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T2VBench: Benchmarking Temporal Dynamics for Text-to-Video Generation

T2VBench: Benchmarking Temporal Dynamics for Text-to-Video G...

引用

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

作者： Pengliang Ji Chuyang Xiao Huilin Tai Mingxiao Huo Carnegie Mellon University ShanghaiTech University McGill University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

While text-to-video (T2V) generative models produce exceptionally realistic videos, they lack a comprehensive evaluation across the temporal dimension, with a limited focus on basic dynamics including camera transitions, movement, and event sequences. In this work, we introduce T2VBench, a comprehensive T2V evaluation benchmark enriched with temporal dynamics lexicons derived from curated temporal words on Wikipedia. T2VBench is a hierarchical evaluation framework comprising over 1,600 temporally rich prompts and 5,000 generated videos with human ratings, spanning 16 critical temporal evaluation dimensions. We assess three leading text-to-video models, including ZeroScope and Pika, to gauge their proficiency in handling temporal dynamics. Our analysis highlights the strengths and limitations of these models across various temporal aspects. Furthermore, we provide insights into future directions for enhancing text-to-video evaluation metrics and offer a detailed analysis of these models’ performance across the temporal dimensions. Overall, T2VBench is the first-of-its-kind comprehensive benchmark fully focused on temporal dynamics for text-to-video evaluation. It aims to facilitate scientific benchmarking of both generative models and automated metrics on text-to-video generation.

关键词： Measurement Analytical models Computational modeling conferences Encyclopedias Benchmark testing pattern recognition

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Language-guided Multi-modal Emotional Mimicry Intensity Estimation

Language-guided Multi-modal Emotional Mimicry Intensity Esti...

引用

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

作者： Feng Qiu Wei Zhang Chen Liu Lincheng Li Heming Du Tianchen Guo Xin Yu Netease Fuxi AI Lab The University of Queensland

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Emotional Mimicry Intensity (EMI) estimation aims to identify the intensity of mimicry exhibited by individuals in response to observed emotions. The challenge in EMI estimation lies in discerning nuanced facial expression cues on mimicry behaviors based on the seed video and the text instructions. In this paper, we propose a multi-modal EMI estimation framework by leveraging visual, auditory, and textual modalities to capture a comprehensive emotional profile. We first extract representations for each modality separately and then fuse the modality-specific representations via a Temporal Segment Network, optimizing for temporal coherence and emotional context. Furthermore, we find that participants demonstrate notable proficiency in mimicking text instructions, yet exhibit less effectiveness in replicating facial expressions and vocal tones. In light of this, we design a contrastive learning mechanism to refine the extracted feature based on textual guidance. By doing so, features derived from similar text instructions are closely aligned, enhancing the estimation of emotional mimicry intensity by leveraging the dominant textual modality. Experiments conducted on the Hume-Vidmimic2 dataset illustrate the effectiveness of our framework in EMI estimation. Our framework is recognized as the leading solution in the Emotional Mimicry Intensity (EMI) Estimation Challenge at the 6th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW). More information for the Competition can be found in: 6th ABAW.

关键词： Visualization Emotion recognition computer vision Fuses conferences Electromagnetic interference Estimation

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MultiPanoWise: holistic deep architecture for multi-task dense prediction from a single panoramic image

MultiPanoWise: holistic deep architecture for multi-task den...

引用

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

作者： Uzair Shah Muhammad Tukur Mahmood Alzubaidi Giovanni Pintore Enrico Gobbetti Mowafa Househ Jens Schneider Marco Agus ICT Division College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Visual and Data-Intensive Computing CRS4 Italy National Research Center in HPC Big Data and Quantum Computing Italy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We present a novel holistic deep-learning approach for multi-task learning from a single indoor panoramic image. Our framework, named MultiPanoWise, extends vision transformers to jointly infer multiple pixel-wise signals, such as depth, normals, and semantic segmentation, as well as signals from intrinsic decomposition, such as reflectance and shading. Our solution leverages a specific architecture combining a transformer-based encoder-decoder with multiple heads, by introducing, in particular, a novel context adjustment approach, to enforce knowledge distillation between the various signals. Moreover, at training time we introduce a hybrid loss scalarization method based on an augmented Chebychev/hypervolume scheme. We illustrate the capabilities of the proposed architecture on public-domain synthetic and real-world datasets. We demonstrate performance improvements with respect to the most recent methods specifically designed for single tasks, like, for example, individual depth estimation or semantic segmentation. To our knowledge, this is the first architecture capable of achieving state-of-the-art performance on the joint extraction of heterogeneous signals from single indoor omnidirectional images.

关键词： Training Reflectivity computer vision Semantic segmentation Estimation computer architecture Multitasking

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DECNet: A Non-Contacting Dual-Modality Emotion Classification Network for Driver Health Monitoring

DECNet: A Non-Contacting Dual-Modality Emotion Classificatio...

