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Found 21 papers, 6 papers with code
HoloAssist: an Egocentric Human Interaction Dataset for Interactive AI Assistants in the Real World
Building an interactive AI assistant that can perceive, reason, and collaborate with humans in the real world has been a long-standing pursuit in the AI community.
Beyond Generation: Harnessing Text to Image Models for Object Detection and Segmentation
A foreground-background segmentation algorithm is then used to generate foreground object masks.
Controllable Text-to-Image Generation with GPT-4
Control-GPT works by querying GPT-4 to write TikZ code, and the generated sketches are used as references alongside the text instructions for diffusion models (e. g., ControlNet) to generate photo-realistic images.
Exploring the Sim2Real Gap Using Digital Twins
An emerging alternative is to use synthetic data, but if the synthetic data is not similar enough to the real data, the performance is typically below that of training with real data.
PatchBlender: A Motion Prior for Video Transformers
We show that our method is successful at enabling vision transformers to encode the temporal component of video data.
Neural-Sim: Learning to Generate Training Data with NeRF
However, existing approaches either require human experts to manually tune each scene property or use automatic methods that provide little to no control; this requires rendering large amounts of random data variations, which is slow and is often suboptimal for the target domain.
Scaling Novel Object Detection with Weakly Supervised Detection Transformers
A critical object detection task is finetuning an existing model to detect novel objects, but the standard workflow requires bounding box annotations which are time-consuming and expensive to collect.
DALL-E for Detection: Language-driven Compositional Image Synthesis for Object Detection
For foreground object mask generation, we use a simple textual template with object class name as input to DALL-E to generate a diverse set of foreground images.
Visual Attention Emerges from Recurrent Sparse Reconstruction
We present VARS, Visual Attention from Recurrent Sparse reconstruction, a new attention formulation built on two prominent features of the human visual attention mechanism: recurrency and sparsity.
One Network Doesn't Rule Them All: Moving Beyond Handcrafted Architectures in Self-Supervised Learning
We conduct a large-scale study with over 100 variants of ResNet and MobileNet architectures and evaluate them across 11 downstream scenarios in the SSL setting.
Robust Contrastive Learning against Noisy Views
Contrastive learning relies on an assumption that positive pairs contain related views, e. g., patches of an image or co-occurring multimodal signals of a video, that share certain underlying information about an instance.
Depth Completion Using a View-constrained Deep Prior
Given color images and noisy and incomplete target depth maps, we optimize a randomly-initialized CNN model to reconstruct a depth map restored by virtue of using the CNN network structure as a prior combined with a view-constrained photo-consistency loss.
A deep active learning system for species identification and counting in camera trap images
However, the accuracy of results depends on the amount, quality, and diversity of the data available to train models, and the literature has focused on projects with millions of relevant, labeled training images.
Synthetic Examples Improve Generalization for Rare Classes
The ability to detect and classify rare occurrences in images has important applications - for example, counting rare and endangered species when studying biodiversity, or detecting infrequent traffic scenarios that pose a danger to self-driving cars.
Learn-to-Score: Efficient 3D Scene Exploration by Predicting View Utility
We propose to learn a better utility function that predicts the usefulness of future viewpoints.
Personalized Cinemagraphs using Semantic Understanding and Collaborative Learning
Cinemagraphs are a compelling way to convey dynamic aspects of a scene.
Highly curved image sensors: a practical approach for improved optical performance
In this work we demonstrate that commercial silicon CMOS image sensors can be thinned and formed into accurate, highly curved optical surfaces with undiminished functionality.
Submodular Trajectory Optimization for Aerial 3D Scanning
Drones equipped with cameras are emerging as a powerful tool for large-scale aerial 3D scanning, but existing automatic flight planners do not exploit all available information about the scene, and can therefore produce inaccurate and incomplete 3D models.
Semantic-driven Generation of Hyperlapse from $360^\circ$ Video
We present a system for converting a fully panoramic ($360^\circ$) video into a normal field-of-view (NFOV) hyperlapse for an optimal viewing experience.
Lens Factory: Automatic Lens Generation Using Off-the-shelf Components
Unfortunately, custom lens design is costly (thousands to tens of thousands of dollars), time consuming (10-12 weeks typical lead time), and requires specialized optics design expertise.
Blind Image Quality Assessment using Semi-supervised Rectifier Networks
The biggest hurdles to these efforts are: 1) the difficulty of generalizing across diverse types of distortions and 2) collecting the enormity of human scored training data that is needed to learn the measure.
