arXiv AI
Jul 9
MiLSD: A Micro Line-Segment Detector for Resource-Constrained Devices
arXiv:2607. 06600v1 Announce Type: cross Abstract: Line segment detection is a key building block in visual SLAM, 3D reconstruction, and industrial inspection.
I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation
arXiv:2509. 10334v2 Announce Type: replace-cross Abstract: Vision Transformers (ViTs) have recently achieved strong results in semantic segmentation, yet their deployment on resource-constrained devices remains limited due to their high memory footprint and computational cost.
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arXiv Computer Vision
Sep 17
Decoder-Agnostic Token Merging for Vision Transformers: A Systematic Study of G2TM
The paper studies Graph-Guided Token Merging (G2TM), a module that reduces token count in Vision Transformers. It evaluates G2TM across multiple segmentation frameworks and decoder types, finding that its performance gains are tied to the encoder rather than the decoder. The authors report consistent reductions in GFLOPs (22‑47%) and throughput improvements (up to 74%) on ADE20K, with optimal hyperparameters depending mainly on backbone pre‑training and target dataset.
arXiv Computer Vision
Sep 22
GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers
arXiv:2506.11784v2 Announce Type: replace Abstract: Vision Transformers (ViTs) are essential in computer vision but are computationally intensive, too. Model quantization, particularly to low bit-wid...
arXiv Machine Learning
Jul 9
Activation Quantization of Vision Encoders Needs Prefixing Registers
arXiv:2510. 04547v5 Announce Type: replace Abstract: Large pretrained vision encoders are central to multimodal intelligence, powering applications from on-device vision processing to vision-language models.
arXiv Computer Vision
Sep 16
Channel-Wise and Token-Aware Post-Training Quantization for Visual State Space Duality
arXiv:2609.16656v1 Announce Type: new Abstract: State space models (SSMs), particularly Mamba, have emerged as efficient alternatives to attention-based architectures and have been extended to vision...
arXiv Machine Learning
Sep 24
RAMP: Robust Adaptive Mixed-Precision Quantization for Edge CPU Vision Models
The paper introduces RAMP, a method for robust adaptive mixed‑precision quantization of vision models on edge CPUs. It evaluates 13 sensitivity metrics across four neural networks, finding that Jensen‑Shannon Divergence consistently identifies layers that can be safely quantized. Using K‑Means clustering on these metrics, RAMP achieves near‑lossless accuracy with an average 1.81× speed‑up, while cautioning against excluding low‑speed‑up layers that can fragment the computational graph.
Hugging Face Trending Papers
Sep 23
RAMP: Robust Adaptive Mixed-Precision Quantization for Edge CPU Vision Models
Deploying deep learning models on edge CPUs is bottlenecked by computational and memory constraints. Mixed-precision quantization promises to reduce inference latency while preserving accuracy. Howeve...
arXiv Computer Vision
Sep 18
MiX: Micro-Inverted-Scaling for End-to-End Low-Bit Vision-Language Model Acceleration
The paper introduces MiX, a micro‑inverted‑scaling format that replaces shared exponents with shared mantissas to avoid microscaling collapse in low‑bit vision‑language models. An adaptive dual‑format inference framework (MiX‑MX) maps this format to a custom accelerator, replacing multipliers with shifters. Experiments show 4.5‑bit MiX matches or outperforms NVFP4 accuracy while improving area efficiency by 25 % and delivering 2.3–4.5× speedup with 1.4–2.9× energy savings over the Focus accelerator.
arXiv Machine Learning
Jul 31
MixFrag: Fragility-Guided Mixed-Precision Post-Training Quantization for Vision Transformers
arXiv:2607. 28589v1 Announce Type: cross Abstract: Post-training quantization (PTQ) has emerged as an effective solution for deploying Vision Transformers (ViTs) on resource-constrained devices.
arXiv AI
Jul 2
LUMA: Benchmarking Segmentation via a Lightweight Universal Mask Adapter
arXiv:2607. 00687v1 Announce Type: cross Abstract: Comparing transformer backbones for image segmentation is confounded: each is paired with a different decoder, recipe, and pretraining, so reported differences rarely reflect the backbone itself.
Hugging Face Trending Papers
Sep 15
Channel-Wise and Token-Aware Post-Training Quantization for Visual State Space Duality
State space models (SSMs), particularly Mamba, have emerged as efficient alternatives to attention-based architectures and have been extended to vision through ViM, VMamba, and Visual State Space Dual...
Hugging Face Trending Papers
Sep 17
MiX: Micro-Inverted-Scaling for End-to-End Low-Bit Vision-Language Model Acceleration
MiX: Micro-Inverted-Scaling for End-to-End Low-Bit Vision-Language Model Acceleration proposes a new quantization format that inverts the traditional microscaling approach by assigning private exponents to each element and a shared mantissa. The adaptive dual-format MiX-MX inference framework maps this format to a custom accelerator, replacing multipliers with shifters. Evaluations show that 4.5-bit MiX matches or surpasses NVFP4 accuracy on multimodal benchmarks while improving area efficiency by 25% and delivering 2.3–4.5× speedup with 1.4–2.9× energy reduction compared to the Focus accelerator.