Segment Anything Model (SAM) exhibits remarkable zero-shot segmentation capability; however, its prohibitive computational costs make edge deployment challenging. Although post-training quantization (PTQ) offers a promising compression solution, existing methods yield unsatisfactory results when applied to SAM, owing to its specialized model components and promptable workflow: (i) The mask decoder's attention exhibits extreme activation outliers, and we find that aggressive clipping (even 100x), without smoothing or isolation, is effective in suppressing outliers while maintaining performance. Unfortunately, traditional distribution-based metrics (e.g., MSE) fail to provide such large-scale clipping. (ii) Existing quantization reconstruction methods neglect semantic interactivity of SAM, leading to misalignment between image feature and prompt intention. To address the above issues, we propose SAQ-SAM in this paper, which boosts PTQ for SAM from the perspective of semantic alignment. Specifically, we propose Perceptual-Consistency Clipping, which exploits attention focus overlap to promote aggressive clipping while preserving semantic capabilities. Furthermore, we propose Prompt-Aware Reconstruction, which incorporates image-prompt interactions by leveraging cross-attention in mask decoder, thus facilitating alignment in both distribution and semantic. Moreover, to ensure the interaction efficiency, we design a layer-skipping strategy for image tokens in encoder. Extensive experiments are conducted on various SAM sizes and tasks, including instance segmentation, oriented object detection, and semantic segmentation, and the results show that our method consistently exhibits advantages. For example, when quantizing SAM-B to 4-bit, SAQ-SAM achieves 11.7% higher mAP than the baseline in instance segmentation task.

Diffusion models have achieved great success in image generation tasks. However, the lengthy denoising process and complex neural networks hinder their low-latency applications in real-world scenarios. Quantization can effectively reduce model complexity, and post-training quantization (PTQ), which does not require fine-tuning, is highly promising for compressing and accelerating diffusion models. Unfortunately, we find that due to the highly dynamic activations, existing PTQ methods suffer from distribution mismatch issues at both calibration sample level and reconstruction output level, which makes the performance far from satisfactory. In this paper, we propose EDA-DM, a standardized PTQ method that efficiently addresses the above issues. Specifically, at the calibration sample level, we extract information from the density and diversity of latent space feature maps, which guides the selection of calibration samples to align with the overall sample distribution; and at the reconstruction output level, we theoretically analyze the reasons for previous reconstruction failures and, based on this insight, optimize block reconstruction using the Hessian loss of layers, aligning the outputs of quantized model and full-precision model at different network granularity. Extensive experiments demonstrate that EDA-DM significantly outperforms the existing PTQ methods across various models and datasets. Our method achieves a $1.83\times $ speedup and $4\times $ compression for the popular Stable-Diffusion on MS-COCO, with only a 0.05 loss in CLIP score. Code is available at http://github.com/BienLuky/EDA-DM.

Model quantization is an effective approach to reduce the complexity of neural networks, enabling them to be deployed on resource-constrained edge devices. Recently, data-free quantization has been widely investigated, since it does not access the original datasets and can address the widely-held data privacy and security concerns. Its idea is to generate fake data depending on the prior information in the full-precision (FP) model, and then fine-tune the quantized model with them under the supervision of the FP model. The quantization performance relies heavily on the validity of the generated data, however, existing methods suffer from two severe issues: mode collapse and (catastrophic) example forgetting, leading to non-trivial accuracy degradation. In this work, we proposeContrastiveLearningQuantization (CoLeQ), which achieves data diversity enhancement and old knowledge restoration via contrastive learning to address the above issues. Specifically, we introduce the MoCo paradigm that maintains a dynamic momentum queue of the encoded features to data-free quantization. The contrastive learning objective is used to improve data diversity by facilitating the separation of generated samples from the already generated ones in previous mini-batches, thus mitigating the mode collapse problem. Moreover, we design a tied-weight decoder to restore the previous samples from the encoded features in the queue without additional parameters and training, hence cost-effectively preventing the example forgetting problem. Extensive experiments are conducted to evaluate the effectiveness of CoLeQ, and the results demonstrate a consistent superiority compared to state-of-the-art methods.

