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CAGE: Circumplex Affect Guided Expression Inference

CAGE: Circumplex Affect Guided Expression Inference

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

作者： Wagner, Niklas Maetzler, Felix Vossberg, Samed R. Schneider, Helen Pavlitska, Svetlana Zoellner, J. Marius Karlsruhe Inst Technol KIT Karlsruhe Germany FZI Res Ctr Informat Technol Karlsruhe Germany

ISBN: (纸本)9798350365474

Understanding emotions and expressions is a task of interest across multiple disciplines, especially for improving user experiences. Contrary to the common perception, it has been shown that emotions are not discrete entities but instead exist along a continuum. People understand discrete emotions differently due to a variety of factors, including cultural background, individual experiences, and cognitive biases. Therefore, most approaches to expression understanding, particularly those relying on discrete categories, are inherently biased. In this paper, we present a comparative in-depth analysis of two common datasets (AffectNet and EMOTIC) equipped with the components of the circumplex model of affect. Further, we propose a model for the prediction of facial expressions tailored for lightweight applications. Using a small-scaled MaxViT-based model architecture, we evaluate the impact of discrete expression category labels in training with the continuous valence and arousal labels. We show that considering valence and arousal in addition to discrete category labels helps to significantly improve expression inference. The proposed model outperforms the current state-of-the-art models on AffectNet, establishing it as the best-performing model for inferring valence and arousal achieving a 7% lower RMSE. Training scripts and trained weights to reproduce our results can be found here: https:// ***/wagner-niklas/CAGE_expression_inference.

关键词： computer vision Expression Inference Transformer

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NTIRE 2024 Challenge on Stereo Image Super-Resolution: Methods and Results

NTIRE 2024 Challenge on Stereo Image Super-Resolution: Metho...

引用

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

作者： Wang, Longguang Guo, Yulan Li, Juncheng Liu, Hongda Zhao, Yang Wang, Yingqian Jin, Zhi Gu, Shuhang Timofte, Radu Aviation University of Air Force Sun Yat-sen University The Shenzhen Campus of Sun Yat-sen University China National University of Defense Technology China Shanghai University China University of Electronic Science and Technology of China China Computer Vision Lab University of Würzburg Germany

ISBN: (纸本)9798350365474

This paper summarizes the 3rd NTIRE challenge on stereo image super-resolution (SR) with a focus on new solutions and results. The task of this challenge is to super-resolve a low-resolution stereo image pair to a high-resolution one with a magnification factor of x4 under a limited computational budget. Compared with single image SR, the major challenge of this challenge lies in how to exploit additional information in another viewpoint and how to maintain stereo consistency in the results. This challenge has 2 tracks, including one track on bicubic degradation and one track on real degradations. In total, 108 and 70 participants were successfully registered for each track, respectively. In the test phase, 14 and 13 teams successfully submitted valid results with PSNR (RGB) scores better than the baseline. This challenge establishes a new benchmark for stereo image SR.

关键词： Stereocenters

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AIGeN: An Adversarial Approach for Instruction Generation in VLN

AIGeN: An Adversarial Approach for Instruction Generation in...

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

作者： Rawal, Niyati Bigazzi, Roberto Baraldi, Lorenzo Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy

ISBN: (纸本)9798350365474

In the last few years, the research interest in vision-and-Language Navigation (VLN) has grown significantly. VLN is a challenging task that involves an agent following human instructions and navigating in a previously unknown environment to reach a specified goal. Recent work in literature focuses on different ways to augment the available datasets of instructions for improving navigation performance by exploiting synthetic training data. In this work, we propose AIGeN, a novel architecture inspired by Generative Adversarial Networks (GANs) that produces meaningful and well-formed synthetic instructions to improve navigation agents' performance. The model is composed of a Transformer decoder (GPT-2) and a Transformer encoder (BERT). During the training phase, the decoder generates sentences for a sequence of images describing the agent's path to a particular point while the encoder discriminates between real and fake instructions. Experimentally, we evaluate the quality of the generated instructions and perform extensive ablation studies. Additionally, we generate synthetic instructions for 217K trajectories using AIGeN on Habitat-Matterport 3D Dataset (HM3D) and show an improvement in the performance of an off-the-shelf VLN method. The validation analysis of our proposal is conducted on REVERIE and R2R and highlights the promising aspects of our proposal, achieving state-of-the-art performance.

