:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Wang, Weitian, Shubham, Rai, De La Parra, Cecilia, Kumar, Akash
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition
Online Access:	https://arxiv.org/abs/2507.19131
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

HTTM: Head-wise Temporal Token Merging for Faster VGGT
by: Wang, Weitian, et al.
Published: (2025)

LampQ: Towards Accurate Layer-wise Mixed Precision Quantization for Vision Transformers
by: Kim, Minjun, et al.
Published: (2025)

Mix-QViT: Mixed-Precision Vision Transformer Quantization Driven by Layer Importance and Quantization Sensitivity
by: Ranjan, Navin, et al.
Published: (2025)

MPTQ-ViT: Mixed-Precision Post-Training Quantization for Vision Transformer
by: Tai, Yu-Shan, et al.
Published: (2024)

MetaMix: Meta-state Precision Searcher for Mixed-precision Activation Quantization
by: Kim, Han-Byul, et al.
Published: (2023)

TreeQ: Pushing the Quantization Boundary of Diffusion Transformer via Tree-Structured Mixed-Precision Search
by: Yang, Kaicheng, et al.
Published: (2025)

LRP-QViT: Mixed-Precision Vision Transformer Quantization via Layer-wise Relevance Propagation
by: Ranjan, Navin, et al.
Published: (2024)

Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model
by: Ranjan, Navin, et al.
Published: (2025)

Mixed Non-linear Quantization for Vision Transformers
by: Kim, Gihwan, et al.
Published: (2024)

ARQ: A Mixed-Precision Quantization Framework for Accurate and Certifiably Robust DNNs
by: Yang, Yuchen, et al.
Published: (2024)

MGRQ: Post-Training Quantization For Vision Transformer With Mixed Granularity Reconstruction
by: Yang, Lianwei, et al.
Published: (2024)

Adaptive Distribution-aware Quantization for Mixed-Precision Neural Networks
by: Jia, Shaohang, et al.
Published: (2025)

MPQ-Diff: Mixed Precision Quantization for Diffusion Models
by: Maruzzelli, Rocco Manz, et al.
Published: (2024)

MPQ-DM: Mixed Precision Quantization for Extremely Low Bit Diffusion Models
by: Feng, Weilun, et al.
Published: (2024)

MoQAE: Mixed-Precision Quantization for Long-Context LLM Inference via Mixture of Quantization-Aware Experts
by: Tao, Wei, et al.
Published: (2025)

Value-Driven Mixed-Precision Quantization for Patch-Based Inference on Microcontrollers
by: Tao, Wei, et al.
Published: (2024)

6Bit-Diffusion: Inference-Time Mixed-Precision Quantization for Video Diffusion Models
by: Su, Rundong, et al.
Published: (2026)

Data-Free Quantization via Mixed-Precision Compensation without Fine-Tuning
by: Chen, Jun, et al.
Published: (2023)

MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with Metric-Decoupled Mixed Precision Quantization
by: Zhao, Tianchen, et al.
Published: (2024)

Amortized-Precision Quantization for Early-Exit Vision Transformers
by: Fang, Rui, et al.
Published: (2026)

SemanticDialect: Semantic-Aware Mixed-Format Quantization for Video Diffusion Transformers
by: Jang, Wonsuk, et al.
Published: (2026)

DynaQuant: Dynamic Mixed-Precision Quantization for Learned Image Compression
by: Bao, Youneng, et al.
Published: (2025)

FLIQS: One-Shot Mixed-Precision Floating-Point and Integer Quantization Search
by: Dotzel, Jordan, et al.
Published: (2023)

FairQuant: Fairness-Aware Mixed-Precision Quantization for Medical Image Classification
by: Woergaard, Thomas, et al.
Published: (2026)

WindowQuant: Mixed-Precision KV Cache Quantization based on Window-Level Similarity for VLMs Inference Optimization
by: Tao, Wei, et al.
Published: (2026)

Memory-Efficient Vision Transformers: An Activation-Aware Mixed-Rank Compression Strategy
by: Azizi, Seyedarmin, et al.
Published: (2024)

ADFQ-ViT: Activation-Distribution-Friendly Post-Training Quantization for Vision Transformers
by: Jiang, Yanfeng, et al.
Published: (2024)

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation
by: Feng, Weilun, et al.
Published: (2025)

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers
by: Bae, Jongseong, et al.
Published: (2024)

Vote&Mix: Plug-and-Play Token Reduction for Efficient Vision Transformer
by: Peng, Shuai, et al.
Published: (2024)

Efficient and Effective Methods for Mixed Precision Neural Network Quantization for Faster, Energy-efficient Inference
by: Bablani, Deepika, et al.
Published: (2023)

Q-VLM: Post-training Quantization for Large Vision-Language Models
by: Wang, Changyuan, et al.
Published: (2024)

ViTmiX: Vision Transformer Explainability Augmented by Mixed Visualization Methods
by: Hogea, Eduard, et al.
Published: (2024)

DopQ-ViT: Towards Distribution-Friendly and Outlier-Aware Post-Training Quantization for Vision Transformers
by: Yang, Lianwei, et al.
Published: (2024)

TransformMix: Learning Transformation and Mixing Strategies from Data
by: Cheung, Tsz-Him, et al.
Published: (2024)

AdaLog: Post-Training Quantization for Vision Transformers with Adaptive Logarithm Quantizer
by: Wu, Zhuguanyu, et al.
Published: (2024)

The Inductive Bottleneck: Data-Driven Emergence of Representational Sparsity in Vision Transformers
by: Awadhiya, Kanishk
Published: (2025)

FIMA-Q: Post-Training Quantization for Vision Transformers by Fisher Information Matrix Approximation
by: Wu, Zhuguanyu, et al.
Published: (2025)

Can Cross-Layer Transcoders Replace Vision Transformer Activations? An Interpretable Perspective on Vision
by: Chatzoudis, Gerasimos, et al.
Published: (2026)

Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning
by: Ma, Lianbo, et al.
Published: (2025)