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Found 13 papers, 7 papers with code
UFO²: A Unified Framework towards Omni-supervised Object Detection
Existing work on object detection often relies on a single form of annotation: the model is trained using either accurate yet costly bounding boxes or cheaper but less expressive image-level tags.
CASA: Category-agnostic Skeletal Animal Reconstruction
Recovering the skeletal shape of an animal from a monocular video is a longstanding challenge.
Occupancy Planes for Single-view RGB-D Human Reconstruction
Specifically, a set of 3D locations within the view-frustum of the camera are first projected independently onto the image and a corresponding feature is subsequently extracted for each 3D location.
Neural Volumetric Object Selection
We introduce an approach for selecting objects in neural volumetric 3D representations, such as multi-plane images (MPI) and neural radiance fields (NeRF).
Total Variation Optimization Layers for Computer Vision
To study question (a), in this work, we propose total variation (TV) minimization as a layer for computer vision.
Class-agnostic Reconstruction of Dynamic Objects from Videos
We introduce REDO, a class-agnostic framework to REconstruct the Dynamic Objects from RGBD or calibrated videos.
Semantic Tracklets: An Object-Centric Representation for Visual Multi-Agent Reinforcement Learning
We evaluate `semantic tracklets' on the visual multi-agent particle environment (VMPE) and on the challenging visual multi-agent GFootball environment.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
3D Spatial Recognition without Spatially Labeled 3D
We introduce WyPR, a Weakly-supervised framework for Point cloud Recognition, requiring only scene-level class tags as supervision.
UFO$^2$: A Unified Framework towards Omni-supervised Object Detection
Existing work on object detection often relies on a single form of annotation: the model is trained using either accurate yet costly bounding boxes or cheaper but less expressive image-level tags.
Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning
Existing semi-supervised learning (SSL) algorithms use a single weight to balance the loss of labeled and unlabeled examples, i. e., all unlabeled examples are equally weighted.
Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection
Weakly supervised learning has emerged as a compelling tool for object detection by reducing the need for strong supervision during training.
Ranked #1 on
Weakly Supervised Object Detection
on COCO test-dev
Learning to Anonymize Faces for Privacy Preserving Action Detection
The end result is a video anonymizer that performs pixel-level modifications to anonymize each person's face, with minimal effect on action detection performance.
Cross-Domain Self-supervised Multi-task Feature Learning using Synthetic Imagery
In human learning, it is common to use multiple sources of information jointly.
