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Muhammad Haris Khan

Topic trends: 32,543 papers · similarity ≥ 0.4 · year ≥ 2024 · Data sourced from Semantic Scholar

34,598 papers | Abstracts: 31,650 (91.5%) | Citations: 34,598 (100.0%) | arXiv: 26,074 (75.4%)

Built: Feb 14, 2026, 11:22 PM AMS

23

papers

281

total citations

papers (23)

AnimalWeb: A Large-Scale Hierarchical Dataset of Annotated Animal Faces

MSI: Maximize Support-Set Information for Few-Shot Segmentation

Towards Improving Calibration in Object Detection Under Domain Shift

NEURIPS 2022arXiv

Improving Single Domain-Generalized Object Detection: A Focus on Diversification and Alignment

Multiclass Confidence and Localization Calibration for Object Detection

Bridging Precision and Confidence: A Train-Time Loss for Calibrating Object Detection

Video Instance Segmentation via Multi-Scale Spatio-Temporal Split Attention Transformer

HM: Hybrid Masking for Few-Shot Segmentation

Why Not Use Your Textbook? Knowledge-Enhanced Procedure Planning of Instructional Videos

Cal-DETR: Calibrated Detection Transformer

NEURIPS 2023arXiv

O-TPT: Orthogonality Constraints for Calibrating Test-time Prompt Tuning in Vision-Language Models

Towards Generalizing to Unseen Domains with Few Labels

SynFER: Towards Boosting Facial Expression Recognition with Synthetic Data

OSLoPrompt: Bridging Low-Supervision Challenges and Open-Set Domain Generalization in CLIP

Discrete Cycle-Consistency Based Unsupervised Deep Graph Matching

Pose-Guided Self-Training with Two-Stage Clustering for Unsupervised Landmark Discovery

Hyperbolic Uncertainty-Aware Few-Shot Incremental Point Cloud Segmentation

SegMASt3R: Geometry Grounded Segment Matching

NEURIPS 2025arXiv

Dual-Stage Value-Guided Inference with Margin-Based Reward Adjustment for Fast and Faithful VLM Captioning

NEURIPS 2025arXiv

Leveraging 2D Priors and SDF Guidance for Urban Scene Rendering

Unsupervised Discovery of Facial Landmarks and Head Pose

SSAL: Synergizing between Self-Training and Adversarial Learning for Domain Adaptive Object Detection

MSAmba: Exploring Multimodal Sentiment Analysis with State Space Models