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Show, Think, and Tell: Thought-Augmented Fine-Tuning of Large Language Models for Video Captioning

Show, Think, and Tell: Thought-Augmented Fine-Tuning of Larg...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Byoungjip Kim Dasol Hwang Sungjun Cho Youngsoo Jang Honglak Lee Moontae Lee LG AI Research University of Illinois Chicago

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Large language models (LLMs) have achieved a great success in natural language processing, and have a significant potential for multi-modal applications. Despite the surprising zero-shot or few-shot ability, it is also required to effectively fine-tune pre-trained language models for specific downstream tasks. In this paper, we introduce CaptionT5, a video captioning model that fine-tunes T5 towards understanding videos and generating descriptive captions. To generate a more corespondent caption, CaptionT5 introduces thought-augmented fine-tuning for video captioning, in which a pre-trained language model is fine-tuned on thought-augmented video inputs. This resembles the process that human see a video, think of visual concepts such as objects and actions, and then tell a correct and natural sentence based on the thoughts. To automatically generate thoughts, we propose (1) CLIP-guided thought sampling that samples thoughts based on the similarity in an image-text multimodal embedding space by leveraging CLIP. We also propose (2) CLIP-guided caption ranking during decoding for further performance gains. Through experimentation on VATEX, MSRVTT, and YC2 datasets, we empirically demonstrate that CaptionT5 performs competitively against prior-art video captioning approaches without using encoders specialized for video data. Further experiments show that CaptionT5 is especially effective under small number of sampled video frames.

关键词： Visualization computer vision Large language models Computational modeling conferences Performance gain Data models

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AR-CP: Uncertainty-Aware Perception in Adverse Conditions with Conformal Prediction and Augmented Reality For Assisted Driving

AR-CP: Uncertainty-Aware Perception in Adverse Conditions wi...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Achref Doula Max Mühlhäuser Alejandro Sanchez Guinea Technical University of Darmstadt Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep learning models are pivotal in enhancing driver assistance systems and improving environmental perception. However, the tendency of neural networks towards overconfident predictions poses a risk of inaccurate predictions, potentially compromising driver safety in adverse conditions. To mitigate this issue, we introduce AR-CP, an uncertainty-aware framework designed to augment driver perception in scenarios characterized by adverse weather and insufficient lighting, through the integration of conformal prediction and augmented reality (AR). Our framework initiates with a conformal prediction step that produces an uncertainty-aware prediction set including potential object classes at a predefined probability level. Subsequently, AR is used to provide a simplified and informative visualization of the closest common parent class of the classes in the prediction set, thereby reducing the likelihood of misinformation. We provide a principled formulation and theoretical analysis of our framework. We evaluate AR-CP on the ROAD dataset, a large dataset containing different difficult situations that induce high uncertainty during prediction time. The results show that our framework outperforms state-of-the-art approaches in providing smaller prediction sets while holding the theoretical guarantees, ensuring an uncertainty-aware prediction, and reducing user confusion. We conduct an immersive user study with 15 participants to investigate the effects of our concept on the quality of perception, situation awareness, and mental load of participants. The results show that our concept facilitates a safer driving experience while holding the mental load low and the situation awareness high.

关键词： Visualization Uncertainty Roads Lighting Reliability theory Safety pattern recognition

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Beyond the Screen: Evaluating Deepfake Detectors under Moiré pattern Effects

Beyond the Screen: Evaluating Deepfake Detectors under Moir...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Razaib Tariq Minji Heo Simon S. Woo Shahroz Tariq Sungkyunkwan University South Korea CSIRO’s Data61 Australia

