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Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for vision Decoding

Seeing Beyond the Brain: Conditional Diffusion Model with Sp...

引用

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

作者： Chen, Zijiao Qing, Jiaxin Xiang, Tiange Yue, Wan Lin Zhou, Juan Helen Natl Univ Singapore Singapore Singapore Chinese Univ Hong Kong Hong Kong Peoples R China Stanford Univ Stanford CA USA

ISBN: (纸本)9798350301298

Decoding visual stimuli from brain recordings aims to deepen our understanding of the human visual system and build a solid foundation for bridging human and computer vision through the Brain-computer Interface. However, reconstructing high-quality images with correct semantics from brain recordings is a challenging problem due to the complex underlying representations of brain signals and the scarcity of data annotations. In this work, we present MinD-Vis: Sparse Masked Brain Modeling with Double-Conditioned Latent Diffusion Model for Human vision Decoding. Firstly, we learn an effective self-supervised representation of fMRI data using mask modeling in a large latent space inspired by the sparse coding of information in the primary visual cortex. Then by augmenting a latent diffusion model with double-conditioning, we show that MinD-Vis can reconstruct highly plausible images with semantically matching details from brain recordings using very few paired annotations. We benchmarked our model qualitatively and quantitatively;the experimental results indicate that our method outperformed state-of-the-art in both semantic mapping (100-way semantic classification) and generation quality (FID) by 66% and 41% respectively. An exhaustive ablation study was also conducted to analyze our framework.

关键词： cell microscopy Medical and biological vision

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Constrained Evolutionary Diffusion Filter for Monocular Endoscope Tracking

Constrained Evolutionary Diffusion Filter for Monocular Endo...

引用

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

作者： Luo, Xiongbiao Xiamen Univ Dept Comp Sci & Technol Xiamen Peoples R China Xiamen Univ Natl Inst Data Sci Hlth & Med Xiamen 361102 Peoples R China

ISBN: (纸本)9798350301298

Stochastic filtering is widely used to deal with nonlinear optimization problems such as 3-D and visual tracking in various computer vision and augmented reality applications. Many current methods suffer from an imbalance between exploration and exploitation due to their particle degeneracy and impoverishment, resulting in local optimums. To address this imbalance, this work proposes a new constrained evolutionary diffusion filter for nonlinear optimization. Specifically, this filter develops spatial state constraints and adaptive history-recall differential evolution embedded evolutionary stochastic diffusion instead of sequential resampling to resolve the degeneracy and impoverishment problem. With application to monocular endoscope 3-D tracking, the experimental results show that the proposed filtering significantly improves the balance between exploration and exploitation and certainly works better than recent 3-D tracking methods. Particularly, the surgical tracking error was reduced from 4.03 mm to 2.59 mm.

关键词： cell microscopy Medical and biological vision

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DisWOT: Student Architecture Search for Distillation WithOut Training

DisWOT: Student Architecture Search for Distillation WithOut...

引用

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

作者： Dong, Peijie Li, Lujun Wei, Zimian Natl Univ Def Technol Changsha Peoples R China Chinese Acad Sci Beijing 100864 Peoples R China

ISBN: (纸本)9798350301298

Knowledge distillation (KD) is an effective training strategy to improve the lightweight student models under the guidance of cumbersome teachers. However, the large architecture difference across the teacher-student pairs limits the distillation gains. In contrast to previous adaptive distillation methods to reduce the teacher-student gap, we explore a novel training-free framework to search for the best student architectures for a given teacher. Our work first empirically show that the optimal model under vanilla training cannot be the winner in distillation. Secondly, we find that the similarity of feature semantics and sample relations between random-initialized teacher-student networks have good correlations with final distillation performances. Thus, we efficiently measure similarity matrixs conditioned on the semantic activation maps to select the optimal student via an evolutionary algorithm without any training. In this way, our student architecture search for Distillation WithOut Training (DisWOT) significantly improves the performance of the model in the distillation stage with at least 180x training acceleration. Additionally, we extend similarity metrics in DisWOT as new distillers and KD-based zero-proxies. Our experiments on CIFAR, ImageNet and NAS-Bench-201 demonstrate that our technique achieves state-of-the-art results on different search spaces. Our project and code are available at https://***/DisWOT-cvpr2023/.

关键词： Efficient and scalable vision

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Learning Debiased Representations via Conditional Attribute Interpolation

Learning Debiased Representations via Conditional Attribute ...

