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Context De-confounded Emotion recognition

Context De-confounded Emotion Recognition

引用

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

作者： Yang, Dingkang Chen, Zhaoyu Wang, Yuzheng Wang, Shunli Li, Mingcheng Liu, Siao Zhao, Xiao Huang, Shuai Dong, Zhiyan Zhai, Peng Zhang, Lihua Fudan Univ Acad Engn & Technol Shanghai Peoples R China Inst Meta Med IPASS Boston MA 02108 USA Jilin Prov Key Lab Intelligence Sci & Engn Changchun Peoples R China Minist Educ Engn Res Ctr AI & Robot Shanghai Peoples R China

ISBN: (纸本)9798350301298

Context-Aware Emotion recognition (CAER) is a crucial and challenging task that aims to perceive the emotional states of the target person with contextual information. Recent approaches invariably focus on designing sophisticated architectures or mechanisms to extract seemingly meaningful representations from subjects and contexts. However, a long-overlooked issue is that a context bias in existing datasets leads to a significantly unbalanced distribution of emotional states among different context scenarios. Concretely, the harmful bias is a confounder that misleads existing models to learn spurious correlations based on conventional likelihood estimation, significantly limiting the models' performance. To tackle the issue, this paper provides a causality-based perspective to disentangle the models from the impact of such bias, and formulate the causalities among variables in the CAER task via a tailored causal graph. Then, we propose a Contextual Causal Intervention Module (CCIM) based on the backdoor adjustment to de-confound the confounder and exploit the true causal effect for model training. CCIM is plug-in and model-agnostic, which improves diverse state-of-the-art approaches by considerable margins. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our CCIM and the significance of causal insight.

关键词： vision applications and systems

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Accelerating vision-Language Pretraining with Free Language Modeling

Accelerating Vision-Language Pretraining with Free Language ...

引用

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

作者： Wang, Teng Ge, Yixiao Zheng, Feng Cheng, Ran Shan, Ying Qie, Xiaohu Luo, Ping Southern Univ Sci & Technol Shenzhen Peoples R China Univ Hong Kong Hong Kong Peoples R China ARC Lab Shenzhen Peoples R China Tencent PCG Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

The state of the arts in vision-language pretraining (VLP) achieves exemplary performance but suffers from high training costs resulting from slow convergence and long training time, especially on large-scale web datasets. An essential obstacle to training efficiency lies in the entangled prediction rate (percentage of tokens for reconstruction) and corruption rate (percentage of corrupted tokens) in masked language modeling (MLM), that is, a proper corruption rate is achieved at the cost of a large portion of output tokens being excluded from prediction loss. To accelerate the convergence of VLP, we propose a new pretraining task, namely, free language modeling (FLM), that enables a 100% prediction rate with arbitrary corruption rates. FLM successfully frees the prediction rate from the tie-up with the corruption rate while allowing the corruption spans to be customized for each token to be predicted. FLM-trained models are encouraged to learn better and faster given the same GPU time by exploiting bidirectional contexts more flexibly. Extensive experiments show FLM could achieve an impressive 2.5x pretraining time reduction in comparison to the MLM-based methods, while keeping competitive performance on both vision-language understanding and generation tasks. Code will be public at https://***/TencentARC/FLM.

关键词： and reasoning language vision

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DR2: Diffusion-based Robust Degradation Remover for Blind Face Restoration

DR2: Diffusion-based Robust Degradation Remover for Blind Fa...

