4 citations · 5 across the 6 of their papers we have counts for
5 papers · 1 filter
Multimodal Learning on Low-Quality Data with Conformal Predictive Self-Calibration
Xun Jiang, Yufan Gu, Disen Hu +5
Multimodal learning often grapples with the challenge of low-quality data, which predominantly manifests as two facets: modality imbalance and noisy corruption. While these issues…
HarmoCLIP: Harmonizing Global and Regional Representations in Contrastive Vision-Language Models
Haoxi Zeng, Haoxuan Li, Yi Bin +4
Contrastive Language-Image Pre-training (CLIP) has demonstrated remarkable generalization ability and strong performance across a wide range of vision-language tasks. However, due…
Truth in the Few: High-Value Data Selection for Efficient Multi-Modal Reasoning
Shenshen Li, Xing Xu, Kaiyuan Deng +3
While multi-modal large language models (MLLMs) have made significant progress in complex reasoning tasks via reinforcement learning, it is commonly believed that extensive trainin…
VQ-Flow: Taming Normalizing Flows for Multi-Class Anomaly Detection via Hierarchical Vector Quantization
Yixuan Zhou, Xing Xu, Zhe Sun +3
Normalizing flows, a category of probabilistic models famed for their capabilities in modeling complex data distributions, have exhibited remarkable efficacy in unsupervised anomal…
BatchNorm-based Weakly Supervised Video Anomaly Detection
Yixuan Zhou, Yi Qu, Xing Xu +3
In weakly supervised video anomaly detection (WVAD), where only video-level labels indicating the presence or absence of abnormal events are available, the primary challenge arises…