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Collaborative Noisy Label Cleaner: Learning Scene-aware Trailers for Multi-modal Highlight Detection in Movies

Collaborative Noisy Label Cleaner: Learning Scene-aware Trai...

引用

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

作者： Gan, Bei Shu, Xiujun Qiao, Ruizhi Wu, Haoqian Chen, Keyu Li, Hanjun Ren, Bo Tencent YouTu Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

Movie highlights stand out of the screenplay for efficient browsing and play a crucial role on social media platforms. Based on existing efforts, this work has two observations: (1) For different annotators, labeling highlight has uncertainty, which leads to inaccurate and time-consuming annotations. (2) Besides previous supervised or unsupervised settings, some existing video corpora can be useful, e.g., trailers, but they are often noisy and incomplete to cover the full highlights. In this work, we study a more practical and promising setting, i.e., reformulating highlight detection as "learning with noisy labels". This setting does not require time-consuming manual annotations and can fully utilize existing abundant video corpora. First, based on movie trailers, we leverage scene segmentation to obtain complete shots, which are regarded as noisy labels. Then, we propose a Collaborative noisy Label Cleaner (CLC) framework to learn from noisy highlight moments. CLC consists of two modules: augmented cross-propagation (ACP) and multi-modality cleaning (MMC). The former aims to exploit the closely related audio-visual signals and fuse them to learn unified multi-modal representations. The latter aims to achieve cleaner highlight labels by observing the changes in losses among different modalities. To verify the effectiveness of CLC, we further collect a large-scale highlight dataset named MovieLights. Comprehensive experiments on MovieLights and YouTube Highlights datasets demonstrate the effectiveness of our approach. Code has been made available at: https://***/TencentYoutuResearch/HighlightDetection-CLC.

关键词： vision applications and systems

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Learning Multi-scale Representations with Single-stream Network for Video Retrieval

Learning Multi-scale Representations with Single-stream Netw...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Wang, Chia-Hui Tseng, Yu-Chee Chiang, Ting-Hui Chen, Yan-Ann National Yang Ming Chiao Tung University Department of Computer Science Taiwan National Yang Ming Chiao Tung University College of AI Taiwan Chunghwa Telecom Laboratories Advanced Technology Laboratory Taoyuan Taiwan Yuan Ze University Department of Computer Science and Engineering Taiwan

ISBN: (纸本)9798350302493

With the explosive growth of video contents in the Internet, video retrieval has become an important issue that can benefit video recommendation and copyright detection. Since the key features of a video may distribute in distant regions of a lengthy video, several works have made a success by exploiting multi-stream, multi-scale architectures to learn and merge distant features. However, a multi-stream network is costly in terms of memory and computing overhead. The number of scales and these scales are handcrafted and fixed once a model is finalized. Further, being more complicated, multi-stream networks are more prone to being overfitting and lead to poorer generalization. This paper proposes a single-stream network with built-in dilated spatial and temporal learning capability. By combining with modern techniques, including Denoising Autoencoder, Squeeze-and-Excitation Attention, and Triplet Comparative Mechanism, our model achieves state-of-the-art performance in several video retrieval tasks on the FIVR200K, CC WEB VIDEO, and EVVE datasets. © 2023 ieee.

关键词： Excited states

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StillFast: An End-to-End Approach for Short-Term Object Interaction Anticipation

StillFast: An End-to-End Approach for Short-Term Object Inte...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Ragusa, Francesco Farinella, Giovanni Maria Furnari, Antonino University of Catania Next Vision S.r.l. - Spin-off of the University of Catania FPV@IPLab Italy

ISBN: (纸本)9798350302493

Anticipation problem has been studied considering different aspects such as predicting humans' locations, predicting hands and objects trajectories, and forecasting actions and human-object interactions. In this paper, we studied the short-term object interaction anticipation problem from the egocentric point of view, proposing a new end-to-end architecture named StillFast. Our approach simultaneously processes a still image and a video detecting and localizing next-active objects, predicting the verb which describes the future interaction and determining when the interaction will start. Experiments on the large-scale egocentric dataset EGO4D [17] show that our method outperformed state-of-the-art approaches on the considered task. Our method is ranked first in the public leaderboard of the EGO4D short term object interaction anticipation challenge 2022 and it is the official baseline for the 2023 one. Please see the project web page for code and additional details: https://***/stillfast/. © 2023 ieee.

关键词： Forecasting

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Bias Mimicking: A Simple Sampling Approach for Bias Mitigation

Bias Mimicking: A Simple Sampling Approach for Bias Mitigati...