引用

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

作者： Wang, Zhixin Zhang, Ziying Zhang, Xiaoyun Zheng, Huangjie Zhou, Mingyuan Zhang, Ya Wang, Yanfeng Shanghai Jiao Tong Univ CMIC Shanghai Peoples R China Univ Texas Austin Austin TX 78712 USA Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

Blind face restoration usually synthesizes degraded low-quality data with a pre-defined degradation model for training, while more complex cases could happen in the real world. This gap between the assumed and actual degradation hurts the restoration performance where artifacts are often observed in the output. However, it is expensive and infeasible to include every type of degradation to cover real-world cases in the training data. To tackle this robustness issue, we propose Diffusion-based Robust Degradation Remover (DR2) to first transform the degraded image to a coarse but degradation-invariant prediction, then employ an enhancement module to restore the coarse prediction to a high-quality image. By leveraging a well-performing denoising diffusion probabilistic model, our DR2 diffuses input images to a noisy status where various types of degradation give way to Gaussian noise, and then captures semantic information through iterative denoising steps. As a result, DR2 is robust against common degradation (e.g. blur, resize, noise and compression) and compatible with different designs of enhancement modules. Experiments in various settings show that our framework outperforms state-of-the-art methods on heavily degraded synthetic and real-world datasets.

关键词： Low-level vision

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Weakly supervised segmentation with point annotations for histopathology images via contrast-based variational model

Weakly supervised segmentation with point annotations for hi...

引用

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

作者： Zhang, Hongrun Burrows, Liam Meng, Yanda Sculthorpe, Declan Mukherjee, Abhik Coupland, Sarah E. Chen, Ke Zheng, Yalin Univ Liverpool Dept Eye & Vis Sci Liverpool Merseyside England Univ Liverpool Dept Math Sci Liverpool Merseyside England Univ Liverpool Ctr Math Imaging Techn Liverpool Merseyside England Univ Nottingham Biodiscovery Inst Sch Med Nottingham England Univ Liverpool Inst Syst Mol & Integrat Biol Liverpool Merseyside England

ISBN: (纸本)9798350301298

Image segmentation is a fundamental task in the field of imaging and vision. Supervised deep learning for segmentation has achieved unparalleled success when sufficient training data with annotated labels are available. However, annotation is known to be expensive to obtain, especially for histopathology images where the target regions are usually with high morphology variations and irregular shapes. Thus, weakly supervised learning with sparse annotations of points is promising to reduce the annotation workload. In this work, we propose a contrast-based variational model to generate segmentation results, which serve as reliable complementary supervision to train a deep segmentation model for histopathology images. The proposed method considers the common characteristics of target regions in histopathology images and can be trained in an end-to-end manner. It can generate more regionally consistent and smoother boundary segmentation, and is more robust to unlabeled 'novel' regions. Experiments on two different histology datasets demonstrate its effectiveness and efficiency in comparison to previous models. Code is available at: https://***/hrzhang1123/CVM_WS_Segmentation.

关键词： cell microscopy Medical and biological vision

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FashionSAP: Symbols and Attributes Prompt for Fine-grained Fashion vision-Language Pre-training

FashionSAP: Symbols and Attributes Prompt for Fine-grained F...

引用

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

作者： Han, Yunpeng Zhang, Lisai Chen, Qingcai Chen, Zhijian Li, Zhonghua Yang, Jianxin Cao, Zhao Harbin Inst Technol Shenzhen Harbin Peoples R China PengCheng Lab Shenzhen Peoples R China Huawei Technol Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Fashion vision-language pre-training models have shown efficacy for a wide range of downstream tasks. However, general vision-language pre-training models pay less attention to fine-grained domain features, while these features are important in distinguishing the specific domain tasks from general tasks. We propose a method for fine-grained fashion vision-language pre-training based on fashion Symbols and Attributes Prompt (FashionSAP) to model fine-grained multi-modalities fashion attributes and characteristics. Firstly, we propose the fashion symbols, a novel abstract fashion concept layer, to represent different fashion items and to generalize various kinds of fine-grained fashion features, making modelling fine-grained attributes more effective. Secondly, the attributes prompt method is proposed to make the model learn specific attributes of fashion items explicitly. We design proper prompt templates according to the format of fashion data. Comprehensive experiments are conducted on two public fashion benchmarks, i.e., FashionGen and FashionIQ, and FashionSAP gets SOTA performances for four popular fashion tasks. The ablation study also shows the proposed abstract fashion symbols, and the attribute prompt method enables the model to acquire fine-grained semantics in the fashion domain effectively. The obvious performance gains from FashionSAP provide a new baseline for future fashion task research.