引用

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

作者： Zhekang Dong Chenhao Hu Shiqi Zhou Liyan Zhu Junfan Wang Yi Chen Xudong Lv Xiaoyue Ji Hangzhou Dianzi University Zhejiang Provincial Key Laboratory of Equipment Electronics Zhejiang University Tsinghua University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Negative emotions have been identified as significant factors influencing driver behavior, easily leading to extremely serious traffic accidents. Hence, there is a pressing need to develop an automatic emotion classification method for driver health monitoring and road safety improvement. Most of the existing methods predominantly focus on single modalities, resulting in suboptimal classification performance due to the underutilization of heterogeneous information. In this work, we propose a novel non-contacting dual-modality driver emotion classification network (DECNet) to address these limitations. DECNet consists of three key modules: 1) facial video modality processing module; 2) driving behavior modality processing module; 3) fusion decision module. Meanwhile, we introduce a combined multi-task learning strategy within DECNet to improve the efficacy in the driver emotion classification task. To evaluate the effectiveness of the proposed DECNet, we conducted experiments on the PPB-Emo dataset, the experimental results showcase the superiority in terms of accuracy (⩾ 6.12% Acc-7) and F1-score (⩾ 7.25% F1-7) compared to existing state-of-the art methods. The model and code will be available at https://***/fqfqngxhs/***

关键词： Smart cities Pressing Feature extraction Multitasking Road safety Safety pattern recognition

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Activity Analysis in Microtubule Videos by Mixture of Hidden Markov Models

Activity Analysis in Microtubule Videos by Mixture of Hidden...

引用

conference on computer vision and pattern recognition (CVPR)

作者： A. Altinok M. El-Saban A.J. Peck L. Wilson S.C. Feinstein B.S. Manjunath K. Rose Computer Science University of California Santa Barbara CA USA Electrical and Computer Engineering University of California Santa Barbara CA USA Molecular Cellular and Developmental Biology University of California Santa Barbara CA USA

We present an automated method for the tracking and dynamics modeling of microtubules -a major component of the cytoskeleton- which provides researchers with a previously unattainable level of data analysis and quantification capabilities. The proposed method improves upon the manual tracking and analysis techniques by i) increasing accuracy and quantified sample size in data collection, ii) eliminating user bias and standardizing analysis, iii) making available new features that are impractical to capture manually, iv) enabling statistical extraction of dynamics patterns from cellular processes, and v) greatly reducing required time for entire studies. An automated procedure is proposed to track each resolvable microtubule, whose aggregate activity is then modeled by mixtures of Hidden Markov Models to uncover dynamics patterns of underlying cellular and experimental conditions. Our results support manually established findings on an actual microtubule dataset and illustrate how automated analysis of spatial and temporal patterns offers previously unattainable insights to cellular processes.

关键词： Videos Hidden Markov models pattern analysis Cells (biology) Biological system modeling computer vision Biology computing Data analysis Data mining Application software

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A Web-based System for Retrieving Document Images from Digital Library

A Web-based System for Retrieving Document Images from Digit...

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

作者： Li Zhang Yue Lu Chew Lim Tan Department of Computer Science School of Computing National University of Singapore Singapore

A web-based system for retrieving imaged documents from a digital library is described in this paper. First, some image preprocessing is performed off-line on the underlying imaged document to extract its word objects. Then, each word object is represented by a string known as its feature code, based on which a feature code file of the corresponding document is constructed. On the web interface side, the system allows the user to input a set of query words and indicate either to perform "AND" or "OR" operation on them. Once receiving user's request, the system will process each query word and combine the results based on the "AND" or "OR" operation the user has chosen. As for each query word, it is first looked up in an index table that stores words being queried before. If matches are found, results will be retrieved from the index table directly and stored temporarily for subsequent merging. This speeds up searching and makes the system an incremental intelligence system. Otherwise, the system will convert the query word to a feature code string and employ a partial word matching approach to perform search on the pre-generated feature code files. Preliminary experimental results with the imaged documents of students' theses provided by our digital library show that the proposed system is efficient and promising for document image retrieval, and thus has potential applications to digital libraries.

关键词： Feature extraction Indexes Servers Libraries Shape Probability density function Image retrieval

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Nonparametric Priors on the Space of Joint Intensity Distributions for Non-Rigid Multi-Modal Image Registration

Nonparametric Priors on the Space of Joint Intensity Distrib...

引用

conference on computer vision and pattern recognition (CVPR)

作者： D. Cremers C. Guetter Chenyang Xu Department of Computer Science University of Bonn Germany Department of Imaging and Visualization Siemens AG Corporate Research and Development Princeton NJ USA

The introduction of prior knowledge has greatly enhanced numerous purely low-level driven image processing algorithms. In this work, we focus on the problem of nonrigid image registration. A number of powerful registration criteria have been developed in the last decade, most prominently the criterion of maximum mutual information. Although this criterion provides for good registration results in many applications, it remains a purely low-level criterion. As a consequence, registration results will deteriorate once this low-level information is corrupted, due to noise, partial occlusions or missing image structure. In this paper, we will develop a Bayesian framework that allows to impose statistically learned prior knowledge about the joint intensity distribution into image registration methods. The prior is given by a kernel density estimate on the space of joint intensity distributions computed from a representative set of pre-registered image pairs. This nonparametric prior accurately models previously learned intensity relations between various image modalities and slice locations. Experimental results demonstrate that the resulting registration process is more robust to missing low-level information as it favors intensity correspondences statistically consistent with the learned intensity distributions.