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

作者： Qraitem, Maan Saenko, Kate Plummer, Bryan A. Boston Univ Boston MA 02215 USA MIT IBM Watson Lab Cambridge MA USA

ISBN: (纸本)9798350301298

Prior work has shown that Visual recognition datasets frequently underrepresent bias groups B (e.g. Female) within class labels Y (e.g. Programmers). This dataset bias can lead to models that learn spurious correlations between class labels and bias groups such as age, gender, or race. Most recent methods that address this problem require significant architectural changes or additional loss functions requiring more hyper-parameter tuning. Alternatively, data sampling baselines from the class imbalance literature (e.g. Undersampling, Upweighting), which can often be implemented in a single line of code and often have no hyper-parameters, offer a cheaper and more efficient solution. However, these methods suffer from significant shortcomings. For example, Undersampling drops a significant part of the input distribution per epoch while Oversampling repeats samples, causing overfitting. To address these shortcomings, we introduce a new class-conditioned sampling method: Bias Mimicking. The method is based on the observation that if a class c bias distribution, i.e. PD(B| Y = c) is mimicked across every c ' not equal c, then Y and B are statistically independent. Using this notion, BM, through a novel training procedure, ensures that the model is exposed to the entire distribution per epoch without repeating samples. Consequently, Bias Mimicking improves underrepresented groups' accuracy of sampling methods by 3% over four benchmarks while maintaining and sometimes improving performance over nonsampling methods. Code: https: //***/mqraitem/Bias-Mimicking

关键词： accountability accountability ethics in vision fairness privacy Transparency

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Image Quality-aware Diagnosis via Meta-knowledge Co-embedding

Image Quality-aware Diagnosis via Meta-knowledge Co-embeddin...

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

作者： Che, Haoxuan Chen, Siyu Chen, Hao Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Medical images usually suffer from image degradation in clinical practice, leading to decreased performance of deep learning-based models. To resolve this problem, most previous works have focused on filtering out degradation-causing low-quality images while ignoring their potential value for models. Through effectively learning and leveraging the knowledge of degradations, models can better resist their adverse effects and avoid misdiagnosis. In this paper, we raise the problem of image quality-aware diagnosis, which aims to take advantage of low-quality images and image quality labels to achieve a more accurate and robust diagnosis. However, the diversity of degradations and superficially unrelated targets between image quality assessment and disease diagnosis makes it still quite challenging to effectively leverage quality labels to assist diagnosis. Thus, to tackle these issues, we propose a novel meta-knowledge co-embedding network, consisting of two subnets: Task Net and Meta Learner. Task Net constructs an explicit quality information utilization mechanism to enhance diagnosis via knowledge co-embedding features, while Meta Learner ensures the effectiveness and constrains the semantics of these features via meta-learning and joint-encoding masking. Superior performance on five datasets with four widely-used medical imaging modalities demonstrates the effectiveness and generalizability of our method.

关键词： cell microscopy Medical and biological vision

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GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Parts

GAPartNet: Cross-Category Domain-Generalizable Object Percep...

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

作者： Geng, Haoran Xu, Helin Zhao, Chengyang Xu, Chao Yi, Li Huang, Siyuan Wang, He Peking Univ CFCS Beijing Peoples R China Peking Univ Sch EECS Beijing Peoples R China Beijing Inst Gen Artificial Intelligence Beijing Peoples R China Tsinghua Univ Beijing Peoples R China Univ Calif Los Angeles Los Angeles CA USA

ISBN: (纸本)9798350301298

For years, researchers have been devoted to generalizable object perception and manipulation, where cross-category generalizability is highly desired yet underexplored. In this work, we propose to learn such cross-category skills via Generalizable and Actionable Parts (GAParts). By identifying and defining 9 GAPart classes (lids, handles, etc.) in 27 object categories, we construct a large-scale part-centric interactive dataset, GAPartNet, where we provide rich, part-level annotations (semantics, poses) for 8,489 part instances on 1,166 objects. Based on GAPartNet, we investigate three cross-category tasks: part segmentation, part pose estimation, and part-based object manipulation. Given the significant domain gaps between seen and unseen object categories, we propose a robust 3D segmentation method from the perspective of domain generalization by integrating adversarial learning techniques. Our method outperforms all existing methods by a large margin, no matter on seen or unseen categories. Furthermore, with part segmentation and pose estimation results, we leverage the GAPart pose definition to design part-based manipulation heuristics that can generalize well to unseen object categories in both the simulator and the real world.