The rapid advancement of visual generative models necessitates efficient and reliable evaluation methods. Arena platform, which gathers user votes on model comparisons, can rank models with human preferences. However, traditional Arena methods, while established, require an excessive number of comparisons for ranking to converge and are vulnerable to preference noise in voting, suggesting the need for better approaches tailored to contemporary evaluation challenges. In this paper, we introduce K-Sort Arena, an efficient and reliable platform based on a key insight: images and videos possess higher perceptual intuitiveness than texts, enabling rapid evaluation of multiple samples simultaneously. Consequently, K-Sort Arena employs K-wise comparisons, allowing K models to engage in free-forall competitions, which yield much richer information than pairwise comparisons. To enhance the robustness of the system, we leverage probabilistic modeling and Bayesian updating techniques. We propose an exploration-exploitation-based matchmaking strategy to facilitate more informative comparisons. In our experiments, K-Sort Arena exhibits 16.3× faster convergence compared to the widely used Elo algorithm. To further validate the superiority and obtain a comprehensive leaderboard, we collect human feedback via crowdsourced evaluations of numerous cutting-edge text-to-image and text-to-video models. Thanks to its high efficiency, K-Sort Arena can continuously incorporate emerging models and update the leaderboard with minimal votes. Our project has undergone several months of internal testing and is now available online.

Diffusion models have gradually gained prominence in the field of image synthesis, showcasing remarkable generative capabilities. Nevertheless, the slow inference and complex networks, resulting from redundancy at both temporal and structural levels, hinder their low-latency applications in real-world scenarios. Current acceleration methods for diffusion models focus separately on temporal and structural levels. However, independent optimization at each level to further push the acceleration limits results in significant performance degradation. On the other hand, integrating optimizations at both levels can compound the acceleration effects. Unfortunately, we find that the optimizations at these two levels are not entirely orthogonal. Performing separate optimizations and then simply integrating them results in unsatisfactory performance. To tackle this issue, we propose CacheQuant, a novel training-free paradigm that comprehensively accelerates diffusion models by jointly optimizing model caching and quantization techniques. Specifically, we employ a dynamic programming approach to determine the optimal cache schedule, in which the properties of caching and quantization are carefully considered to minimize errors. Additionally, we propose decoupled error correction to further mitigate the coupled and accumulated errors step by step. Experimental results show that CacheQuant achieves a 5.18× speedup and 4× compression for Stable Diffusion on MS-COCO, with only a 0.02 loss in CLIP score. Our code are open-sourced.

Vision-language models (VLMs) have shown remarkable success across various multi-modal tasks, yet large VLMs encounter significant efficiency challenges due to processing numerous visual tokens. A promising approach to accelerating large VLM inference is using partial information, such as attention maps from specific layers, to assess token importance and prune less essential tokens. However, our study reveals three key insights: (i) Partial attention information is insufficient for accurately identifying critical visual tokens, resulting in suboptimal performance, especially at low token retention ratios; (ii) Global attention information, such as the attention map aggregated across all layers, more effectively preserves essential tokens and maintains comparable performance under aggressive pruning. However, the attention maps from all layers require a full inference pass, which increases computational load and is therefore impractical in existing methods; and (iii) The global attention map aggregated from a small VLM closely resembles that of a large VLM, suggesting an efficient alternative. Based on these findings, we introduce a training-free method, Small VLM Guidance for accelerating Large VLMs (SGL). Specifically, we employ the attention map aggregated from a small VLM to guide visual token pruning in a large VLM. Additionally, an early exiting mechanism is developed to fully use the small VLM’s predictions, dynamically invoking the larger VLM only when necessary, yielding a superior trade-off between accuracy and computation. Extensive evaluations across 11 benchmarks demonstrate the effectiveness and generalizability of SGL, achieving up to 91% pruning ratio for visual tokens while retaining competitive performance. The code is publicly available at https://github.com/NUS-HPC-AI-Lab/SGL.

Class distribution mismatch (CDM) refers to the discrepancy between class distributions in training data and target tasks. Previous methods address this by designing classifiers to categorize classes known during training, while grouping unknown or new classes into an "other" category. However, they focus on semi-supervised scenarios and heavily rely on labeled data, limiting their applicability and performance. To address this, we propose Unsupervised Learning for Class Distribution Mismatch (UCDM), which constructs positive-negative pairs from unlabeled data for classifier training. Our approach randomly samples images and uses a diffusion model to add or erase semantic classes, synthesizing diverse training pairs. Additionally, we introduce a confidence-based labeling mechanism that iteratively assigns pseudo-labels to valuable real-world data and incorporates them into the training process. Extensive experiments on three datasets demonstrate UCDM’s superiority over previous semi-supervised methods. Specifically, with a 60\% mismatch proportion on Tiny-ImageNet dataset, our approach, without relying on labeled data, surpasses OpenMatch (with 40 labels per class) by 35.1%, 63.7%, and 72.5% in classifying known, unknown, and new classes.

