16 citations · 23 across the 9 of their papers we have counts for
11 papers
VLM-AD: End-to-End Autonomous Driving through Vision-Language Model Supervision
Yi Xu, Yuxin Hu, Zaiwei Zhang +5
Human drivers rely on commonsense reasoning to navigate diverse and dynamic real-world scenarios. Existing end-to-end (E2E) autonomous driving (AD) models are typically optimized t…
VLMine: Long-Tail Data Mining with Vision Language Models
Mao Ye, Gregory P. Meyer, Zaiwei Zhang +4
Ensuring robust performance on long-tail examples is an important problem for many real-world applications of machine learning, such as autonomous driving. This work focuses on the…
ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts
Mu Cai, Haotian Liu, Dennis Park +4
While existing large vision-language multimodal models focus on whole image understanding, there is a prominent gap in achieving region-specific comprehension. Current approaches t…
NOVA: NOvel View Augmentation for Neural Composition of Dynamic Objects
Dakshit Agrawal, Jiajie Xu, Siva Karthik Mustikovela +3
We propose a novel-view augmentation (NOVA) strategy to train NeRFs for photo-realistic 3D composition of dynamic objects in a static scene. Compared to prior work, our framework s…
Self-Supervised Object Detection via Generative Image Synthesis
Siva Karthik Mustikovela, Shalini De Mello, Aayush Prakash +5
We present SSOD, the first end-to-end analysis-by synthesis framework with controllable GANs for the task of self-supervised object detection. We use collections of real world imag…
Intrinsic Autoencoders for Joint Neural Rendering and Intrinsic Image Decomposition
Hassan Abu Alhaija, Siva Karthik Mustikovela, Justus Thies +4
Neural rendering techniques promise efficient photo-realistic image synthesis while at the same time providing rich control over scene parameters by learning the physical image for…
Self-Supervised Viewpoint Learning From Image Collections
Siva Karthik Mustikovela, Varun Jampani, Shalini De Mello +4
Training deep neural networks to estimate the viewpoint of objects requires large labeled training datasets. However, manually labeling viewpoints is notoriously hard, error-prone,…
Geometric Image Synthesis
Hassan Abu Alhaija, Siva Karthik Mustikovela, Andreas Geiger +1
The task of generating natural images from 3D scenes has been a long standing goal in computer graphics. On the other hand, recent developments in deep neural networks allow for tr…
iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects
Omid Hosseini Jafari, Siva Karthik Mustikovela, Karl Pertsch +2
We address the task of 6D pose estimation of known rigid objects from single input images in scenarios where the objects are partly occluded. Recent RGB-D-based methods are robust…
Augmented Reality Meets Computer Vision : Efficient Data Generation for Urban Driving Scenes
Hassan Abu Alhaija, Siva Karthik Mustikovela, Lars Mescheder +2
The success of deep learning in computer vision is based on availability of large annotated datasets. To lower the need for hand labeled images, virtually rendered 3D worlds have r…
Can Ground Truth Label Propagation from Video help Semantic Segmentation?
Siva Karthik Mustikovela, Michael Ying Yang, Carsten Rother
For state-of-the-art semantic segmentation task, training convolutional neural networks (CNNs) requires dense pixelwise ground truth (GT) labeling, which is expensive and involves…