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PairDETR : Joint Detection and Association of Human Bodies and Faces

PairDETR : Joint Detection and Association of Human Bodies a...

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

作者： Ali, Ammar Gaikov, Georgii Rybalchenko, Denis Chigorin, Alexander Laptev, Ivan Zagoruyko, Sergey MTS AI ITMO Moscow Russia MTS AI Moscow Russia VisionLabs Hyderabad Telangana India MBZUAI Abu Dhabi U Arab Emirates MTS AI Skoltech Moscow Russia

ISBN: (纸本)9798350353013;9798350353006

Image and video analysis requires not only accurate object detection but also the understanding of relationships among detected objects. Common solutions to relation modeling typically resort to stand-alone object detectors followed by non-differentiable post-processing techniques. Recently introduced detection transformers (DETR) perform end-to-end object detection based on a bipartite matching loss. Such methods, however, lack the ability to jointly detect objects and resolve object associations. In this paper, we build on the DETR approach and extend it to the joint detection of objects and their relationships by introducing an approximated bipartite matching. While our method can generalize to an arbitrary number of objects, we here focus on the modeling of object pairs and their relations. In particular, we apply our method PairDETR to the problem of detecting human bodies and faces, and associating them for the same person. Our approach not only eliminates the need for hand-designed post-processing but also achieves excellent results for body-face associations. We evaluate PairDETR on the challenging CrowdHuman and CityPersons datasets and demonstrate a large improvement over the state of the art. Our training code and pre-trained models are available at https://***/mts-ai/pairdetr

关键词： Association CityPersons computer vision CrowdHuman DETR end-to-end Object detection Transformers

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Summarize the Past to Predict the Future: Natural Language Descriptions of Context Boost Multimodal Object Interaction Anticipation

Summarize the Past to Predict the Future: Natural Language D...

引用

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

作者： Pasca, Razvan-George Gavryushin, Alexey Hamza, Muhammad Kuo, Yen-Ling Mo, Kaichun Van Gool, Luc Hilliges, Otmar Wang, Xi Swiss Fed Inst Technol Zurich Switzerland Univ Zurich Zurich Switzerland Univ Virginia Charlottesville VA USA NVIDIA Santa Clara CA USA Katholieke Univ Leuven Leuven Belgium INSAIT Sofia Bulgaria

ISBN: (纸本)9798350353006

We study object interaction anticipation in egocentric videos. This task requires an understanding of the spatio-temporal context formed by past actions on objects, coined action context. We propose TransFusion, a multimodal transformer-based architecture for short-term object interaction anticipation. Our method exploits the representational power of language by summarizing the action context textually, after leveraging pre-trained vision-language foundation models to extract the action context from past video frames. The summarized action context and the last observed video frame are processed by the multimodal fusion module to forecast the next object interaction. Experiments on the Ego4D next active object interaction dataset show the effectiveness of our multimodal fusion model and highlight the benefits of using the power of foundation models and language-based context summaries in a task where vision may appear to suffice. Our novel approach outperforms all state-of-the-art methods on both versions of the Ego4D dataset. A project video and code are available at https://***/transfusion-proj/.

关键词： computer vision Egocentric vision Human Behavior Prediction Multimodal Learning Object Interaction Anticipation vision-Language Models

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Learning to Predict Activity Progress by Self-Supervised Video Alignment

Learning to Predict Activity Progress by Self-Supervised Vid...

引用

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

作者： Donahue, Gerard Elhamifar, Ehsan Northwestern Univ Boston MA 02115 USA

ISBN: (纸本)9798350353006

In this paper, we tackle the problem of self-supervised video alignment and activity progress prediction using in-the-wild videos. Our proposed self-supervised representation learning method carefully addresses different action orderings, redundant actions, and background frames to generate improved video representations compared to previous methods. Our model generalizes temporal cycleconsistency learning to allow for more flexibility in determining cycle-consistent neighbors. More specifically, to handle repeated actions, we propose a multi-neighbor cycle consistency and a multi-cycle-back regression loss by finding multiple soft nearest neighbors using a Gaussian Mixture Model. To handle background and redundant frames, we introduce a context-dependent drop function in our framework, discouraging the alignment of droppable frames. On the other hand, to learn from videos of multiple activities jointly, we propose a multi-head crosstask network, allowing us to embed a video and estimate progress without knowing its activity label. Experiments on multiple datasets show that our method outperforms the state-of-the-art for video alignment and progress prediction. (1)

关键词： computer vision in the wild procedural learning progress progress prediction representation learning self-supervised self-supervised representation learning unconstrained unconstrained videos Video Alignment video understanding

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Unlocking Pre-trained Image Backbones for Semantic Image Synthesis

Unlocking Pre-trained Image Backbones for Semantic Image Syn...