Model quantization is a promising method for accelerating and compressing diffusion models. Nevertheless, since post-training quantization (PTQ) fails catastrophically at low-bit cases, quantization-aware training (QAT) is essential. Unfortunately, the wide range and time-varying activations in diffusion models sharply increase the complexity of quantization, making existing QAT methods inefficient. Equivalent scaling can effectively reduce activation range, but previous methods remain the overall quantization error unchanged. More critically, these methods significantly disrupt the original weight distribution, resulting in poor weight initialization and challenging convergence during QAT training. In this paper, we propose a novel QAT framework for diffusion models, called DilateQuant. Specifically, we propose Weight Dilation (WD) that maximally dilates the unsaturated in-channel weights to a constrained range through equivalent scaling. WD decreases the activation range while preserving the original weight range, which steadily reduces the quantization error and ensures model convergence. To further enhance accuracy and efficiency, we design a Temporal Parallel Quantizer (TPQ) to address the time-varying activations and introduce a Block-wise Knowledge Distillation (BKD) to reduce resource consumption in training. Extensive experiments demonstrate that DilateQuant significantly outperforms existing methods in terms of accuracy and efficiency.

Vision Transformers (ViTs) have emerged as the new fundamental architecture for most computer vision fields. However, the considerable memory and computation costs also hinder their application on resource-limited devices. Currently, binarization has demonstrated remarkable potential as a model compression technique in traditional Convolutional Neural Networks (CNNs), albeit with some accuracy loss. In this paper, we focus on binarization of ViTs, which is still under-studied and suffering a significant performance drop. We start with constructing a strong baseline of binary ViTs, integrating some of the best practices from binary CNNs, which forms the foundation of our exploration. Subsequently, we identify that the severe performance degradation of the baseline is mainly caused by the weight oscillation around the quantization boundary and the information distortion in the activation of ViTs. To address these challenges, we introduce BinaryViT, a precise full binarization framework tailored for Vision Transformers (ViTs), effectively pushing the binarization of ViTs to its limit. Specifically, we propose a novel gradient regularization scheme (GRS), which mitigates oscillations by fostering a smooth moving of latent weights to be away from the quantization boundary during the training process. Additionally, we have devised an Activation Shift Module (ASM) that dynamically adjusts the activation distribution prior to the sign function, thereby minimizing the information distortion stemming from the significant inter-channel variations. Extensive experiments on ImageNet dataset show that our BinaryViT consistently surpasses the strong baseline by 2.05% and improves the accuracy of fully binarized ViTs to a usable level. Furthermore, our method achieves impressive savings of$16.2\times $and$17.7\times $in model size and OPs compared to the full-precision DeiT-S.

With the growing concern of AI safety, there is a need to trust the computations done by machine learning (ML) models. Blockchain technology, known for recording data and running computations transparently and in a tamper-proof manner, can offer this trust. One significant challenge in deploying ML Classifiers on-chain is that while ML models are typically written in Python using an ML library such as Pytorch, smart contracts deployed on EVM-compatible blockchains are written in Solidity. We introduce Machine Learning to Smart Contract (ML2SC), a PyTorch to Solidity translator that can automatically translate multi-layer perceptron (MLP) models written in Pytorch to Solidity smart contract versions. ML2SC uses a fixedpoint math library to approximate floating-point computation. After deploying the generated smart contract, we can train our models off-chain using PyTorch and then further transfer the acquired weights and biases to the smart contract using a function call. Finally, the model inference can also be done with a function call providing the input. We mathematically model the gas costs associated with deploying, updating model parameters, and running inference on these models on-chain, showing that the gas costs increase linearly in various parameters associated with an MLP. We present empirical results matching our modeling. We also evaluate the classification accuracy showing that the outputs obtained by our transparent on-chain implementation are identical to the original off-chain implementation with Pytorch.

Post-training quantization (PTQ) efficiently compresses vision models, but unfortunately, it accompanies a certain degree of accuracy degradation. Reconstruction methods aim to enhance model performance by narrowing the gap between the quantized model and the full-precision model, often yielding promising results. However, efforts to significantly improve the performance of PTQ through reconstruction in the Vision Transformer (ViT) have shown limited efficacy. In this paper, we conduct a thorough analysis of the reasons for this limited effectiveness and propose MGRQ (Mixed Granularity Reconstruction Quantization) as a solution to address this issue. Unlike previous reconstruction schemes, MGRQ introduces a mixed granularity reconstruction approach. Specifically, MGRQ enhances the performance of PTQ by introducing Extra-Block Global Supervision and Intra-Block Local Supervision, building upon Optimized Block-wise Reconstruction. Extra-Block Global Supervision considers the relationship between block outputs and the model’s output, aiding block-wise reconstruction through global supervision. Meanwhile, Intra-Block Local Supervision reduces generalization errors by aligning the distribution of outputs at each layer within a block. Subsequently, MGRQ is further optimized for reconstruction through Mixed Granularity Loss Fusion. Extensive experiments conducted on various ViT models illustrate the effectiveness of MGRQ. Notably, MGRQ demonstrates robust performance in low-bit quantization, thereby enhancing the practicality of the quantized model.

