Search Results for author:
Found 48 papers, 23 papers with code
UniScene: Unified Occupancy-centric Driving Scene Generation
UniScene employs a progressive generation process that decomposes the complex task of scene generation into two hierarchical steps: (a) first generating semantic occupancy from a customized scene layout as a meta scene representation rich in both semantic and geometric information, and then (b) conditioned on occupancy, generating video and LiDAR data, respectively, with two novel transfer strategies of Gaussian-based Joint Rendering and Prior-guided Sparse Modeling.
Advancing Auto-Regressive Continuation for Video Frames
Recent advances in auto-regressive large language models (LLMs) have shown their potential in generating high-quality text, inspiring researchers to apply them to image and video generation.
ARIC: An Activity Recognition Dataset in Classroom Surveillance Images
The ARIC dataset has advantages of multiple perspectives, 32 activity categories, three modalities, and real-world classroom scenarios.
Recent Advances in Attack and Defense Approaches of Large Language Models
Given the extensive research in the field of LLM security, we believe that summarizing the current state of affairs will help the research community better understand the present landscape and inform future developments.
Chat-Edit-3D: Interactive 3D Scene Editing via Text Prompts
Recent work on image content manipulation based on vision-language pre-training models has been effectively extended to text-driven 3D scene editing.
A Survey on Future Frame Synthesis: Bridging Deterministic and Generative Approaches
Future Frame Synthesis (FFS) aims to enable models to generate sequences of future frames based on existing content.
Scale-Adaptive Feature Aggregation for Efficient Space-Time Video Super-Resolution
The Space-Time Video Super-Resolution (STVSR) task aims to enhance the visual quality of videos, by simultaneously performing video frame interpolation (VFI) and video super-resolution (VSR).
Space-time Video Super-resolution
Video Frame Interpolation
+1
Editing 3D Scenes via Text Prompts without Retraining
To tackle these issues, we introduce a text-driven editing method, termed DN2N, which allows for the direct acquisition of a NeRF model with universal editing capabilities, eliminating the requirement for retraining.
Proximal Policy Optimization Actual Combat: Manipulating Output Tokenizer Length
In this paper, we introduce a simple task designed to employ Gloden as a reward model that validates the effectiveness of PPO and inspires it, primarily explaining the task of utilizing PPO to manipulate the tokenizer length of the output generated by the model.
Progressive Volume Distillation with Active Learning for Efficient NeRF Architecture Conversion
For instance, PVD-AL can distill an MLP-based model from a Hashtables-based model at a 10~20X faster speed and 0. 8dB~2dB higher PSNR than training the MLP-based model from scratch.
UniDistill: A Universal Cross-Modality Knowledge Distillation Framework for 3D Object Detection in Bird's-Eye View
In the field of 3D object detection for autonomous driving, the sensor portfolio including multi-modality and single-modality is diverse and complex.
A Dynamic Multi-Scale Voxel Flow Network for Video Prediction
The performance of video prediction has been greatly boosted by advanced deep neural networks.
Ranked #1 on
Video Prediction
on Cityscapes
Three Guidelines You Should Know for Universally Slimmable Self-Supervised Learning
We propose universally slimmable self-supervised learning (dubbed as US3L) to achieve better accuracy-efficiency trade-offs for deploying self-supervised models across different devices.
OccDepth: A Depth-Aware Method for 3D Semantic Scene Completion
3D Semantic Scene Completion (SSC) can provide dense geometric and semantic scene representations, which can be applied in the field of autonomous driving and robotic systems.
Ranked #15 on
3D Semantic Scene Completion
on SemanticKITTI
Occ^2Net: Robust Image Matching Based on 3D Occupancy Estimation for Occluded Regions
Image matching is a fundamental and critical task in various visual applications, such as Simultaneous Localization and Mapping (SLAM) and image retrieval, which require accurate pose estimation.
One is All: Bridging the Gap Between Neural Radiance Fields Architectures with Progressive Volume Distillation
In this paper, we propose Progressive Volume Distillation (PVD), a systematic distillation method that allows any-to-any conversions between different architectures, including MLP, sparse or low-rank tensors, hashtables and their compositions.
Ranked #1 on
Novel View Synthesis
on NeRF
(Average PSNR metric)
Synergistic Self-supervised and Quantization Learning
In this paper, we propose a method called synergistic self-supervised and quantization learning (SSQL) to pretrain quantization-friendly self-supervised models facilitating downstream deployment.
Perceptual Conversational Head Generation with Regularized Driver and Enhanced Renderer
This paper reports our solution for ACM Multimedia ViCo 2022 Conversational Head Generation Challenge, which aims to generate vivid face-to-face conversation videos based on audio and reference images.
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
Combinatorial optimization is a well-established area in operations research and computer science.
ML4CO-KIDA: Knowledge Inheritance in Dataset Aggregation
The Machine Learning for Combinatorial Optimization (ML4CO) NeurIPS 2021 competition aims to improve state-of-the-art combinatorial optimization solvers by replacing key heuristic components with machine learning models.
FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer
Network quantization significantly reduces model inference complexity and has been widely used in real-world deployments.
Ranked #1 on
Quantization
on ImageNet
Arch-Net: Model Distillation for Architecture Agnostic Model Deployment
For example, Transformer Networks do not have native support on many popular chips, and hence are difficult to deploy.
Multi-split Reversible Transformers Can Enhance Neural Machine Translation
Large-scale transformers have been shown the state-of-the-art on neural machine translation.
RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation
We propose RIFE, a Real-time Intermediate Flow Estimation algorithm for Video Frame Interpolation (VFI).
Active Learning Approaches to Enhancing Neural Machine Translation
Active learning is an efficient approach for mitigating data dependency when training neural machine translation (NMT) models.
Optimal Quantization for Batch Normalization in Neural Network Deployments and Beyond
Quantized Neural Networks (QNNs) use low bit-width fixed-point numbers for representing weight parameters and activations, and are often used in real-world applications due to their saving of computation resources and reproducibility of results.
Learning to Paint With Model-based Deep Reinforcement Learning
We show how to teach machines to paint like human painters, who can use a small number of strokes to create fantastic paintings.
Accelerated Value Iteration via Anderson Mixing
A2VI is more efficient than the modified policy iteration, which is a classical approximate method for policy evaluation.
Harmonic Adversarial Attack Method
The property of edge-free guarantees that the generated adversarial images can still preserve visual quality, even when perturbations are of large magnitudes.
Stroke-based Character Reconstruction
Background elimination for noisy character images or character images from real scene is still a challenging problem, due to the bewildering backgrounds, uneven illumination, low resolution and different distortions.
Interpolatron: Interpolation or Extrapolation Schemes to Accelerate Optimization for Deep Neural Networks
In this paper we explore acceleration techniques for large scale nonconvex optimization problems with special focuses on deep neural networks.
Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments
In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course.
Learning to Run with Actor-Critic Ensemble
We introduce an Actor-Critic Ensemble(ACE) method for improving the performance of Deep Deterministic Policy Gradient(DDPG) algorithm.
Balanced Quantization: An Effective and Efficient Approach to Quantized Neural Networks
Overall, our method improves the prediction accuracies of QNNs without introducing extra computation during inference, has negligible impact on training speed, and is applicable to both Convolutional Neural Networks and Recurrent Neural Networks.
GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data
In this work, we propose a model that can learn object transfiguration from two unpaired sets of images: one set containing images that "have" that kind of object, and the other set being the opposite, with the mild constraint that the objects be located approximately at the same place.
EAST: An Efficient and Accurate Scene Text Detector
Previous approaches for scene text detection have already achieved promising performances across various benchmarks.
Ranked #3 on
Scene Text Detection
on COCO-Text
Curved Text Detection
Optical Character Recognition (OCR)
+1
Training Bit Fully Convolutional Network for Fast Semantic Segmentation
Fully convolutional neural networks give accurate, per-pixel prediction for input images and have applications like semantic segmentation.
Effective Quantization Methods for Recurrent Neural Networks
In addition, we propose balanced quantization methods for weights to further reduce performance degradation.
Scene Text Detection via Holistic, Multi-Channel Prediction
Recently, scene text detection has become an active research topic in computer vision and document analysis, because of its great importance and significant challenge.
Ranked #6 on
Scene Text Detection
on COCO-Text
DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
We propose DoReFa-Net, a method to train convolutional neural networks that have low bitwidth weights and activations using low bitwidth parameter gradients.
Exploiting Local Structures with the Kronecker Layer in Convolutional Networks
In this paper, we propose and study a technique to reduce the number of parameters and computation time in convolutional neural networks.
Incidental Scene Text Understanding: Recent Progresses on ICDAR 2015 Robust Reading Competition Challenge 4
Different from focused texts present in natural images, which are captured with user's intention and intervention, incidental texts usually exhibit much more diversity, variability and complexity, thus posing significant difficulties and challenges for scene text detection and recognition algorithms.
Multilinear Map Layer: Prediction Regularization by Structural Constraint
In this paper we propose and study a technique to impose structural constraints on the output of a neural network, which can reduce amount of computation and number of parameters besides improving prediction accuracy when the output is known to approximately conform to the low-rankness prior.
Compression of Fully-Connected Layer in Neural Network by Kronecker Product
In this paper we propose and study a technique to reduce the number of parameters and computation time in fully-connected layers of neural networks using Kronecker product, at a mild cost of the prediction quality.
ICDAR 2015 Text Reading in the Wild Competition
Recently, text detection and recognition in natural scenes are becoming increasing popular in the computer vision community as well as the document analysis community.
Group Orbit Optimization: A Unified Approach to Data Normalization
In this paper we propose and study an optimization problem over a matrix group orbit that we call \emph{Group Orbit Optimization} (GOO).
Kinetic Energy Plus Penalty Functions for Sparse Estimation
In this paper we propose and study a family of sparsity-inducing penalty functions.
