2 citations · 2 across the 11 of their papers we have counts for
11 papers
LLaVAFlow: Preserving Latent Alignment Flow for Parameter-Efficient Multimodal Fine-Tuning
Muyao Yuan, Muyan Jiao, Jiangyong Ying +5
While Multimodal Large Language Models (MLLMs) exhibit strong generalization, visual instruction tuning for downstream tasks inevitably causes catastrophic forgetting, impairing ov…
STAR: Structured Tokenization and Target-Aware Interest Representation for PCVR Prediction
Yimeng Xu, Ruihao Zhang, Yingqi Song +2
Post-click conversion rate (PCVR) prediction is a core ranking task in industrial recommender systems. Modern ranking models must jointly capture heterogeneous non-sequential featu…
DepthArb: Training-Free Depth-Arbitrated Generation for Occlusion-Robust Image Synthesis
Hongjin Niu, Jiahao Wang, Xirui Hu +4
Text-to-image models often struggle to synthesize correct occlusion relationships among multiple objects, especially in densely overlapping regions. Many training-free layout-guide…
InfoCLIP: Bridging Vision-Language Pretraining and Open-Vocabulary Semantic Segmentation via Information-Theoretic Alignment Transfer
Muyao Yuan, Yuanhong Zhang, Weizhan Zhang +4
Recently, the strong generalization ability of CLIP has facilitated open-vocabulary semantic segmentation, which labels pixels using arbitrary text. However, existing methods that…
LLaVA-RE: Binary Image-Text Relevancy Evaluation with Multimodal Large Language Model
Tao Sun, Oliver Liu, JinJin Li +1
Multimodal generative AI usually involves generating image or text responses given inputs in another modality. The evaluation of image-text relevancy is essential for measuring res…
CardioLive: Empowering Video Streaming with Online Cardiac Monitoring
Sheng Lyu, Ruiming Huang, Sijie Ji +3
Online Cardiac Monitoring (OCM) emerges as a compelling enhancement for the next-generation video streaming platforms. It enables various applications including remote health, onli…
FedRepOpt: Gradient Re-parametrized Optimizers in Federated Learning
Kin Wai Lau, Yasar Abbas Ur Rehman, Pedro Porto Buarque de Gusmão +3
Federated Learning (FL) has emerged as a privacy-preserving method for training machine learning models in a distributed manner on edge devices. However, on-device models face inhe…
RS-DPO: A Hybrid Rejection Sampling and Direct Preference Optimization Method for Alignment of Large Language Models
Saeed Khaki, JinJin Li, Lan Ma +2
Reinforcement learning from human feedback (RLHF) has been extensively employed to align large language models with user intent. However, proximal policy optimization (PPO) based R…
Exploring Federated Self-Supervised Learning for General Purpose Audio Understanding
Yasar Abbas Ur Rehman, Kin Wai Lau, Yuyang Xie +2
The integration of Federated Learning (FL) and Self-supervised Learning (SSL) offers a unique and synergetic combination to exploit the audio data for general-purpose audio underst…
Uncovering Drift in Textual Data: An Unsupervised Method for Detecting and Mitigating Drift in Machine Learning Models
Saeed Khaki, Akhouri Abhinav Aditya, Zohar Karnin +3
Drift in machine learning refers to the phenomenon where the statistical properties of data or context, in which the model operates, change over time leading to a decrease in its p…
AudioInceptionNeXt: TCL AI LAB Submission to EPIC-SOUND Audio-Based-Interaction-Recognition Challenge 2023
Kin Wai Lau, Yasar Abbas Ur Rehman, Yuyang Xie +1
This report presents the technical details of our submission to the 2023 Epic-Kitchen EPIC-SOUNDS Audio-Based Interaction Recognition Challenge. The task is to learn the mapping fr…