🚀 Daily Paper Reading Tracker

Welcome to the Daily Paper Reading Tracker! Stay on top of your research by keeping track of what you're reading, what's on your radar, and what you've found insightful. 🌟

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📌 Legend

• ❓ Plan to Read → Papers on the reading list
• ⭐ Good Paper → Recommended reads
• ✅ Completed → Finished this week
• 📌 Discussion → Group meeting topic

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E2E Driving

Diffusion-Based Planning for Autonomous Driving with Flexible Guidance

Machine Learning

Surrogate Gap Minimization Improves Sharpness-Aware Training

❓ Open-Set Recognition: A Good Closed-Set Classifier is All You Need

Image as Set of Points

Test-time Adaptation

A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts

A Versatile Framework for Continual Test-Time Domain Adaptation: Balancing Discriminability and Generalizability

Revisiting Test Time Adaptation under Online Evaluation

Evaluating Continual Test-Time Adaptation for Contextual and Semantic Domain Shifts

Test-time Training

ActMAD: Activation Matching to Align Distributions for Test-Time-Training

Test-Time Training with Masked Autoencoders

TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive?

Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering

Test-Time Training with Self-Supervision for Generalization under Distribution Shifts

Test-time Augmentation

❓ Better Aggregation in Test-Time Augmentation

Test-time DG

Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models

Improved Test-time Adaptation for Domain Generalization

TTA

❓SODA: Robust Training of Test-Time Data Adaptors

ViDA: Homeostatic Visual Domain Adapter for Continual Test Time Adaptation

Towards Open-Set Test-Time Adaptation Utilizing the Wisdom of Crowds in Entropy Minimization

Label Shift Adapter for Test-Time Adaptation under Covariate and Label Shifts

Uncovering Adversarial Risks of Test-Time Adaptation

Gradual Test-Time Adaptation by Self-Training and Style Transfer

❓ Introducing Intermediate Domains for Effective Self-Training during Test-Time

Rethinking Precision of Pseudo Label: Test-Time Adaptation via Complementary Learning Done but haven't been updated

✔️ Adaptive Domain Generalization via Online Disagreement Minimization

✔️ Covariance-aware Feature Alignment with Pre-computed Source Statistics for Test-time Adaptation

✔️ SATA: Source Anchoring and Target Alignment Network for Continual Test Time Adaptation

✔️ CAFA: Class-Aware Feature Alignment for Test-Time Adaptation

❓ Towards Understanding GD with Hard and Conjugate Pseudo-labels for Test-Time Adaptation

✔️ Benchmarking Test-time Unsupervised Deep Neural Network Adaptation on Edge Devices

✔️ Learning to Adapt to Online Streams with Distribution Shifts

✔️ A Simple Test-time Adaptation Method for Source-free Domain Generalization

Robustifying vision transformer without retraining from scratch by test-time class-conditional feature alignment

Test-time batch normalization

On Pitfalls of Test-Time Adaptation

Feature Alignment and Uniformity for Test Time Adaptation

A Probabilistic Framework for Lifelong Test-Time Adaptation

TIPI: Test time adaptation with transformation invariance

EcoTTA: Memory-Efficient Continual Test-time Adaptation via Self-distilled Regularization

Robust mean teacher for continual and gradual test-time adaptation

Multi-step test-time adaptation with entropy minimization and pseudo-labeling

Robust Test-Time Adaptation in Dynamic Scenarios

TeSLA: Test-time self-learning with automatic adversarial augmentation

DELTA: degradation-free fully test-time adaptation

MECTA: Memory-Economic Continual Test-Time Model Adaptation

Parameter-free Online Test-time Adaptation

Decorate the Newcomers: Visual Domain Prompt for Continual Test Time Adaptation

MixNorm: Test-Time Adaptation Through Online Normalization Estimation

Domain Alignment Meets Fully Test-Time Adaptation

Test-time Adaptation via Conjugate Pseudo-Labels

Test-Time Adaptation to Distribution Shifts by Confidence Maximization and Input Transformation

Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization

Test-time Batch Statistics Calibration for Covariate Shift

Domain-agnostic Test-time Adaptation by Prototypical Training with Auxiliary Data

Test time Adaptation through Perturbation Robustness

Continual Test-Time Domain Adaptation

Improving Test-Time Adaptation via Shift-agnostic Weight Regularization and Nearest Source Prototypes

MEMO: Test Time Robustness via Adaptation and Augmentation

❓ Extrapolative Continuous-time Bayesian Neural Network for Fast Training-free Test-time Adaptation

