Multi-Task Learning papers, 2023–2026

Key findings

• Multi-Task Learning went from 288 papers in 2023 to 402 in 2026, roughly 1.4x in three years.
• NeurIPS publishes the most Multi-Task Learning work: 399 papers from 2023 to 2026, 34% of the four-venue total.
• The fastest-growing overlap is with Datasets and Benchmarks: papers tagged with both rose from 6 in 2023 to 16 in 2026.
• Among the 5 topics under Meta-Learning and Transfer, it ranks #2 by 2026 paper count; the fastest grower in the group, Foundation Model Adaptation, is up 3.8x versus 1.4x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026A Mahalanobis Margin \texorpdfstring{$\gamma_{\min}$}{gamma\_min} Bound on Task Confusion in Pretrained Class-Incremental Learning: From Infeasibility to Exponential Attenuation
• ICML 2026A Systematic Study of Behavioral Cloning for Scientific Data Annotation
• ICLR 2026Automatic Stage Lighting Control: Is it a Rule-Driven Process or Generative Task?
• CVPR 20263D-Aware Multi-Task Learning with Cross-View Correlations for Dense Scene Understanding
• NeurIPS 2025A High-Dimensional Statistical Method for Optimizing Transfer Quantities in Multi-Source Transfer Learning
• ICML 2025ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning
• ICLR 2025CodeMMLU: A Multi-Task Benchmark for Assessing Code Understanding & Reasoning Capabilities of CodeLLMs
• CVPR 2025ACAttack: Adaptive Cross Attacking RGB-T Tracker via Multi-Modal Response Decoupling
• NeurIPS 2024CoMix: A Comprehensive Benchmark for Multi-Task Comic Understanding
• ICML 2024Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning
• ICLR 2024Class Incremental Learning via Likelihood Ratio Based Task Prediction
• CVPR 2024Differentiable Information Bottleneck for Deterministic Multi-view Clustering
• NeurIPS 2023A Spectral Algorithm for List-Decodable Covariance Estimation in Relative Frobenius Norm
• ICML 2023A Conditional Normalizing Flow for Accelerated Multi-Coil MR Imaging
• ICLR 2023An Extensible Multi-modal Multi-task Object Dataset with Materials
• CVPR 2023AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation
• NeurIPS 2026A Scalable Multi-Task Model for Virtual Sensors
• ICML 2026Alleviating Observation Bias via Causal-Invariant Meta-Learning for Unbalanced Incomplete Multi-view Clustering
• ICLR 2026Beyond Hearing: Learning Task-agnostic ExG Representations from Earphones via Physiology-informed Tokenization
• CVPR 2026Any Resolution Any Geometry From Multi-View To Multi-Patch

