This paper proposes DPNeXt, a streamlined multi-scale feature fusion decoder for Multi-Task Learning (MTL) in robotics perception systems, improving frozen VFM utilization and mitigating negative indu…

The paper proposes xModel-KD, a cross-modal knowledge distillation framework, to improve 3D point cloud segmentation by effectively transferring rich appearance cues from 2D images to sparse 3D geomet…

The paper demonstrates a coordinated, cross-modal spoofing attack that successfully deceives state-of-the-art multi-sensor fusion systems in autonomous vehicles by making multiple sensors agree on a f…

This paper presents Fusion Embedding family, which adds audio to a pre-trained vision-language embedding base using a connector and deep adapters.

The paper proposes a novel geometric embedding hashing method to recover object correspondences (vector links) between two embedding clouds generated by different black-box encoders using only a small…

This paper explores the use of Large Language Models (LLMs) in data fusion tasks for tabular data and shows their superiority over traditional methods.

The paper presents SenseNova-Vision, a unified multimodal model for computer vision tasks using natural language instructions and optional visual prompts, trained primarily on a new corpus and requiri…

The paper introduces MetricScenes, a new large-scale, in-the-wild dataset, and demonstrates that fine-tuning existing geometry models on this dataset significantly mitigates the scale-collapse problem…

The paper proposes VLM3, a simple, scalable method that demonstrates standard Vision Language Models (VLMs) can natively learn 3D understanding by focusing on architectural simplicity and specific dat…

The paper introduces TrojanMerge, a framework demonstrating that model merging can be exploited to systematically compromise the safety alignment of multiple individually safe LLMs.

This paper proposes a four-layer technical architecture for large model inference optimization, including Multi-model Fusion, Model Optimization, Compute-Model Fusion, and Compute-Network-Model Fusion…

The paper proposes SharpRec, a framework for LLM-based Cross-Domain Sequential Recommendation to address the bottlenecks of cross-domain knowledge conflict and performance saturation in multi-domain f…

This paper introduces Align4D, a framework for generating coherent video-3D pairs using any-modal input, achieving state-of-the-art quality and consistency in X-to-4D generation.

FLORO is a multimodal geospatial foundation model that learns transferable remote sensing representations from a small, diverse corpus, achieving strong performance across various sensor types and res…

The paper identifies a fundamental mismatch between standard pairwise ranking metrics (like AP and FPR-95) and the true assignment objective in multi-view object association, proposing a Sinkhorn-base…

The paper proposes Preference Delta Aggregation (PDA), a framework that aggregates multiple weak preference signals derived from smaller model pairs using LoRA merging to significantly boost the perfo…

CLIFE is an edge-native camera-LiDAR fusion framework that enhances perception of vulnerable road users under varied environmental and traffic conditions, with adaptive calibration, O(N log N) per-fra…

This paper proposes a novel layer-wise decision fusion method for fake audio detection using deep speech models, achieving the best cross-dataset performance.