DiffCrossGait proposes a novel trajectory-level alignment method using latent diffusion to overcome domain discrepancies in 2D-3D gait recognition, achieving state-of-the-art performance.

The paper proposes a Variational Adapter (VACSR) to improve cross-modal similarity representation by treating fine-grained image-text matching as a variational inference problem, thereby mitigating th…

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.

The paper proposes FedMChain, a novel federated learning framework that structures multimodal training into sequential phases to mitigate modality competition and improve model performance while reduc…

This paper identifies modality-order sensitivity as a failure in vision-language models and introduces a test-time training method to mitigate it, resulting in improved performance.

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…

The paper introduces UniKE, a benchmark showing that successful knowledge edits in text-only multimodal models do not reliably transfer to image generation, revealing a significant modality gap.

The paper proposes UF-AMA, a unified framework that achieves state-of-the-art cross-domain emotion recognition by adaptively aligning and fusing multimodal physiological signals like EEG and eye-track…

The paper proposes a novel global multi-modal alignment framework to robustly learn video representations from noisy and complementary sensor data, significantly improving driver distraction detection…

The paper introduces COMET, a novel PLS-SVD framework, to analyze the audio-text modality gap in CLAP models, showing that shared concepts are captured by a small subset of axes, and proposes a spectr…

The paper introduces AnyMo, a unified multimodal framework that enables high-quality, scalable conditional human motion generation by leveraging a massive, cross-modal dataset and a masked modeling tr…

V-LynX is a framework that enhances Video LLMs by integrating new modalities into their existing token interface, achieving state-of-the-art performance across diverse video understanding tasks.

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 proposes a sequence-alignment framework using Soft Dynamic Time Warping to evaluate audio-driven talking-head generation, demonstrating that this approach provides more robust and fair compa…

The paper proposes Progressive Loading-Aware Hierarchical Contrastive Learning (PL-HCL), a framework to detect misalignment between a skill's description and its true behavior in large language model…

The paper introduces CrossMPI, a novel cross-modal prompt injection attack that uses image-only perturbations to steer the interpretation of both textual and visual inputs in Large Vision-Language Mod…

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 a Conflict-aware Penalty (CP) and Statistical Loss (SL) framework to stabilize and balance the training of multimodal sentiment analysis models, achieving state-of-the-art perform…