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The detection of deepfakes is crucial for mitigating the societal impact of falsified video content. Despite the development of various algorithms for this purpose, challenges arise for detectors in real-world scenarios, especially when users capture deepfake content from screens and upload it online or when detectors operate on external devices like smartphones, requiring the capture of potential deepfakes through the camera for evaluation. A significant challenge in these scenarios is the presence of Moiré patterns, which degrade image quality and complicate conventional classification methods, notably deep neural networks (DNNs). However, the impact of Moiré patterns on the effectiveness of deepfake detection systems has not been adequately explored. This study aims to investigate how capturing deepfake videos via digital screen cameras affects the accuracy of detection mechanisms. We introduced the Moiré patterns by capturing the display of a monitor using a smartphone camera and conducted empirical evaluations using four widely recognized datasets: CelebDF, DFD, DFDC, and FF++. We compare the performance of twelve SOTA detectors on deepfake videos captured under the influence of Moiré patterns. Our findings reveal a performance decrease of up to 33.1 and 31.3 percentage points for image- and video-based detectors. Therefore, highlighting the challenges posed by Moiré patterns and other naturally induced artifacts is critical for improving the effectiveness of real-world deepfake detection efforts. To facilitate further research, we will release the Moiré pattern impact version of CelebDF, DFD, DFDC, and FF++ datasets with this paper. Our code is available here: https://***/Razaib-Tariq/deepmoire

关键词： Image quality Deepfakes computer vision conferences Refining Detectors Cameras

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Retina : Low-Power Eye Tracking with Event Camera and Spiking Hardware

Retina : Low-Power Eye Tracking with Event Camera and Spikin...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Pietro Bonazzi Sizhen Bian Giovanni Lippolis Yawei Li Sadique Sheik Michele Magno ETH Zürich Inivation AG Synsense AG

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper introduces a neuromorphic dataset and methodology for eye tracking, harnessing event data captured streamed continuously by a Dynamic vision Sensor (DVS). The framework integrates a directly trained Spiking Neuron Network (SNN) regression model and leverages a state-of-the-art low power edge neuromorphic processor - Speck. First, it introduces a representative event-based eye-tracking dataset, "Ini-30," which was collected with two glass-mounted DVS cameras from thirty volunteers. Then, a SNN model, based on Integrate And Fire (IAF) neurons, named "Retina", is described , featuring only 64k parameters (6.63x fewer than 3ET) and achieving pupil tracking error of only 3.24 pixels in a 64x64 DVS input. The continuous regression output is obtained by means of temporal convolution using a non-spiking 1D filter slided across the output spiking layer over time. Retina is evaluated on the neuromorphic processor, showing an end-to-end power between 2.89-4.8 mW and a latency of 5.57-8.01 ms dependent on the time to slice the event-based video recording. The model is more precise than the latest event-based eye-tracking method, "3ET", on Ini-30, and shows comparable performance with significant model compression (35 times fewer MAC operations) in the synthetic dataset used in "3ET". We hope this work will open avenues for further investigation of close-loop neuromorphic solutions and true event-based training pursuing edge performance.

关键词： Neuromorphics Computational modeling Neurons Gaze tracking vision sensors Retina Cameras

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Deploying Machine Learning Anomaly Detection Models to Flight Ready AI Boards

Deploying Machine Learning Anomaly Detection Models to Fligh...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： James Murphy Maria Buckley Léonie Buckley Adam Taylor Jake O’Brien Brian Mac Namee Réaltra Space Systems Engineering Ubotica Technologies Adiuvo Engineering & Training School of Computer Science University College Dublin

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This study explores the development and implementation of machine learning (ML) models on Edge-AI boards, aiming to identify the most effective solution for anomaly detection systems on space missions. We investigate ML anomaly detection techniques including Autoencoders, Long Short-Term Memory (LSTM) cells, Isolation Forests, and Transformers. These models were trained on a univariate dataset derived from real space missions and deployed on diverse hardware platforms engineered for space environments to comprehensively assess performance. Specifically, we explore space flight ready boards (Ubotica CogniSAT-XE1 and XE2, which incorporate Intel’s Myriad 2 and X chips, respectively); commercial, non-space flight ready, edge-AI boards (NVIDIA’s Jetson Nano and Google Coral); and Field Programmable Gate Array (FPGA) implementations (from Microchip, AMD, and NanoXplore). We compare the performance of anomaly detection models run on space flight ready and commercial boards (using CPU performance as a benchmark) to provide a thorough comparison of available platforms for onboard anomaly detection. This paper provides a detailed examination of both the optimal ML models and hardware platforms for deploying univariate anomaly detection systems in space flight contexts and draws conclusions about which ones are most suitable.

