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NeuFlow: A runtime reconfigurable dataflow processor for vision

NeuFlow: A runtime reconfigurable dataflow processor for vis...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Clément Farabet Berin Martini Benoit Corda Polina Akselrod Eugenio Culurciello Yann LeCun Electrical Engineering Department Yale University New Heaven CT USA Courant Institute of Mathematical Sciences New York University New York NY USA

In this paper we present a scalable dataflow hardware architecture optimized for the computation of general-purpose vision algorithms - neuFlow - and a dataflow compiler - luaFlow - that transforms high-level flow-graph representations of these algorithms into machine code for neuFlow. This system was designed with the goal of providing real-time detection, categorization and localization of objects in complex scenes, while consuming 10 Watts when implemented on a Xilinx Virtex 6 FPGA platform, or about ten times less than a laptop computer, and producing speedups of up to 100 times in real-world applications. We present an application of the system on street scene analysis, segmenting 20 categories on 500 × 375 frames at 12 frames per second on our custom hardware neuFlow.

关键词： Tiles computer architecture Runtime Field programmable gate arrays Hardware Convolvers Feature extraction

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Probing Conceptual Understanding of Large Visual-Language Models

Probing Conceptual Understanding of Large Visual-Language Mo...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Madeline Schiappa Raiyaan Abdullah Shehreen Azad Jared Claypoole Michael Cogswell Ajay Divakaran Yogesh Rawat Center for Research in Computer Vision University of Central Florida SRI International

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In recent years large visual-language (V+L) models have achieved great success in various downstream tasks. However, it is not well studied whether these models have a conceptual grasp of the visual content. In this work we focus on conceptual understanding of these large V+L models. To facilitate this study, we propose novel benchmarking datasets for probing three different aspects of content understanding, 1) relations, 2) composition, and 3) context. Our probes are grounded in cognitive science and help determine if a V+L model can, for example, determine if snow garnished with a man is implausible, or if it can identify beach furniture by knowing it is located on a beach. We experimented with many recent state-of-the-art V+L models and observe that these models mostly fail to demonstrate a conceptual understanding. This study reveals several interesting insights such as that cross-attention helps learning conceptual understanding, and that CNNs are better with texture and patterns, while Transformers are better at color and shape. We further utilize some of these insights and investigate a simple finetuning technique that rewards the three conceptual understanding measures with promising initial results. The proposed benchmarks will drive the community to delve deeper into conceptual understanding and foster advancements in the capabilities of large V+L models. The code and dataset is available at: https://***/vlm-robustness

关键词： Training Visualization Shape Snow Color Benchmark testing Transformers

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Adaptive Step Size Window Matching for Detection

Adaptive Step Size Window Matching for Detection

引用

International conference on pattern recognition

作者： N. Mekuz K.G. Derpanis J.K. Tsotsos Department of Computer Science and Center for Vision Research (CVR) York University Toronto ONT Canada

ISBN: (纸本)0769525210

An often overlooked problem in matching lies in selecting an appropriate step size. The selection of the step size for real-time applications is critical both from the point of view of computational efficiency and detection performance. Current systems set the step size in an ad hoc manner. This paper describes an algorithm for selecting the step size based on a theoretical worst case analysis. We have implemented this adaptive step size method in an object detection algorithm. Experimental evaluation demonstrates the effectiveness of our proposed algorithm

关键词： Algorithm design and analysis Computational efficiency Object detection computer vision computer science Application software Stereo image processing Stereo vision Image reconstruction Optical devices

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SAM-CLIP: Merging vision Foundation Models towards Semantic and Spatial Understanding

SAM-CLIP: Merging Vision Foundation Models towards Semantic ...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Haoxiang Wang Pavan Kumar Anasosalu Vasu Fartash Faghri Raviteja Vemulapalli Mehrdad Farajtabar Sachin Mehta Mohammad Rastegari Oncel Tuzel Hadi Pouransari University of Illinois Urbana-Champaign Apple

