activity
20212026
most citedEvaluating the Robustness of Bayesian Neural Networks Against Different Types of Attacks

10 citations · 17 across the 9 of their papers we have counts for

collaborators
Showing cs.CVShow all

7 papers · 1 filter

cs.CV2025

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…

cs.CV2024

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…

cs.CV2024

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…

cs.CV2023★ 2 cited

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…

cs.CV2023

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…

cs.CV2023★ 2 cited

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…

cs.CV2022

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…