Jose M. Alvarez
57 papers · 2016–2026 · 9 venues · across top CS/AI venues
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Achievements
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Research topics
Machine Learning
(32)
Computer Vision
(31)
Deep Learning
(30)
Analysis
(26)
Artificial Intelligence
(24)
Core AI
(21)
Application Areas
(16)
Architectures
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Domain-Specific
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Learning Types Β· Deep Learning
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Optimization & Theory Β· Machine Learning
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Optimization & Theory Β· Deep Learning
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Techniques
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Keywords
model compression
(11)
object detection
(9)
semantic segmentation
(8)
depth estimation
(8)
neural network
(5)
network pruning
(5)
neural network optimization
(5)
knowledge distillation
(5)
self-supervised learning
(5)
vision transformer
(5)
autonomous driving
(4)
neural scaling law
(4)
active learning
(3)
multi-view stereo
(3)
end-to-end autonomous driving
(3)
3d reconstruction
(3)
image classification
(3)
instance segmentation
(3)
3d object detection
(3)
convolutional neural network
(3)
Papers
4DSurf: High-Fidelity Dynamic Scene Surface Reconstruction
CVPR 2026
DriveSuprim: Towards Precise Trajectory Selection for End-to-End Planning
AAAI 2026
GHOST: Getting to the Bottom of Hallucinations with A Multi-round Consistency Benchmark
WACV 2026
Mitigating Multimodal Hallucinations via Gradient-based Self-Reflection
CVPR 2026
Scaling-Aware Data Selection for End-to-End Autonomous Driving Systems
CVPR 2026
VideoITG: Multimodal Video Understanding with Instructed Temporal Grounding
CVPR 2026
Advancing Weight and Channel Sparsification with Enhanced Saliency
WACV 2025
Hydra-NeXt: Robust Closed-Loop Driving with Open-Loop Training
ICCV 2025
Joint Optimization of Neural Radiance Fields and Continuous Camera Motion from a Monocular Video
CVPR 2025
MDP: Multidimensional Vision Model Pruning with Latency Constraint
CVPR 2025
OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning
CVPR 2025
Optimizing Data Collection for Machine Learning
JMLR 2025
PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models
CVPR 2025
Adaptive Sharpness-Aware Pruning for Robust Sparse Networks
ICLR 2024
BEVNeXt: Reviving Dense BEV Frameworks for 3D Object Detection
CVPR 2024
FasterViT: Fast Vision Transformers with Hierarchical Attention
ICLR 2024
Improving Distant 3D Object Detection Using 2D Box Supervision
CVPR 2024
Is Ego Status All You Need for Open-Loop End-to-End Autonomous Driving?
CVPR 2024
Memorize What Matters: Emergent Scene Decomposition from Multitraverse
NeurIPS 2024
Mining Supervision for Dynamic Regions in Self-Supervised Monocular Depth Estimation
CVPR 2024
Can We Trust Fair-AI?
AAAI 2023
FB-BEV: BEV Representation from Forward-Backward View Transformations
ICCV 2023
FocalFormer3D: Focusing on Hard Instance for 3D Object Detection
ICCV 2023
Fully Attentional Networks with Self-emerging Token Labeling
ICCV 2023
Knowledge Distillation for 6D Pose Estimation by Aligning Distributions of Local Predictions
CVPR 2023
Parametric Depth Based Feature Representation Learning for Object Detection and Segmentation in Bird's-Eye View
ICCV 2023
Towards Viewpoint Robustness in Bird's Eye View Segmentation
ICCV 2023
Vision Transformers Are Good Mask Auto-Labelers
CVPR 2023
VoxFormer: Sparse Voxel Transformer for Camera-Based 3D Semantic Scene Completion
CVPR 2023
A-ViT: Adaptive Tokens for Efficient Vision Transformer
CVPR 2022
FreeSOLO: Learning To Segment Objects Without Annotations
CVPR 2022
How Much More Data Do I Need? Estimating Requirements for Downstream Tasks
CVPR 2022
Non-Parametric Depth Distribution Modelling Based Depth Inference for Multi-View Stereo
CVPR 2022
Not All Labels Are Equal: Rationalizing the Labeling Costs for Training Object Detection
CVPR 2022
Optimizing Data Collection for Machine Learning
NeurIPS 2022
Panoptic SegFormer: Delving Deeper Into Panoptic Segmentation With Transformers
CVPR 2022
Structural Pruning via Latency-Saliency Knapsack
NeurIPS 2022
Understanding The Robustness in Vision Transformers
ICML 2022
When To Prune? A Policy Towards Early Structural Pruning
CVPR 2022
Active Learning for Deep Object Detection via Probabilistic Modeling
ICCV 2021
Contrastive Syn-to-Real Generalization
ICLR 2021
Distilling Image Classifiers in Object Detectors
NeurIPS 2021
Optimal Quantization Using Scaled Codebook
CVPR 2021
Personalized Federated Learning with First Order Model Optimization
ICLR 2021
See Through Gradients: Image Batch Recovery via GradInversion
CVPR 2021
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
NeurIPS 2021
Self-Supervised Learning of Depth Inference for Multi-View Stereo
CVPR 2021
Cost Volume Pyramid Based Depth Inference for Multi-View Stereo
CVPR 2020
Dreaming to Distill: Data-Free Knowledge Transfer via DeepInversion
CVPR 2020
ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks
NeurIPS 2020
Quadtree Generating Networks: Efficient Hierarchical Scene Parsing with Sparse Convolutions
WACV 2020
Effective Use of Synthetic Data for Urban Scene Semantic Segmentation
ECCV 2018
Bringing Background Into the Foreground: Making All Classes Equal in Weakly-Supervised Video Semantic Segmentation
ICCV 2017
Compression-aware Training of Deep Networks
NeurIPS 2017
Domain-Adaptive Deep Network Compression
ICCV 2017
Emotion Recognition in Context
CVPR 2017
Learning the Number of Neurons in Deep Networks
NeurIPS 2016