3 citations · 14 across the 20 of their papers we have counts for
21 papers
DiffusionBench: On Holistic Evaluation of Diffusion Transformers
Xingjian Leng, Jaskirat Singh, Zhanhao Liang +5
Diffusion transformer (DiT) research on image generation has converged to a single evaluation setup: class-conditional generation on ImageNet. While methods improve the FID and rel…
Improved Baselines with Representation Autoencoders
Jaskirat Singh, Boyang Zheng, Zongze Wu +3
Representation Autoencoders (RAE) replace traditional VAE with pretrained vision encoders. In this paper, we systematically investigate several design choices and find three insigh…
What matters for Representation Alignment: Global Information or Spatial Structure?
Jaskirat Singh, Xingjian Leng, Zongze Wu +4
Representation alignment (REPA) guides generative training by distilling representations from a strong, pretrained vision encoder to intermediate diffusion features. We investigate…
Vec2Face+ for Face Dataset Generation
Haiyu Wu, Jaskirat Singh, Sicong Tian +2
When synthesizing identities as face recognition training data, it is generally believed that large inter-class separability and intra-class attribute variation are essential for s…
DiSA: Diffusion Step Annealing in Autoregressive Image Generation
Qinyu Zhao, Jaskirat Singh, Ming Xu +3
An increasing number of autoregressive models, such as MAR, FlowAR, xAR, and Harmon adopt diffusion sampling to improve the quality of image generation. However, this strategy lead…
R2E-Gym: Procedural Environments and Hybrid Verifiers for Scaling Open-Weights SWE Agents
Naman Jain, Jaskirat Singh, Manish Shetty +3
Improving open-source models on real-world SWE tasks (solving GITHUB issues) faces two key challenges: 1) scalable curation of execution environments to train these models, and, 2)…
REPA-E: Unlocking VAE for End-to-End Tuning with Latent Diffusion Transformers
Xingjian Leng, Jaskirat Singh, Yunzhong Hou +3
In this paper we tackle a fundamental question: "Can we train latent diffusion models together with the variational auto-encoder (VAE) tokenizer in an end-to-end manner?" Tradition…
Negative Token Merging: Image-based Adversarial Feature Guidance
Jaskirat Singh, Lindsey Li, Weijia Shi +7
Text-based adversarial guidance using a negative prompt has emerged as a widely adopted approach to steer diffusion models away from producing undesired concepts. While useful, per…
Vec2Face: Scaling Face Dataset Generation with Loosely Constrained Vectors
Haiyu Wu, Jaskirat Singh, Sicong Tian +2
This paper studies how to synthesize face images of non-existent persons, to create a dataset that allows effective training of face recognition (FR) models. Besides generating rea…
Towards Hierarchical Multi-Agent Workflows for Zero-Shot Prompt Optimization
Yuchi Liu, Jaskirat Singh, Gaowen Liu +2
Large language models (LLMs) have shown great progress in responding to user questions, allowing for a multitude of diverse applications. Yet, the quality of LLM outputs heavily de…
SmartMask: Context Aware High-Fidelity Mask Generation for Fine-grained Object Insertion and Layout Control
Jaskirat Singh, Jianming Zhang, Qing Liu +3
The field of generative image inpainting and object insertion has made significant progress with the recent advent of latent diffusion models. Utilizing a precise object mask can g…
Divide, Evaluate, and Refine: Evaluating and Improving Text-to-Image Alignment with Iterative VQA Feedback
Jaskirat Singh, Liang Zheng
The field of text-conditioned image generation has made unparalleled progress with the recent advent of latent diffusion models. While remarkable, as the complexity of given text i…
Probabilistic and Semantic Descriptions of Image Manifolds and Their Applications
Peter Tu, Zhaoyuan Yang, Richard Hartley +6
This paper begins with a description of methods for estimating image probability density functions that reflects the observation that such data is usually constrained to lie in res…
High-Fidelity Guided Image Synthesis with Latent Diffusion Models
Jaskirat Singh, Stephen Gould, Liang Zheng
Controllable image synthesis with user scribbles has gained huge public interest with the recent advent of text-conditioned latent diffusion models. The user scribbles control the…
Real-Time Heuristic Framework for Safe Landing of UAVs in Dynamic Scenarios
Jaskirat Singh, Neel Adwani, Harikumar Kandath +1
The world we live in is full of technology and with each passing day the advancement and usage of UAVs increases efficiently. As a result of the many application scenarios, there a…
Paint2Pix: Interactive Painting based Progressive Image Synthesis and Editing
Jaskirat Singh, Liang Zheng, Cameron Smith +1
Controllable image synthesis with user scribbles is a topic of keen interest in the computer vision community. In this paper, for the first time we study the problem of photorealis…
Intelli-Paint: Towards Developing Human-like Painting Agents
Jaskirat Singh, Cameron Smith, Jose Echevarria +1
The generation of well-designed artwork is often quite time-consuming and assumes a high degree of proficiency on part of the human painter. In order to facilitate the human painti…
Sparse Attention Guided Dynamic Value Estimation for Single-Task Multi-Scene Reinforcement Learning
Jaskirat Singh, Liang Zheng
Training deep reinforcement learning agents on environments with multiple levels / scenes from the same task, has become essential for many applications aiming to achieve generaliz…
Enhanced Scene Specificity with Sparse Dynamic Value Estimation
Jaskirat Singh, Liang Zheng
Multi-scene reinforcement learning involves training the RL agent across multiple scenes / levels from the same task, and has become essential for many generalization applications.…
Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level Paintings
Jaskirat Singh, Liang Zheng
Generation of stroke-based non-photorealistic imagery, is an important problem in the computer vision community. As an endeavor in this direction, substantial recent research effor…
Dynamic Value Estimation for Single-Task Multi-Scene Reinforcement Learning
Jaskirat Singh, Liang Zheng
Training deep reinforcement learning agents on environments with multiple levels / scenes / conditions from the same task, has become essential for many applications aiming to achi…