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Found 17 papers, 10 papers with code
DORSal: Diffusion for Object-centric Representations of Scenes et al
In this paper, we leverage recent progress in diffusion models to equip 3D scene representation learning models with the ability to render high-fidelity novel views, while retaining benefits such as object-level scene editing to a large degree.
Diffusion Self-Guidance for Controllable Image Generation
However, many aspects of an image are difficult or impossible to convey through text.
Scalable Adaptive Computation for Iterative Generation
We show how to leverage recurrence by conditioning the latent tokens at each forward pass of the reverse diffusion process with those from prior computation, i. e. latent self-conditioning.
Ranked #1 on
Image Generation
on ImageNet 64x64
Object Permanence Emerges in a Random Walk along Memory
This paper proposes a self-supervised objective for learning representations that localize objects under occlusion - a property known as object permanence.
Discovering Objects that Can Move
Our experiments demonstrate that, despite only capturing a small subset of the objects that move, this signal is enough to generalize to segment both moving and static instances of dynamic objects.
Learning Pixel Trajectories with Multiscale Contrastive Random Walks
A range of video modeling tasks, from optical flow to multiple object tracking, share the same fundamental challenge: establishing space-time correspondence.
Space-Time Correspondence as a Contrastive Random Walk
We cast correspondence as prediction of links in a space-time graph constructed from video.
Towards Practical Multi-Object Manipulation using Relational Reinforcement Learning
Learning robotic manipulation tasks using reinforcement learning with sparse rewards is currently impractical due to the outrageous data requirements.
Unsupervised Curricula for Visual Meta-Reinforcement Learning
In experiments on vision-based navigation and manipulation domains, we show that the algorithm allows for unsupervised meta-learning that transfers to downstream tasks specified by hand-crafted reward functions and serves as pre-training for more efficient supervised meta-learning of test task distributions.
Learning Correspondence from the Cycle-Consistency of Time
We introduce a self-supervised method for learning visual correspondence from unlabeled video.
Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control
A key challenge in complex visuomotor control is learning abstract representations that are effective for specifying goals, planning, and generalization.
Universal Planning Networks
We find that the representations learned are not only effective for goal-directed visual imitation via gradient-based trajectory optimization, but can also provide a metric for specifying goals using images.
Learning Visually Grounded Sentence Representations
We introduce a variety of models, trained on a supervised image captioning corpus to predict the image features for a given caption, to perform sentence representation grounding.
CommAI: Evaluating the first steps towards a useful general AI
With machine learning successfully applied to new daunting problems almost every day, general AI starts looking like an attainable goal.
Learning Visual N-Grams from Web Data
Real-world image recognition systems need to recognize tens of thousands of classes that constitute a plethora of visual concepts.
Ranked #2 on
Zero-Shot Transfer Image Classification
on SUN
Revisiting Visual Question Answering Baselines
Visual question answering (VQA) is an interesting learning setting for evaluating the abilities and shortcomings of current systems for image understanding.
Learning Visual Features from Large Weakly Supervised Data
We train convolutional networks on a dataset of 100 million Flickr photos and captions, and show that these networks produce features that perform well in a range of vision problems.