关键词： Image registration Biomedical imaging Mutual information Motion estimation computer vision Image motion analysis computer science Visualization Image processing Bayesian methods

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Is Neuron Coverage Needed to Make Person Detection More Robust?

Is Neuron Coverage Needed to Make Person Detection More Robu...

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

作者： Svetlana Pavlitskaya Ş iyar Yı kmı ş J. Marius Zö llner FZI Research Center for Information Technology Karlsruhe Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

The growing use of deep neural networks (DNNs) in safety- and security-critical areas like autonomous driving raises the need for their systematic testing. Coverage-guided testing (CGT) is an approach that applies mutation or fuzzing according to a predefined coverage metric to find inputs that cause misbehavior. With the introduction of a neuron coverage metric, CGT has also recently been applied to DNNs. In this work, we apply CGT to the task of person detection in crowded scenes. The proposed pipeline uses YOLOv3 for person detection and includes finding DNN bugs via sampling and mutation, and subsequent DNN retraining on the updated training set. To be a bug, we require a mutated image to cause a significant performance drop compared to a clean input. In accordance with the CGT, we also consider an additional requirement of increased coverage in the bug definition. In order to explore several types of robustness, our approach includes natural image transformations, corruptions, and adversarial examples generated with the Daedalus attack. The proposed framework has uncovered several thousand cases of incorrect DNN behavior. The relative change in mAP performance of the retrained models reached on average between 26.21% and 64.24% for different robustness types. However, we have found no evidence that the investigated coverage metrics can be advantageously used to improve robustness.

关键词： Measurement Deep learning computer vision computer bugs Neurons Neural networks Training data

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The revenge of BiSeNet: Efficient Multi-Task Image Segmentation

The revenge of BiSeNet: Efficient Multi-Task Image Segmentat...

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

作者： Gabriele Rosi Claudia Cuttano Niccolò Cavagnero Giuseppe Averta Fabio Cermelli Politecnico di Torino Focoos AI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent advancements in image segmentation have focused on enhancing the efficiency of the models to meet the demands of real-time applications, especially on edge devices. However, existing research has primarily concentrated on single-task settings, especially on semantic segmentation, leading to redundant efforts and specialized architectures for different tasks. To address this limitation, we propose a novel architecture for efficient multi-task image segmentation, capable of handling various segmentation tasks without sacrificing efficiency or accuracy. We introduce BiSeNetFormer, that leverages the efficiency of two-stream semantic segmentation architectures and it extends them into a mask classification framework. Our approach maintains the efficient spatial and context paths to capture detailed and semantic information, respectively, while leveraging an efficient transformed-based segmentation head that computes the binary masks and class probabilities. By seamlessly supporting multiple tasks, namely semantic and panoptic segmentation, BiSeNetFormer offers a versatile solution for multi-task segmentation. We evaluate our approach on popular datasets, Cityscapes and ADE20K, demonstrating impressive inference speeds while maintaining competitive accuracy compared to state-of-the-art architectures. Our results indicate that BiSeNetFormer represents a significant advancement towards fast, efficient, and multi-task segmentation networks, bridging the gap between model efficiency and task adaptability.

关键词： computer vision Accuracy Semantic segmentation Semantics Refining Redundancy computer architecture

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Ternary MobileNets via Per-Layer Hybrid Filter Banks

Ternary MobileNets via Per-Layer Hybrid Filter Banks

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

作者： Dibakar Gope Jesse Beu Urmish Thakker Matthew Mattina Arm ML Research Lab

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

MobileNets family of computer vision neural networks have fueled tremendous progress in the design and organization of resource-efficient architectures in recent years. New applications with stringent real-time requirements on highly constrained devices require further compression of MobileNets-like compute-efficient networks. Model quantization is a widely used technique to compress and accelerate neural network inference and prior works have quantized MobileNets to 4 - 6 bits, albeit with a modest to significant drop in accuracy. While quantization to sub-byte values (i.e. precision ≤ 8 bits) has been valuable, even further quantization of MobileNets to binary or ternary values is necessary to realize significant energy savings and possibly runtime speedups on specialized hardware, such as ASICs and FPGAs. Under the key observation that convolutional filters at each layer of a deep neural network may respond differently to ternary quantization, we propose a novel quantization method that generates per-layer hybrid filter banks consisting of full-precision and ternary weight filters for MobileNets. Using this proposed quantization method, we quantize a substantial portion of weight filters of MobileNets to ternary values resulting in a 27.98% savings in energy, and a 51.07% reduction in the model size, while achieving comparable accuracy and no degradation in throughput on specialized hardware in comparison to the baseline full-precision MobileNets. Finally, we demonstrate the generalizability and effectiveness of hybrid filter banks to other neural network architectures.

关键词： Convolution Quantization (signal) computer architecture Neural networks Throughput Hardware Computational modeling

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