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Found 23 papers, 5 papers with code
Reasoning Paths with Reference Objects Elicit Quantitative Spatial Reasoning in Large Vision-Language Models
Inspired by this observation, we develop a zero-shot prompting technique, SpatialPrompt, that encourages VLMs to answer quantitative spatial questions using reference objects as visual cues.
Photorealistic Object Insertion with Diffusion-Guided Inverse Rendering
The correct insertion of virtual objects in images of real-world scenes requires a deep understanding of the scene's lighting, geometry and materials, as well as the image formation process.
RefFusion: Reference Adapted Diffusion Models for 3D Scene Inpainting
In this work, we propose an approach for 3D scene inpainting -- the task of coherently replacing parts of the reconstructed scene with desired content.
Can Feedback Enhance Semantic Grounding in Large Vision-Language Models?
We find that if prompted appropriately, VLMs can utilize feedback both in a single step and iteratively, showcasing the potential of feedback as an alternative technique to improve grounding in internet-scale VLMs.
DreamTeacher: Pretraining Image Backbones with Deep Generative Models
In this work, we introduce a self-supervised feature representation learning framework DreamTeacher that utilizes generative networks for pre-training downstream image backbones.
Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweighting
Sim2Real domain adaptation (DA) research focuses on the constrained setting of adapting from a labeled synthetic source domain to an unlabeled or sparsely labeled real target domain.
Neural Light Field Estimation for Street Scenes with Differentiable Virtual Object Insertion
In this work, we propose a neural approach that estimates the 5D HDR light field from a single image, and a differentiable object insertion formulation that enables end-to-end training with image-based losses that encourage realism.
How Much More Data Do I Need? Estimating Requirements for Downstream Tasks
Given a small training data set and a learning algorithm, how much more data is necessary to reach a target validation or test performance?
Scalable Neural Data Server: A Data Recommender for Transfer Learning
NDS uses a mixture of experts trained on data sources to estimate similarity between each source and the downstream task.
Domain Adversarial Training: A Game Perspective
Defining optimal solutions in domain-adversarial training as a local Nash equilibrium, we show that gradient descent in domain-adversarial training can violate the asymptotic convergence guarantees of the optimizer, oftentimes hindering the transfer performance.
Federated Learning with Heterogeneous Architectures using Graph HyperNetworks
Standard Federated Learning (FL) techniques are limited to clients with identical network architectures.
Towards Optimal Strategies for Training Self-Driving Perception Models in Simulation
Alternative solutions seek to exploit driving simulators that can generate large amounts of labeled data with a plethora of content variations.
f-Domain-Adversarial Learning: Theory and Algorithms
Unsupervised domain adaptation is used in many machine learning applications where, during training, a model has access to unlabeled data in the target domain, and a related labeled dataset.
Complex Momentum for Optimization in Games
We generalize gradient descent with momentum for optimization in differentiable games to have complex-valued momentum.
f-Domain-Adversarial Learning: Theory and Algorithms for Unsupervised Domain Adaptation with Neural Networks
We provide empirical results for several f-divergences and show that some, not considered previously in domain-adversarial learning, achieve state-of-the-art results in practice.
Neural Data Server: A Large-Scale Search Engine for Transfer Learning Data
NDS consists of a dataserver which indexes several large popular image datasets, and aims to recommend data to a client, an end-user with a target application with its own small labeled dataset.
Neural Turtle Graphics for Modeling City Road Layouts
We propose Neural Turtle Graphics (NTG), a novel generative model for spatial graphs, and demonstrate its applications in modeling city road layouts.
Gated-SCNN: Gated Shape CNNs for Semantic Segmentation
Here, we propose a new two-stream CNN architecture for semantic segmentation that explicitly wires shape information as a separate processing branch, i. e. shape stream, that processes information in parallel to the classical stream.
Ranked #24 on
Semantic Segmentation
on Cityscapes test
Meta-Sim: Learning to Generate Synthetic Datasets
Training models to high-end performance requires availability of large labeled datasets, which are expensive to get.
Devil is in the Edges: Learning Semantic Boundaries from Noisy Annotations
We further reason about true object boundaries during training using a level set formulation, which allows the network to learn from misaligned labels in an end-to-end fashion.
Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data
Moreover, synthetic SDR data combined with real KITTI data outperforms real KITTI data alone.
Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization
We present a system for training deep neural networks for object detection using synthetic images.
Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++
Manually labeling datasets with object masks is extremely time consuming.