关键词： Generative Adversarial Networks Text Generation vision-and-Language Navigation

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DVMSR: Distillated vision Mamba for Efficient Super-Resolution

DVMSR: Distillated Vision Mamba for Efficient Super-Resoluti...

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

作者： Lei, Xiaoyan Zhang, Wenlong Cao, Weifeng Zhengzhou Univ Light Ind Zhengzhou Peoples R China HongKong Polytech Univ Hong Kong Peoples R China

ISBN: (纸本)9798350365474

Efficient Image Super-Resolution (SR) aims to accelerate SR network inference by minimizing computational complexity and network parameters while preserving performance. Existing state-of-the-art Efficient Image Super-Resolution methods are based on convolutional neural networks. Few attempts have been made with Mamba to harness its long-range modeling capability and efficient computational complexity, which have shown impressive performance on high-level vision tasks. In this paper, we propose DVMSR, a novel lightweight Image SR network that incorporates vision Mamba and a distillation strategy. The network of DVMSR consists of three modules: feature extraction convolution, multiple stacked Residual State Space Blocks (RSSBs), and a reconstruction module. Specifically, the deep feature extraction module is composed of several residual state space blocks (RSSB), each of which has several vision Mamba Moudles(ViMM) together with a residual connection. To achieve efficiency improvement while maintaining comparable performance, we employ a distillation strategy to the vision Mamba network for superior performance. Specifically, we leverage the rich representation knowledge of teacher network as additional supervision for the output of lightweight student networks. Extensive experiments have demonstrated that our proposed DVMSR can outperform state-of-the-art efficient SR methods in terms of model parameters while maintaining the performance of both PSNR and SSIM. The source code is available at https://***/nathan66666/***

关键词： Efficient Image Super-Resolution vision Mamba

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Exploring the Zero-Shot Capabilities of vision-Language Models for Improving Gaze Following

Exploring the Zero-Shot Capabilities of Vision-Language Mode...

引用

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

作者： Gupta, Anshul Vuillecard, Pierre Farkhondeh, Arya Odobez, Jean-Marc Idiap Res Inst Martigny Switzerland Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (纸本)9798350365474

Contextual cues related to a person's pose and interactions with objects and other people in the scene can provide valuable information for gaze following. While existing methods have focused on dedicated cue extraction methods, in this work we investigate the zero-shot capabilities of vision-Language Models (VLMs) for extracting a wide array of contextual cues to improve gaze following performance. We first evaluate various VLMs, prompting strategies, and in-context learning (ICL) techniques for zero-shot cue recognition performance. We then use these insights to extract contextual cues for gaze following, and investigate their impact when incorporated into a state of the art model for the task. Our analysis indicates that BLIP-2 is the overall top performing VLM and that ICL can improve performance. We also observe that VLMs are sensitive to the choice of the text prompt although ensembling over multiple text prompts can provide more robust performance. Additionally, we discover that using the entire image along with an ellipse drawn around the target person is the most effective strategy for visual prompting. For gaze following, incorporating the extracted cues results in better generalization performance, especially when considering a larger set of cues, highlighting the potential of this approach.

关键词： Gaze Following vision-Language Zero-Shot Evaluation

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NICE: CVPR 2023 Challenge on Zero-shot Image Captioning

NICE: CVPR 2023 Challenge on Zero-shot Image Captioning

引用

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

作者： Kim, Taehoon Ahn, Pyunghwan Kim, Sangyun Lee, Sihaeng Marsden, Mark Sala, Alessandra Kim, Seung Hwan Han, Bohyung Lee, Kyoung Mu Lee, Honglak Bae, Kyounghoon Wu, Xiangyu Gao, Yi Zhang, Hailiang Yang, Yang Guo, Weili Lu, Jianfeng Oh, Youngtaek Cho, Jae Won Kim, Dong-Jin Kweon, In So Kim, Junmo Kang, Wooyoung Jhoo, Won Young Roh, Byungseok Mun, Jonghwan Oh, Solgil Ak, Kenan Emir Lee, Gwang-Gook Xu, Yan Shen, Mingwei Hwang, Kyomin Shin, Wonsik Lee, Kamin Park, Wonhark Lee, Dongkwan Kwak, Nojun Wang, Yujin Wang, Yimu Gu, Tiancheng Lv, Xingchang Sun, Mingmao

