10 citations · 17 across the 9 of their papers we have counts for
11 papers
Learning to Plan from Random Exploration
Deqian Kong, Guangyan Sun, Sheng Cheng +6
Random exploration reveals how an environment can be traversed before a goal is specified. Can this experience support long-range planning without policy-improvement training? Our…
BEVDiffuser: Plug-and-Play Diffusion Model for BEV Denoising with Ground-Truth Guidance
Xin Ye, Burhaneddin Yaman, Sheng Cheng +3
Bird's-eye-view (BEV) representations play a crucial role in autonomous driving tasks. Despite recent advancements in BEV generation, inherent noise, stemming from sensor limitatio…
Precision or Recall? An Analysis of Image Captions for Training Text-to-Image Generation Model
Sheng Cheng, Maitreya Patel, Yezhou Yang
Despite advancements in text-to-image models, generating images that precisely align with textual descriptions remains challenging due to misalignment in training data. In this pap…
TripletCLIP: Improving Compositional Reasoning of CLIP via Synthetic Vision-Language Negatives
Maitreya Patel, Abhiram Kusumba, Sheng Cheng +4
Contrastive Language-Image Pretraining (CLIP) models maximize the mutual information between text and visual modalities to learn representations. This makes the nature of the train…
Latent Space Energy-based Neural ODEs
Sheng Cheng, Deqian Kong, Jianwen Xie +3
This paper introduces novel deep dynamical models designed to represent continuous-time sequences. Our approach employs a neural emission model to generate each data point in the t…
ECLIPSE: A Resource-Efficient Text-to-Image Prior for Image Generations
Maitreya Patel, Changhoon Kim, Sheng Cheng +2
Text-to-image (T2I) diffusion models, notably the unCLIP models (e.g., DALL-E-2), achieve state-of-the-art (SOTA) performance on various compositional T2I benchmarks, at the cost o…
Adversarial Bayesian Augmentation for Single-Source Domain Generalization
Sheng Cheng, Tejas Gokhale, Yezhou Yang
Generalizing to unseen image domains is a challenging problem primarily due to the lack of diverse training data, inaccessible target data, and the large domain shift that may exis…
WOUAF: Weight Modulation for User Attribution and Fingerprinting in Text-to-Image Diffusion Models
Changhoon Kim, Kyle Min, Maitreya Patel +2
The rapid advancement of generative models, facilitating the creation of hyper-realistic images from textual descriptions, has concurrently escalated critical societal concerns suc…
SSR-GNNs: Stroke-based Sketch Representation with Graph Neural Networks
Sheng Cheng, Yi Ren, Yezhou Yang
This paper follows cognitive studies to investigate a graph representation for sketches, where the information of strokes, i.e., parts of a sketch, are encoded on vertices and info…
Data-Driven Learning of 3-Point Correlation Functions as Microstructure Representations
Sheng Cheng, Yang Jiao, Yi Ren
This paper considers the open challenge of identifying complete, concise, and explainable quantitative microstructure representations for disordered heterogeneous material systems.…
Evaluating the Robustness of Bayesian Neural Networks Against Different Types of Attacks
Yutian Pang, Sheng Cheng, Jueming Hu +1
To evaluate the robustness gain of Bayesian neural networks on image classification tasks, we perform input perturbations, and adversarial attacks to the state-of-the-art Bayesian…