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Found 27 papers, 12 papers with code
Text2Reward: Automated Dense Reward Function Generation for Reinforcement Learning
Unlike inverse RL and recent work that uses LLMs to write sparse reward codes, Text2Reward produces interpretable, free-form dense reward codes that cover a wide range of tasks, utilize existing packages, and allow iterative refinement with human feedback.
Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slots
We introduce a method to segment the visual field into independently moving regions, trained with no ground truth or supervision.
JacobiNeRF: NeRF Shaping with Mutual Information Gradients
We propose a method that trains a neural radiance field (NeRF) to encode not only the appearance of the scene but also semantic correlations between scene points, regions, or entities -- aiming to capture their mutual co-variation patterns.
VDN-NeRF: Resolving Shape-Radiance Ambiguity via View-Dependence Normalization
We propose VDN-NeRF, a method to train neural radiance fields (NeRFs) for better geometry under non-Lambertian surface and dynamic lighting conditions that cause significant variation in the radiance of a point when viewed from different angles.
Structure-Aware Surface Reconstruction via Primitive Assembly
We propose a novel and efficient method for reconstructing manifold surfaces from point clouds.
COPILOT: Human-Environment Collision Prediction and Localization from Egocentric Videos
In this work, we introduce the challenging problem of predicting collisions in diverse environments from multi-view egocentric videos captured from body-mounted cameras.
6D Camera Relocalization in Visually Ambiguous Extreme Environments
To this end, we propose: (i) a hierarchical localization system, where we leverage temporal information and (ii) a novel environment-aware image enhancement method to boost the robustness and accuracy.
SpOT: Spatiotemporal Modeling for 3D Object Tracking
In this work, we develop a holistic representation of traffic scenes that leverages both spatial and temporal information of the actors in the scene.
GIMO: Gaze-Informed Human Motion Prediction in Context
We perform an extensive study of the benefits of leveraging the eye gaze for ego-centric human motion prediction with various state-of-the-art architectures.
ADeLA: Automatic Dense Labeling With Attention for Viewpoint Shift in Semantic Segmentation
We describe a method to deal with performance drop in semantic segmentation caused by viewpoint changes within multi-camera systems, where temporally paired images are readily available, but the annotations may only be abundant for a few typical views.
Domain Adaptation on Point Clouds via Geometry-Aware Implicits
Here we propose a simple yet effective method for unsupervised domain adaptation on point clouds by employing a self-supervised task of learning geometry-aware implicits, which plays two critical roles in one shot.
Object Pursuit: Building a Space of Objects via Discriminative Weight Generation
We perform an extensive study of the key features of the proposed framework and analyze the characteristics of the learned representations.
IFR-Explore: Learning Inter-object Functional Relationships in 3D Indoor Scenes
While most works focus on single-object or agent-object visual functionality and affordances, our work proposes to study a new kind of visual relationship that is also important to perceive and model -- inter-object functional relationships (e. g., a switch on the wall turns on or off the light, a remote control operates the TV).
ADeLA: Automatic Dense Labeling with Attention for Viewpoint Adaptation in Semantic Segmentation
Furthermore, to resolve ambiguities in converting the semantic images to semantic labels, we treat the view transformation network as a functional representation of an unknown mapping implied by the color images and propose functional label hallucination to generate pseudo-labels in the target domain.
DCL: Differential Contrastive Learning for Geometry-Aware Depth Synthesis
We describe a method for unpaired realistic depth synthesis that learns diverse variations from the real-world depth scans and ensures geometric consistency between the synthetic and synthesized depth.
Learning Semantic-Aware Dynamics for Video Prediction
The appearance of the scene is warped from past frames using the predicted motion in co-visible regions; dis-occluded regions are synthesized with content-aware inpainting utilizing the predicted scene layout.
DyStaB: Unsupervised Object Segmentation via Dynamic-Static Bootstrapping
Then, it uses the segments to learn object models that can be used for detection in a static image.
FDA: Fourier Domain Adaptation for Semantic Segmentation
We describe a simple method for unsupervised domain adaptation, whereby the discrepancy between the source and target distributions is reduced by swapping the low-frequency spectrum of one with the other.
Ranked #3 on
Domain Adaptation
on Panoptic SYNTHIA-to-Mapillary
Learning to Manipulate Individual Objects in an Image
We describe a method to train a generative model with latent factors that are (approximately) independent and localized.
Phase Consistent Ecological Domain Adaptation
We introduce two criteria to regularize the optimization involved in learning a classifier in a domain where no annotated data are available, leveraging annotated data in a different domain, a problem known as unsupervised domain adaptation.
Dense Depth Posterior (DDP) from Single Image and Sparse Range
We present a deep learning system to infer the posterior distribution of a dense depth map associated with an image, by exploiting sparse range measurements, for instance from a lidar.
Ranked #5 on
Depth Completion
on VOID
Unsupervised Moving Object Detection via Contextual Information Separation
We propose an adversarial contextual model for detecting moving objects in images.
Conditional Prior Networks for Optical Flow
On the other hand, fully supervised methods learn the regularity in the annotated data, without explicit regularization and with the risk of overfitting.
S2F: Slow-To-Fast Interpolator Flow
We introduce a method to compute optical flow at multiple scales of motion, without resorting to multi- resolution or combinatorial methods.
Self-Occlusions and Disocclusions in Causal Video Object Segmentation
We propose a method to detect disocclusion in video sequences of three-dimensional scenes and to partition the disoccluded regions into objects, defined by coherent deformation corresponding to surfaces in the scene.
Shape Tracking With Occlusions via Coarse-To-Fine Region-Based Sobolev Descent
In cases of 3D object motion and viewpoint change, self-occlusions and dis-occlusions of the object are prominent, and current methods employing joint shape and appearance models are unable to adapt to new shape and appearance information, leading to inaccurate shape detection.