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

作者： Berrada, Tariq Verbeek, Jakob Couprie, Camille Alahari, Karteek Meta FAIR Menlo Pk CA 94025 USA Univ Grenoble Alpes LJK Grenoble INP InriaCNRS Grenoble France

ISBN: (纸本)9798350353013;9798350353006

Semantic image synthesis, i.e., generating images from user-provided semantic label maps, is an important conditional image generation task as it allows to control both the content as well as the spatial layout of generated images. Although diffusion models have pushed the state of the art in generative image modeling, the iterative nature of their inference process makes them computationally demanding. Other approaches such as GANs are more efficient as they only need a single feed-forward pass for generation, but the image quality tends to suffer when modeling large and diverse datasets. In this work, we propose a new class of GAN discriminators for semantic image synthesis that generates highly realistic images by exploiting feature backbones pre-trained for tasks such as image classification. We also introduce a new generator architecture with better context modeling and using cross-attention to inject noise into latent variables, leading to more diverse generated images. Our model, which we dub DP-SIMS, achieves state-of-the-art results in terms of image quality and consistency with the in-put label maps on ADE-20K, COCO-Stuff, and Cityscapes, surpassing recent diffusion models while requiring two orders of magnitude less compute for inference.

关键词： computer vision GAN Generative Modeling Image-to-image Semantic Synthesis

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ZePT: Zero-Shot Pan-Tumor Segmentation via Query-Disentangling and Self-Prompting

ZePT: Zero-Shot Pan-Tumor Segmentation via Query-Disentangli...

引用

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

作者： Jiang, Yankai Huang, Zhongzhen Zhang, Rongzhao Zhang, Xiaofan Zhang, Shaoting Shanghai AI Lab Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China SenseTime Res Shanghai Peoples R China

ISBN: (纸本)9798350353006

The long-tailed distribution problem in medical image analysis reflects a high prevalence of common conditions and a low prevalence of rare ones, which poses a significant challenge in developing a unified model capable of identifying rare or novel tumor categories not encountered during training. In this paper, we propose a new Zero-shot Pan-Tumor segmentation framework (ZePT) based on query-disentangling and self-prompting to segment unseen tumor categories beyond the training set. ZePT disentangles the object queries into two subsets and trains them in two stages. Initially, it learns a set of fundamental queries for organ segmentation through an object-aware feature grouping strategy, which gathers organ-level visual features. Subsequently, it refines the other set of advanced queries that focus on the auto-generated visual prompts for unseen tumor segmentation. Moreover, we introduce query-knowledge alignment at the feature level to enhance each query's discriminative representation and generalizability. Extensive experiments on various tumor segmentation tasks demonstrate the performance superiority of ZePT, which surpasses the previous counterparts and evidences the promising ability for zero-shot tumor segmentation in real-world settings.

关键词： Medical Image Segmentation vision-Language Model

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SonicvisionLM: Playing Sound with vision Language Models

SonicVisionLM: Playing Sound with Vision Language Models

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

作者： Xie, Zhifeng Yu, Shengye He, Qile Li, Mengtian Shanghai Univ Shanghai Peoples R China Shanghai Engn Res Ctr Mot Picture Special Effects Shanghai Peoples R China

ISBN: (纸本)9798350353006

There has been a growing interest in the task of generating sound for silent videos, primarily because of its practicality in streamlining video post-production. However, existing methods for video-sound generation attempt to directly create sound from visual representations, which can be challenging due to the difficulty of aligning visual representations with audio representations. In this paper, we present SonicvisionLM, a novel framework aimed at generating a wide range of sound effects by leveraging vision-language models(VLMs). Instead of generating audio directly from video, we use the capabilities of powerful VLMs. When provided with a silent video, our approach first identifies events within the video using a VLM to suggest possible sounds that match the video content. This shift in approach transforms the challenging task of aligning image and audio into more well-studied sub-problems of aligning image-to-text and text-to-audio through the popular diffusion models. To improve the quality of audio recommendations with LLMs, we have collected an extensive dataset that maps text descriptions to specific sound effects and developed a time-controlled audio adapter. Our approach surpasses current state-of-the-art methods for converting video to audio, enhancing synchronization with the visuals, and improving alignment between audio and video components. Project page: https://***. io/***/

关键词： audio generation multi-modal vision-language model

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WALT3D: Generating Realistic Training Data from Time-Lapse Imagery for Reconstructing Dynamic Objects under Occlusion

WALT3D: Generating Realistic Training Data from Time-Lapse I...

