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Found 15 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.
GoMAvatar: Efficient Animatable Human Modeling from Monocular Video Using Gaussians-on-Mesh
We introduce GoMAvatar, a novel approach for real-time, memory-efficient, high-quality animatable human modeling.
StableDreamer: Taming Noisy Score Distillation Sampling for Text-to-3D
Our analysis identifies the core of these challenges as the interaction among noise levels in the 2D diffusion process, the architecture of the diffusion network, and the 3D model representation.
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.
