15 citations · 100 across the 85 of their papers we have counts for
35 papers · 1 filter
DrivingRecon: Large 4D Gaussian Reconstruction Model For Autonomous Driving
Hao Lu, Tianshuo Xu, Wenzhao Zheng +6
Photorealistic 4D reconstruction of street scenes is essential for developing real-world simulators in autonomous driving. However, most existing methods perform this task offline…
Driv3R: Learning Dense 4D Reconstruction for Autonomous Driving
Xin Fei, Wenzhao Zheng, Yueqi Duan +4
Realtime 4D reconstruction for dynamic scenes remains a crucial challenge for autonomous driving perception. Most existing methods rely on depth estimation through self-supervision…
A Lesson in Splats: Teacher-Guided Diffusion for 3D Gaussian Splats Generation with 2D Supervision
Chensheng Peng, Ido Sobol, Masayoshi Tomizuka +3
We present a novel framework for training 3D image-conditioned diffusion models using only 2D supervision. Recovering 3D structure from 2D images is inherently ill-posed due to the…
Stochastic Communication Avoidance for Recommendation Systems
Lutfi Eren Erdogan, Vijay Anand Raghava Kanakagiri, Kurt Keutzer +1
One of the major bottlenecks for efficient deployment of neural network based recommendation systems is the memory footprint of their embedding tables. Although many neural network…
DeSiRe-GS: 4D Street Gaussians for Static-Dynamic Decomposition and Surface Reconstruction for Urban Driving Scenes
Chensheng Peng, Chengwei Zhang, Yixiao Wang +6
We present DeSiRe-GS, a self-supervised gaussian splatting representation, enabling effective static-dynamic decomposition and high-fidelity surface reconstruction in complex drivi…
Squeezed Attention: Accelerating Long Context Length LLM Inference
Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh +6
Emerging Large Language Model (LLM) applications require long input context in order to perform complex tasks like document analysis and code generation. For these long context len…
TinyAgent: Function Calling at the Edge
Lutfi Eren Erdogan, Nicholas Lee, Siddharth Jha +7
Recent large language models (LLMs) have enabled the development of advanced agentic systems that can integrate various tools and APIs to fulfill user queries through function call…
PixelGaussian: Generalizable 3D Gaussian Reconstruction from Arbitrary Views
Xin Fei, Wenzhao Zheng, Yueqi Duan +4
We propose PixelGaussian, an efficient feed-forward framework for learning generalizable 3D Gaussian reconstruction from arbitrary views. Most existing methods rely on uniform pixe…
Interpolating Video-LLMs: Toward Longer-sequence LMMs in a Training-free Manner
Yuzhang Shang, Bingxin Xu, Weitai Kang +7
Advancements in Large Language Models (LLMs) inspire various strategies for integrating video modalities. A key approach is Video-LLMs, which incorporate an optimizable interface l…
COAT: Compressing Optimizer states and Activation for Memory-Efficient FP8 Training
Haocheng Xi, Han Cai, Ligeng Zhu +4
FP8 training has emerged as a promising method for improving training efficiency. Existing frameworks accelerate training by applying FP8 computation to linear layers while leaving…
UniDrive: Towards Universal Driving Perception Across Camera Configurations
Ye Li, Wenzhao Zheng, Xiaonan Huang +1
Vision-centric autonomous driving has demonstrated excellent performance with economical sensors. As the fundamental step, 3D perception aims to infer 3D information from 2D images…
SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference
Yuan Zhang, Chun-Kai Fan, Junpeng Ma +8
In vision-language models (VLMs), visual tokens usually bear a significant amount of computational overhead despite sparsity of information in them when compared to text tokens. To…
RoVi-Aug: Robot and Viewpoint Augmentation for Cross-Embodiment Robot Learning
Lawrence Yunliang Chen, Chenfeng Xu, Karthik Dharmarajan +6
Scaling up robot learning requires large and diverse datasets, and how to efficiently reuse collected data and transfer policies to new embodiments remains an open question. Emergi…
Efficient and Scalable Estimation of Tool Representations in Vector Space
Suhong Moon, Siddharth Jha, Lutfi Eren Erdogan +4
Recent advancements in function calling and tool use have significantly enhanced the capabilities of large language models (LLMs) by enabling them to interact with external informa…
FactorLLM: Factorizing Knowledge via Mixture of Experts for Large Language Models
Zhongyu Zhao, Menghang Dong, Rongyu Zhang +6
Recent research has demonstrated that Feed-Forward Networks (FFNs) in Large Language Models (LLMs) play a pivotal role in storing diverse linguistic and factual knowledge. Conventi…
K-Sort Arena: Efficient and Reliable Benchmarking for Generative Models via K-wise Human Preferences
Zhikai Li, Xuewen Liu, Dongrong Joe Fu +4
The rapid advancement of visual generative models necessitates efficient and reliable evaluation methods. Arena platform, which gathers user votes on model comparisons, can rank mo…
Sparse Refinement for Efficient High-Resolution Semantic Segmentation
Zhijian Liu, Zhuoyang Zhang, Samir Khaki +5
Semantic segmentation empowers numerous real-world applications, such as autonomous driving and augmented/mixed reality. These applications often operate on high-resolution images…
Sharpness-diversity tradeoff: improving flat ensembles with SharpBalance
Haiquan Lu, Xiaotian Liu, Yefan Zhou +6
Recent studies on deep ensembles have identified the sharpness of the local minima of individual learners and the diversity of the ensemble members as key factors in improving test…
Characterizing Prompt Compression Methods for Long Context Inference
Siddharth Jha, Lutfi Eren Erdogan, Sehoon Kim +2
Long context inference presents challenges at the system level with increased compute and memory requirements, as well as from an accuracy perspective in being able to reason over…
Fisher-aware Quantization for DETR Detectors with Critical-category Objectives
Huanrui Yang, Yafeng Huang, Zhen Dong +6
The impact of quantization on the overall performance of deep learning models is a well-studied problem. However, understanding and mitigating its effects on a more fine-grained le…
Instruct Large Language Models to Drive like Humans
Ruijun Zhang, Xianda Guo, Wenzhao Zheng +3
Motion planning in complex scenarios is the core challenge in autonomous driving. Conventional methods apply predefined rules or learn from driving data to plan the future trajecto…
Immiscible Diffusion: Accelerating Diffusion Training with Noise Assignment
Yiheng Li, Heyang Jiang, Akio Kodaira +3
In this paper, we point out that suboptimal noise-data mapping leads to slow training of diffusion models. During diffusion training, current methods diffuse each image across the…
Gaussian: Self-Supervised Street Gaussians for Autonomous Driving
Nan Huang, Xiaobao Wei, Wenzhao Zheng +6
Photorealistic 3D reconstruction of street scenes is a critical technique for developing real-world simulators for autonomous driving. Despite the efficacy of Neural Radiance Field…
Looking Backward: Streaming Video-to-Video Translation with Feature Banks
Feng Liang, Akio Kodaira, Chenfeng Xu +3
This paper introduces StreamV2V, a diffusion model that achieves real-time streaming video-to-video (V2V) translation with user prompts. Unlike prior V2V methods using batches to p…