30 citations · 164 across the 19 of their papers we have counts for
19 papers
Improving Chain-of-Thought Efficiency for Autoregressive Image Generation
Zeqi Gu, Markos Georgopoulos, Xiaoliang Dai +10
Autoregressive multimodal large language models have recently gained popularity for image generation, driven by advances in foundation models. To enhance alignment and detail, newe…
Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models
Xu Ma, Peize Sun, Haoyu Ma +22
Autoregressive (AR) models, long dominant in language generation, are increasingly applied to image synthesis but are often considered less competitive than Diffusion-based models.…
Movie Gen: A Cast of Media Foundation Models
Adam Polyak, Amit Zohar, Andrew Brown +85
We present Movie Gen, a cast of foundation models that generates high-quality, 1080p HD videos with different aspect ratios and synchronized audio. We also show additional capabili…
Pixel-Space Post-Training of Latent Diffusion Models
Christina Zhang, Simran Motwani, Matthew Yu +6
Latent diffusion models (LDMs) have made significant advancements in the field of image generation in recent years. One major advantage of LDMs is their ability to operate in a com…
Cache Me if You Can: Accelerating Diffusion Models through Block Caching
Felix Wimbauer, Bichen Wu, Edgar Schoenfeld +11
Diffusion models have recently revolutionized the field of image synthesis due to their ability to generate photorealistic images. However, one of the major drawbacks of diffusion…
ControlRoom3D: Room Generation using Semantic Proxy Rooms
Jonas Schult, Sam Tsai, Lukas Höllein +11
Manually creating 3D environments for AR/VR applications is a complex process requiring expert knowledge in 3D modeling software. Pioneering works facilitate this process by genera…
Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack
Xiaoliang Dai, Ji Hou, Chih-Yao Ma +23
Training text-to-image models with web scale image-text pairs enables the generation of a wide range of visual concepts from text. However, these pre-trained models often face chal…
NeRF-Det: Learning Geometry-Aware Volumetric Representation for Multi-View 3D Object Detection
Chenfeng Xu, Bichen Wu, Ji Hou +8
We present NeRF-Det, a novel method for indoor 3D detection with posed RGB images as input. Unlike existing indoor 3D detection methods that struggle to model scene geometry, our m…
Mask3D: Pre-training 2D Vision Transformers by Learning Masked 3D Priors
Ji Hou, Xiaoliang Dai, Zijian He +2
Current popular backbones in computer vision, such as Vision Transformers (ViT) and ResNets are trained to perceive the world from 2D images. However, to more effectively understan…
A Practical Stereo Depth System for Smart Glasses
Jialiang Wang, Daniel Scharstein, Akash Bapat +11
We present the design of a productionized end-to-end stereo depth sensing system that does pre-processing, online stereo rectification, and stereo depth estimation with a fallback…
Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks
Yen-Cheng Liu, Chih-Yao Ma, Junjiao Tian +2
Adapting large-scale pretrained models to various downstream tasks via fine-tuning is a standard method in machine learning. Recently, parameter-efficient fine-tuning methods show…
Open-Set Semi-Supervised Object Detection
Yen-Cheng Liu, Chih-Yao Ma, Xiaoliang Dai +4
Recent developments for Semi-Supervised Object Detection (SSOD) have shown the promise of leveraging unlabeled data to improve an object detector. However, thus far these methods h…
Cross-Domain Adaptive Teacher for Object Detection
Yu-Jhe Li, Xiaoliang Dai, Chih-Yao Ma +6
We address the task of domain adaptation in object detection, where there is a domain gap between a domain with annotations (source) and a domain of interest without annotations (t…
Unbiased Teacher for Semi-Supervised Object Detection
Yen-Cheng Liu, Chih-Yao Ma, Zijian He +6
Semi-supervised learning, i.e., training networks with both labeled and unlabeled data, has made significant progress recently. However, existing works have primarily focused on im…
One Shot 3D Photography
Johannes Kopf, Kevin Matzen, Suhib Alsisan +12
3D photography is a new medium that allows viewers to more fully experience a captured moment. In this work, we refer to a 3D photo as one that displays parallax induced by moving…
FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining
Xiaoliang Dai, Alvin Wan, Peizhao Zhang +8
Neural Architecture Search (NAS) yields state-of-the-art neural networks that outperform their best manually-designed counterparts. However, previous NAS methods search for archite…
FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions
Alvin Wan, Xiaoliang Dai, Peizhao Zhang +9
Differentiable Neural Architecture Search (DNAS) has demonstrated great success in designing state-of-the-art, efficient neural networks. However, DARTS-based DNAS's search space i…
Efficient Segmentation: Learning Downsampling Near Semantic Boundaries
Dmitrii Marin, Zijian He, Peter Vajda +4
Many automated processes such as auto-piloting rely on a good semantic segmentation as a critical component. To speed up performance, it is common to downsample the input frame. Ho…
RF-Net: An End-to-End Image Matching Network based on Receptive Field
Xuelun Shen, Cheng Wang, Xin Li +5
This paper proposes a new end-to-end trainable matching network based on receptive field, RF-Net, to compute sparse correspondence between images. Building end-to-end trainable mat…