引用

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

作者： Zhang, Yi-Kai Wang, Qi-Wei Zhan, De-Chuan Ye, Han-Jia Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9798350301298

An image is usually described by more than one attribute like "shape" and "color". When a dataset is biased, i.e., most samples have attributes spuriously correlated with the target label, a Deep Neural Network (DNN) is prone to make predictions by the "unintended" attribute, especially if it is easier to learn. To improve the generalization ability when training on such a biased dataset, we propose a chi(2)-model to learn debiased representations. First, we design a.-shape pattern to match the training dynamics of a DNN and find Intermediate Attribute Samples (IASs) samples near the attribute decision boundaries, which indicate how the value of an attribute changes from one extreme to another. Then we rectify the representation with a chi-structured metric learning objective. Conditional interpolation among IASs eliminates the negative effect of peripheral attributes and facilitates retaining the intra-class compactness. Experiments show that chi(2)-model learns debiased representation effectively and achieves remarkable improvements on various datasets. Code is available at: https: //***/ZhangYikaii/chi-square

关键词： accountability ethics in vision fairness privacy Transparency

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Learning to Detect and Segment for Open Vocabulary Object Detection

Learning to Detect and Segment for Open Vocabulary Object De...

引用

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

作者： Wang, Tao Sichuan Univ Chengdu Peoples R China

ISBN: (纸本)9798350301298

Open vocabulary object detection has been greatly advanced by the recent development of vision-language pretrained model, which helps recognize novel objects with only semantic categories. The prior works mainly focus on knowledge transferring to the object proposal classification and employ class-agnostic box and mask prediction. In this work, we propose CondHead, a principled dynamic network design to better generalize the box regression and mask segmentation for open vocabulary setting. The core idea is to conditionally parameterize the network heads on semantic embedding and thus the model is guided with class-specific knowledge to better detect novel categories. Specifically, CondHead is composed of two streams of network heads, the dynamically aggregated head and dynamically generated head. The former is instantiated with a set of static heads that are conditionally aggregated, these heads are optimized as experts and are expected to learn sophisticated prediction. The latter is instantiated with dynamically generated parameters and encodes general class-specific information. With such a conditional design, the detection model is bridged by the semantic embedding to offer strongly generalizable class-wise box and mask prediction. Our method brings significant improvement to the state-of-the-art open vocabulary object detection methods with very minor overhead, e.g., it surpasses a RegionClip model by 3.0 detection AP on novel categories, with only 1.1% more computation.

关键词： detection recognition: Categorization retrieval

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Generalizable Implicit Neural Representations via Instance pattern Composers

Generalizable Implicit Neural Representations via Instance P...

引用

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

作者： Kim, Chiheon Lee, Doyup Kim, Saehoon Cho, Minsu Han, Wook-Shin Kakao Brain Seongnam South Korea POSTECH Pohang South Korea

ISBN: (纸本)9798350301298

Despite recent advances in implicit neural representations (INRs), it remains challenging for a coordinate-based multi-layer perceptron (MLP) of INRs to learn a common representation across data instances and generalize it for unseen instances. In this work, we introduce a simple yet effective framework for generalizable INRs that enables a coordinate-based MLP to represent complex data instances by modulating only a small set of weights in an early MLP layer as an instance pattern composer;the remaining MLP weights learn pattern composition rules for common representations across instances. Our generalizable INR framework is fully compatible with existing meta-learning and hypernetworks in learning to predict the modulated weight for unseen instances. Extensive experiments demonstrate that our method achieves high performance on a wide range of domains such as an audio, image, and 3D object, while the ablation study validates our weight modulation.

关键词： Self-supervised or unsupervised representation learning

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Improving Robustness of vision Transformers by Reducing Sensitivity to Patch Corruptions

Improving Robustness of Vision Transformers by Reducing Sens...

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

作者： Guo, Yong Stutz, David Schiele, Bernt Saarland Informat Campus Max Planck Inst Informat Saarbrucken Germany

ISBN: (纸本)9798350301298

Despite their success, vision transformers still remain vulnerable to image corruptions, such as noise or blur. Indeed, we find that the vulnerability mainly stems from the unstable self-attention mechanism, which is inherently built upon patch-based inputs and often becomes overly sensitive to the corruptions across patches. For example, when we only occlude a small number of patches with random noise (e.g., 10%), these patch corruptions would lead to severe accuracy drops and greatly distract intermediate attention layers. To address this, we propose a new training method that improves the robustness of transformers from a new perspective - reducing sensitivity to patch corruptions (RSPC). Specifically, we first identify and occlude/corrupt the most vulnerable patches and then explicitly reduce sensitivity to them by aligning the intermediate features between clean and corrupted examples. We highlight that the construction of patch corruptions is learned adversarially to the following feature alignment process, which is particularly effective and essentially different from existing methods. In experiments, our RSPC greatly improves the stability of attention layers and consistently yields better robustness on various benchmarks, including CIFAR-10/100-C, ImageNet-A, ImageNet-C, and ImageNet-P.