All Multi-Task Learning papers

363 papers, page 4 of 8

151. CVPR 2025Enhanced then Progressive Fusion with View Graph for Multi-View Clustering
152. CVPR 2025Focusing on Tracks for Online Multi-Object Tracking
153. CVPR 2025Free Lunch Enhancements for Multi-modal Crowd Counting
154. CVPR 2025GeoMM: On Geodesic Perspective for Multi-modal Learning
155. CVPR 2025GET: Unlocking the Multi-modal Potential of CLIP for Generalized Category Discovery
156. CVPR 2025Hierarchical Knowledge Prompt Tuning for Multi-task Test-Time Adaptation
157. CVPR 2025Identifying and Mitigating Spurious Correlation in Multi-Task Learning
158. CVPR 2025IMFine: 3D Inpainting via Geometry-guided Multi-view Refinement
159. CVPR 2025Inst3D-LMM: Instance-Aware 3D Scene Understanding with Multi-modal Instruction Tuning
160. CVPR 2025Joint Scheduling of Causal Prompts and Tasks for Multi-Task Learning
161. CVPR 2025KMD: Koopman Multi-modality Decomposition for Generalized Brain Tumor Segmentation under Incomplete Modalities
162. CVPR 2025Large-scale Multi-view Tensor Clustering with Implicit Linear Kernels
163. CVPR 2025Learning from Synchronization: Self-Supervised Uncalibrated Multi-View Person Association in Challenging Scenes
164. CVPR 2025Learning to Detect Objects from Multi-Agent LiDAR Scans without Manual Labels
165. CVPR 2025Less is More: Efficient Model Merging with Binary Task Switch
166. CVPR 2025MangaNinja: Line Art Colorization with Precise Reference Following
167. CVPR 2025MIRE: Matched Implicit Neural Representations
168. CVPR 2025MLVU: Benchmarking Multi-task Long Video Understanding
169. CVPR 2025MMAR: Towards Lossless Multi-Modal Auto-Regressive Probabilistic Modeling
170. CVPR 2025MMRL: Multi-Modal Representation Learning for Vision-Language Models
171. CVPR 2025MMTL-UniAD: A Unified Framework for Multimodal and Multi-Task Learning in Assistive Driving Perception
172. CVPR 2025MOVIS: Enhancing Multi-Object Novel View Synthesis for Indoor Scenes
173. CVPR 2025Mr. DETR: Instructive Multi-Route Training for Detection Transformers
174. CVPR 2025Multi-Group Proportional Representations for Text-to-Image Models
175. CVPR 2025Multi-Modal Contrastive Masked Autoencoders: A Two-Stage Progressive Pre-training Approach for RGBD Datasets
176. CVPR 2025Multi-Modal Synergistic Implicit Image Enhancement for Efficient Optical Flow Estimation
177. CVPR 2025Multi-modal Topology-embedded Graph Learning for Spatially Resolved Genes Prediction from Pathology Images with Prior Gene Similarity Information
178. CVPR 2025Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representation
179. CVPR 2025MultiGO: Towards Multi-level Geometry Learning for Monocular 3D Textured Human Reconstruction
180. CVPR 2025MVPaint: Synchronized Multi-View Diffusion for Painting Anything 3D
181. CVPR 2025One Model for ALL: Low-Level Task Interaction Is a Key to Task-Agnostic Image Fusion
182. CVPR 2025PatchDEMUX: A Certifiably Robust Framework for Multi-label Classifiers Against Adversarial Patches
183. CVPR 2025PDFactor: Learning Tri-Perspective View Policy Diffusion Field for Multi-Task Robotic Manipulation
184. CVPR 2025ProHOC: Probabilistic Hierarchical Out-of-Distribution Classification via Multi-Depth Networks
185. CVPR 2025RANGE: Retrieval Augmented Neural Fields for Multi-Resolution Geo-Embeddings
186. CVPR 2025ROLL: Robust Noisy Pseudo-label Learning for Multi-View Clustering with Noisy Correspondence
187. CVPR 2025SINR: Sparsity Driven Compressed Implicit Neural Representations
188. CVPR 2025SIR-DIFF: Sparse Image Sets Restoration with Multi-View Diffusion Model
189. CVPR 2025SocialGesture: Delving into Multi-person Gesture Understanding
190. CVPR 2025SplatFlow: Multi-View Rectified Flow Model for 3D Gaussian Splatting Synthesis
191. CVPR 2025STiL: Semi-supervised Tabular-Image Learning for Comprehensive Task-Relevant Information Exploration in Multimodal Classification
192. CVPR 2025TADFormer: Task-Adaptive Dynamic TransFormer for Efficient Multi-Task Learning
193. CVPR 2025Task Singular Vectors: Reducing Task Interference in Model Merging
194. CVPR 2025Task-Agnostic Guided Feature Expansion for Class-Incremental Learning
195. CVPR 2025Test-Time Fine-Tuning of Image Compression Models for Multi-Task Adaptability
196. CVPR 2025Theory-Inspired Deep Multi-View Multi-Label Learning with Incomplete Views and Noisy Labels
197. CVPR 2025TokenHSI: Unified Synthesis of Physical Human-Scene Interactions through Task Tokenization
198. CVPR 2025Towards Consistent Multi-Task Learning: Unlocking the Potential of Task-Specific Parameters
199. CVPR 2025Towards Optimizing Large-Scale Multi-Graph Matching in Bioimaging
200. CVPR 2025Two by Two: Learning Multi-Task Pairwise Objects Assembly for Generalizable Robot Manipulation

FAQ

How many Multi-Task Learning papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

1,181 in total: 288 in 2023, 227 in 2024, 264 in 2025, 402 in 2026.

How many Multi-Task Learning papers are at NeurIPS 2026?

NeurIPS 2026 has 126 Multi-Task Learning papers, versus 101 the year before.

How many Multi-Task Learning papers are at ICLR 2026?

ICLR 2026 has 57 Multi-Task Learning papers, versus 40 the year before.

How many Multi-Task Learning papers are at ICML 2026?

ICML 2026 has 91 Multi-Task Learning papers, versus 47 the year before.

How many Multi-Task Learning papers are at CVPR 2026?

CVPR 2026 has 128 Multi-Task Learning papers, versus 76 the year before.

Which conference publishes the most Multi-Task Learning papers?

NeurIPS, with 399 papers from 2023 to 2026.