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Evaluating Multimodal Large Language Models across Distribution Shifts and Augmentations

Evaluating Multimodal Large Language Models across Distribut...

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

作者： Aayush Atul Verma Amir Saeidi Shamanthak Hegde Ajay Therala Fenil Denish Bardoliya Nagaraju Machavarapu Shri Ajay Kumar Ravindhiran Srija Malyala Agneet Chatterjee Yezhou Yang Chitta Baral Arizona State University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Foundational models such as Multimodal Large Language Models (MLLMs) with their ability to interpret images and generate intricate responses has led to their widespread adoption across multiple computer vision and natural language processing tasks. However, they suffer from hallucinations and struggle to reason over complex reasoning tasks. In this work, we evaluate the performance of MLLMs across multiple multimodal augmentations and evaluate their performance in out-of-distribution settings. We benchmark 3 models, across 2 vision-language datasets, VQAv2 and CLEVR, and assess their performance across adversarial transformations in both the vision and language modalities. We introduce image perturbations using various augmentations, including noise addition, blurring, and median filtering and generate adversarial questions which contain conjunctions, disjunctions and negations. Additionally, we conduct a detailed fine-grained analysis to assess the model’s performance on particular question categories, such as those related to shape and color, across images featuring identical or varying objects. Our findings indicate a notable decrease in the performance of current MLLMs for synthetic images, with a gradual decline observed across both vision and language augmentations. Specifically, Gaussian Noise Addition emerges as the most detrimental augmentation, and we observe a significant drop in performance with complex questions containing multiple connectives. In these times of rapid development and deployment of MLLMs in real-world settings, we believe our findings are a first step towards benchmarking the robustness and out-of-distribution behavior of such models.

关键词： computer vision Analytical models Shape Large language models Computational modeling Perturbation methods Benchmark testing

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TAME: Task Agnostic Continual Learning using Multiple Experts

TAME: Task Agnostic Continual Learning using Multiple Expert...

引用

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

作者： Haoran Zhu Maryam Majzoubi Arihant Jain Anna Choromanska New York University Google

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The goal of lifelong learning is to continuously learn from non-stationary distributions, where the non-stationarity is typically imposed by a sequence of distinct tasks. Prior works have mostly considered idealistic settings, where the identity of tasks is known at least at training. In this paper we focus on a fundamentally harder, so-called task-agnostic, setting where the task identities are not known and the learning machine needs to infer them from the observations. Our algorithm, which we call TAME (Task-Agnostic continual learning using Multiple Experts), automatically detects the shift in data distributions and switches between task expert networks in an online manner. At training, the strategy for switching between tasks hinges on an extremely simple observation that for each new coming task there occurs a statistically-significant deviation in the value of the loss function that marks the onset of this new task. At inference, the switching between experts is governed by the selector network that forwards the test sample to its relevant expert network. The selector network is trained on a small subset of data drawn uniformly at random. We control the growth of the task expert networks as well as selector network by employing pruning. Our experimental results show the efficacy of our approach on benchmark continual learning data sets, outperforming the previous task-agnostic methods and even the techniques that admit task identities at both training and testing, while at the same time using a comparable model size.

关键词： Training Continuing education computer vision conferences Computational modeling Switches Fasteners

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Feature Corrective Transfer Learning: End-to-End Solutions to Object Detection in Non-Ideal Visual Conditions

Feature Corrective Transfer Learning: End-to-End Solutions t...

引用

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

作者： Chuheng Wei Guoyuan Wu Matthew J. Barth University of California Riverside

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

A significant challenge in the field of object detection lies in the system’s performance under non-ideal imaging conditions, such as rain, fog, low illumination, or raw Bayer images that lack ISP processing. Our study introduces ‘Feature Corrective Transfer Learning’, a novel approach that leverages transfer learning and a bespoke loss function to facilitate the end-to-end detection of objects in these challenging scenarios without the need to convert non-ideal images into their RGB counterparts. In our methodology, we initially train a comprehensive model on a pristine RGB image dataset. Subsequently, non-ideal images are processed by comparing their feature maps against those from the initial ideal RGB model. This comparison employs the Extended Area Novel Structural Discrepancy Loss (EANSDL), a novel loss function designed to quantify similarities and integrate them into the detection loss. This approach refines the model’s ability to perform object detection across varying conditions through direct feature map correction, encapsulating the essence of Feature Corrective Transfer Learning. Experimental validation on variants of the KITTI dataset demonstrates a significant improvement in mean Average Precision (mAP), resulting in a 3.8-8.1% relative enhancement in detection under non-ideal conditions compared to the baseline model, and a less marginal performance difference within 1.3% of the mAP@[0.5:0.95] achieved under ideal conditions by the standard Faster RCNN algorithm.