TTN: A Domain-Shift Aware Batch Normalization in Test-Time Adaptation

The Norm Must Go On: Dynamic Unsupervised Domain Adaptation by Normalization

Online Adaptation to Label Distribution Shift

Improving robustness against common corruptions by covariate shift adaptation

MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentation

Efficient Test-Time Model Adaptation without Forgetting

❓ Back to the Source: Diffusion-Driven Test-Time Adaptation

Test-Time Adaptation via Self-Training with Nearest Neighbor Information

NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation

Towards Stable Test-time Adaptation in Dynamic Wild World

📌 Neuro-Modulated Hebbian Learning for Fully Test-Time Adaptation

❓ medical Test-Time Unsupervised Domain Adaptation

Imbalanced Data

Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification

❓ ACE: Ally Complementary Experts for Solving Long-Tailed Recognition in One-Shot

✔️ Posterior Re-calibration for Imbalanced Datasets

Transfer Learning things

❓ Does Robustness on ImageNet Transfer to Downstream Tasks?

Domain Adaptation (DA)

Category Contrast for Unsupervised Domain Adaptation in Visual Tasks

Graph-Relational Domain Adaptation

Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data

Taskonomy: Disentangling Task Transfer Learning

Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration

f-Domain-Adversarial Learning: Theory and Algorithms

⭐ Dirichlet-based Uncertainty Calibration for Active Domain Adaptation

❓ Addressing Parameter Choice Issues in Unsupervised Domain Adaptation by Aggregation

DA for Image Classification

Cycle Self-Training for Domain Adaptation

Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning

Toalign: Task-oriented alignment for unsupervised domain adaptation

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

❓ Upcycling Models under Domain and Category Shift

HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks

DA for Semantic Segmentation

Unsupervised Domain Adaptation for Semantic Segmentation using Depth Distribution

Pixel-by-Pixel Cross-Domain Alignment for Few-Shot Semantic Segmentation

TACS: Taxonomy Adaptive Cross-Domain Semantic Segmentation

❓ Domain Transfer through Deep Activation Matching

DA for online 3D segmentation

GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentation

3D point cloud

4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks

3D Semantic Occupancy Prediction

Diffusion

Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think

Semantic Segmentation

Transformer-based segmentation

Segmenter: Transformer for Semantic Segmentation

Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Segmentation model

❓ Denoising Pretraining for Semantic Segmentation

❓ Language-driven Semantic Segmentation

Active Boundary Loss for Semantic Segmentation

Segfix: Model-agnostic boundary refinement for segmentation

❓ Segment Anything

Domain Generalization

SWAD: Domain Generalization by Seeking Flat Minima

Domain Generalization by Learning and Removing Domain-specific Features

Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization

❓ Sparse Mixture-of-Experts are Domain Generalizable Learners

OOD

Assaying Out-Of-Distribution Generalization in Transfer Learning

Visual Prompting via Image Inpainting

Uncertainty Modeling for Out-of-Distribution Generalization

Delving Deep into the Generalization of Vision Transformers under Distribution Shifts

A Fine-Grained Analysis on Distribution Shift

❓ Generalization to Out-of-Distribution transformations

❓ Agree to Disagree: Diversity through Disagreement for Better Transferability

OOD Detection

Mitigating Neural Network Overconfidence with Logit Normalization

❓Beyond AUROC & Co. for Evaluating Out-of-Distribution Detection Performance

❓Decoupling MaxLogit for Out-of-Distribution Detection

PU Learning

Positive-Unlabeled Learning with Non-Negative Risk Estimator

semi-supervised SS

Re-distributing Biased Pseudo Labels for Semi-supervised Semantic Segmentation: A Baseline Investigation

Continual Learning

Learning To Prompt for Continual Learning

Instance Segmentation

SOLO: Segmenting Objects by Locations

SOLOv2: Dynamic and Fast Instance Segmentation

Unsupervised instance segmentation

Freesolo: Learning to segment objects without annotations

Dense Contrastive Learning for Self-Supervised Visual Pre-Training

❓ Cut and Learn for Unsupervised Object Detection and Instance Segmentation

Transformer

❓ Transformers are Sample-Efficient World Models

Calibration

Revisiting the Calibration of Modern Neural Networks

❓ Mitigating Bias in Calibration Error Estimation

Model Selection

❓ LogME: Practical Assessment of Pre-trained Models for Transfer Learning

📌 Transferability Estimation Using Bhattacharyya Class Separability

📌 LEEP: A new measure to evaluate transferability of learned representations

❓ Scalable Diverse Model Selection for Accessible Transfer Learning

❓ Transferability Metrics for Selecting Source Model Ensembles

❓ Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs

Dataset Distillation

Dataset Distillation by Matching Training Trajectories

Open Vocabulary

A Simple Framework for Open-Vocabulary Segmentation and Detection