关键词： Multi-modal learning

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Self-supervised Implicit Glyph Attention for Text recognition

Self-supervised Implicit Glyph Attention for Text Recognitio...

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

作者： Guan, Tongkun Gu, Chaochen Tu, Jingzheng Yang, Xue Feng, Qi Zhao, Yudi Shen, Wei Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China

ISBN: (纸本)9798350301298

The attention mechanism has become the de facto module in scene text recognition (STR) methods, due to its capability of extracting character-level representations. These methods can be summarized into implicit attention based and supervised attention based, depended on how the attention is computed, i.e., implicit attention and supervised attention are learned from sequence-level text annotations and or character-level bounding box annotations, respectively. Implicit attention, as it may extract coarse or even incorrect spatial regions as character attention, is prone to suffering from an alignment-drifted issue. Supervised attention can alleviate the above issue, but it is character category-specific, which requires extra laborious character-level bounding box annotations and would be memory-intensive when handling languages with larger character categories. To address the aforementioned issues, we propose a novel attention mechanism for STR, self-supervised implicit glyph attention (SIGA). SIGA delineates the glyph structures of text images by jointly self-supervised text segmentation and implicit attention alignment, which serve as the supervision to improve attention correctness without extra character-level annotations. Experimental results demonstrate that SIGA performs consistently and significantly better than previous attention-based STR methods, in terms of both attention correctness and final recognition performance on publicly available context benchmarks and our contributed contextless benchmarks.

关键词： detection recognition: Categorization retrieval

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On the Difficulty of Unpaired Infrared-to-Visible Video Translation: Fine-Grained Content-Rich Patches Transfer

On the Difficulty of Unpaired Infrared-to-Visible Video Tran...

引用

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

作者： Yu, Zhenjie Li, Shuang Shen, Yirui Liu, Chi Harold Wang, Shuigen Beijing Inst Technol Beijing Peoples R China Yantai IRay Technol Ltd Co Yantai Peoples R China

ISBN: (纸本)9798350301298

Explicit visible videos can provide sufficient visual information and facilitate vision applications. Unfortunately, the image sensors of visible cameras are sensitive to light conditions like darkness or overexposure. To make up for this, recently, infrared sensors capable of stable imaging have received increasing attention in autonomous driving and monitoring. However, most prosperous vision models are still trained on massive clear visible data, facing huge visual gaps when deploying to infrared imaging scenarios. In such cases, transferring the infrared video to a distinct visible one with fine-grained semantic patterns is a worthwhile endeavor. Previous works improve the outputs by equally optimizing each patch on the translated visible results, which is unfair for enhancing the details on content-rich patches due to the long-tail effect of pixel distribution. Here we propose a novel CPTrans framework to tackle the challenge via balancing gradients of different patches, achieving the fine-grained Content-rich Patches Transferring. Specifically, the content-aware optimization module encourages model optimization along gradients of target patches, ensuring the improvement of visual details. Additionally, the content-aware temporal normalization module enforces the generator to be robust to the motions of target patches. Moreover, we extend the existing dataset InfraredCity to more challenging adverse weather conditions (rain and snow), dubbed as InfraredCity-Adverse1. Extensive experiments show that the proposed CPTrans achieves state-of-the-art performance under diverse scenes while requiring less training time than competitive methods.

关键词： computer vision for social good

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Blind Image Quality Assessment via vision-Language Correspondence: A Multitask Learning Perspective

Blind Image Quality Assessment via Vision-Language Correspon...

