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Shared Interest…Sometimes: Understanding the Alignment between Human Perception, vision Architectures, and Saliency Map Techniques

Shared Interest…Sometimes: Understanding the Alignment betw...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Katelyn Morrison Ankita Mehra Adam Perer

Empirical studies have shown that attention-based architectures outperform traditional convolutional neural networks (CNN) in terms of accuracy and robustness. As a result, attention-based architectures are increasingly used in high-stakes domains such as radiology and wildlife conservation to aid in decision-making. However, understanding how attention-based architectures compare to CNNs regarding alignment with human perception is still under-explored. Previous studies exploring how vision architectures align with human perception evaluate a single architecture with multiple explainability techniques or multiple architectures with a single explainability technique. Through an empirical analysis, we investigate how two attention-based architectures and two CNNs for two saliency map techniques align with the ground truth for human perception on 100 images from an interpretability benchmark dataset. Using the Shared Interest metrics, we found that CNNs align more with human perception when using the XRAI saliency map technique. However, we found the opposite for Grad-CAM. We discuss the implications of our analysis for human-centered explainable AI and introduce directions for future work.

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CLIP-Guided vision-Language Pre-training for Question Answering in 3D Scenes

CLIP-Guided Vision-Language Pre-training for Question Answer...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Maria Parelli Alexandros Delitzas Nikolas Hars Georgios Vlassis Sotirios Anagnostidis Gregor Bachmann Thomas Hofmann ETH Zurich Switzerland

Training models to apply linguistic knowledge and visual concepts from 2D images to 3D world understanding is a promising direction that researchers have only recently started to explore. In this work, we design a novel 3D pre-training vision-Language method that helps a model learn semantically meaningful and transferable 3D scene point cloud representations. We inject the representational power of the popular CLIP model into our 3D encoder by aligning the encoded 3D scene features with the corresponding 2D image and text embeddings produced by CLIP. To assess our model’s 3D world reasoning capability, we evaluate it on the downstream task of 3D Visual Question Answering. Experimental quantitative and qualitative results show that our pre-training method outperforms state-of-the-art works in this task and leads to an interpretable representation of 3D scene features.

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Interactiveness Field in Human-Object Interactions

Interactiveness Field in Human-Object Interactions

引用

2022 ieee/CVF conference on computer vision and pattern recognition, CVPR 2022

作者： Liu, Xinpeng Li, Yong-Lu Wu, Xiaoqian Tai, Yu-Wing Lu, Cewu Tang, Chi-Keung Shanghai Jiao Tong University China HKUST Hong Kong Kuaishou Technology China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Human-Object Interaction (HOI) detection plays a core role in activity understanding. Though recent two/one-stage methods have achieved impressive results, as an essential step, discovering interactive human-object pairs remains challenging. Both one/two-stage methods fail to effectively extract interactive pairs instead of generating redundant negative pairs. In this work, we introduce a previously overlooked interactiveness bimodal prior: given an object in an image, after pairing it with the humans, the generated pairs are either mostly non-interactive, or mostly interactive, with the former more frequent than the latter. Based on this interactiveness bimodal prior we propose the 'interactiveness field'. To make the learned field compatible with real HOI image considerations, we propose new energy constraints based on the cardinality and difference in the inherent 'interactiveness field' underlying interactive versus non-interactive pairs. Consequently, our method can detect more precise pairs and thus significantly boost HOI detection performance, which is validated on widely-used benchmarks where we achieve decent improvements over state-of-the-arts. Our code is available at https://***-Field. © 2022 ieee.

关键词： computer vision Codes Benchmark testing pattern recognition

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ViTA: An Efficient Video-to-Text Algorithm using VLM for RAG-based Video Analysis System

ViTA: An Efficient Video-to-Text Algorithm using VLM for RAG...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Md Adnan Arefeen Biplob Debnath Md Yusuf Sarwar Uddin Srimat Chakradhar NEC Laboratories America University of Missouri-Kansas City

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Retrieval-augmented generation (RAG) is used in natural language processing (NLP) to provide query-relevant information in enterprise documents to large language models (LLMs). Such enterprise context enables the LLMs to generate more informed and accurate responses. When enterprise data is primarily videos, AI models like vision language models (VLMs) are necessary to convert information in videos into text. While essential, this conversion is a bottleneck, especially for large corpus of videos. It delays the timely use of enterprise videos to generate useful *** propose ViTA, a novel method that leverages two unique characteristics of VLMs to expedite the conversion process. As VLMs output more text tokens, they incur higher latency. In addition, large (heavyweight) VLMs can extract intricate details from images and videos, but they incur much higher latency per output token when compared to smaller (lightweight) VLMs that may miss details. To expedite conversion, ViTA first employs a lightweight VLM to quickly understand the gist or overview of an image or a video clip, and directs a heavyweight VLM (through prompt engineering) to extract additional details by using only a few (preset number of) output tokens. Our experimental results show that ViTA expedites the conversion time by as much as 43%, without compromising the accuracy of responses when compared to a baseline system that only uses a heavyweight VLM.