关键词： Visualization computer vision Accuracy Computational modeling Surveillance Transfer learning Imaging

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Specular Flow and the Recovery of Surface Structure

Specular Flow and the Recovery of Surface Structure

引用

conference on computer vision and pattern recognition (CVPR)

作者： S. Roth M.J. Black Department of Computer Science Brown University Providence RI USA

In scenes containing specular objects, the image motion observed by a moving camera may be an intermixed combination of optical flow resulting from diffuse reflectance (diffuse flow) and specular reflection (specular flow). Here, with few assumptions, we formalize the notion of specular flow, show how it relates to the 3D structure of the world, and develop an algorithm for estimating scene structure from 2D image motion. Unlike previous work on isolated specular highlights we use two image frames and estimate the semi-dense flow arising from the specular reflections of textured scenes. We parametrically model the image motion of a quadratic surface patch viewed from a moving camera. The flow is modeled as a probabilistic mixture of diffuse and specular components and the 3D shape is recovered using an Expectation-Maximization algorithm. Rather than treating specular reflections as noise to be removed or ignored, we show that the specular flow provides additional constraints on scene geometry that improve estimation of 3D structure when compared with reconstruction from diffuse flow alone. We demonstrate this for a set of synthetic and real sequences of mixed specular-diffuse objects.

关键词： Surface structures Layout Image motion analysis Optical reflection Cameras Acoustic reflection Reflectivity Motion estimation Surface treatment Shape

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A Regression Model in TensorPCA Subspace for Face Image Super-resolution Reconstruction

A Regression Model in TensorPCA Subspace for Face Image Supe...

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International conference on pattern recognition

作者： Junwen Wu M.M. Trivedi Computer Vision and Robotics Research Laboratory Department of Electrical and Computer Engineering University of California San Diego CA USA

ISBN: (纸本)0769525210

A regression model in the tensorPCA subspace is proposed in this paper for face super-resolution reconstruction. An approximate conditional probability model is used for the tensor subspace coefficients and maximum-likelihood estimator gives a linear regression model. The approximation is corrected by adding non-linear component from a RBF-type regressor. Experiments on face images from FERET database validate the algorithm. Although each projection coefficient is estimated by a local estimator, tensorPCA subspace analysis is still a global descriptor, which makes the algorithm have certain ability to deal with partially occluded images

关键词： Image resolution Image reconstruction Tensile stress Spatial resolution Principal component analysis Face detection Image analysis computer vision Robot vision systems Laboratories

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Trident Dehazing Network

Trident Dehazing Network

引用

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

作者： Jing Liu Haiyan Wu Yuan Xie Yanyun Qu Lizhuang Ma School of Computer Science and Technology East China Normal University China School of Information Science and Engineering Xiamen University China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Most existing dehazing methods are not robust to nonhomogeneous haze. Meanwhile, the information of dense haze region is usually unknown and hard to estimate, leading to blurry in dehaze result for those regions. Focusing on these two issues, we propose a novel coarse-to-fine model, namely Trident Dehazing Network (TDN), to learn the hazy to hazy- free image mapping with automatic haze density recognition. In detail, TDN is composed of three sub-nets: the Encoder-Decoder Net (EDN) is the main net of TDN to reconstruct the coarse hazy-free feature; the Detail Refinement sub-Net (DRN) helps to refine the high frequency details that was easily lost in the pooling layers in the encoder; and the Haze Density Map Generation sub-Net (HDMGN) can automatically distinguish the thick haze region with thin one, to prevent over-dehazing or under-dehazing in regions of different haze density. Moreover, we propose a frequency domain loss function to make supervision of different frequency band more uniform. Extensive experimental results on synthetic and real datasets demonstrate that our proposed TDN outperforms the state-of-the-arts with better fidelity and perceptual, generalizing well on both dense haze and nonhomogeneous haze scene. Our method won the first place in NTIRE2020 nonhomogeneous dehazing challenge.

关键词： Image reconstruction Frequency-domain analysis Feature extraction Training Atmospheric modeling Convolution

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Photorealistic Arm Robot Simulation for 3D Plant Reconstruction and Automatic Annotation using Unreal Engine 5

Photorealistic Arm Robot Simulation for 3D Plant Reconstruct...

引用

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

作者： Xingjian Li Jeremy Park Chris Reberg-Horton Steven Mirsky Edgar Lobaton Lirong Xiang Department of Biological and Agricultural Engineering North Carolina State University Department of Electrical and Computer Engineering North Carolina State University Department of Computer Science North Carolina State University Department of Crop and Soil Science North Carolina State University Sustainable Agricultural Systems Laboratory USDA-ARS

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Robotics paired with computer vision are widely used in precision agriculture. Simulations are critical for safety and performance estimation by verifying their routine in a virtual world before real-world testing and deployment. However, many simulators used in agricultural robots lack photorealism in their virtual worlds compared to the real world. We implemented Unreal Engine 5 (UE5) and the Robot Operating System (ROS) to develop a robot simulator tailored to agricultural tasks and synthetic data generation with RGB, segmentation, and depth images. We designed a method for assigning multiple segmentation labels within a single plant mesh. We experimented with a semi-spherical routine for two robot arms to perform 3D point cloud reconstruction across 10 plant assets. We showed our simulator produces much more accurate segmentation images and reconstruction compared to existing UE5 solutions. We extend our results with Neural Radiance Field (NeRF) reconstructions. The packaged simulator, UE5 project, and ROS package with the Python routine can be found at https://***/NCSU-BAE-ARLab/AgriRoboSimUE5.

