuned thresholds.We provide a basic theoretical result showing that discarding an arbitrary subset of atom mass incurs a penalty bounded by its total mass, independent of transport distances.Experiments on multi-view action recognition benchmarks under simulated missing views, missing-rate shift, and feature-space corruption demonstrate consistently improved stability under severe missingness with modest overhead on top of strong backbones.

Incomplete multi-view multi-label classification addresses scenarios where views and labels are partially missing. While existing methods treat labels solely as supervision signals, they overlook the semantic structure inherent in partial annotations. We propose Label-Guided Representation Learning (LGRL) that systematically exploits label semantics as structural priors throughout learning. Our framework constructs a semantic-informed mixture prior via learnable category prototypes to guide representation extraction, and introduces category-specific conditional posteriors where prototypes act as Bayesian experts for multi-view fusion. We further derive a principled label-driven information bottleneck objective balancing reconstruction sufficiency with cross-view consistency, enabling category-conditional reasoning. Extensive experimental results demonstrate the effectiveness of LGRL across benchmark datasets as well as real-world applications in sports analytics and medical imaging.

Evidential Deep Learning (EDL) enables trustworthy multi-view classification, yet suffers from a critical vulnerability: the Scale Mismatch Problem. We theoretically demonstrate that existing evidential fusion rules erroneously equate logit magnitude with semantic confidence, rendering them susceptible to semantic hijacking by inflated but uninformative views. To resolve this, we propose Scale-Invariant Evidential Fusion (SAEF), a framework utilizing instance-wise standardization to strictly decouple confidence from scale. Instead of relying on magnitude dominance, SAEF aggregates views based on statistical consensus. Theoretically, SAEF guarantees invariance to global scaling and robustness to asymmetric dominance. Experiments on four diverse datasets confirm that SAEF outperforms state-of-the-art baselines in accuracy and robustness to semantic conflicts and noise, ensuring stability against severe scale perturbations.

Real-world multimodal systems must be robust against low-quality data, such as sensor noise, incomplete multimodal data and conflicting inputs. However, existing trustworthy fusion methods rely on the model's own prediction confidence to judge data quality. This creates a circular dependency: when a model is confident but wrong (overconfident), these methods fail to detect the error. To break this loop, we propose Geometry-based Multimodal Fusion (GMF). Instead of relying on predictions, we evaluate reliability by measuring the physical effort required to map input data back to the valid data manifold. We implement this using Diffusion Schrödinger Bridges with Rectified Flow, which allows us to calculate Transport Energy as a direct metric for quality. The logic is simple: valid data sits on the manifold (low energy), while noisy, incomplete data or conflicting data requires high energy to be restored. This geometric metric acts as an independent judge, effectively flagging unreliable inputs even when the classifier is fooled. Extensive experiments demonstrate that GMF significantly improves robustness against severe sensor noise and semantic conflicts compared to confidence-based baselines.

Model fusion seeks to combine independently trained neural networks into a single model without retraining, but is complicated by representational divergence arising from permutation invariance, random initialization, and heterogeneous training data. Existing methods struggle particularly in zero-shot settings under non-IID data distributions, and are often limited to specific architectures or pairwise fusion. We introduce a neuron-centric family of fusion algorithms that frames fusion as a principled representation-matching problem: intermediate neurons across parent models are grouped into target representations, which the fused model's corresponding sub-networks are then trained to approximate. Unlike prior work, our approach incorporates neuron attribution scores to bias alignment toward salient features, and can be applied to any architecture modularizable as a DAG of levels—empirically validated on VGGs, ResNets, and ViTs. Experiments across standard benchmarks show consistent improvements over existing fusion methods, with the largest gains in zero-shot and non-IID scenarios. Code is available at https://github.com/AndrewSpano/model-fusion-via-retrofitting}{https://github.com/AndrewSpano/model-fusion-via-retrofitting.

In RGB--D salient object detection, multimodal fusion is commonly implemented via symmetric token interaction, implicitly allowing information to flow in both directions. Under *modality imbalance*---when an auxiliary stream is substantially noisier than a designated primary stream---such symmetry creates a *backflow channel* that injects auxiliary noise into the primary representation and amplifies errors across iterative refinement stages. We formulate fusion in this regime as *directed refinement with one-way safety*: the primary modality defines a guidance field, while only auxiliary representations are iteratively purified, and primary perturbations induced by the auxiliary stream are explicitly bounded. We propose *Hamiltonian Asymmetric Fusion* (HAF), a lightweight unrolled refinement block that updates auxiliary tokens with momentum regularization and gated driving. The refinement force is instantiated by FFT-based spectral global correlation and modulated by a shared learnable spectral response to emphasize reliable frequency components with minimal parameters; a leaky momentum gate and a stable integrator improve multi-step refinement stability. We provide guarantees of auxiliary error contraction and bounded primary perturbation, which symmetric fusion operators do not satisfy under imbalance. Experiments on six RGB--D SOD benchmarks show consistent gains and substantially more graceful degradation under controlled auxiliary corruption.

