AHMAD: Adaptive Hybrid Multi-task Vision Learning with Assisted Distillation for Keypoint Detection

arXiv:2609.35490 · cs.CV · Submitted 2026-09-28 · Read on arXiv

cs.CV

Submitted: 2026-09-28

Updated: 2026-09-30

Terminology

• Object Detection — 30× in this paper
• Transformer — 23× in this paper · explained in 2 episodes
• Keypoint Detection — 19× in this paper
• Distillation — 18× in this paper · explained in 2 episodes
• Semantic Segmentation — 13× in this paper
• Knowledge Distillation — 11× in this paper · explained in 11 episodes
• Depth Estimation — 10× in this paper
• Instance Segmentation — 10× in this paper · explained in 3 episodes
• Loss Function — 10× in this paper · explained in 2 episodes
• Grounding — 7× in this paper · explained in 3 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Positional Embedding — 4× in this paper
• Accuracy — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Edge Distance Sampling — 2× in this paper
• Multimodal Learning — 2× in this paper · explained in 2 episodes
• Self-Supervised Learning — 2× in this paper · explained in 8 episodes
• Unified Framework — 2× in this paper · explained in 5 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Active Inference — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Homogenization — 1× in this paper · explained in 2 episodes
• Non-Maximum Suppression — 1× in this paper
• Pseudo-labels — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Root Mean Squared Error — 1× in this paper
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Visual Grounding — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• GPT-4 Technical Report
• Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Open-vocabulary Object Detection via Vision and Language Knowledge Distillation
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
• Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks
• DINOv2: Learning Robust Visual Features without Supervision
• A Simple and Generalist Approach for Panoptic Segmentation
• SAM 2: Segment Anything in Images and Videos
• DINO-X: A Unified Vision Model for Open-World Object Detection and Understanding
• Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection
• Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks
• EarthDial: Turning Multi-sensory Earth Observations to Interactive Dialogues

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