引用

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

作者： Khiem Vuong Reddy, N. Dinesh Tamburo, Robert Narasimhan, Srinivasa G. Carnegie Mellon Univ Pittsburgh PA 15213 USA Amazon Seattle WA USA

ISBN: (纸本)9798350353006

Current methods for 2D and 3D object understanding struggle with severe occlusions in busy urban environments, partly due to the lack of large-scale labeled groundtruth annotations for learning occlusion. In this work, we introduce a novel framework for automatically generating a large, realistic dataset of dynamic objects under occlusions using freely available time-lapse imagery. By leveraging off-the-shelf 2D (bounding box, segmentation, keypoint) and 3D (pose, shape) predictions as pseudo-groundtruth, unoccluded 3D objects are identified automatically and composited into the background in a clip-art style, ensuring realistic appearances and physically accurate occlusion configurations. The resulting clip-art image with pseudo-groundtruth enables efficient training of object reconstruction methods that are robust to occlusions. Our method demonstrates significant improvements in both 2D and 3D reconstruction, particularly in scenarios with heavily occluded objects like vehicles and people in urban scenes.

关键词： 3D from single images computer vision

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HomoFormer: Homogenized Transformer for Image Shadow Removal

HomoFormer: Homogenized Transformer for Image Shadow Removal

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

作者： Xiao, Jie Fu, Xueyang Zhu, Yurui Li, Dong Huang, Jie Zhu, Kai Zha, Zheng-Jun Univ Sci & Technol China Hefei Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9798350353006

The spatial non-uniformity and diverse patterns of shadow degradation conflict with the weight sharing manner of dominant models, which may lead to an unsatisfactory compromise. To tackle with this issue, we present a novel strategy from the view of shadow transformation in this paper: directly homogenizing the spatial distribution of shadow degradation. Our key design is the random shuffle operation and its corresponding inverse operation. Specifically, random shuffle operation stochastically rear-ranges the pixels across spatial space and the inverse operation recovers the original order. After randomly shuffling, the shadow diffuses in the whole image and the degradation appears in a homogenized way, which can be effectively processed by the local self-attention layer. Moreover, we further devise a new feed forward network with position modeling to exploit image structural information. Based on these elements, we construct the final local window based transformer named HomoFormer for image shadow removal. Our HomoFormer can enjoy the linear complexity of local transformers while bypassing challenges of non-uniformity and diversity of shadow. Extensive experiments are conducted to verify the superiority of our HomoFormer across public datasets. Code is available at https://***/jiexiaou/HomoFormer.

关键词： Image Restoration Image Shadow Removal vision Transformer

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Evaluating the Integration of Morph Attack Detection in Automated Face recognition Systems

Evaluating the Integration of Morph Attack Detection in Auto...

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

作者： Panzino, Andrea la Cava, Simone Maurizio Orru, Giulia Marcialis, Gian Luca Univ Cagliari Piazza Armi I-09123 Cagliari Italy

ISBN: (纸本)9798350365474

Due to the possibility of automatically verifying an individual's identity by comparing his/her face with that present in a personal identification document, systems providing identification must be equipped with digital manipulation detectors. Morphed facial images can be considered a threat among other manipulations because they are visually indistinguishable from authentic facial photos. They can have characteristics of many possible subjects due to the nature of the attack. Thus, morphing attack detection methods (MADs) must be integrated into automated face recognition. Following the recent advances in MADs, we investigate their effectiveness by proposing an integrated system simulator of real application contexts, moving from known to never-seen-before attacks.

关键词： detection face integration morphing

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SleepVST: Sleep Staging from Near-Infrared Video Signals using Pre-Trained Transformers

SleepVST: Sleep Staging from Near-Infrared Video Signals usi...

引用

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

作者： Carter, Jonathan F. Jorge, Joao Gibson, Oliver Tarassenkol, Lionel Univ Oxford Inst Biomed Engn Oxford England Oxehealth Ltd Oxford England

ISBN: (纸本)9798350353006

Advances in camera-based physiological monitoring have enabled the robust, non-contact measurement of respiration and the cardiac pulse, which are known to be indicative of the sleep stage. This has led to research into camera-based sleep monitoring as a promising alternative to "gold-standard" polysomnography, which is cumbersome, expensive to administer, and hence unsuitable for longer-term clinical studies. In this paper, we introduce SleepVST, a transformer model which enables state-of-the-art performance in camera-based sleep stage classification (sleep staging). After pre-training on contact sensor data, SleepVST outperforms existing methods for cardio-respiratory sleep staging on the SHHS and MESA datasets, achieving total Cohen's kappa scores of 0.75 and 0.77 respectively. We then show that SleepVST can be successfully transferred to cardio-respiratory waveforms extracted from video, enabling fully contact-free sleep staging. Using a video dataset of 50 nights, we achieve a total accuracy of 78.8% and a Cohen's. of 0.71 in four-class video-based sleep staging, setting a new state-of-the-art in the domain.

关键词： computer vision remote monitoring sleep staging transformers

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