关键词： Performance evaluation Space missions Image edge detection Computational modeling Machine learning Transformers Hardware

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Video Based Computational Coding of Movement Anomalies in ASD Children

Video Based Computational Coding of Movement Anomalies in AS...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Priya Singh Abhishek Pathak Umer Jon Ganai Braj Bhushan Venkatesh K. Subramanian TCS Research India Airbase Labs India Pvt Ltd India Indian Institute of Technology Kanpur India

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Autism spectrum disorder (ASD) is a neurodevelopmental disorder. Early detection and diagnosis are instrumental in early intervention, yet diagnosis often remains delayed due to the limited availability of clinical practitioners and specialists. We propose a computer vision and Machine Learning based novel framework for quantitative screening of autism spectrum disorder (ASD). This is aimed to minimize the need for trained professionals at the initial stage but not substitute for it. We designed simple activities in consultation with ASD clinical psychologists and therapists for children in the 3-7 years age group that could be performed in their natural environment (home). The temporal features extracted from these activities encode the behavioral differences between Autism Spectrum Disorder (ASD) and Typically Developing (TD) control groups. Due to the unavailability of a public dataset of children performing the designed task, we created our own video dataset of 210 videos taken in unconstrained natural settings. The dataset was collected from a single RGB camera. The proposed vision and learning-based algorithms extract features from the collected data for a comprehensive set of indicators including the visual attention span, name-calling response, neck pose of the subjects, gross motor movement and establish a parametrized automated protocol for early detection without the need to take the subjects out of their natural daily environment. This forestalls the possibility of mis-performance by the subject out of nervousness due to unfamiliar surroundings. Results show that our ASD screening methodology can achieve superior performance compared to the single phenotype approaches, and thus has a prognostic value that could be helpful for both clinical and research applications.

关键词： Autism computer vision Visualization Protocols Phenotypes Psychology Variable speed drives

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Uncovering Hidden Emotions with Adaptive Multi-Attention Graph Networks

Uncovering Hidden Emotions with Adaptive Multi-Attention Gra...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Ankith Jain Rakesh Kumar Bir Bhanu Department of Electrical and Computer Engineering University of California Riverside

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Micro-expressions (MEs) are subtle expressions lasting a fraction of a second, offering valuable cues for understanding human emotions and intentions. However, effectively classifying these subtle expressions from video data poses several challenges due to their short duration and low intensity. This paper addresses these issues and presents a novel 2-stream Adaptive Multi-Attention ((Self-Attention and Gaussian Attention) Graph Network (2S-AMAGN) based approach for ME classification in videos. The Self-Attention mechanism captures the global and local dependencies between nodes in a graph. The Gaussian attention mechanism computes weights based on the Gaussian distribution, considering the mean and variance of features across each edge, offering a nuanced understanding of spatial and temporal relationships within MEs. It meticulously analyzes node pair features and edge features, capturing the significance of facial regions. An adaptive learnable weight is introduced to learn the contributions of each attention mechanism, facilitating adaptive attention fusion. The network utilizes a three-frame graph structure to extract spatio-temporal information. The approach incorporates a dynamic frame selection mechanism, which utilizes a sliding window optical flow method to filter out low-intensity emotion frames, thereby refining the extraction of spatio-temporal information from the video data. The results are presented and compared with state-of-the-art methods for SMIC and SAMM databases. Additionally, cross-dataset experiments are conducted, and the results are reported.

关键词： Optical filters Attention mechanisms Adaptive systems Databases Refining Gaussian distribution Information filters

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Deep Learning-Based Identification of Arctic Ocean Boundaries and Near-Surface Phenomena in Underwater Echograms

