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HumMUSS: Human Motion Understanding using State Space Models

HumMUSS: Human Motion Understanding using State Space Models

引用

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

作者： Mondal, Arnab Alletto, Stefano Tome, Denis Mila Montreal PQ Canada Apple Cupertino CA 95014 USA

ISBN: (纸本)9798350353013;9798350353006

Understanding human motion from video is essential for a range of applications, including pose estimation, mesh recovery and action recognition. While state-of-the-art methods predominantly rely on transformer-based architectures, these approaches have limitations in practical scenarios. Transformers are slower when sequentially predicting on a continuous stream of frames in real-time, and do not generalize to new frame rates. In light of these constraints, we propose a novel attention-free spatiotemporal model for human motion understanding building upon recent advancements in state space models. Our model not only matches the performance of transformer-based models in various motion understanding tasks but also brings added benefits like adaptability to different video frame rates and enhanced training speed when working with longer sequences of keypoints. Moreover, the proposed model supports both offline and real-time applications. For real-time sequential prediction, our model is both memory efficient and several times faster than transformer-based approaches while maintaining their high accuracy.

关键词： action recognition human motion understanding mesh recovery pose estimation self-supervised learning spatiotemporal modeling state space models

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SHViT: Single-Head vision Transformer with Memory Efficient Macro Design

SHViT: Single-Head Vision Transformer with Memory Efficient ...

引用

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

作者： Yun, Seokju Ro, Youngmin Univ Seoul Machine Intelligence Lab Seoul South Korea

ISBN: (纸本)9798350353013;9798350353006

Recently, efficient vision Transformers have shown great performance with low latency on resource-constrained devices. Conventionally, they use 4x4 patch embeddings and a 4-stage structure at the macro level, while utilizing sophisticated attention with multi-head configuration at the micro level. This paper aims to address computational redundancy at all design levels in a memory-efficient manner. We discover that using larger-stride patchify stem not only reduces memory access costs but also achieves competitive performance by leveraging token representations with reduced spatial redundancy from the early stages. Furthermore, our preliminary analyses suggest that attention layers in the early stages can be substituted with convolutions, and several attention heads in the latter stages are computationally redundant. To handle this, we introduce a single-head attention module that inherently prevents head redundancy and simultaneously boosts accuracy by parallelly combining global and local information. Building upon our solutions, we introduce SHViT, a SingleHead vision Transformer that obtains the state-of-the-art speed-accuracy tradeoff. For example, on ImageNet-1k, our SHViT-S4 is 3.3x, 8.1x, and 2.4x faster than MobileViTv2 x1.0 on GPU, CPU, and iPhone12 mobile device, respectively, while being 1.3% more accurate. For object detection and instance segmentation on MS COCO using MaskRCNN head, our model achieves performance comparable to FastViT-SA12 while exhibiting 3.8x and 2.0x lower backbone latency on GPU and mobile device, respectively.

关键词： CNNs efficient visual backbone vision Transformer

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PEEKABOO: Interactive Video Generation via Masked-Diffusion

PEEKABOO: Interactive Video Generation via Masked-Diffusion

引用

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

作者： Jain, Yash Nasery, Anshul Vineet, Vibhav Behl, Harkirat Microsoft Redmond WA 98052 USA Univ Washington Seattle WA USA

ISBN: (纸本)9798350353013;9798350353006

Modern video generation models like Sora have achieved remarkable success in producing high-quality videos. However, a significant limitation is their inability to offer interactive control to users, a feature that promises to open up unprecedented applications and creativity. In this work, we introduce the first solution to equip diffusion-based video generation models with spatio-temporal control. We present PEEKABOO, a novel masked attention module, which seamlessly integrates with current video generation models offering control without the need for additional training or inference overhead. To facilitate future research, we also introduce a comprehensive benchmark for interactive video generation. This benchmark offers a standardized framework for the community to assess the efficacy of emerging interactive video generation models. Our extensive qualitative and quantitative assessments reveal that PEEKABOO achieves up to a 3.8x improvement in mIoU over baseline models, all while maintaining the same latency. Code and benchmark are available on the webpage.