Data-free quantization can potentially address data privacy and security concerns in model compression and thus has been widely investigated. Recently, patch similarity aware data-free quantization for vision transformers (PSAQ-ViT) designs a relative value metric, patch similarity, to generate data from pretrained vision transformers (ViTs), achieving the first attempt at data-free quantization for ViTs. In this article, we propose PSAQ-ViT V2, a more accurate and general data-free quantization framework for ViTs, built on top of PSAQ-ViT. More specifically, following the patch similarity metric in PSAQ-ViT, we introduce an adaptive teacher-student strategy, which facilitates the constant cyclic evolution of the generated samples and the quantized model (student) in a competitive and interactive fashion under the supervision of the full-precision (FP) model (teacher), thus significantly improving the accuracy of the quantized model. Moreover, without the auxiliary category guidance, we employ the task- and model-independent prior information, making the general-purpose scheme compatible with a broad range of vision tasks and models. Extensive experiments are conducted on various models on image classification, object detection, and semantic segmentation tasks, and PSAQ-ViT V2, with the naive quantization strategy and without access to real-world data, consistently achieves competitive results, showing potential as a powerful baseline on data-free quantization for ViTs. For instance, with Swin-S as the (backbone) model, 8-bit quantization reaches 82.13 top-1 accuracy on ImageNet, 50.9 box AP and 44.1 mask AP on COCO, and 47.2 mean Intersection over Union (mIoU) on ADE20K. We hope that accurate and general PSAQ-ViT V2 can serve as a potential and practice solution in real-world applications involving sensitive data. Code is released and merged at: https://github.com/zkkli/PSAQ-ViT.

Vision Transformers (ViTs) have achieved state-of-the-art performance on various computer vision applications. However, these models have considerable storage and computational overheads, making their deployment and efficient inference on edge devices challenging. Quantization is a promising approach to reducing model complexity, and the dyadic arithmetic pipeline can allow the quantized models to perform efficient integer-only inference. Unfortunately, dyadic arithmetic is based on the homogeneity condition in convolutional neural networks, which is not applicable to the non-linear components in ViTs, making integer-only inference of ViTs an open issue. In this paper, we propose I-ViT, an integer-only quantization scheme for ViTs, to enable ViTs to perform the entire computational graph of inference with integer arithmetic and bit-shifting, and without any floating-point arithmetic. In I-ViT, linear operations (e.g., MatMul and Dense) follow the integer-only pipeline with dyadic arithmetic, and non-linear operations (e.g., Softmax, GELU, and LayerNorm) are approximated by the proposed light-weight integer-only arithmetic methods. More specifically, I-ViT applies the proposed Shiftmax and ShiftGELU, which are designed to use integer bit-shifting to approximate the corresponding floating-point operations. We evaluate I-ViT on various benchmark models and the results show that integer-only INT8 quantization achieves comparable (or even slightly higher) accuracy to the full-precision (FP) baseline. Furthermore, we utilize TVM for practical hardware deployment on the GPU’s integer arithmetic units, achieving 3.72 ~ 4.11 inference speedup compared to the FP model. Code of both Pytorch and TVM is released at https://github.com/zkkli/I-ViT.

Post-training quantization (PTQ), which only requires a tiny dataset for calibration without end-to-end retraining, is a light and practical model compression technique. Recently, several PTQ schemes for vision transformers (ViTs) have been presented; unfortunately, they typically suffer from non-trivial accuracy degradation, especially in low-bit cases. In this paper, we propose RepQ-ViT, a novel PTQ framework for ViTs based on quantization scale reparameterization, to address the above issues. RepQ-ViT decouples the quantization and inference processes, where the former employs complex quantizers and the latter employs scale-reparameterized simplified quantizers. This ensures both accurate quantization and efficient inference, which distinguishes it from existing approaches that sacrifice quantization performance to meet the target hardware. More specifically, we focus on two components with extreme distributions: post-LayerNorm activations with severe inter-channel variation and post-Softmax activations with power-law features, and initially apply channel-wise quantization and log$\sqrt 2 $ quantization, respectively. Then, we reparameterize the scales to hardware-friendly layer-wise quantization and log2 quantization for inference, with only slight accuracy or computational costs. Extensive experiments are conducted on multiple vision tasks with different model variants, proving that RepQ-ViT, without hyperparameters and expensive reconstruction procedures, can outperform existing strong baselines and encouragingly improve the accuracy of 4-bit PTQ of ViTs to a usable level. Code is available at https://github.com/zkkli/RepQ-ViT.