关键词： computer vision Accuracy Large language models conferences Natural language processing Data models pattern recognition

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Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Report

Fast and Accurate Quantized Camera Scene Detection on Smartp...

引用

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

作者： Ignatov, Andrey Malivenko, Grigory Timofte, Radu Chen, Sheng Xia, Xin Liu, Zhaoyan Zhang, Yuwei Zhu, Feng Li, Jiashi Xiao, Xuefeng Tian, Yuan Wu, Xinglong Kyrkou, Christos Chen, Yixin Zhang, Zexin Peng, Yunbo Lin, Yue Dutta, Saikat Das, Sourya Dipta Shah, Nisarg A. Kumar, Himanshu Ge, Chao Wu, Pei-Lin Du, Jin-Hua Batutin, Andrew Federico, Juan Pablo Lyda, Konrad Khojoyan, Levon Thanki, Abhishek Paul, Sayak Siddiqui, Shahid Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland AI Witchlabs Lausanne Switzerland ByteDance Inc Beijing Peoples R China Univ Cyprus KIOS Res & Innovat Ctr Excellence Nicosia Cyprus Netease Games AI Lab Beijing Peoples R China Indian Inst Technol Madras Chennai Tamil Nadu India Jadavpur Univ Kolkata India Indian Inst Technol Jodhpur Karwar India Chinese Acad Sci Inst Automat Nanjing Artificial Intelligence Chip Res Beijing Peoples R China DataArt Inc New York NY USA PyImageSearch Mumbai Maharashtra India Univ Cyprus KIOS Ctr Excellence Nicosia Cyprus

ISBN: (纸本)9781665448994

Camera scene detection is among the most popular computer vision problem on smartphones. While many custom solutions were developed for this task by phone vendors, none of the designed models were available publicly up until now. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop quantized deep learning-based camera scene classification solutions that can demonstrate a real-time performance on smartphones and IoT platforms. For this, the participants were provided with a large-scale CamSDD dataset consisting of more than 11K images belonging to the 30 most important scene categories. The runtime of all models was evaluated on the popular Apple Bionic A11 platform that can be found in many iOS devices. The proposed solutions are fully compatible with all major mobile AI accelerators and can demonstrate more than 100-200 FPS on the majority of recent smartphone platforms while achieving a top-3 accuracy of more than 98%. A detailed description of all models developed in the challenge is provided in this paper.

关键词： computer vision Runtime Image analysis Computational modeling conferences Cameras Real-time systems

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Dual Attention Poser: Dual Path Body Tracking Based on Attention

Dual Attention Poser: Dual Path Body Tracking Based on Atten...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Xinhan Di Xiaokun Dai Xinkang Zhang Xinrong Chen Deepearthgo Academy for Engineering&Technology Fudan Universiry Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Fudan University

Currently, mixed reality head-mounted displays tracking the full body of users is an important human-computer interaction mode through the pose of the head and the hands. Unfortunately, users’ virtual representation and experience is limited due to high reconstruction error when simple transformer network architecture is applied. In this paper, we present a novel model, named Dual Attention Poser, which can learn the whole body reconstruction at a high accuracy. The proposed model consists of three key modules. Among them, dual-path attention encoder is designed to extract feature of the sparse signals. Cross attention mixer module enable the fusion of representation in the double path. Attention-gated-mlp decoder is applied to decode the hidden feature from the sparse input through attention gate. Test results on the AMASS dataset show that Dual Attention Poser can reduce the error by up to 18.2% in comparison with the state-of-the-art results.

