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Found 7 papers, 0 papers with code
RING-NeRF : Rethinking Inductive Biases for Versatile and Efficient Neural Fields
Recent advances in Neural Fields mostly rely on developing task-specific supervision which often complicates the models.
The Background Also Matters: Background-Aware Motion-Guided Objects Discovery
This is a critical limitation given the unsupervised setting, where object segments and noise are not distinguishable.
Image Segmentation-based Unsupervised Multiple Objects Discovery
To address this problem, we propose a fully unsupervised, bottom-up approach, for multiple objects discovery.
Self-Supervised Pre-training of Vision Transformers for Dense Prediction Tasks
We present a new self-supervised pre-training of Vision Transformers for dense prediction tasks.
PandaNet : Anchor-Based Single-Shot Multi-Person 3D Pose Estimation
It does not need any post-processing to regroup joints since the network predicts a full 3D pose for each bounding box and allows the pose estimation of a possibly large number of people at low resolution.
LapNet : Automatic Balanced Loss and Optimal Assignment for Real-Time Dense Object Detection
In this paper, we propose a new way to efficiently learn a single-shot detector which offers a very good compromise between these two objectives.
Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image
In this paper, we present a novel approach, called Deep MANTA (Deep Many-Tasks), for many-task vehicle analysis from a given image.
Ranked #2 on
Vehicle Pose Estimation
on KITTI Cars Hard
(using extra training data)
