Search Results for author:
Found 18 papers, 12 papers with code
NeRRF: 3D Reconstruction and View Synthesis for Transparent and Specular Objects with Neural Refractive-Reflective Fields
In this paper, we introduce the refractive-reflective field.
Conditional Perceptual Quality Preserving Image Compression
We propose conditional perceptual quality, an extension of the perceptual quality defined in \citet{blau2018perception}, by conditioning it on user defined information.
Query-Policy Misalignment in Preference-Based Reinforcement Learning
To unravel this mystery, we identify a long-neglected issue in the query selection schemes of existing PbRL studies: Query-Policy Misalignment.
PROTO: Iterative Policy Regularized Offline-to-Online Reinforcement Learning
Offline-to-online reinforcement learning (RL), by combining the benefits of offline pretraining and online finetuning, promises enhanced sample efficiency and policy performance.
DPF: Learning Dense Prediction Fields with Weak Supervision
We showcase the effectiveness of DPFs using two substantially different tasks: high-level semantic parsing and low-level intrinsic image decomposition.
VIMI: Vehicle-Infrastructure Multi-view Intermediate Fusion for Camera-based 3D Object Detection
In autonomous driving, Vehicle-Infrastructure Cooperative 3D Object Detection (VIC3D) makes use of multi-view cameras from both vehicles and traffic infrastructure, providing a global vantage point with rich semantic context of road conditions beyond a single vehicle viewpoint.
AdaptiveNet: Post-deployment Neural Architecture Adaptation for Diverse Edge Environments
Model elastification generates a high-quality search space of model architectures with the guidance of a developer-specified oracle model.
LODE: Locally Conditioned Eikonal Implicit Scene Completion from Sparse LiDAR
In this paper, we propose a novel Eikonal formulation that conditions the implicit representation on localized shape priors which function as dense boundary value constraints, and demonstrate it works on SemanticKITTI and SemanticPOSS.
Mind the Gap: Offline Policy Optimization for Imperfect Rewards
RGM is formulated as a bi-level optimization problem: the upper layer optimizes a reward correction term that performs visitation distribution matching w. r. t.
Mutual Information Regularization for Vertical Federated Learning
However, previous works show that parties without labels (passive parties) in VFL can infer the sensitive label information owned by the party with labels (active party) or execute backdoor attacks to VFL.
Vertical Federated Learning
Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with different features about the same set of users jointly train machine learning models without exposing their raw data or model parameters.
TOIST: Task Oriented Instance Segmentation Transformer with Noun-Pronoun Distillation
As such, we study the challenging problem of task oriented detection, which aims to find objects that best afford an action indicated by verbs like sit comfortably on.
Correcting the Sub-optimal Bit Allocation
In this paper, we investigate the problem of bit allocation in Neural Video Compression (NVC).
Bit Allocation using Optimization
In this paper, we consider the problem of bit allocation in Neural Video Compression (NVC).
When Data Geometry Meets Deep Function: Generalizing Offline Reinforcement Learning
In offline reinforcement learning (RL), one detrimental issue to policy learning is the error accumulation of deep Q function in out-of-distribution (OOD) areas.
Semi-supervised Implicit Scene Completion from Sparse LiDAR
Recent advances show that semi-supervised implicit representation learning can be achieved through physical constraints like Eikonal equations.
Cerberus Transformer: Joint Semantic, Affordance and Attribute Parsing
Multi-task indoor scene understanding is widely considered as an intriguing formulation, as the affinity of different tasks may lead to improved performance.
Ranked #42 on
Semantic Segmentation
on NYU Depth v2
PQ-Transformer: Jointly Parsing 3D Objects and Layouts from Point Clouds
Such a scheme has two limitations: 1) Storing and running several networks for different tasks are expensive for typical robotic platforms.