关键词： computer vision diffusion interactive text to video video generation

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Open-Set Domain Adaptation for Semantic Segmentation

Open-Set Domain Adaptation for Semantic Segmentation

引用

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

作者： Choe, Seun-An Shin, Ah-Hyung Park, Keon-Hee Choi, Jinwoo Park, Gyeong-Moon Kyung Hee Univ Yongin South Korea

ISBN: (纸本)9798350353006

Unsupervised domain adaptation (UDA) for semantic segmentation aims to transfer the pixel-wise knowledge from the labeled source domain to the unlabeled target domain. However, current UDA methods typically assume a shared label space between source and target, limiting their applicability in real-world scenarios where novel categories may emerge in the target domain. In this paper, we introduce Open-Set Domain Adaptation for Semantic Segmentation (OSDA-SS) for the first time, where the target domain includes unknown classes. We identify two major problems in the OSDA-SS scenario as follows: 1) the existing UDA methods struggle to predict the exact boundary of the unknown classes, and 2) they fail to accurately predict the shape of the unknown classes. To address these issues, we propose Boundary and Unknown Shape-Aware open-set domain adaptation, coined BUS. Our BUS can accurately discern the boundaries between known and unknown classes in a contrastive manner using a novel dilation-erosion-based contrastive loss. In addition, we propose OpenReMix, a new domain mixing augmentation method that guides our model to effectively learn domain and size-invariant features for improving the shape detection of the known and unknown classes. Through extensive experiments, we demonstrate that our proposed BUS effectively detects unknown classes in the challenging OSDA-SS scenario compared to the previous methods by a large margin. The code is available at https://***/KHU-AGI/BUS.

关键词： Contrastive Learning Domain Adaptation Domain Mixing Augmentation Open-Set recognition Semantic Segmentation

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VLP: vision Language Planning for Autonomous Driving

VLP: Vision Language Planning for Autonomous Driving

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

作者： Pan, Chenbin Yaman, Burhaneddin Nesti, Tommaso Mallik, Abhirup Allievi, Alessandro G. Velipasalar, Senem Rene, Liu Syracuse Univ Syracuse NY USA Bosch Res North Amer & Bosch Ctr Artificial Intel Sunnyvale CA 94085 USA

ISBN: (纸本)9798350353006

Autonomous driving is a complex and challenging task that aims at safe motion planning through scene understanding and reasoning. While vision-only autonomous driving methods have recently achieved notable performance, through enhanced scene understanding, several key issues, including lack of reasoning, low generalization performance and long-tail scenarios, still need to be addressed. In this paper, we present VLP, a novel vision-Language-Planning framework that exploits language models to bridge the gap between linguistic understanding and autonomous driving. VLP enhances autonomous driving systems by strengthening both the source memory foundation and the self-driving car's contextual understanding. VLP achieves state-of-the-art end-to-end planning performance on the challenging NuScenes dataset by achieving 35.9% and 60.5% reduction in terms of average L2 error and collision rates, respectively, compared to the previous best method. Moreover, VLP shows improved performance in challenging long-tail scenarios and strong generalization capabilities when faced with new urban environments.

关键词： Visual languages

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HEAL-SWIN: A vision Transformer On The Sphere

HEAL-SWIN: A Vision Transformer On The Sphere

引用

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

作者： Carlsson, Oscar Gerken, Jan E. Linander, Hampus Spiess, Heiner Ohlsson, Fredrik Petersson, Christoffer Persson, Daniel Univ Gothenburg Chalmers Univ Technol Dept Math Sci SE-41296 Gothenburg Sweden Tech Univ Berlin Neural Informat Proc Sci Intelligence DE-10623 Berlin Germany Umea Univ Dept Math & Math Stat Umea degrees SE-90187 Umea Sweden Zenseact SE-41756 Gothenburg Sweden

ISBN: (纸本)9798350353013;9798350353006

High-resolution wide-angle fisheye images are becoming more and more important for robotics applications such as autonomous driving. However, using ordinary convolutional neural networks or vision transformers on this data is problematic due to projection and distortion losses introduced when projecting to a rectangular grid on the plane. We introduce the HEAL-SWIN transformer, which combines the highly uniform Hierarchical Equal Area iso-Latitude Pixelation (HEALPix) grid used in astrophysics and cosmology with the Hierarchical Shifted-Window (SWIN) transformer to yield an efficient and flexible model capable of training on high-resolution, distortion-free spherical data. In HEAL-SWIN, the nested structure of the HEALPix grid is used to perform the patching and windowing operations of the SWIN transformer, enabling the network to process spherical representations with minimal computational overhead. We demonstrate the superior performance of our model on both synthetic and real automotive datasets, as well as a selection of other image datasets, for semantic segmentation, depth regression and classification tasks. Our code is publicly available(1).