ISBN: (纸本)9798350365474

In this report, we introduce NICE (New frontiers for zero-shot Image Captioning Evaluation) project1 and share the results and outcomes of 2023 challenge. This project is designed to challenge the computer vision community to develop robust image captioning models that advance the state-of-the-art both in terms of accuracy and fairness. Through the challenge, the image captioning models were tested using a new evaluation dataset that includes a large variety of visual concepts from many domains. There was no specific training data provided for the challenge, and therefore the challenge entries were required to adapt to new types of image descriptions that had not been seen during training. This report includes information on the newly proposed NICE dataset, evaluation methods, challenge results, and technical details of top-ranking entries. We expect that the outcomes of the challenge will contribute to the improvement of AI models on various vision-language tasks.

关键词： Image captioning Multimodal representation vision-language models

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Hierarchical NeuroSymbolic Approach for Comprehensive and Explainable Action Quality Assessment

Hierarchical NeuroSymbolic Approach for Comprehensive and Ex...

引用

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

作者： Okamoto, Lauren Parmar, Paritosh Princeton Univ Princeton NJ 08544 USA ASTAR IHPC Singapore Singapore

ISBN: (纸本)9798350365474

Action quality assessment (AQA) applies computer vision to quantitatively assess the performance or execution of a human action. Current AQA approaches are end-to-end neural models, which lack transparency and tend to be biased because they are trained on subjective human judgements as ground-truth. To address these issues, we introduce a neuro-symbolic paradigm for AQA, which uses neural networks to abstract interpretable symbols from video data and makes quality assessments by applying rules to those symbols. We take diving as the case study. We found that domain experts prefer our system and find it more informative than purely neural approaches to AQA in diving. Our system also achieves state-of-the-art action recognition and temporal segmentation, and automatically generates a detailed report that breaks the dive down into its elements and provides objective scoring with visual evidence. As verified by a group of domain experts, this report may be used to assist judges in scoring, help train judges, and provide feedback to divers. Annotated training data and code: https://***/laurenok24/NSAQA.

关键词： action quality assessment action recognition AI Coach AI Diving Coach AI Diving Judge AI Olympics Judge explainable AI fairness in AI interpretable action analysis interpretable action quality assessment interpretable fine-grained action quality assessment neuro-symbolic computer vision neurosymbolic action assessment neurosymbolic action scoring neurosymbolic AI neurosymbolic fine-grained action analysis neurosymbolic fine-grained action quality assessment neurosymbolic fine-grained action recogntion neurosymbolic fine-grained action understanding neurosymbolic skills assessment neurosymbolic temporal segmentation neurosymbolic video understanding Olympics Scoring representation learning skills assessment temporal segmentation transparent AI XAI

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InVERGe: Intelligent Visual Encoder for Bridging Modalities in Report Generation

InVERGe: Intelligent Visual Encoder for Bridging Modalities ...

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

作者： Deria, Ankan Kumar, Komal Chakraborty, Snehashis Mahapatra, Dwarikanath Roy, Sudipta Jio Inst Artificial Intelligence & Data Sci Navi Mumbai 410206 India Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350365474

