10 citations · 30 across the 15 of their papers we have counts for
16 papers
Search-Aware On-Policy Self-Distillation with Sibling Alignment for Generative Recommendation
Rongfeng Guo, Zhuang Zhuang, Ji Dai +6
Generative recommendation retrieves items by autoregressively generating semantic IDs, typically via beam search. On-policy self-distillation (OPSD) enhances training supervision b…
GramLoop: Training-Free Gram-Gated Replay for Robust Dense Prediction
Yang Chen, Canyu Shen, Xinzhe Rao +5
We aim to improve frozen DINOv3 dense-prediction models under distribution shift by adding inference computation inside the visual backbone, without changing model weights, task ad…
Training-Free Hidden-State Refinement for Flow-Matching Image Generators
Yuanyi Yan, Xinzhe Rao, Canyu Shen +5
We aim to improve frozen flow-matching image generators by adding inference computation inside the denoiser, without changing model weights or the outer sampler. Existing generator…
From State to Action: OODA-Tool for Reliable Multi-Turn Tool Use
Rongfeng Guo, Yinxuan Huang, Yusen Wu +5
Reliable multi-turn tool use requires an agent to preserve an evolving task state and ensure that each action remains consistent with it. However, direct function-calling and ReAct…
ZigMa: A DiT-style Zigzag Mamba Diffusion Model
Vincent Tao Hu, Stefan Andreas Baumann, Ming Gui +4
The diffusion model has long been plagued by scalability and quadratic complexity issues, especially within transformer-based structures. In this study, we aim to leverage the long…
DepthFM: Fast Monocular Depth Estimation with Flow Matching
Ming Gui, Johannes Schusterbauer, Ulrich Prestel +6
Current discriminative depth estimation methods often produce blurry artifacts, while generative approaches suffer from slow sampling due to curvatures in the noise-to-depth transp…
Training Class-Imbalanced Diffusion Model Via Overlap Optimization
Divin Yan, Lu Qi, Vincent Tao Hu +2
Diffusion models have made significant advances recently in high-quality image synthesis and related tasks. However, diffusion models trained on real-world datasets, which often fo…
Latent Space Editing in Transformer-Based Flow Matching
Vincent Tao Hu, David W Zhang, Pascal Mettes +3
This paper strives for image editing via generative models. Flow Matching is an emerging generative modeling technique that offers the advantage of simple and efficient training. S…
Motion Flow Matching for Human Motion Synthesis and Editing
Vincent Tao Hu, Wenzhe Yin, Pingchuan Ma +7
Human motion synthesis is a fundamental task in computer animation. Recent methods based on diffusion models or GPT structure demonstrate commendable performance but exhibit drawba…
Guided Diffusion from Self-Supervised Diffusion Features
Vincent Tao Hu, Yunlu Chen, Mathilde Caron +3
Guidance serves as a key concept in diffusion models, yet its effectiveness is often limited by the need for extra data annotation or classifier pretraining. That is why guidance w…
Boosting Latent Diffusion with Flow Matching
Johannes Schusterbauer, Ming Gui, Pingchuan Ma +4
Visual synthesis has recently seen significant leaps in performance, largely due to breakthroughs in generative models. Diffusion models have been a key enabler, as they excel in i…
ScribbleGen: Generative Data Augmentation Improves Scribble-supervised Semantic Segmentation
Jacob Schnell, Jieke Wang, Lu Qi +2
Recent advances in generative models, such as diffusion models, have made generating high-quality synthetic images widely accessible. Prior works have shown that training on synthe…
Self-Guided Diffusion Models
Vincent Tao Hu, David W Zhang, Yuki M. Asano +2
Diffusion models have demonstrated remarkable progress in image generation quality, especially when guidance is used to control the generative process. However, guidance requires a…
Self-supervised Video Representation Learning with Cross-Stream Prototypical Contrasting
Martine Toering, Ioannis Gatopoulos, Maarten Stol +1
Instance-level contrastive learning techniques, which rely on data augmentation and a contrastive loss function, have found great success in the domain of visual representation lea…
Localizing the Common Action Among a Few Videos
Pengwan Yang, Vincent Tao Hu, Pascal Mettes +1
This paper strives to localize the temporal extent of an action in a long untrimmed video. Where existing work leverages many examples with their start, their ending, and/or the cl…
PointMixup: Augmentation for Point Clouds
Yunlu Chen, Vincent Tao Hu, Efstratios Gavves +4
This paper introduces data augmentation for point clouds by interpolation between examples. Data augmentation by interpolation has shown to be a simple and effective approach in th…