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NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Quantization for vision Transformers

NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Qua...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Liu, Yijiang Yang, Huanrui Dong, Zhen Keutzer, Kurt Du, Li Zhang, Shanghang Nanjing Univ Nanjing Peoples R China Univ Calif Berkeley Berkeley CA USA Peking Univ Sch Comp Sci Natl Key Lab Multimedia Informat Proc Beijing Peoples R China

ISBN: (纸本)9798350301298

The complicated architecture and high training cost of vision transformers urge the exploration of post-training quantization. However, the heavy-tailed distribution of vision transformer activations hinders the effectiveness of previous post-training quantization methods, even with advanced quantizer designs. Instead of tuning the quantizer to better fit the complicated activation distribution, this paper proposes NoisyQuant, a quantizer-agnostic enhancement for the post-training activation quantization performance of vision transformers. We make a surprising theoretical discovery that for a given quantizer, adding a fixed Uniform noisy bias to the values being quantized can significantly reduce the quantization error under provable conditions. Building on the theoretical insight, NoisyQuant achieves the first success on actively altering the heavy-tailed activation distribution with additive noisy bias to fit a given quantizer. Extensive experiments show NoisyQuant largely improves the post-training quantization performance of vision transformer with minimal computation overhead. For instance, on linear uniform 6-bit activation quantization, NoisyQuant improves SOTA top-1 accuracy on ImageNet by up to 1.7%, 1.1% and 0.5% for ViT, DeiT, and Swin Transformer respectively, achieving on-par or even higher performance than previous nonlinear, mixed-precision quantization.

关键词： Efficient and scalable vision

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Visual recognition-Driven Image Restoration for Multiple Degradation with Intrinsic Semantics Recovery

Visual Recognition-Driven Image Restoration for Multiple Deg...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yang, Zizheng Huang, Jie Chang, Jiahao Zhou, Man Yu, Hu Zhang, Jinghao Zhao, Feng Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9798350301298

Deep image recognition models suffer a significant performance drop when applied to low-quality images since they are trained on high-quality images. Although many studies have investigated to solve the issue through image restoration or domain adaptation, the former focuses on visual quality rather than recognition quality, while the latter requires semantic annotations for task-specific training. In this paper, to address more practical scenarios, we propose a Visual recognition-Driven Image Restoration network for multiple degradation, dubbed VRD-IR, to recover high-quality images from various unknown corruption types from the perspective of visual recognition within one model. Concretely, we harmonize the semantic representations of diverse degraded images into a unified space in a dynamic manner, and then optimize them towards intrinsic semantics recovery. Moreover, a prior-ascribing optimization strategy is introduced to encourage VRD-IR to couple with various downstream recognition tasks better. Our VRD-IR is corruption- and recognition-agnostic, and can be inserted into various recognition tasks directly as an image enhancement module. Extensive experiments on multiple image distortions demonstrate that our VRD-IR surpasses existing image restoration methods and show superior performance on diverse high-level tasks, including classification, detection, and person re-identification.

关键词： Low-level vision

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CompletionFormer: Depth Completion with Convolutions and vision Transformers

CompletionFormer: Depth Completion with Convolutions and Vis...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Youmin Guo, Xianda Poggi, Matteo Zhu, Zheng Huang, Guan Mattoccia, Stefano Univ Bologna Bologna Italy PhiGent Robot Beijing Peoples R China

ISBN: (纸本)9798350301298

Given sparse depths and the corresponding RGB images, depth completion aims at spatially propagating the sparse measurements throughout the whole image to get a dense depth prediction. Despite the tremendous progress of deep-learning-based depth completion methods, the locality of the convolutional layer or graph model makes it hard for the network to model the long-range relationship between pixels. While recent fully Transformer-based architecture has reported encouraging results with the global receptive field, the performance and efficiency gaps to the well-developed CNN models still exist because of its deteriorative local feature details. This paper proposes a Joint Convolutional Attention and Transformer block (JCAT), which deeply couples the convolutional attention layer and vision Transformer into one block, as the basic unit to construct our depth completion model in a pyramidal structure. This hybrid architecture naturally benefits both the local connectivity of convolutions and the global context of the Transformer in one single model. As a result, our Completion-Former outperforms state-of-the-art CNNs-based methods on the outdoor KITTI Depth Completion benchmark and indoor NYUv2 dataset, achieving significantly higher efficiency (nearly 1/3 FLOPs) compared to pure Transformer-based methods. Code is available at https://***/youmi-zym/CompletionFormer.