Medical image captioning plays an important role in modern healthcare, improving clinical report generation and aiding radiologists in detecting abnormalities and reducing misdiagnosis. The complex visual and textual data biases make this task more challenging. Recent advancements in transformer-based models have significantly improved the generation of radiology reports from medical images. However, these models require substantial computational resources for training and have been observed to produce unnatural language outputs when trained solely on raw image-text pairs. Our aim is to generate more detailed reports specific to images and to explain the reasoning behind the generated text through image-text alignment. Given the high computational demands of end-to-end model training, we introduce a two-step training methodology with an Intelligent Visual Encoder for Bridging Modalities in Report Generation (InVERGe) model. This model incorporates a lightweight transformer known as the Cross-Modal Query Fusion Layer (CMQFL), which utilizes the output from a frozen encoder to identify the most relevant text-grounded image embedding. This layer bridges the gap between the encoder and decoder, significantly reducing the workload on the decoder and enhancing the alignment between vision and language. Our experimental results, conducted using the MIMIC-CXR, Indiana University chest X-ray images, and CDD-CESM breast images datasets, demonstrate the effectiveness of our approach. Code: https://***/labsroy007/InVERGe

关键词： computer vision Deep Learning Large Language Model Medical Imaging Report Generation

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How to Benchmark vision Foundation Models for Semantic Segmentation?

How to Benchmark Vision Foundation Models for Semantic Segme...

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

作者： Kerssies, Tommie de Geus, Daan Dubbelman, Gijs Eindhoven Univ Technol Eindhoven Netherlands

ISBN: (纸本)9798350365474

Recent vision foundation models (VFMs) have demonstrated proficiency in various tasks but require supervised fine-tuning to perform the task of semantic segmentation effectively. Benchmarking their performance is essential for selecting current models and guiding future model developments for this task. The lack of a standardized benchmark complicates comparisons. Therefore, the primary objective of this paper is to study how VFMs should be benchmarked for semantic segmentation. To do so, various VFMs are finetuned under various settings, and the impact of individual settings on the performance ranking and training time is assessed. Based on the results, the recommendation is to finetune the ViT-B variants of VFMs with a 16 x 16 patch size and a linear decoder, as these settings are representative of using a larger model, more advanced decoder and smaller patch size, while reducing training time by more than 13 times. Using multiple datasets for training and evaluation is also recommended, as the performance ranking across datasets and domain shifts varies. Linear probing, a common practice for some VFMs, is not recommended, as it is not representative of end-to-end fine-tuning. The benchmarking setup recommended in this paper enables a performance analysis of VFMs for semantic segmentation. The findings of such an analysis reveal that pretraining with promptable segmentation is not beneficial, whereas masked image modeling (MIM) with abstract representations is crucial, even more important than the type of supervision used. The code for efficiently fine-tuning VFMs for semantic segmentation can be accessed through the project page(1).

关键词： benchmark semantic segmentation vision foundation models

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Block Selective Reprogramming for On-device Training of vision Transformers

Block Selective Reprogramming for On-device Training of Visi...

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

作者： Sarkar, Sreetama Kundu, Souvik Zheng, Kai Beerel, Peter A. Univ Southern Calif Los Angeles CA 90007 USA Intel Labs San Diego CA USA

ISBN: (纸本)9798350365474

The ubiquity of vision transformers (ViTs) for various edge applications, including personalized learning, has created the demand for on-device fine-tuning. However, training with the limited memory and computation power of edge devices remains a significant challenge. In particular, the memory required for training is much higher than that needed for inference, primarily due to the need to store activations across all layers in order to compute the gradients needed for weight updates. Previous works have explored reducing this memory requirement via frozen-weight training as well storing the activations in a compressed format. However, these methods are deemed inefficient due to their inability to provide training or inference speedup. In this paper, we first investigate the limitations of existing on-device training methods aimed at reducing memory and compute requirements. We then present block selective reprogramming (BSR) in which we fine-tune only a fraction of total blocks of a pre-trained model and selectively drop tokens based on self-attention scores of the frozen layers. To show the efficacy of BSR, we present extensive evaluations on ViT-B and DeiT-S with five different datasets. Compared to the existing alternatives, our approach simultaneously reduces training memory by up to 1.4x and compute cost by up to 2x while maintaining similar accuracy. We also showcase results for Mixture-of-Expert (MoE) models, demonstrating the effectiveness of our approach in multitask learning scenarios. Code will be available at: https://***/sreetamasarkar/BSR.

关键词： on-device training token pruning vision transformer

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