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The landscape of publicly available vision foundation models (VFMs), such as CLIP and Segment Anything Model (SAM), is expanding rapidly. VFMs are endowed with distinct capabilities stemming from their pre-training objectives. For instance, CLIP excels in semantic understanding, while SAM specializes in spatial understanding for segmentation. In this work, we introduce a simple recipe to efficiently merge VFMs into a unified model that absorbs their expertise. Our method integrates techniques of multitask learning, continual learning, and distillation. Further, it demands significantly less computational cost compared to traditional multi-task training from scratch, and it only needs a small fraction of the pre-training datasets that were initially used to train individual models. By applying our method to SAM and CLIP, we obtain SAM-CLIP : a unified model that combines the capabilities of SAM and CLIP into a single vision transformer. Compared with deploying SAM and CLIP independently, our merged model, SAM-CLIP, reduces storage and compute costs for inference, making it well-suited for edge device applications. We show that SAM-CLIP not only retains the foundational strengths of SAM and CLIP, but also introduces synergistic functionalities, notably in zero-shot semantic segmentation, where SAM-CLIP establishes new state-of-the-art results on 5 benchmarks. It outperforms previous models that are specifically designed for this task by a large margin, including +6.8% and +5.9% mean IoU improvement on Pascal-VOC and COCO-Stuff datasets, respectively.

关键词： Training computer vision Visualization Computational modeling Semantic segmentation Semantics Merging

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Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero-shot Medical Image Segmentation

Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP f...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Sidra Aleem Fangyijie Wang Mayug Maniparambil Eric Arazo Julia Dietlmeier Kathleen Curran Noel E. O’Connor Suzanne Little ML-Labs Dublin City University ML-Labs University College Dublin Centre for Applied AI (CeADAR) University College Dublin Ireland Insight SFI Centre for Data Analytics Dublin City University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The Segment Anything Model (SAM) and CLIP are remarkable vision foundation models (VFMs). SAM, a prompt-driven segmentation model, excels in segmentation tasks across diverse domains, while CLIP is renowned for its zero-shot recognition capabilities. However, their unified potential has not yet been explored in medical image segmentation. To adapt SAM, to medical imaging, existing methods primarily rely on tuning strategies that require extensive data or prior prompts tailored to the specific task, making it particularly challenging when only a limited number of data samples are available. This work presents an in-depth exploration of integrating SAM and CLIP into a unified framework for medical image segmentation. Specifically, we propose a simple unified framework, SaLIP, for organ segmentation. Initially, SAM is used for part-based segmentation within the image, followed by CLIP to retrieve the mask corresponding to the region of interest (ROI) from the pool of SAM’s generated masks. Finally, SAM is prompted by the retrieved ROI to segment a specific organ. Thus, SaLIP is training/fine-tuning free and does not rely on domain expertise or labeled data for prompt engineering. Our method shows substantial enhancements in zero-shot segmentation, showcasing notable improvements in DICE scores across diverse segmentation tasks like brain (63.46%), lung (50.11%), and fetal head (30.82%), when compared to un-prompted SAM. Code and text prompts are available at SaLIP.

关键词： Image segmentation Adaptation models Filtering Inference mechanisms Lung Robustness pattern recognition

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Toward Robust Distance Metric Analysis for Similarity Estimation

Toward Robust Distance Metric Analysis for Similarity Estima...

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conference on computer vision and pattern recognition (CVPR)

作者： Jie Yu Qi Tian J. Amores N. Sebe Department of Computer Science University of Texas San Antonio TX USA IMEDIA Research Group INRIA Rocquencourt France Faculty of Science University of Amsterdam Netherlands

In this paper, we present a general guideline to establish the relation between a distribution model and its corresponding similarity estimation. A rich set of distance metrics, such as harmonic distance and geometric distance, is derived according to Maximum Likelihood theory. These metrics can provide a more accurate feature model than the conventional Euclidean distance (SSD) and Manhattan distance (SAD). Because the feature elements are from heterogeneous sources and may have different influence on similarity estimation, the assumption of single isotropic distribution model is often inappropriate. We propose a novel boosted distance metric that not only finds the best distance metric that fits the distribution of the underlying elements but also selects the most important feature elements with respect to similarity. We experiment with different distance metrics for similarity estimation and compute the accuracy of different methods in two applications: stereo matching and motion tracking in video sequences. The boosted distance metric is tested on fifteen benchmark data sets from the UCI repository and two image retrieval applications. In all the experiments, robust results are obtained based on the proposed methods.