关键词： Embodied vision: Active agents simulation

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AltFreezing for More General Video Face Forgery Detection

AltFreezing for More General Video Face Forgery Detection

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

作者： Wang, Zhendong Bao, Jianmin Zhou, Wengang Wangi, Weilun Li, Hougiang Univ Sci & Technol China EELS Dept CAS Key Lab GIPAS Langfang Hebei Peoples R China Microsoft Res Asia Beijing Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China

ISBN: (纸本)9798350301298

Existing face forgery detection models try to discriminate fake images by detecting only spatial artifacts (e.g., generative artifacts, blending) or mainly temporal artifacts (e.g., flickering, discontinuity). They may experience significant performance degradation when facing out-domain artifacts. In this paper, we propose to capture both spatial and temporal artifacts in one model for face forgery detection. A simple idea is to leverage a spatiotemporal model (3D ConvNet). However, we find that it may easily rely on one type of artifact and ignore the other. To address this issue, we present a novel training strategy called AltFreezing for more general face forgery detection. The AltFreezing aims to encourage the model to detect both spatial and temporal artifacts. It divides the weights of a spatiotemporal network into two groups: spatial-related and temporal-related. Then the two groups of weights are alternately frozen during the training process so that the model can learn spatial and temporal features to distinguish real or fake videos. Furthermore, we introduce various video-level data augmentation methods to improve the generalization capability of the forgery detection model. Extensive experiments show that our framework outperforms existing methods in terms of generalization to unseen manipulations and datasets.

关键词： accountability ethics in vision fairness privacy Transparency

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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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Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring

Efficient Frequency Domain-based Transformers for High-Quali...

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

作者： Kong, Lingshun Dong, Jiangxin Ge, Jianjun Li, Mingqiang Pan, Jinshan Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China China Elect Technol Grp Corp Informat Sci Acad Beijing Peoples R China

ISBN: (纸本)9798350301298

We present an effective and efficient method that explores the properties of Transformers in the frequency domain for high-quality image deblurring. Our method is motivated by the convolution theorem that the correlation or convolution of two signals in the spatial domain is equivalent to an element-wise product of them in the frequency domain. This inspires us to develop an efficient frequency domain-based self-attention solver (FSAS) to estimate the scaled dot-product attention by an element-wise product operation instead of the matrix multiplication in the spatial domain. In addition, we note that simply using the naive feed-forward network (FFN) in Transformers does not generate good deblurred results. To overcome this problem, we propose a simple yet effective discriminative frequency domain-based FFN (DFFN), where we introduce a gated mechanism in the FFN based on the Joint Photographic Experts Group (JPEG) compression algorithm to discriminatively determine which low- and high-frequency information of the features should be preserved for latent clear image restoration. We formulate the proposed FSAS and DFFN into an asymmetrical network based on an encoder and decoder architecture, where the FSAS is only used in the decoder module for better image deblurring. Experimental results show that the proposed method performs favorably against the state-of-the-art approaches.

关键词： Low-level vision

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Cross-Domain Image Captioning with Discriminative Finetuning

Cross-Domain Image Captioning with Discriminative Finetuning

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

作者： Dessi, Roberto Bevilacqua, Michele Gualdoni, Eleonora Carraz Rakotonirina, Nathanael Franzon, Francesca Baroni, Marco UPF Meta AI Barcelona Spain Samaya AI Mountain View CA USA UPF Barcelona Spain UPF ICREA Barcelona Spain

ISBN: (纸本)9798350301298

Neural captioners are typically trained to mimic human-generated references without optimizing for any specific communication goal, leading to problems such as the generation of vague captions. In this paper, we show that fine-tuning an out-of-the-box neural captioner with a self-supervised discriminative communication objective helps to recover a plain, visually descriptive language that is more informative about image contents. Given a target image, the system must learn to produce a description that enables an out-of-the-box text-conditioned image retriever to identify such image among a set of candidates. We experiment with the popular ClipCap captioner, also replicating the main results with BLIP. In terms of similarity to ground-truth human descriptions, the captions emerging from discriminative finetuning lag slightly behind those generated by the non-finetuned model, when the latter is trained and tested on the same caption dataset. However, when the model is used without further tuning to generate captions for out-of-domain datasets, our discriminatively-finetuned captioner generates descriptions that resemble human references more than those produced by the same captioner wihtout finetuning. We further show that, on the Conceptual Captions dataset, discriminatively finetuned captions are more helpful than either vanilla ClipCap captions or ground-truth captions for human annotators tasked with an image discrimination task.(1)

关键词： language reasoning vision

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