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

作者： Zhang, Weixia Zhai, Guangtao Wei, Ying Yang, Xiaokang Ma, Kede Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China City Univ Hong Kong Shenzhen Res Inst Hong Kong Peoples R China

ISBN: (纸本)9798350301298

We aim at advancing blind image quality assessment (BIQA), which predicts the human perception of image quality without any reference information. We develop a general and automated multitask learning scheme for BIQA to exploit auxiliary knowledge from other tasks, in a way that the model parameter sharing and the loss weighting are determined automatically. Specifically, we first describe all candidate label combinations (from multiple tasks) using a textual template, and compute the joint probability from the cosine similarities of the visual-textual embeddings. Predictions of each task can be inferred from the joint distribution, and optimized by carefully designed loss functions. Through comprehensive experiments on learning three tasks - BIQA, scene classification, and distortion type identification, we verify that the proposed BIQA method 1) benefits from the scene classification and distortion type identification tasks and outperforms the state-of-the-art on multiple IQA datasets, 2) is more robust in the group maximum differentiation competition, and 3) realigns the quality annotations from different IQA datasets more effectively. The source code is available at https://***/zwx8981/LIQE.

关键词： Datasets and evaluation

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Indescribable Multi-modal Spatial Evaluator

Indescribable Multi-modal Spatial Evaluator

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

作者： Kong, Lingke Qi, X. Sharon Shen, Qijin Wang, Jiacheng Zhang, Jingyi Hu, Yanle Zhou, Qichao Manteia Tech Xiamen Peoples R China Univ Calif Los Angeles Los Angeles CA USA Fuzhou Univ Fuzhou Peoples R China Xiamen Univ Xiamen Peoples R China Mayo Clin Arizona Phoenix AZ 85054 USA

ISBN: (纸本)9798350301298

Multi-modal image registration spatially aligns two images with different distributions. One of its major challenges is that images acquired from different imaging machines have different imaging distributions, making it difficult to focus only on the spatial aspect of the images and ignore differences in distributions. In this study, we developed a self-supervised approach, Indescribable Multi-model Spatial Evaluator (IMSE), to address multi-modal image registration. IMSE creates an accurate multi-modal spatial evaluator to measure spatial differences between two images, and then optimizes registration by minimizing the error predicted of the evaluator. To optimize IMSE performance, we also proposed a new style enhancement method called Shuffle Remap which randomizes the image distribution into multiple segments, and then randomly disorders and remaps these segments, so that the distribution of the original image is changed. Shuffle Remap can help IMSE to predict the difference in spatial location from unseen target distributions. Our results show that IMSE outperformed the existing methods for registration using T1-T2 and CT-MRI datasets. IMSE also can be easily integrated into the traditional registration process, and can provide a convenient way to evaluate and visualize registration results. IMSE also has the potential to be used as a new paradigm for image-to-image translation. Our code is available at https://***/Kid-Liet/IMSE.

关键词： cell microscopy Medical and biological vision

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Watch or Listen: Robust Audio-Visual Speech recognition with Visual Corruption Modeling and Reliability Scoring

Watch or Listen: Robust Audio-Visual Speech Recognition with...

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

作者： Hong, Joanna Kim, Minsu Choi, Jeongsoo Ro, Yong Man Korea Adv Inst Sci & Technol Image & Video Syst Lab Daejeon South Korea

ISBN: (纸本)9798350301298

This paper deals with Audio-Visual Speech recognition (AVSR) under multimodal input corruption situations where audio inputs and visual inputs are both corrupted, which is not well addressed in previous research directions. Previous studies have focused on how to complement the corrupted audio inputs with the clean visual inputs with the assumption of the availability of clean visual inputs. However, in real life, clean visual inputs are not always accessible and can even be corrupted by occluded lip regions or noises. Thus, we firstly analyze that the previous AVSR models are not indeed robust to the corruption of multimodal input streams, the audio and the visual inputs, compared to uni-modal models. Then, we design multimodal input corruption modeling to develop robust AVSR models. Lastly, we propose a novel AVSR framework, namely Audio-Visual Reliability Scoring module (AV-RelScore), that is robust to the corrupted multimodal inputs. The AV-RelScore can determine which input modal stream is reliable or not for the prediction and also can exploit the more reliable streams in prediction. The effectiveness of the proposed method is evaluated with comprehensive experiments on popular benchmark databases, LRS2 and LRS3. We also show that the reliability scores obtained by AV-RelScore well reflect the degree of corruption and make the proposed model focus on the reliable multimodal representations.

关键词： Multi-modal learning

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