As emerging hardware begins to support mixed bit-width arithmetic computation, mixed-precision quantization is widely used to reduce the complexity of neural networks. However, Vision Transformers (ViT ViTs) require complex self-attention computation to guarantee the learning of powerful feature representations, which makes mixed-precision quantization of ViTs still challenging. In this paper, we propose a novel patch-wise mixed-precision quantization (PMQ) for efficient inference of ViT$s$. Specifically, we design a lightweight global metric, which is faster than existing methods, to measure the sensitivity of each component in ViT$s$to quantization errors. Moreover, we also introduce a pareto frontier approach to automatically allocate the optimal bit-precision according to the sensitivity. To further reduce the computational complexity of self-attention in inference stage, we propose a patch-wise module to reallocate bit-width of patches in each layer. Extensive experiments on the ImageNet dataset shows that our method greatly reduces the search cost and facilitates the application of mixed-precision quantization to ViTs.

Abstract Exploiting multi‐scale features is one of the most effective methods to recognize objects of different scales in object detection. Since image pyramid is time‐consuming, Feature Pyramid Network (FPN) becomes the most popular component used for obtaining pyramidal features. Despite its effectiveness, there still exist some intrinsic defects. In this work, it is attributed to insufficient information flow and a Deformable Cross‐scale Interaction Feature Pyramid Network (DCIFPN) is proposed, which aims to promote the information transfer process with content‐aware sampling and dynamic aggregation weights. More specifically, Deformable Semantic Enhancement Module (DSEM) is designed that can construct accurate information flow with dynamic aggregation weights. In addition, Deformable Spatial Refinement Module (DSRM) is proposed to enhance high‐level features with low‐level location details. When DCIFPN is deployed on RetinaNet and FCOS with ResNet‐50, the performance is improved by 1.6 AP and 1.1 AP, respectively, on the challenging MS COCO benchmark. Apart from one‐stage detectors, DCIFPN is also applicable to two‐stage methods such as Faster R‐CNN and Mask R‐CNN. Further experiments on Pascal VOC and CrowdHuman datasets can verify the effectiveness and generalization of the method.

Many indoor spaces have constantly changing layouts and may not be mapped by an autonomous vehicle, yet maps such as floor plans or evacuation maps of these places are common. We propose a method for an autonomous robot to localize itself on such maps with inconsistent scale using Stochastic Gradient Descent (SGD) with scan matching using a 2D LiDAR. We also introduce a new scale state in 2D localization to manage the possible inconsistent scale of the input map. Experiments are conducted in an indoor corridor using three different input maps - a point cloud, a floor plan, and a hand-drawn map. The SGD localization algorithm is bench-marked to Adaptive Monte Carlo Localization (AMCL). In a point cloud mapped environment, our algorithm achieves 0.264m and 5.26° average position and heading error respectively. On the hand-drawn map, our SGD localization algorithm remains robust while AMCL fails. The role of the scale state in our SGD localization algorithm is demonstrated in poorly scaled maps.

Simultaneous Localization and Mapping (SLAM) is difficult to deploy in the embedded systems due to its high computation cost and stable input requirements. Building on excellent algorithms of recent years, we present Em-SLAM, a monocular SLAM method which is fast and robust in the embedded system. We present Em-SLAM in three stages comprising initial pose estimation, iterative pose optimization and correspondences, and mapping with nearest frame queue. During the first stage, we perform stable initial pose estimation based on the matched ORB features extracted around the selected key points. Regarding initial pose and corresponding key points as input, the second stage of Em-SLAM iteratively optimizes these inputs values by tracking key points in the new frames. At the last stage, we firstly determine keyframes with the help of the proposed nearest frame queue and then design a greedy search algorithm to find matched ORB features between keyframes, which are adopted for compact and robust map reconstruction. Due to the special designs for the embedded systems, Em-SLAM demonstrates a high accurate and fast performance on the embedded system for all SLAM tasks: tracking, mapping and loop closing. We evaluate Em-SLAM on he most popular datasets by comparing with one latest SLAM method.