2 citations · 3 across the 13 of their papers we have counts for
13 papers
MURPHY: Feedback-Aware GRPO with Retrospective Credit Assignment for Multi-Turn Code Generation
Chanakya Ekbote, Vijay Lingam, Sujay Sanghavi +4
Reinforcement Learning with Verifiable Rewards (RLVR) has become a standard recipe for post-training LLMs on reasoning tasks, with Group Relative Policy Optimization (GRPO) emergin…
Robust Planning for Autonomous Driving via Mixed Adversarial Diffusion Predictions
Albert Zhao, Stefano Soatto
We describe a robust planning method for autonomous driving that mixes normal and adversarial agent predictions output by a diffusion model trained for motion prediction. We first…
Efficient Scaling of Diffusion Transformers for Text-to-Image Generation
Hao Li, Shamit Lal, Zhiheng Li +9
We empirically study the scaling properties of various Diffusion Transformers (DiTs) for text-to-image generation by performing extensive and rigorous ablations, including training…
DocKD: Knowledge Distillation from LLMs for Open-World Document Understanding Models
Sungnyun Kim, Haofu Liao, Srikar Appalaraju +6
Visual document understanding (VDU) is a challenging task that involves understanding documents across various modalities (text and image) and layouts (forms, tables, etc.). This s…
NAVERO: Unlocking Fine-Grained Semantics for Video-Language Compositionality
Chaofan Tao, Gukyeong Kwon, Varad Gunjal +7
We study the capability of Video-Language (VidL) models in understanding compositions between objects, attributes, actions and their relations. Composition understanding becomes pa…
Diffusion Soup: Model Merging for Text-to-Image Diffusion Models
Benjamin Biggs, Arjun Seshadri, Yang Zou +6
We present Diffusion Soup, a compartmentalization method for Text-to-Image Generation that averages the weights of diffusion models trained on sharded data. By construction, our ap…
Fewer Truncations Improve Language Modeling
Hantian Ding, Zijian Wang, Giovanni Paolini +4
In large language model training, input documents are typically concatenated together and then split into sequences of equal length to avoid padding tokens. Despite its efficiency,…
THRONE: An Object-based Hallucination Benchmark for the Free-form Generations of Large Vision-Language Models
Prannay Kaul, Zhizhong Li, Hao Yang +4
Mitigating hallucinations in large vision-language models (LVLMs) remains an open problem. Recent benchmarks do not address hallucinations in open-ended free-form responses, which…
Grounded Compositional and Diverse Text-to-3D with Pretrained Multi-View Diffusion Model
Xiaolong Li, Jiawei Mo, Ying Wang +7
In this paper, we propose an effective two-stage approach named Grounded-Dreamer to generate 3D assets that can accurately follow complex, compositional text prompts while achievin…
Mixed-Query Transformer: A Unified Image Segmentation Architecture
Pei Wang, Zhaowei Cai, Hao Yang +3
Existing unified image segmentation models either employ a unified architecture across multiple tasks but use separate weights tailored to each dataset, or apply a single set of we…
On the Scalability of Diffusion-based Text-to-Image Generation
Hao Li, Yang Zou, Ying Wang +7
Scaling up model and data size has been quite successful for the evolution of LLMs. However, the scaling law for the diffusion based text-to-image (T2I) models is not fully explore…
Fast Sparse View Guided NeRF Update for Object Reconfigurations
Ziqi Lu, Jianbo Ye, Xiaohan Fei +4
Neural Radiance Field (NeRF), as an implicit 3D scene representation, lacks inherent ability to accommodate changes made to the initial static scene. If objects are reconfigured, i…
A Quantitative Evaluation of Score Distillation Sampling Based Text-to-3D
Xiaohan Fei, Chethan Parameshwara, Jiawei Mo +5
The development of generative models that create 3D content from a text prompt has made considerable strides thanks to the use of the score distillation sampling (SDS) method on pr…