53 citations · 134 across the 24 of their papers we have counts for
24 papers
Cosmos 3: Omnimodal World Models for Physical AI
NVIDIA, :, Aditi +293
We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences within a unified mixture-of-t…
The Pragmatic Persona: Discovering LLM Persona through Bridging Inference
Jisoo Yang, Jongwon Ryu, Minuk Ma +2
Large Language Models (LLMs) reveal inherent and distinctive personas through dialogue. However, most existing persona discovery approaches rely on surface-level lexical or stylist…
A Hidden Semantic Bottleneck in Conditional Embeddings of Diffusion Transformers
Trung X. Pham, Kang Zhang, Ji Woo Hong +1
Diffusion Transformers have achieved state-of-the-art performance in class-conditional and multimodal generation, yet the structure of their learned conditional embeddings remains…
World Simulation with Video Foundation Models for Physical AI
NVIDIA, :, Arslan Ali +87
We introduce [Cosmos-Predict2.5], the latest generation of the Cosmos World Foundation Models for Physical AI. Built on a flow-based architecture, [Cosmos-Predict2.5] unifies Text2…
Model-Guided Dual-Role Alignment for High-Fidelity Open-Domain Video-to-Audio Generation
Kang Zhang, Trung X. Pham, Suyeon Lee +3
We present MGAudio, a novel flow-based framework for open-domain video-to-audio generation, which introduces model-guided dual-role alignment as a central design principle. Unlike…
ITA-MDT: Image-Timestep-Adaptive Masked Diffusion Transformer Framework for Image-Based Virtual Try-On
Ji Woo Hong, Tri Ton, Trung X. Pham +3
This paper introduces ITA-MDT, the Image-Timestep-Adaptive Masked Diffusion Transformer Framework for Image-Based Virtual Try-On (IVTON), designed to overcome the limitations of pr…
E-MD3C: Taming Masked Diffusion Transformers for Efficient Zero-Shot Object Customization
Trung X. Pham, Zhang Kang, Ji Woo Hong +2
We propose E-MD3C (fficient asked iffusion Transformer with Disentangled onditions and ompact $\underline…
MDSGen: Fast and Efficient Masked Diffusion Temporal-Aware Transformers for Open-Domain Sound Generation
Trung X. Pham, Tri Ton, Chang D. Yoo
We introduce MDSGen, a novel framework for vision-guided open-domain sound generation optimized for model parameter size, memory consumption, and inference speed. This framework in…
Cross-view Masked Diffusion Transformers for Person Image Synthesis
Trung X. Pham, Zhang Kang, Chang D. Yoo
We present X-MDPT (-view asked iffusion rediction ransformers), a novel diffusion model designed for…
DifAugGAN: A Practical Diffusion-style Data Augmentation for GAN-based Single Image Super-resolution
Axi Niu, Kang Zhang, Joshua Tian Jin Tee +5
It is well known the adversarial optimization of GAN-based image super-resolution (SR) methods makes the preceding SR model generate unpleasant and undesirable artifacts, leading t…
DimCL: Dimensional Contrastive Learning For Improving Self-Supervised Learning
Thanh Nguyen, Trung Pham, Chaoning Zhang +3
Self-supervised learning (SSL) has gained remarkable success, for which contrastive learning (CL) plays a key role. However, the recent development of new non-CL frameworks has ach…
ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution
Axi Niu, Pham Xuan Trung, Kang Zhang +4
Diffusion models have gained significant popularity in the field of image-to-image translation. Previous efforts applying diffusion models to image super-resolution (SR) have demon…
Learning from Multi-Perception Features for Real-Word Image Super-resolution
Axi Niu, Kang Zhang, Trung X. Pham +4
Currently, there are two popular approaches for addressing real-world image super-resolution problems: degradation-estimation-based and blind-based methods. However, degradation-es…
CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution
Axi Niu, Kang Zhang, Trung X. Pham +4
Diffusion probabilistic models (DPM) have been widely adopted in image-to-image translation to generate high-quality images. Prior attempts at applying the DPM to image super-resol…
Self-Supervised Visual Representation Learning via Residual Momentum
Trung X. Pham, Axi Niu, Zhang Kang +5
Self-supervised learning (SSL) approaches have shown promising capabilities in learning the representation from unlabeled data. Amongst them, momentum-based frameworks have attract…
LAD: A Hybrid Deep Learning System for Benign Paroxysmal Positional Vertigo Disorders Diagnostic
Trung Xuan Pham, Jin Woong Choi, Rusty John Lloyd Mina +3
Herein, we introduce "Look and Diagnose" (LAD), a hybrid deep learning-based system that aims to support doctors in the medical field in diagnosing effectively the Benign Paroxysma…
On the Pros and Cons of Momentum Encoder in Self-Supervised Visual Representation Learning
Trung Pham, Chaoning Zhang, Axi Niu +2
Exponential Moving Average (EMA or momentum) is widely used in modern self-supervised learning (SSL) approaches, such as MoCo, for enhancing performance. We demonstrate that such m…
Dual Temperature Helps Contrastive Learning Without Many Negative Samples: Towards Understanding and Simplifying MoCo
Chaoning Zhang, Kang Zhang, Trung X. Pham +4
Contrastive learning (CL) is widely known to require many negative samples, 65536 in MoCo for instance, for which the performance of a dictionary-free framework is often inferior b…
How Does SimSiam Avoid Collapse Without Negative Samples? A Unified Understanding with Self-supervised Contrastive Learning
Chaoning Zhang, Kang Zhang, Chenshuang Zhang +3
To avoid collapse in self-supervised learning (SSL), a contrastive loss is widely used but often requires a large number of negative samples. Without negative samples yet achieving…
Self-supervised Learning with Local Attention-Aware Feature
Trung X. Pham, Rusty John Lloyd Mina, Dias Issa +1
In this work, we propose a novel methodology for self-supervised learning for generating global and local attention-aware visual features. Our approach is based on training a model…
Robust MAML: Prioritization task buffer with adaptive learning process for model-agnostic meta-learning
Thanh Nguyen, Tung Luu, Trung Pham +2
Model agnostic meta-learning (MAML) is a popular state-of-the-art meta-learning algorithm that provides good weight initialization of a model given a variety of learning tasks. The…
Modality Shifting Attention Network for Multi-modal Video Question Answering
Junyeong Kim, Minuk Ma, Trung Pham +2
This paper considers a network referred to as Modality Shifting Attention Network (MSAN) for Multimodal Video Question Answering (MVQA) task. MSAN decomposes the task into two sub-…
Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution
Thang Vu, Hyunjun Jang, Trung X. Pham +1
This paper considers an architecture referred to as Cascade Region Proposal Network (Cascade RPN) for improving the region-proposal quality and detection performance by \textit{sys…
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
Andrey Ignatov, Radu Timofte, Thang Van Vu +45
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones. The challenge consisted of two tra…