Search Results for author:
Found 32 papers, 14 papers with code
LoRA3D: Low-Rank Self-Calibration of 3D Geometric Foundation Models
Emerging 3D geometric foundation models, such as DUSt3R, offer a promising approach for in-the-wild 3D vision tasks.
Extrapolated Urban View Synthesis Benchmark
Our results show that Gaussian Splatting is prone to overfitting to training views.
Promptable Closed-loop Traffic Simulation
Simulation stands as a cornerstone for safe and efficient autonomous driving development.
Wolf: Captioning Everything with a World Summarization Framework
Finally, we establish a benchmark for video captioning and introduce a leaderboard, aiming to accelerate advancements in video understanding, captioning, and data alignment.
Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving
The autonomous driving industry is increasingly adopting end-to-end learning from sensory inputs to minimize human biases in system design.
DiffuBox: Refining 3D Object Detection with Point Diffusion
Ensuring robust 3D object detection and localization is crucial for many applications in robotics and autonomous driving.
Language-Image Models with 3D Understanding
Our experiments on outdoor benchmarks demonstrate that Cube-LLM significantly outperforms existing baselines by 21. 3 points of AP-BEV on the Talk2Car dataset for 3D grounded reasoning and 17. 7 points on the DriveLM dataset for complex reasoning about driving scenarios, respectively.
Crypto Inverse-Power Options and Fractional Stochastic Volatility
Key insights from our theoretical analysis and empirical findings include: (1) the superior performance of fractional stochastic-volatility models compared to various benchmark models, including those incorporating jumps and stochastic volatility, along with high computational efficiency when utilizing a piecewise kernel, (2) the practical necessity of considering jumps in both price and volatility, along with rough volatility, in pricing and hedging cryptocurrency options, (3) stability of calibrated parameter values in line with stylized facts.
Efficient Video Diffusion Models via Content-Frame Motion-Latent Decomposition
To tackle this issue, we propose content-motion latent diffusion model (CMD), a novel efficient extension of pretrained image diffusion models for video generation.
Driving Everywhere with Large Language Model Policy Adaptation
Adapting driving behavior to new environments, customs, and laws is a long-standing problem in autonomous driving, precluding the widespread deployment of autonomous vehicles (AVs).
Synthesizing Moving People with 3D Control
This disentangled approach allows our method to generate a sequence of images that are faithful to the target motion in the 3D pose and, to the input image in terms of visual similarity.
Self-correcting LLM-controlled Diffusion Models
Steered by an LLM controller, SLD turns text-to-image generation into an iterative closed-loop process, ensuring correctness in the resulting image.
From Wrong To Right: A Recursive Approach Towards Vision-Language Explanation
Addressing the challenge of adapting pre-trained vision-language models for generating insightful explanations for visual reasoning tasks with limited annotations, we present ReVisE: a $\textbf{Re}$cursive $\textbf{Vis}$ual $\textbf{E}$xplanation algorithm.
EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision
We present EmerNeRF, a simple yet powerful approach for learning spatial-temporal representations of dynamic driving scenes.
Interactive Task Planning with Language Models
An interactive robot framework accomplishes long-horizon task planning and can easily generalize to new goals or distinct tasks, even during execution.
LLM-grounded Video Diffusion Models
We show that LLMs are able to understand complex spatiotemporal dynamics from text alone and generate layouts that align closely with both the prompts and the object motion patterns typically observed in the real world.
LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models
Our method significantly outperforms the base diffusion model and several strong baselines in accurately generating images according to prompts that require various capabilities, doubling the generation accuracy across four tasks on average.
Re-evaluating the Need for Multimodal Signals in Unsupervised Grammar Induction
Are multimodal inputs necessary for grammar induction?
WiCV 2021: The Eighth Women In Computer Vision Workshop
In this paper, we present the details of Women in Computer Vision Workshop - WiCV 2021, organized alongside the virtual CVPR 2021.
Language-driven Semantic Segmentation
We present LSeg, a novel model for language-driven semantic image segmentation.
Ranked #1 on
Few-Shot Semantic Segmentation
on FSS-1000
Fixed Neural Network Steganography: Train the images, not the network
Recent attempts at image steganography make use of advances in deep learning to train an encoder-decoder network pair to hide and retrieve secret messages in images.
SITTA: Single Image Texture Translation for Data Augmentation
In this paper, we propose and explore the problem of image translation for data augmentation.
WiCV 2020: The Seventh Women In Computer Vision Workshop
It provides a voice to a minority (female) group in computer vision community and focuses on increasingly the visibility of these researchers, both in academia and industry.
On Feature Normalization and Data Augmentation
The moments (a. k. a., mean and standard deviation) of latent features are often removed as noise when training image recognition models, to increase stability and reduce training time.
Ranked #32 on
Domain Generalization
on ImageNet-A
Integrated Triaging for Fast Reading Comprehension
This paper introduces Integrated Triaging, a framework that prunes almost all context in early layers of a network, leaving the remaining (deep) layers to scan only a tiny fraction of the full corpus.
Neural Network Out-of-Distribution Detection for Regression Tasks
Neural network out-of-distribution (OOD) detection aims to identify when a model is unable to generalize to new inputs, either due to covariate shift or anomalous data.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
+1
Positional Normalization
A popular method to reduce the training time of deep neural networks is to normalize activations at each layer.
FastFusionNet: New State-of-the-Art for DAWNBench SQuAD
In this technical report, we introduce FastFusionNet, an efficient variant of FusionNet [12].
Benchmarking Single Image Dehazing and Beyond
We present a comprehensive study and evaluation of existing single image dehazing algorithms, using a new large-scale benchmark consisting of both synthetic and real-world hazy images, called REalistic Single Image DEhazing (RESIDE).
AOD-Net: All-In-One Dehazing Network
This paper proposes an image dehazing model built with a convolutional neural network (CNN), called All-in-One Dehazing Network (AOD-Net).
Ranked #25 on
Image Dehazing
on SOTS Outdoor
End-to-End United Video Dehazing and Detection
Furthermore, we build an End-to-End United Video Dehazing and Detection Network(EVDD-Net), which concatenates and jointly trains EVD-Net with a video object detection model.
An All-in-One Network for Dehazing and Beyond
This paper proposes an image dehazing model built with a convolutional neural network (CNN), called All-in-One Dehazing Network (AOD-Net).