关键词： 3D from multi-view and sensors

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POPE: 6-DoF Promptable Pose Estimation of Any Object, in Any Scene, with One Reference

POPE: 6-DoF Promptable Pose Estimation of Any Object, in Any...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Fan, Zhiwen Pan, Panwang Wang, Peihao Jiang, Yifan Xu, Dejia Wang, Zhangyang ByteDance Beijing Peoples R China Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9798350365474

Despite the significant progress in six degrees-of-freedom (6DoF) object pose estimation, existing methods have limited applicability in real-world scenarios involving embodied agents and downstream 3D vision tasks. These limitations mainly come from the necessity of 3D models, closed-category detection, and a large number of densely annotated support views. To mitigate this issue, we propose a general paradigm for object pose estimation, called Promptable Object Pose Estimation (POPE). The proposed approach POPE enables zero-shot 6DoF object pose estimation for any target object in any scene, while only a single reference is adopted as the support view. To achieve this, POPE lever-ages the power of the pre-trained large-scale 2D foundation model, employs a framework with hierarchical feature representation and 3D geometry principles. Moreover, it estimates the relative camera pose between object prompts and the target object in new views, enabling both two-view and multi-view 6DoF pose estimation tasks. Comprehensive experimental results demonstrate that POPE exhibits unrivaled robust performance in zero-shot settings, by achieving a significant reduction in the averaged Median Pose Error by 52.38% and 50.47% on the LINEMOD [22] and OnePose [54] datasets, respectively. We also conduct more challenging testings in causally captured images (see Figure 1), which further demonstrates the robustness of POPE.

关键词： Object recognition

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Causally-Aware Intraoperative Imputation for Overall Survival Time Prediction

Causally-Aware Intraoperative Imputation for Overall Surviva...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Li, Xiang Qian, Xuelin Liang, Litian Kong, Lingjie Dong, Qiaole Chen, Jiejun Liu, Dingxia Yao, Xiuzhong Fu, Yanwei Fudan Univ Sch Data Sci Shanghai Peoples R China Fudan Univ Zhongshan Hosp Dept Radiol Shanghai Peoples R China

ISBN: (纸本)9798350301298

Previous efforts in vision community are mostly made on learning good representations from visual patterns. Beyond this, this paper emphasizes the high-level ability of causal reasoning. We thus present a case study of solving the challenging task of Overall Survival (OS) time in primary liver cancers. Critically, the prediction of OS time at the early stage remains challenging, due to the unobvious image patterns of reflecting the OS. To this end, we propose a causal inference system by leveraging the intraoperative attributes and the correlation among them, as an intermediate supervision to bridge the gap between the images and the final OS. Particularly, we build a causal graph, and train the images to estimate the intraoperative attributes for final OS prediction. We present a novel Causally-aware Intraoperative Imputation Model (CAWIM) that can sequentially predict each attribute using its parent nodes in the estimated causal graph. To determine the causal directions, we propose a splitting-voting mechanism, which votes for the direction for each pair of adjacent nodes among multiple predictions obtained via causal discovery from heterogeneity. The practicability and effectiveness of our method are demonstrated by the promising results on liver cancer dataset of 361 patients with long-term observations.

关键词： cell microscopy Medical and biological vision

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Unbalanced Optimal Transport: A Unified Framework for Object Detection

Unbalanced Optimal Transport: A Unified Framework for Object...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： De Plaen, Henri De Plaen, Pierre-Francois Suykens, Johan A. K. Proesmans, Marc Tuytelaars, Tinne Van Gool, Luc Katholieke Univ Leuven ESAT STADIUS Leuven Belgium Katholieke Univ Leuven ESAT PSI Leuven Belgium Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (纸本)9798350301298

During training, supervised object detection tries to correctly match the predicted bounding boxes and associated classification scores to the ground truth. This is essential to determine which predictions are to be pushed towards which solutions, or to be discarded. Popular matching strategies include matching to the closest ground truth box (mostly used in combination with anchors), or matching via the Hungarian algorithm (mostly used in anchor-free methods). Each of these strategies comes with its own properties, underlying losses, and heuristics. We show how Unbalanced Optimal Transport unifies these different approaches and opens a whole continuum of methods in between. This allows for a finer selection of the desired properties. Experimentally, we show that training an object detection model with Unbalanced Optimal Transport is able to reach the state-of-the-art both in terms of Average Precision and Average Recall as well as to provide a faster initial convergence. The approach is well suited for GPU implementation, which proves to be an advantage for large-scale models.

关键词： computer vision theory

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Physics-Guided ISO-Dependent Sensor Noise Modeling for Extreme Low-Light Photography

Physics-Guided ISO-Dependent Sensor Noise Modeling for Extre...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Cao, Yue Liu, Ming Liu, Shuai Wang, Xiaotao Lei, Lei Zuo, Wangmeng Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Although deep neural networks have achieved astonishing performance in many vision tasks, existing learning-based methods are far inferior to the physical model-based solutions in extreme low-light sensor noise modeling. To tap the potential of learning-based sensor noise modeling, we investigate the noise formation in a typical imaging process and propose a novel physics-guided ISO-dependent sensor noise modeling approach. Specifically, we build a normalizing flow-based framework to represent the complex noise characteristics of CMOS camera sensors. Each component of the noise model is dedicated to a particular kind of noise under the guidance of physical models. Moreover, we take into consideration of the ISO dependence in the noise model, which is not completely considered by the existing learning-based methods. For training the proposed noise model, a new dataset is further collected with paired noisy-clean images, as well as flat-field and bias frames covering a wide range of ISO settings. Compared to existing methods, the proposed noise model is equipped with a flexible structure and accurate modeling capabilities, which is beneficial for better denoising performance in extreme low-light scenes. The dataset and code are available at https://***/happycaoyue/LLD.