Medical diagnosis tasks often rely on heterogeneous information from multiple sources, such as medical images and clinical text. Multimodal fusion is therefore essential for improving classification performance and robustness. However, most existing methods assume a fixed and known modality set, making them less effective when the number or composition of modalities changes. To address this limitation, we propose a modality-agnostic medical multimodal fusion framework that can naturally accommodate an arbitrary number of input modalities. At the coarse-grained modality level, we represent each modality’s estimation of latent semantics as an uncertainty-aware probability distribution, and impose symmetric consistency constraints to achieve global cross-modal semantic alignment. At the fine-grained token level, we further design a token-level consistency constraint based on linear reconstruction. This constraint enables structured mutual verification of local semantics across modalities. Finally, for multimodal fusion, we adopt a multi-view consistency strategy to obtain a unified representation for diagnosis prediction. In particular, each modality is sequentially treated as a conditional view to suppress noise in the remaining modalities and extract shared semantics. Extensive experiments on five public and self-constructed multimodal medical datasets demonstrate the effectiveness and scalability of the proposed approach. Code is available at https://github.com/gjhgjbkg/SMMF.

Most reinforcement learning (RL) baselines maximize future cumulative rewards with a fixed single discount factor, which limits their performance in complex sequential decision-making tasks due to a failure to balance short-term objectives and long-term planning. To address this issue, this paper focuses on a multi-timescale critic framework, where each component corresponds to a Q-value with a distinct discount factor. Two key improvements are proposed: (1) A Neural Reward Decoder reconstructs the reward sequence from multi-scale Q-values, with value and reward reconstruction losses enhancing Q-value estimation consistency; (2) A cross-attention-based Q-weight predictor adaptively adjusts Q-value weights via current observations to generate the final Q-value for policy optimization. Extensive experiments on DMControl and CARLA benchmarks demonstrate that our method significantly outperforms state-of-the-art (SOTA) baselines. Furthermore, we validate the framework's generalizability by integrating it with both off-policy (SAC, DrQ-v2) and on-policy (PPO) algorithms, achieving consistent performance gains. The code is available in the supplementary material.

Current deep learning models for Multispectral and Hyperspectral Image Fusion (MS/HS fusion) are typically designed for fixed spectral bands and spatial scales, which limits their transferability across diverse sensors. To address this, we propose SSA, a unified framework for MS/HS fusion with spectral-band and fusion-scale agnosticism. Specifically, we introduce Matryoshka Kernel (MK), a novel operator that enables a single model to process varying ordered spectral band counts within a supported range. Meanwhile, we build SSA upon an Implicit Neural Representation (INR) backbone that models the HS signal as a continuous function, enabling reconstruction at arbitrary spatial resolutions. Together, these two mechanisms support a single MS/HS fusion model for heterogeneous sensors and arbitrary query scales. Extensive experiments demonstrate that our single jointly trained model achieves state-of-the-art performance, generalizes to unseen spatial scales, and transfers to unseen sensors with few-step adaptation. The code can be obtained at https://github.com/vg219/SSA.

Multimodal systems encounter partial observability from sensor dropout and degradation, yet adverse tails can remain unreliable even when average performance is preserved. Under a locked same-support contract, matched-average runs can exhibit same-support tail failure, diverging on worst-case and shift-reweighted metrics over the same observable support. The failure arises because conditional interaction separates environment coverage from parameter exposure. Gated routing sends information through different groups, so high-leverage groups may receive few updates despite complete coverage, and tail aggregation amplifies their errors. We audit this mismatch with TailPressure, an exposure-normalized statistic of tail-leveraged interaction from gating logs. We propose Heterogeneity-aware Closed-loop Exposure Stabilizer (H-CES), a lightweight controller that stabilizes per-group pressure via deterministic increment-branch gating and group-wise decoupled weight decay, without changing loss or inference. Across multimodal settings and backbones, H-CES improves same-support tail reliability while preserving clean performance.