关键词： Deep learning architectures and techniques

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Segment Anything Model for Road Network Graph Extraction

Segment Anything Model for Road Network Graph Extraction

引用

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

作者： Hetang, Congrui Xue, Haoru Le, Cindy Yue, Tianwei Wang, Wenping He, Yihui Carnegie Mellon Univ Pittsburgh PA 15213 USA Columbia Univ New York NY USA

ISBN: (纸本)9798350365474

We propose SAM-Road, an adaptation of the Segment Anything Model (SAM) [27] for extracting large-scale, vectorized road network graphs from satellite imagery. To predict graph geometry, we formulate it as a dense semantic segmentation task, leveraging the inherent strengths of SAM. The image encoder of SAM is fine-tuned to produce probability masks for roads and intersections, from which the graph vertices are extracted via simple non-maximum suppression. To predict graph topology, we designed a lightweight transformer-based graph neural network, which leverages the SAM image embeddings to estimate the edge existence probabilities between vertices. Our approach directly predicts the graph vertices and edges for large regions without expensive and complex post-processing heuristics and is capable of building complete road network graphs spanning multiple square kilometers in a matter of seconds. With its simple, straightforward, and minimalist design, SAM-Road achieves comparable accuracy with the state-of-the-art method RNGDet++[57], while being 40 times faster on the City-scale dataset. We thus demonstrate the power of a foundational vision model when applied to a graph learning task. The code is available at https://***/htcr/sam_road.

关键词： autonomous driving computer vision foundation model graph graph neural network mapping navigation remote sensing segment anything semantic segmentation transformer

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Bias in Pruned vision Models: In-Depth Analysis and Countermeasures

Bias in Pruned Vision Models: In-Depth Analysis and Counterm...

引用

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

作者： Iofinova, Eugenia Peste, Alexandra Alistarh, Dan IST Austria Klosterneuburg Austria Neural Magic Somerville NJ USA

ISBN: (纸本)9798350301298

Pruning-that is, setting a significant subset of the parameters of a neural network to zero-is one of the most popular methods of model compression. Yet, several recent works have raised the issue that pruning may induce or exacerbate bias in the output of the compressed model. Despite existing evidence for this phenomenon, the relationship between neural network pruning and induced bias is not well-understood. In this work, we systematically investigate and characterize this phenomenon in Convolutional Neural Networks for computer vision. First, we show that it is in fact possible to obtain highly-sparse models, e.g. with less than 10% remaining weights, which do not decrease in accuracy nor substantially increase in bias when compared to dense models. At the same time, we also find that, at higher sparsities, pruned models exhibit higher uncertainty in their outputs, as well as increased correlations, which we directly link to increased bias. We propose easy-to-use criteria which, based only on the uncompressed model, establish whether bias will increase with pruning, and identify the samples most susceptible to biased predictions post-compression. Our code can be found at https://***/IST-DASLab/pruned-vision-model-bias.

关键词： accountability ethics in vision fairness privacy Transparency

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Scaling Graph Convolutions for Mobile vision

Scaling Graph Convolutions for Mobile Vision

引用

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

作者： Avery, William Munir, Mustafa Marculescu, Radu Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9798350365474

To compete with existing mobile architectures, MobileViG introduces Sparse vision Graph Attention (SVGA), a fast token-mixing operator based on the principles of GNNs. However, MobileViG scales poorly with model size, falling at most 1% behind models with similar latency. This paper introduces Mobile Graph Convolution (MGC), a new vision graph neural network (ViG) module that solves this scaling problem. Our proposed mobile vision architecture, Mobile-ViGv2, uses MGC to demonstrate the effectiveness of our approach. MGC improves on SVGA by increasing graph sparsity and introducing conditional positional encodings to the graph operation. Our smallest model, MobileViGv2-Ti, achieves a 77.7% top-1 accuracy on ImageNet-1K, 2% higher than MobileViG-Ti, with 0.9 ms inference latency on the iPhone 13 Mini NPU. Our largest model, MobileViGv2-B, achieves an 83.4% top-1 accuracy, 0.8% higher than MobileViG-B, with 2.7 ms inference latency. Besides image classification, we show that MobileViGv2 generalizes well to other tasks. For object detection and instance segmentation on MS COCO 2017, MobileViGv2-M outperforms MobileViG-M by 1.2 AP(box) and 0.7 AP(mask), and MobileViGv2-B outperforms MobileViG-B by 1.0 AP(box) and 0.7 APmask. For semantic segmentation on ADE20K, MobileViGv2-M achieves 42.9% mIoU and MobileViGv2-B achieves 44.3% mIoU (1).

关键词： computer vision Deep Learning Edge AI Graph Neural Networks

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