Summary of Explainability in Ai Based Applications: a Framework For Comparing Different Techniques, by Arne Grobrugge et al.
Explainability in AI Based Applications: A Framework for Comparing Different Techniquesby Arne Grobrugge, Nidhi Mishra,…
Explainability in AI Based Applications: A Framework for Comparing Different Techniquesby Arne Grobrugge, Nidhi Mishra,…
ED-ViT: Splitting Vision Transformer for Distributed Inference on Edge Devicesby Xiang Liu, Yijun Song, Xia…
Token Pruning using a Lightweight Background Aware Vision Transformerby Sudhakar Sah, Ravish Kumar, Honnesh Rohmetra,…
Tackling the Abstraction and Reasoning Corpus with Vision Transformers: the Importance of 2D Representation, Positions,…
Logic-Free Building Automation: Learning the Control of Room Facilities with Wall Switches and Ceiling Cameraby…
SC-Phi2: A Fine-tuned Small Language Model for StarCraft II Macromanagement Tasksby Muhammad Junaid Khan, Gita…
Ophthalmic Biomarker Detection with Parallel Prediction of Transformer and Convolutional Architectureby Md. Touhidul Islam, Md.…
Intrapartum Ultrasound Image Segmentation of Pubic Symphysis and Fetal Head Using Dual Student-Teacher Framework with…
LMLT: Low-to-high Multi-Level Vision Transformer for Image Super-Resolutionby Jeongsoo Kim, Jongho Nang, Junsuk ChoeFirst submitted…
HiRED: Attention-Guided Token Dropping for Efficient Inference of High-Resolution Vision-Language Modelsby Kazi Hasan Ibn Arif,…