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Motion Information Propagation for Neural Video Compression

Motion Information Propagation for Neural Video Compression

引用

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

作者： Qi, Linfeng Li, Jiahao Li, Bin Li, Houqiang Lu, Yan Univ Sci & Technol China Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

In most existing neural video codecs, the information flow therein is uni-directional, where only motion coding provides motion vectors for frame coding. In this paper, we argue that, through information interactions, the synergy between motion coding and frame coding can be achieved. We effectively introduce bi-directional information interactions between motion coding and frame coding via our Motion Information Propagation. When generating the temporal contexts for frame coding, the high-dimension motion feature from the motion decoder serves as motion guidance to mitigate the alignment errors. Meanwhile, besides assisting frame coding at the current time step, the feature from context generation will be propagated as motion condition when coding the subsequent motion latent. Through the cycle of such interactions, feature propagation on motion coding is built, strengthening the capacity of exploiting long-range temporal correlation. In addition, we propose hybrid context generation to exploit the multi-scale context features and provide better motion condition. Experiments show that our method can achieve 12.9% bit rate saving over the previous SOTA neural video codec.

关键词： Low-level vision

来源：评论

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Joint Multimodal Transformer for Emotion recognition in the Wild

Joint Multimodal Transformer for Emotion Recognition in the ...

引用

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

作者： Waligora, Paul Aslam, Muhammad Haseeb Zeeshan, Muhammad Osama Belharbi, Soufiane Koerich, Alessandro Lameiras Pedersoli, Marco Bacon, Simon Granger, Eric ETS Montreal LIVIA Dept Syst Engn Montreal PQ Canada Concordia Univ Dept Hlth Kinesiol & Appl Physiol Montreal PQ Canada

ISBN: (纸本)9798350365474

Multimodal emotion recognition (MMER) systems typically outperform unimodal systems by leveraging the interand intra-modal relationships between, e.g., visual, textual, physiological, and auditory modalities. This paper proposes an MMER method that relies on a joint multimodal transformer (JMT) for fusion with key-based cross-attention. This framework can exploit the complementary nature of diverse modalities to improve predictive accuracy. Separate backbones capture intra-modal spatiotemporal dependencies within each modality over video sequences. Subsequently, our JMT fusion architecture integrates the individual modality embeddings, allowing the model to effectively capture inter- and intra-modal relationships. Extensive experiments on two challenging expression recognition tasks - (1) dimensional emotion recognition on the Affwild2 dataset (with face and voice) and (2) pain estimation on the Biovid dataset (with face and biosensors) - indicate that our JMT fusion can provide a cost-effective solution for MMER. Empirical results show that MMER systems with our proposed fusion allow us to outperform relevant baseline and state-of-the-art methods. Code is available at: https://***/PoloWlg/Joint-Multimodal-Transformer-6th-ABAW

关键词： Cross Attention Joint Multimodal Transformer Multimodal Emotion recognition Pain Estimation Valence Arousal

来源：评论

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MEDIC: Remove Model Backdoors via Importance Driven Cloning

MEDIC: Remove Model Backdoors via Importance Driven Cloning

引用

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

作者： Xu, Qiuling Tao, Guanhong Honorio, Jean Liu, Yingqi An, Shengwei Shen, Guangyu Cheng, Siyuan Zhang, Xiangyu Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9798350301298

We develop a novel method to remove injected backdoors in deep learning models. It works by cloning the benign behaviors of a trojaned model to a new model of the same structure. It trains the clone model from scratch on a very small subset of samples and aims to minimize a cloning loss that denotes the differences between the activations of important neurons across the two models. The set of important neurons varies for each input, depending on their magnitude of activations and their impact on the classification result. We theoretically show our method can better recover benign functions of the backdoor model. Meanwhile, we prove our method can be more effective in removing backdoors compared with fine-tuning. Our experiments show that our technique can effectively remove nine different types of backdoors with minor benign accuracy degradation, outperforming the state-of-the-art backdoor removal techniques that are based on fine-tuning, knowledge distillation, and neuron pruning.(1)

关键词： ethics in vision fairness*accountability privacy Transparency

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Balanced Energy Regularization Loss for Out-of-distribution Detection

Balanced Energy Regularization Loss for Out-of-distribution ...

引用

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

作者： Choi, Hyunjun Jeong, Hawook Choi, Jin Young Seoul Natl Univ ASRI ECE Seoul South Korea RideFlux Inc Jeju South Korea

ISBN: (纸本)9798350301298

In the field of out-of-distribution (OOD) detection, a previous method that use auxiliary data as OOD data has shown promising performance. However, the method provides an equal loss to all auxiliary data to differentiate them from inliers. However, based on our observation, in various tasks, there is a general imbalance in the distribution of the auxiliary OOD data across classes. We propose a balanced energy regularization loss that is simple but generally effective for a variety of tasks. Our balanced energy regularization loss utilizes class-wise different prior probabilities for auxiliary data to address the class imbalance in OOD data. The main concept is to regularize auxiliary samples from majority classes, more heavily than those from minority classes. Our approach performs better for OOD detection in semantic segmentation, long-tailed image classification, and image classification than the prior energy regularization loss. Furthermore, our approach achieves state-of-the-art performance in two tasks: OOD detection in semantic segmentation and long-tailed image classification.

关键词： vision applications and systems

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CAGE: Circumplex Affect Guided Expression Inference

CAGE: Circumplex Affect Guided Expression Inference

引用

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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Cascaded Local Implicit Transformer for Arbitrary-Scale Super-Resolution

Cascaded Local Implicit Transformer for Arbitrary-Scale Supe...

