activity
20162023
most citedVisual Transformers: Token-based Image Representation and Processing for Computer Vision

380 citations · 1.3k across the 41 of their papers we have counts for

collaborators

43 papers

cs.CV2023★ 4 cited

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…

cs.CV2023★ 2 cited

Pruning Compact ConvNets for Efficient Inference

Sayan Ghosh, Karthik Prasad, Xiaoliang Dai +4

Neural network pruning is frequently used to compress over-parameterized networks by large amounts, while incurring only marginal drops in generalization performance. However, the…

cs.CV2022

INGeo: Accelerating Instant Neural Scene Reconstruction with Noisy Geometry Priors

Chaojian Li, Bichen Wu, Albert Pumarola +3

We present a method that accelerates reconstruction of 3D scenes and objects, aiming to enable instant reconstruction on edge devices such as mobile phones and AR/VR headsets. Whil…

cs.CV2022★ 4 cited

3D-Aware Encoding for Style-based Neural Radiance Fields

Yu-Jhe Li, Tao Xu, Bichen Wu +6

We tackle the task of NeRF inversion for style-based neural radiance fields, (e.g., StyleNeRF). In the task, we aim to learn an inversion function to project an input image to the…

cs.CV2022★ 3 cited

Castling-ViT: Compressing Self-Attention via Switching Towards Linear-Angular Attention at Vision Transformer Inference

Haoran You, Yunyang Xiong, Xiaoliang Dai +5

Vision Transformers (ViTs) have shown impressive performance but still require a high computation cost as compared to convolutional neural networks (CNNs), one reason is that ViTs'…

cs.CV2022★ 10 cited

Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP

Feng Liang, Bichen Wu, Xiaoliang Dai +6

Open-vocabulary semantic segmentation aims to segment an image into semantic regions according to text descriptions, which may not have been seen during training. Recent two-stage…