arXiv subjects

Manning Gao

Publications and source records attributed to Manning Gao.

3 recordsLinked to original sources

arXiv2026

Uncovering Latent Depression Severity for Binary Depression Detection via Advantage-weighting Ranking

Automatic depression detection using audio-visual data faces significant challenges, particularly in disentangling overlapping feature distributions and establishing robust decision boundaries. To address this, we propose a fine-grained multimodal framework featuring a temporal encoder and a mutual transformer to facilitate deep cross-modal fusion. Our core contribution is the Binary Advantage-weighting Ranking Loss, which optimizes the latent space distribution through two complementary mechanisms: Advantage-weighted Separation, which mines hard pairs by computing a pairwise prediction difference matrix and dynamically weighting them based on their difficulty; and Advantage-weighted Compactness, which minimizes intra-class variance to force features to cluster around their respective class centers. Extensive experiments on D-vlog and LMVD demonstrate that our model reconstructs the latent ordinal structure by prioritizing hard pairs, thereby achieving state-of-the-art performance.

cs.AI↗

arXiv2026

GRCF: Two-Stage Groupwise Ranking and Calibration Framework for Multimodal Sentiment Analysis

Most Multimodal Sentiment Analysis research has focused on point-wise regression. While straightforward, this approach is sensitive to label noise and neglects whether one sample is more positive than another, resulting in unstable predictions and poor correlation alignment. Pairwise ordinal learning frameworks emerged to address this gap, capturing relative order by learning from comparisons. Yet, they introduce two new trade-offs: First, they assign uniform importance to all comparisons, failing to adaptively focus on hard-to-rank samples. Second, they employ static ranking margins, which fail to reflect the varying semantic distances between sentiment groups. To address this, we propose a Two-Stage Group-wise Ranking and Calibration Framework (GRCF) that adapts the philosophy of Group Relative Policy Optimization (GRPO). Our framework resolves these trade-offs by simultaneously preserving relative ordinal structure, ensuring absolute score calibration, and adaptively focusing on difficult samples. Specifically, Stage 1 introduces a GRPO-inspired Advantage-Weighted Dynamic Margin Ranking Loss to build a fine-grained ordinal structure. Stage 2 then employs an MAE-driven objective to align prediction magnitudes. To validate its generalizability, we extend GRCF to classification tasks, including multimodal humor detection and sarcasm detection. GRCF achieves state-of-the-art performance on core regression benchmarks, while also showing strong generalizability in classification tasks.

cs.CV↗

arXiv2025

Uncertainty-Aware Collaborative System of Large and Small Models for Multimodal Sentiment Analysis

Multimodal Large Language Models (MLLMs) have notably enhanced the performance of Multimodal Sentiment Analysis (MSA), yet their massive parameter scale leads to excessive resource consumption in training and inference, severely limiting model efficiency. To balance performance and efficiency for MSA, this paper innovatively proposes a novel Uncertainty-Aware Collaborative System (U-ACS) that integrates Uncertainty-aware Baseline Model (UBM) with MLLMs. U-ACS operates in three stages: First, all samples are processed by the UBM, retain high-confidence samples and forward low-confidence samples to the MLLM. Notably, to address the challenge that continuous outputs of regression tasks hinder uncertainty calculation, we innovatively convert the continuous sentiment label prediction task to a classification task, enabling a more accurate calculation of entropy and uncertainty. Second, the MLLM performs initial process. In this stage, high-confidence samples or low-confidence samples whose predictive sentiment polarity matches that of the UBM are deemed acceptable, while unqualified samples are forwarded for further processing. Finally, the MLLM performs secondary inference on remaining low-confidence samples using prompts augmented with prior rounds predictions as references. By aggregating results from the three stages, U-ACS preserves high MSA prediction accuracy while drastically boosting efficiency via offloading most simple samples to the UBM and minimizing MLLM processing volume. Extensive experiments verify that U-ACS maintains superior performance while significantly reducing computational overhead and resource consumption.

cs.CL↗