Search Results for author:
Found 13 papers, 10 papers with code
BIFRÖST: 3D-Aware Image compositing with Language Instructions
This paper introduces Bifr\"ost, a novel 3D-aware framework that is built upon diffusion models to perform instruction-based image composition.
Anti-Collapse Loss for Deep Metric Learning Based on Coding Rate Metric
Deep metric learning (DML) aims to learn a discriminative high-dimensional embedding space for downstream tasks like classification, clustering, and retrieval.
Ctrl123: Consistent Novel View Synthesis via Closed-Loop Transcription
This consequently limits the performance of downstream tasks, such as image-to-multiview generation and 3D reconstruction.
Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Models
In this paper, we propose a novel image clustering pipeline that leverages the powerful feature representation of large pre-trained models such as CLIP and cluster images effectively and efficiently at scale.
Closed-Loop Transcription via Convolutional Sparse Coding
Our method is arguably the first to demonstrate that a concatenation of multiple convolution sparse coding/decoding layers leads to an interpretable and effective autoencoder for modeling the distribution of large-scale natural image datasets.
Unsupervised Manifold Linearizing and Clustering
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 learning and computer vision.
Unsupervised Learning of Structured Representations via Closed-Loop Transcription
This paper proposes an unsupervised method for learning a unified representation that serves both discriminative and generative purposes.
Revisiting Sparse Convolutional Model for Visual Recognition
We show that such models have equally strong empirical performance on CIFAR-10, CIFAR-100, and ImageNet datasets when compared to conventional neural networks.
Incremental Learning of Structured Memory via Closed-Loop Transcription
Our method is simpler than existing approaches for incremental learning, and more efficient in terms of model size, storage, and computation: it requires only a single, fixed-capacity autoencoding network with a feature space that is used for both discriminative and generative purposes.
Closed-Loop Data Transcription to an LDR via Minimaxing Rate Reduction
In particular, we propose to learn a closed-loop transcription between a multi-class multi-dimensional data distribution and a linear discriminative representation (LDR) in the feature space that consists of multiple independent multi-dimensional linear subspaces.
Fully Convolutional Line Parsing
We conduct extensive experiments and show that our method achieves a significantly better trade-off between efficiency and accuracy, resulting in a real-time line detector at up to 73 FPS on a single GPU.
Ranked #1 on
Line Segment Detection
on York Urban Dataset
Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image
Single-image room layout reconstruction aims to reconstruct the enclosed 3D structure of a room from a single image.
HoliCity: A City-Scale Data Platform for Learning Holistic 3D Structures
We present HoliCity, a city-scale 3D dataset with rich structural information.