关键词： Image segmentation computer vision Three-dimensional displays Computational modeling Neural radiance field Manipulators Image reconstruction

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An Integrated Segmentation and Classification Approach Applied to Multiple Sclerosis Analysis

An Integrated Segmentation and Classification Approach Appli...

引用

conference on computer vision and pattern recognition (CVPR)

作者： A. Akselrod-Ballin M. Galun R. Basri A. Brandt M.J. Gomori M. Filippi P. Valsasina Dept. of Computer Science and Applied Math Weizmann Institute of Science Rehovot Israel Dept. of Radiology Hadassah University Hospital Jerusalem Israel Neuroimaging Research Unit Hospital San Raffaele Milan Italy

We present a novel multiscale approach that combines segmentation with classification to detect abnormal brain structures in medical imagery, and demonstrate its utility in detecting multiple sclerosis lesions in 3D MRI data. Our method uses segmentation to obtain a hierarchical decomposition of a multi-channel, anisotropic MRI scan. It then produces a rich set of features describing the segments in terms of intensity, shape, location, and neighborhood relations. These features are then fed into a decision tree-based classifier, trained with data labeled by experts, enabling the detection of lesions in all scales. Unlike common approaches that use voxel-by-voxel analysis, our system can utilize regional properties that are often important for characterizing abnormal brain structures. We provide experiments showing successful detections of lesions in both simulated and real MR images.

关键词： Multiple sclerosis Image segmentation Lesions Brain Magnetic resonance imaging Biomedical imaging Anisotropic magnetoresistance Shape Decision trees Classification tree analysis

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TlTScore: Towards Long-Tail Effects in Text-to-Visual Evaluation with Generative Foundation Models

TlTScore: Towards Long-Tail Effects in Text-to-Visual Evalua...

引用

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

作者： Pengliang Ji Junchen Liu Carnegie Mellon University UC Berkeley

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Evaluation of generative foundation models (GenFMs) for text-to-visual tasks has been enhanced by automatic alignment metrics such as CLIPScore, complementing human feedback. However, existing evaluation methods suffer from a severe long-tail effect, where the balance between token count and semantic validity in the initial step, hinders the accurate evaluation of advanced aspects such as composition. We analyze this drawback and attribute it to a lack of symbolic reasoning attention, while GenFMs demonstrate strong discriminative abilities in handling symbolism. To this end, we propose a pioneering paradigm for evaluating GenFMs’ text-to-visual (T2V) generation using neuro-symbolic thinking to mitigate the long-tail effect. By explicitly embedding Mixture-of-experts (MoE) Large vision Models (LVMs), we introduce symbolic-level understanding while maintaining the strong neuro-level reasoning capability. Through the fusion of semantic and compositional knowledge at the neuro-to-symbolic level, our approach outperforms state-of-the-art T2V evaluation methods, exhibiting stronger compositional reasoning ability on Winoground and better alignment with human judgment. We also demonstrate our impressive effectiveness on diverse tasks, including text-to-3D and text-to-video. To further advance the T2V evaluation of GenFMs, we propose a challenging benchmark that includes richer and more diverse compositional and semantic information compared to Winoground. Overall, our work opens a new direction for neuro-to-symbolic visio-linguistic evaluation of GenFMs and aims to drive further progress in the field.

关键词： Measurement computer vision Accuracy conferences Semantics Benchmark testing Cognition

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Event detection in coarsely annotated sports videos via parallel multi receptive field 1D convolutions

Event detection in coarsely annotated sports videos via para...

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

作者： Kanav Vats Mehrnaz Fani Pascale Walters David A. Clausi John Zelek University of Waterloo Waterloo Ontario Canada

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

In problems such as sports video analytics, it is difficult to obtain accurate frame level annotations and exact event duration because of the lengthy videos and sheer volume of video data. This issue is even more pronounced in fast-paced sports such as ice hockey. Obtaining annotations on a coarse scale can be much more practical and time efficient. We propose the task of event detection in coarsely annotated videos. We introduce a multi-tower temporal convolutional network architecture for the proposed task. The network, with the help of multiple receptive fields, processes information at various temporal scales to account for the uncertainty with regard to the exact event location and duration. We demonstrate the effectiveness of the multi-receptive field architecture through appropriate ablation studies. The method is evaluated on two tasks - event detection in coarsely annotated hockey videos in the NHL dataset and event spotting in soccer on the SoccerNet dataset. The two datasets lack frame-level annotations and have very distinct event frequencies. Experimental results demonstrate the effectiveness of the network by obtaining a 55% average F1 score on the NHL dataset and by achieving competitive performance compared to the state of the art on the SoccerNet dataset. We believe our approach will help develop more practical pipelines for event detection in sports video.

关键词： Videos Poles and towers Event detection Games Task analysis computer vision

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