关键词： Low-level vision

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FineRehab: A Multi-modality and Multi-task Dataset for Rehabilitation Analysis

FineRehab: A Multi-modality and Multi-task Dataset for Rehab...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Li, Jianwei Xue, Jun Cao, Rui Du, Xiaoxia Mo, Siyu Ran, Kehao Zhang, Zeyan Beijing Sport Univ Sch Sports Engn Beijing Peoples R China Rehabil Res Ctr Dept Neurorehabil Beijing Peoples R China

ISBN: (纸本)9798350365474

The assessment of rehabilitation exercises for neurological and musculoskeletal disorders are crucial for recovery. Traditionally, assessment methods have been subjective, with inherent uncertainty and limitations. This paper introduces a novel multi-modality dataset named FineRehab to prompt the study of rehabilitation movement analysis, leveraging advancements in sensor technology and artificial intelligence. FineRehab collects 16 actions from 50 participants, including both patients with musculoskeletal disorders and healthy individuals, and consists of 4,215 action samples captured by two Kinect cameras and 17 IMUs. To benchmark FineRehab, we present a reliable approach to analyze rehabilitation exercises, and make experiments to evaluate the comprehensive movement quality from across multi-dimensions. Comparative experimental analyses have verified the validity of our dataset in distinguishing between the movement of the normal population and patients, which can offer a quantifiable basis for personalized rehabilitation feedback. The introduction of FineRehab will encourage researchers to apply, develop and adapt various methods for rehabilitation exercise analysis.

关键词： Action quality assessment Action recognition Dataset Rehabilitation exercise

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Nighttime Smartphone Reflective Flare Removal Using Optical Center Symmetry Prior

Nighttime Smartphone Reflective Flare Removal Using Optical ...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Dai, Yuekun Luo, Yihang Zhou, Shangchen Li, Chongyi Loy, Chen Change Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (纸本)9798350301298

Reflective flare is a phenomenon that occurs when light reflects inside lenses, causing bright spots or a "ghosting effect" in photos, which can impact their quality. Eliminating reflective flare is highly desirable but challenging. Many existing methods rely on manually designed features to detect these bright spots, but they often fail to identify reflective flares created by various types of light and may even mistakenly remove the light sources in scenarios with multiple light sources. To address these challenges, we propose an optical center symmetry prior, which suggests that the reflective flare and light source are always symmetrical around the lens's optical center. This prior helps to locate the reflective flare's proposal region more accurately and can be applied to most smartphone cameras. Building on this prior, we create the first reflective flare removal dataset called BracketFlare, which contains diverse and realistic reflective flare patterns. We use continuous bracketing to capture the reflective flare pattern in the underexposed image and combine it with a normally exposed image to synthesize a pair of flare-corrupted and flare-free images. With the dataset, neural networks can be trained to remove the reflective flares effectively. Extensive experiments demonstrate the effectiveness of our method on both synthetic and real-world datasets.

关键词： Low-level vision

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Contrastive Grouping with Transformer for Referring Image Segmentation

Contrastive Grouping with Transformer for Referring Image Se...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Tang, Jiajin Zheng, Ge Shi, Cheng Yang, Sibei ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China

ISBN: (纸本)9798350301298

Referring image segmentation aims to segment the target referent in an image conditioning on a natural language expression. Existing one-stage methods employ per-pixel classification frameworks, which attempt straightforwardly to align vision and language at the pixel level, thus failing to capture critical object-level information. In this paper, we propose a mask classification framework, Contrastive Grouping with Transformer network (CGFormer), which explicitly captures object-level information via token-based querying and grouping strategy. Specifically, CGFormer first introduces learnable query tokens to represent objects and then alternately queries linguistic features and groups visual features into the query tokens for object-aware cross-modal reasoning. In addition, CGFormer achieves crosslevel interaction by jointly updating the query tokens and decoding masks in every two consecutive layers. Finally, CGFormer cooperates contrastive learning to the grouping strategy to identify the token and its mask corresponding to the referent. Experimental results demonstrate that CGFormer outperforms state-of-the-art methods in both segmentation and generalization settings consistently and significantly. Code is available at https://***/Toneyaya/CGFormer.

关键词： language reasoning vision

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