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Found 22 papers, 11 papers with code
3D molecule generation by denoising voxel grids
We propose a new score-based approach to generate 3D molecules represented as atomic densities on regular grids.
Learning to Adapt to Semantic Shift
These models outperform both previous work and static models under both \emph{static} and \emph{continual} semantic shifts, suggesting that ``learning to adapt'' is a useful capability for models and agents in a changing world.
Touch-based Curiosity for Sparse-Reward Tasks
Robots in many real-world settings have access to force/torque sensors in their gripper and tactile sensing is often necessary in tasks that involve contact-rich motion.
Unsupervised Learning of Dense Visual Representations
VADeR provides a natural representation for dense prediction tasks and transfers well to downstream tasks.
Reinforced active learning for image segmentation
Our method proposes a new modification of the deep Q-network (DQN) formulation for active learning, adapting it to the large-scale nature of semantic segmentation problems.
Neural Multisensory Scene Inference
For embodied agents to infer representations of the underlying 3D physical world they inhabit, they should efficiently combine multisensory cues from numerous trials, e. g., by looking at and touching objects.
Instance Segmentation with Point Supervision
Instance segmentation methods often require costly per-pixel labels.
Predicting Global Variations in Outdoor PM2.5 Concentrations using Satellite Images and Deep Convolutional Neural Networks
Here we present a new method of estimating global variations in outdoor PM$_{2. 5}$ concentrations using satellite images combined with ground-level measurements and deep convolutional neural networks.
CLAREL: Classification via retrieval loss for zero-shot learning
We address the problem of learning fine-grained cross-modal representations.
Reinforced Imitation Learning from Observations
Imitation learning is an effective alternative approach to learn a policy when the reward function is sparse.
Reinforced Imitation in Heterogeneous Action Space
Imitation learning is an effective alternative approach to learn a policy when the reward function is sparse.
Adaptive Cross-Modal Few-Shot Learning
Through a series of experiments, we show that by this adaptive combination of the two modalities, our model outperforms current uni-modality few-shot learning methods and modality-alignment methods by a large margin on all benchmarks and few-shot scenarios tested.
Adversarial Framing for Image and Video Classification
Neural networks are prone to adversarial attacks.
Domain-Adaptive Single-View 3D Reconstruction
In this paper, we propose a framework to improve over these challenges using adversarial training.
Where are the Blobs: Counting by Localization with Point Supervision
However, we propose a detection-based method that does not need to estimate the size and shape of the objects and that outperforms regression-based methods.
Ranked #1 on
Object Counting
on Pascal VOC 2007 count-test
Unsupervised Domain Adaptation with Similarity Learning
The objective of unsupervised domain adaptation is to leverage features from a labeled source domain and learn a classifier for an unlabeled target domain, with a similar but different data distribution.
A MultiPath Network for Object Detection
To address these challenges, we test three modifications to the standard Fast R-CNN object detector: (1) skip connections that give the detector access to features at multiple network layers, (2) a foveal structure to exploit object context at multiple object resolutions, and (3) an integral loss function and corresponding network adjustment that improve localization.
Ranked #104 on
Instance Segmentation
on COCO test-dev
Learning to Refine Object Segments
In this work we propose to augment feedforward nets for object segmentation with a novel top-down refinement approach.
Ranked #4 on
Region Proposal
on COCO test-dev
Learning to Segment Object Candidates
Recent object detection systems rely on two critical steps: (1) a set of object proposals is predicted as efficiently as possible, and (2) this set of candidate proposals is then passed to an object classifier.
Phrase-based Image Captioning
Generating a novel textual description of an image is an interesting problem that connects computer vision and natural language processing.
Simple Image Description Generator via a Linear Phrase-Based Approach
Generating a novel textual description of an image is an interesting problem that connects computer vision and natural language processing.
From Image-level to Pixel-level Labeling with Convolutional Networks
We are interested in inferring object segmentation by leveraging only object class information, and by considering only minimal priors on the object segmentation task.
