25 citations · 73 across the 11 of their papers we have counts for
11 papers
Ctrl123: Consistent Novel View Synthesis via Closed-Loop Transcription
Hongxiang Zhao, Xili Dai, Jianan Wang +5
Large image diffusion models have demonstrated zero-shot capability in novel view synthesis (NVS). However, existing diffusion-based NVS methods struggle to generate novel views th…
Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Models
Tianzhe Chu, Shengbang Tong, Tianjiao Ding +4
The advent of large pre-trained models has brought about a paradigm shift in both visual representation learning and natural language processing. However, clustering unlabeled imag…
Closed-Loop Transcription via Convolutional Sparse Coding
Xili Dai, Ke Chen, Shengbang Tong +9
Autoencoding has achieved great empirical success as a framework for learning generative models for natural images. Autoencoders often use generic deep networks as the encoder or d…
Unsupervised Manifold Linearizing and Clustering
Tianjiao Ding, Shengbang Tong, Kwan Ho Ryan Chan +3
We consider the problem of simultaneously clustering and learning a linear representation of data lying close to a union of low-dimensional manifolds, a fundamental task in machine…
Unsupervised Learning of Structured Representations via Closed-Loop Transcription
Shengbang Tong, Xili Dai, Yubei Chen +5
This paper proposes an unsupervised method for learning a unified representation that serves both discriminative and generative purposes. While most existing unsupervised learning…
Revisiting Sparse Convolutional Model for Visual Recognition
Xili Dai, Mingyang Li, Pengyuan Zhai +6
Despite strong empirical performance for image classification, deep neural networks are often regarded as ``black boxes'' and they are difficult to interpret. On the other hand, sp…
Incremental Learning of Structured Memory via Closed-Loop Transcription
Shengbang Tong, Xili Dai, Ziyang Wu +3
This work proposes a minimal computational model for learning structured memories of multiple object classes in an incremental setting. Our approach is based on establishing a clos…
Closed-Loop Data Transcription to an LDR via Minimaxing Rate Reduction
Xili Dai, Shengbang Tong, Mingyang Li +8
This work proposes a new computational framework for learning a structured generative model for real-world datasets. In particular, we propose to learn a closed-loop transcription…
Fully Convolutional Line Parsing
Xili Dai, Haigang Gong, Shuai Wu +2
We present a one-stage Fully Convolutional Line Parsing network (F-Clip) that detects line segments from images. The proposed network is very simple and flexible with variations th…
Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image
Cheng Yang, Jia Zheng, Xili Dai +3
Single-image room layout reconstruction aims to reconstruct the enclosed 3D structure of a room from a single image. Most previous work relies on the cuboid-shape prior. This paper…
HoliCity: A City-Scale Data Platform for Learning Holistic 3D Structures
Yichao Zhou, Jingwei Huang, Xili Dai +4
We present HoliCity, a city-scale 3D dataset with rich structural information. Currently, this dataset has 6,300 real-world panoramas of resolution that are acc…