关键词： Robustness Guidelines Maximum likelihood estimation Euclidean distance Motion estimation Tracking Video sequences Benchmark testing Information retrieval Image retrieval

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AMC-Loss: Angular Margin Contrastive Loss for Improved Explainability in Image Classification

AMC-Loss: Angular Margin Contrastive Loss for Improved Expla...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Hongjun Choi Anirudh Som Pavan Turaga Geometric Media Lab School of Arts Media and Engineering Arizona State University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Deep-learning architectures for classification problems involve the cross-entropy loss sometimes assisted with auxiliary loss functions like center loss, contrastive loss and triplet loss. These auxiliary loss functions facilitate better discrimination between the different classes of interest. However, recent studies hint at the fact that these loss functions do not take into account the intrinsic angular distribution exhibited by the low-level and high-level feature representations. This results in less compactness between samples from the same class and unclear boundary separations between data clusters of different classes. In this paper, we address this issue by proposing the use of geometric constraints, rooted in Riemannian geometry. Specifically, we propose Angular Margin Contrastive Loss (AMC-Loss), a new loss function to be used along with the traditional cross-entropy loss. The AMC-Loss employs the discriminative angular distance metric that is equivalent to geodesic distance on a hypersphere manifold such that it can serve a clear geometric interpretation. We demonstrate the effectiveness of AMC-Loss by providing quantitative and qualitative results. We find that although the proposed geometrically constrained loss-function improves quantitative results modestly, it has a qualitatively surprisingly beneficial effect on increasing the interpretability of deep-net decisions as seen by the visual explanations generated by techniques such as the Grad-CAM. Our code is available at https://***/hchoi71/AMC-Loss.

关键词： Training Task analysis Feature extraction Euclidean distance Airplanes computer vision Media

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Resolving occlusion in multiframe reconstruction of deformable surfaces

Resolving occlusion in multiframe reconstruction of deformab...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Appu Shaji Aydin Varol Pascal Fua Yashoteja Ankush Jain Sharat Chandran Computer Vision Laboratory EPF Lausanne Switzerland Indian Institute of Technology Bombay Indian Institute of Technology Bombay India

Occlusion is troublesome for almost all computer vision algorithms. To a certain extent, the difficulty is alleviated when multiple frames are given. On the other hand, when we consider the recovery of shapes of moving deformable objects, observed using a monocular camera, the problem appears difficult again. In this paper, we show a method that outperforms previous approaches to reconstruction when feature data is unavailable, perhaps due to occlusion. Our key intuition is that portions of the surface that are visible in some frame can be reliably reconstructed in that frame; further, the reliable portions can be stitched together to find even missing portions, much the way a human eye would hallucinate. Our techniques are based on optimization in Riemannian shape spaces, and is demonstrated on isometric surfaces without involving any kind of machine learning methods.

关键词： Shape Three dimensional displays Mathematical model Optimization Surface reconstruction Image reconstruction Deformable models

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Event-driven stereo vision for fall detection

Event-driven stereo vision for fall detection

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ahmed Nabil Belbachir Stephan Schraml Aneta Nowakowska New Sensor Technologies Safety & Security Department Austrian Institute of Technology GmbH Vienna Austria

This paper presents a system concept for efficient fall detection in real-time for elderly security in ambient assisted living applications. Event-driven sensors are biologically-inspired and autonomously reacting to scene dynamics and generating events upon relative light intensity change. Their wide dynamic range and high temporal resolution properties enable efficient activity monitoring in natural environment. Using a stereo pair of event-driven sensor chip, it is possible to represent the scene dynamics in a 3D volume at high temporal resolution. Therefore, the person's activity in a home environment can be efficiently recorded, with a low data volume and high temporal resolution that allows efficient incident detection, like person's falls. In this paper, a dataset with scenarios including 68 person's falls has been analyzed for real-time detection with event-driven stereo vision systems and the results are promising.

关键词： Stereo vision Digital signal processing Real time systems Voltage control Three dimensional displays Machine vision Cameras

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A Bimodal Face and Body Gesture Database for Automatic Analysis of Human Nonverbal Affective Behavior

A Bimodal Face and Body Gesture Database for Automatic Analy...

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International conference on pattern recognition

作者： H. Gunes M. Piccardi Computer Vision Research Group University of Technology Sydney NSW Australia

ISBN: (纸本)0769525210

To be able to develop and test robust affective multimodal systems, researchers need access to novel databases containing representative samples of human multi-modal expressive behavior. The creation of such databases requires a major effort in the definition of representative behaviors, the choice of expressive modalities, and the collection and labeling of large amount of data. At present, public databases only exist for single expressive modalities such as facial expression analysis. There also exist a number of gesture databases of static and dynamic hand postures and dynamic hand gestures. However, there is not a readily available database combining affective face and body information in a genuine bimodal manner. Accordingly, in this paper, we present a bimodal database recorded by two high-resolution cameras simultaneously for use in automatic analysis of human nonverbal affective behavior

关键词： Databases Data analysis Emotion recognition Face recognition computer vision pattern recognition Human computer interaction Face detection Australia System testing

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