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Found 26 papers, 8 papers with code
Neural Radiance Fields with Torch Units
In this paper, we design a novel inference pattern that encourages a single camera ray possessing more contextual information, and models the relationship among sample points on each camera ray.
HUGS: Holistic Urban 3D Scene Understanding via Gaussian Splatting
Holistic understanding of urban scenes based on RGB images is a challenging yet important problem.
Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities
The rise of large foundation models, trained on extensive datasets, is revolutionizing the field of AI.
NeRFVS: Neural Radiance Fields for Free View Synthesis via Geometry Scaffolds
We present NeRFVS, a novel neural radiance fields (NeRF) based method to enable free navigation in a room.
NeRF-MS: Neural Radiance Fields with Multi-Sequence
Neural radiance fields (NeRF) achieve impressive performance in novel view synthesis when trained on only single sequence data.
Nuisance-Label Supervision: Robustness Improvement by Free Labels
Nuisance factors are those irrelevant to a task, and an ideal model should be invariant to them.
Simulated Adversarial Testing of Face Recognition Models
In this work, we propose a framework for learning how to test machine learning algorithms using simulators in an adversarial manner in order to find weaknesses in the model before deploying it in critical scenarios.
A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representation
To deal with the large shape variance, we introduce Articulated Signed Distance Functions (A-SDF) to represent articulated shapes with a disentangled latent space, where we have separate codes for encoding shape and articulation.
Learning Part Segmentation through Unsupervised Domain Adaptation from Synthetic Vehicles
We believe our dataset provides a rich testbed to study UDA for part segmentation and will help to significantly push forward research in this area.
Nothing But Geometric Constraints: A Model-Free Method for Articulated Object Pose Estimation
We propose an unsupervised vision-based system to estimate the joint configurations of the robot arm from a sequence of RGB or RGB-D images without knowing the model a priori, and then adapt it to the task of category-independent articulated object pose estimation.
AdaFuse: Adaptive Multiview Fusion for Accurate Human Pose Estimation in the Wild
To make the task truly unconstrained, we present AdaFuse, an adaptive multiview fusion method, which can enhance the features in occluded views by leveraging those in visible views.
Ranked #1 on
3D Human Pose Estimation
on Total Capture
Learning from Synthetic Animals
Despite great success in human parsing, progress for parsing other deformable articulated objects, like animals, is still limited by the lack of labeled data.
Identity Preserve Transform: Understand What Activity Classification Models Have Learnt
This can explain why an activity classification model usually fails to generalize to datasets it is not trained on.
Car Pose in Context: Accurate Pose Estimation with Ground Plane Constraints
Scene context is a powerful constraint on the geometry of objects within the scene in cases, such as surveillance, where the camera geometry is unknown and image quality may be poor.
DASZL: Dynamic Action Signatures for Zero-shot Learning
There are many realistic applications of activity recognition where the set of potential activity descriptions is combinatorially large.
RSA: Randomized Simulation as Augmentation for Robust Human Action Recognition
In this paper, we propose the Randomized Simulation as Augmentation (RSA) framework which augments real-world training data with synthetic data to improve the robustness of action recognition networks.
Identifying Model Weakness with Adversarial Examiner
Different from the existing strategy to always give the same (distribution of) test data, the adversarial examiner will dynamically select the next test data to hand out based on the testing history so far, with the goal being to undermine the model's performance.
Patch-based 3D Human Pose Refinement
State-of-the-art 3D human pose estimation approaches typically estimate pose from the entire RGB image in a single forward run.
CRAVES: Controlling Robotic Arm with a Vision-based Economic System
We also construct a vision-based control system for task accomplishment, for which we train a reinforcement learning agent in a virtual environment and apply it to the real-world.
Semantic Part Detection via Matching: Learning to Generalize to Novel Viewpoints from Limited Training Data
In particular, this enables images in the training dataset to be matched to a virtual 3D model of the object (for simplicity, we assume that the object viewpoint can be estimated by standard techniques).
SampleAhead: Online Classifier-Sampler Communication for Learning from Synthesized Data
But, this raises an important problem in active vision: given an {\bf infinite} data space, how to effectively sample a {\bf finite} subset to train a visual classifier?
Adversarial Attacks Beyond the Image Space
Though image-space adversaries can be interpreted as per-pixel albedo change, we verify that they cannot be well explained along these physically meaningful dimensions, which often have a non-local effect.
ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond
We argue that estimation of object scales in images is helpful for generating object proposals, especially for supermarket images where object scales are usually within a small range.
UnrealStereo: Controlling Hazardous Factors to Analyze Stereo Vision
We generate a large synthetic image dataset with automatically computed hazardous regions and analyze algorithms on these regions.
UnrealCV: Connecting Computer Vision to Unreal Engine
Computer graphics can not only generate synthetic images and ground truth but it also offers the possibility of constructing virtual worlds in which: (i) an agent can perceive, navigate, and take actions guided by AI algorithms, (ii) properties of the worlds can be modified (e. g., material and reflectance), (iii) physical simulations can be performed, and (iv) algorithms can be learnt and evaluated.
Ground-truth dataset and baseline evaluations for image base-detail separation algorithms
Base-detail separation is a fundamental computer vision problem consisting of modeling a smooth base layer with the coarse structures, and a detail layer containing the texture-like structures.