Deep Learning-Based Identification of Arctic Ocean Boundarie...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Femina Senjaliya Melissa Cote Amanda Dash Alexandra Branzan Albu Andrea Niemi Stéphane Gauthier Julek Chawarski Steve Pearce Kaan Ersahin Keath Borg Electrical and Computer Engineering University of Victoria Victoria Canada ASL Environmental Sciences Victoria Canada Fisheries and Oceans Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Monitoring marine environments is a crucial part of understanding the impact of oceans on global climate and their importance for biodiversity and ecological systems, particularly in the Arctic region. Underwater active acoustic surveys with moored multi-frequency echosounders allow for the continuous collection of valuable data reflecting the complex dynamics of these environments. This paper addresses the automatic identification of sea surface boundaries and near-surface phenomena in echograms using deep learning methods to support researchers such as biologists in their work, who typically rely on time-consuming manual analyses. We propose a two-step process that first characterizes echograms according to the surface conditions using an image classification paradigm and then identifies the sea surface boundary and near-surface bubbles and their extent in the water column using a semantic segmentation paradigm. Segmentation is carried out using surface type-specific models, which perform better than a single global segmentation model. We also propose learning strategies, such as a custom boundary loss function, that further improve performance. Experiments with various image classification and semantic segmentation architectures allow us to select the most efficient models for Arctic echogram analysis that, when used in conjunction within our proposed pipeline and our learning strategies, offer excellent results.

关键词： Surveys Sea surface Analytical models Biological system modeling Semantic segmentation Arctic Surface treatment

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DiCo-NeRF: Difference of Cosine Similarity for Neural Rendering of Fisheye Driving Scenes

DiCo-NeRF: Difference of Cosine Similarity for Neural Render...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jiho Choi Gyutae Hwang Sang Jun Lee Jeonbuk National University Republic of Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural radiance fields have emerged in the field of autonomous driving, which contributes to improve perception of the complex 3D environment through the reconstruction of geometry and appearance. Moving objects and sky for outdoor environment is challenging to optimize the NeRF model. Previous work addresses these challenges through preprocessing such as masking; however, the masking process requires additional ground-truth data and a segmentation network. We propose DiCo-NeRF, an approach for driving scenes by leveraging cosine similarity map differences of vision-language aligned model. DiCo-NeRF investigates the correlation between rendered patches and predefined text and adjusts the loss of challenging patches, such as moving objects and the sky. Our neural radiance field utilizes embedding vectors from a pre-trained CLIP to obtain the cosine similarity maps. We introduce SimLoss, a loss function aimed at regulating the color field of NeRF based on the quantified distribution differences between ground-truth and rendered similarity maps. Unlike previous NeRF models that used driving datasets, our approach does not require additional input, such as sensor data, to the model. Experimental results demonstrate that the incorporation of language semantic cues improves the performance of the novel view synthesis task, particularly in complex driving environments. We conducted experiments that included fisheye driving scenes from the KITTI-360 and real-world datasets. Our code is available at https://***/ziiho08/DiCoNeRF.

关键词： Three-dimensional displays Image color analysis Computational modeling Semantics Neural radiance field Rendering (computer graphics) Vectors

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Do More With What You Have: Transferring Depth-Scale from Labeled to Unlabeled Domains

Do More With What You Have: Transferring Depth-Scale from La...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Alexandra Dana Nadav Carmel Amit Shomer Ofer Manela Tomer Peleg Samsung Israel R&D Center Tel Aviv Israel

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Transferring the absolute depth prediction capabilities of an estimator to a new domain is a task with significant real-world applications. This task is specifically challenging when images from the new domain are collected without ground-truth depth measurements, and possibly with sensors of different intrinsics. To overcome such limitations, a recent zero-shot solution was trained on an extensive training dataset and encoded the various camera intrinsics. Other solutions generated synthetic data with depth labels that matched the intrinsics of the new target data to enable depth-scale transfer between the *** this work we present an alternative solution that can utilize any existing synthetic or real dataset, that has a small number of images annotated with ground truth depth labels. Specifically, we show that self-supervised depth estimators result in up-to-scale predictions that are linearly correlated to their absolute depth values across the domain, a property that we model in this work using a single scalar. In addition, aligning the field-of-view of two datasets prior to training, results in a common linear relationship for both domains. We use this observed property to transfer the depth-scale from source datasets that have absolute depth labels to new target datasets that lack these measurements, enabling absolute depth predictions in the target *** suggested method was successfully demonstrated on the KITTI, DDAD and nuScenes datasets, while using other existing real or synthetic source datasets, that have a different field-of-view, other image style or structural content, achieving comparable or better accuracy than other existing methods that do not use target ground-truth depths.

关键词： Training Image sensors computer vision Accuracy conferences Predictive models Cameras

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