引用

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

作者： Chen, Hao-Wei Xu, Yu-Syuan Hong, Min-Fong Tsai, Yi-Min Kuo, Hsien-Kai Lee, Chun-Yi Natl Tsing Hua Univ Elsa Lab Hsinchu Taiwan MediaTek Inc Hsinchu Taiwan

ISBN: (纸本)9798350301298

Implicit neural representation has recently shown a promising ability in representing images with arbitrary resolutions. In this paper, we present a Local Implicit Transformer (LIT), which integrates the attention mechanism and frequency encoding technique into a local implicit image function. We design a cross-scale local attention block to effectively aggregate local features and a local frequency encoding block to combine positional encoding with Fourier domain information for constructing high-resolution images. To further improve representative power, we propose a Cascaded LIT (CLIT) that exploits multi-scale features, along with a cumulative training strategy that gradually increases the upsampling scales during training. We have conducted extensive experiments to validate the effectiveness of these components and analyze various training strategies. The qualitative and quantitative results demonstrate that LIT and CLIT achieve favorable results and outperform the prior works in arbitrary super-resolution tasks.

关键词： Low-level vision

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A Simple Framework for Text-Supervised Semantic Segmentation

A Simple Framework for Text-Supervised Semantic Segmentation

引用

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

作者： Yi, Muyang Cui, Quan Wu, Hao Yang, Cheng Yoshie, Osamu Lu, Hongtao Shanghai Jiao Tong Univ AI Inst Dept Comp Sci & Engn MoE Key Lab Artificial Intelligence Shanghai Peoples R China Waseda Univ Tokyo Japan ByteDance Inc Beijing Peoples R China

ISBN: (纸本)9798350301298

Text-supervised semantic segmentation is a novel research topic that allows semantic segments to emerge with image-text contrasting. However, pioneering methods could be subject to specifically designed network architectures. This paper shows that a vanilla contrastive language-image pre-training (CLIP) model is an effective text-supervised semantic segmentor by itself. First, we reveal that a vanilla CLIP is inferior to localization and segmentation due to its optimization being driven by densely aligning visual and language representations. Second, we propose the locality-driven alignment (LoDA) to address the problem, where CLIP optimization is driven by sparsely aligning local representations. Third, we propose a simple segmentation (SimSeg) framework. LoDA and SimSeg jointly ameliorate a vanilla CLIP to produce impressive semantic segmentation results. Our method outperforms previous state-of-the-art methods on PASCAL VOC 2012, PASCAL Context and COCO datasets by large margins. Code and models are available at ***/muyangyi/SimSeg.

关键词： and reasoning language vision

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Automatic High Resolution Wire Segmentation and Removal

Automatic High Resolution Wire Segmentation and Removal

引用

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

作者： Chiu, Mang Tik Zhang, Xuaner Wei, Zijun Zhou, Yuqian Shechtman, Eli Barnes, Connelly Lin, Zhe Kainz, Florian Amirghodsi, Sohrab Shi, Humphrey UIUC Champaign IL 61801 USA Adobe San Jose CA USA Univ Oregon Eugene OR USA

ISBN: (纸本)9798350301298

Wires and powerlines are common visual distractions that often undermine the aesthetics of photographs. The manual process of precisely segmenting and removing them is extremely tedious and may take up hours, especially on high-resolution photos where wires may span the entire space. In this paper, we present an automatic wire clean-up system that eases the process of wire segmentation and removal/inpainting to within a few seconds. We observe several unique challenges: wires are thin, lengthy, and sparse. These are rare properties of subjects that common segmentation tasks cannot handle, especially in high-resolution images. We thus propose a two-stage method that leverages both global and local contexts to accurately segment wires in high-resolution images efficiently, and a tile-based inpainting strategy to remove the wires given our predicted segmentation masks. We also introduce the first wire segmentation benchmark dataset, WireSegHR. Finally, we demonstrate quantitatively and qualitatively that our wire clean-up system enables fully automated wire removal with great generalization to various wire appearances.

关键词： vision applications and systems

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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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Multi-Modal Representation Learning with Text-Driven Soft Masks

Multi-Modal Representation Learning with Text-Driven Soft Ma...

引用

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

作者： Park, Jaeyoo Han, Bohyung Seoul Natl Univ Comp Vis Lab ECE Seoul South Korea Seoul Natl Univ IPAI Seoul South Korea

ISBN: (纸本)9798350301298

We propose a visual-linguistic representation learning approach within a self-supervised learning framework by introducing a new operation, loss, and data augmentation strategy. First, we generate diverse features for the image-text matching (ITM) task via soft-masking the regions in an image, which are most relevant to a certain word in the corresponding caption, instead of completely removing them. Since our framework relies only on image-caption pairs with no fine-grained annotations, we identify the relevant regions to each word by computing the word-conditional visual attention using multi-modal encoder. Second, we encourage the model to focus more on hard but diverse examples by proposing a focal loss for the image-text contrastive learning (ITC) objective, which alleviates the inherent limitations of overfitting and bias issues. Last, we perform multi-modal data augmentations for self-supervised learning via mining various examples by masking texts and rendering distortions on images. We show that the combination of these three innovations is effective for learning a pretrained model, leading to outstanding performance on multiple vision-language downstream tasks.

关键词： language reasoning vision

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