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nnMobileNet: Rethinking CNN for Retinopathy Research

nnMobileNet: Rethinking CNN for Retinopathy Research

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Wenhui Zhu Peijie Qiu Xiwen Chen Xin Li Natasha Lepore Oana M. Dumitrascu Yalin Wang School of Computing and Augmented Intelligence Arizona State University McKeley School of Engineering Washington University in St. Louis School of Computing Clemson University Department of Radiology Children’s Hospital Los Angeles CIBORG Lab Department of Neurology Mayo Clinic

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Over the past few decades, convolutional neural networks (CNNs) have been at the forefront of the detection and tracking of various retinal diseases (RD). Despite their success, the emergence of vision transformers (ViT) in the 2020s has shifted the trajectory of RD model development. The leading-edge performance of ViT-based models in RD can be largely credited to their scalability—their ability to improve as more parameters are added. As a result, ViT-based models tend to outshine traditional CNNs in RD applications, albeit at the cost of increased data and computational demands. ViTs also differ from CNNs in their approach to processing images, working with patches rather than local regions, which can complicate the precise localization of small, variably presented lesions in RD. In our study, we revisited and updated the architecture of a CNN model, specifically MobileNet, to enhance its utility in RD diagnostics. We found that an optimized MobileNet, through selective modifications, can surpass ViT-based models in various RD benchmarks, including diabetic retinopathy grading, detection of multiple fundus diseases, and classification of diabetic macular edema. The code is available at https://***/Retinal-Research/NN-MOBILENET

关键词： Location awareness computer vision Computational modeling Transformers Retina Trajectory pattern recognition

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Towards Learning Image Similarity from General Triplet Labels

Towards Learning Image Similarity from General Triplet Label...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Radu Dondera Greenfield Vision

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Metric learning for images has so far focused overwhelmingly on a class-based definition of similarity: two images are similar if they belong to the same class and dissimilar otherwise. Impressive results were achieved on datasets for fine grained categorization, but performance is nearing saturation. Recent work in neuroscience and psychology produced datasets with other types of similarity labels, e.g. the outlier in a group of three, but traditional metric learning methods are ill-suited to such data because of low density of labels. To overcome this difficulty, we propose a novel approach in the teacher-student learning paradigm. Multiple teacher models learn to embed images based only on relations with other images, and then a student model learns to embed images based on both content and dense relations provided by the teachers. We show significant improvement over existing triplet based metric learning methods, both in result quality and in training efficiency. Additionally, through experiments on class based datasets, we show the generality of approaching metric learning via knowledge transfer. Code is available at ***/greenfieldvision/tdml.

关键词： Measurement Training Learning systems Visualization Neuroscience Psychology Focusing

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Learning Tracking Representations from Single Point Annotations

Learning Tracking Representations from Single Point Annotati...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Qiangqiang Wu Antoni B. Chan Department of Computer Science City University of Hong Kong

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Existing deep trackers are typically trained with large-scale video frames with annotated bounding boxes. However, these bounding boxes are expensive and time-consuming to annotate, in particular for large scale datasets. In this paper, we propose to learn tracking representations from single point annotations (i.e., 4.5 × faster to annotate than the traditional bounding box) in a weakly supervised manner. Specifically, we propose a soft contrastive learning (SoCL) framework that incorporates target objectness prior into end-to-end contrastive learning. Our SoCL consists of adaptive positive and negative sample generation, which is memory-efficient and effective for learning tracking representations. We apply the learned representation of SoCL to visual tracking and show that our method can 1) achieve better performance than the fully supervised baseline trained with box annotations under the same annotation time cost; 2) achieve comparable performance of the fully supervised baseline by using the same number of training frames and meanwhile reducing annotation time cost by 78% and total fees by 85%; 3) be robust to annotation noise.

关键词： Training Visualization Target tracking Costs Correlation Annotations Noise

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Large-Scale Bidirectional Training for Zero-Shot Image Captioning

Large-Scale Bidirectional Training for Zero-Shot Image Capti...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Taehoon Kim Mark Marsden Pyunghwan Ahn Sangyun Kim Sihaeng Lee Alessandra Sala Seung Hwan Kim LG AI Research Shutterstock

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

When trained on large-scale datasets, image captioning models can understand the content of images from a general domain but often fail to generate accurate, detailed captions. To improve performance, pretraining-and-finetuning has been a key strategy for image captioning. However, we find that large-scale bidirectional training between image and text enables zero-shot image captioning. In this paper, we introduce Bidirectional Image Text Training in largER Scale, BITTERS, an efficient training and inference framework for zero-shot image captioning. We also propose a new evaluation benchmark which comprises of high quality datasets and an extensive set of metrics to properly evaluate zero-shot captioning accuracy and societal bias. We additionally provide an efficient finetuning approach for keyword extraction. We show that careful selection of large-scale training set and model architecture is the key to achieving zero-shot image captioning.

关键词： Training Measurement Accuracy conferences computer architecture Feature extraction Data models

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