关键词： depth estimation fisheye images image classification omni-directional images semantic segmentation spherical grid transformer

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Contrasting intra-modal and ranking cross-modal hard negatives to enhance visio-linguistic compositional understanding

Contrasting intra-modal and ranking cross-modal hard negativ...

引用

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

作者： Zhang, Le Awal, Rabiul Agrawal, Aishwarya Mila Quebec AI Inst Montreal PQ Canada Univ Montreal Montreal PQ Canada Canada CIFAR AI Chair Montreal PQ Canada

ISBN: (纸本)9798350353006

vision-Language Models (VLMs), such as CLIP, exhibit strong image-text comprehension abilities, facilitating advances in several downstream tasks such as zero-shot image classification, image-text retrieval, and text-to-image generation. However, the compositional reasoning abilities of existing VLMs remains subpar. The root of this limitation lies in the inadequate alignment between the images and captions in the pretraining datasets. Additionally, the current contrastive learning objective fails to focus on fine-grained grounding components like relations, actions, and attributes, resulting in "bag-of-words" representations. We introduce a simple and effective method to improve compositional reasoning in VLMs. Our method better leverages available datasets by refining and expanding the standard image-text contrastive learning framework. Our approach does not require specific annotations and does not incur extra parameters. When integrated with CLIP, our technique yields notable improvement over state-of-the-art baselines across five vision-language compositional benchmarks.(1)

关键词： compositional understanding contrastive learning vision-language models

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Object recognition as Next Token Prediction

Object Recognition as Next Token Prediction

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

作者： Yue, Kaiyu Chen, Bor-Chun Geiping, Jonas Li, Hengduo Goldstein, Tom Lim, Ser-Nam Meta Menlo Pk CA 94025 USA Univ Maryland College Pk MD 20742 USA ELLIS Inst Tubingen Germany MPI IS Tubingen Tubingen Germany Univ Cent Florida Orlando FL 32816 USA Meta AI Menlo Pk CA USA

ISBN: (纸本)9798350353006

We present an approach to pose object recognition as next token prediction. The idea is to apply a language decoder that auto-regressively predicts the text tokens from image embeddings to form labels. To ground this prediction process in auto-regression, we customize a non-causal attention mask for the decoder, incorporating two key features: modeling tokens from different labels to be independent, and treating image tokens as a prefix. This masking mechanism inspires an efficient method - one-shot sampling - to simultaneously sample tokens of multiple labels in parallel and rank generated labels by their probabilities during inference. To further enhance the efficiency, we propose a simple strategy to construct a compact decoder by simply discarding the intermediate blocks of a pretrained language model. This approach yields a decoder that matches the full model's performance while being notably more efficient. The code is available at ***/kaiyuyue/nxtp.

关键词： Decoding

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Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding

Chat-UniVi: Unified Visual Representation Empowers Large Lan...

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

作者： Jin, Peng Takanobu, Ryuichi Zhang, Wancai Cao, Xiaochun Yuan, Li Peking Univ Sch Elect & Comp Engn Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Peking Univ Ai Sci AI4S Preferred Program Shenzhen Grad Sch Shenzhen Peoples R China Nari Technol Co Ltd Beijing Peoples R China Sun Yat Sen Univ Sch Cyber Sci & Tech Shenzhen Campus Shenzhen Peoples R China

ISBN: (纸本)9798350353006

Large language models have demonstrated impressive universal capabilities across a wide range of open-ended tasks and have extended their utility to encompass multi-modal conversations. However, existing methods encounter challenges in effectively handling both image and video understanding, particularly with limited visual tokens. In this work, we introduce Chat-UniVi, a Unified vision-language model capable of comprehending and engaging in conversations involving images and videos through a unified visual representation. Specifically, we employ a set of dynamic visual tokens to uniformly represent images and videos. This representation framework empowers the model to efficiently utilize a limited number of visual tokens to simultaneously capture the spatial details necessary for images and the comprehensive temporal relationship required for videos. Moreover, we leverage a multi-scale representation, enabling the model to perceive both high-level semantic concepts and low-level visual details. Notably, ChatUniVi is trained on a mixed dataset containing both images and videos, allowing direct application to tasks involving both mediums without requiring any modifications. Extensive experimental results demonstrate that Chat-UniVi consistently outperforms even existing methods exclusively designed for either images or videos. Code is available at https://***/PKU-YuanGroup/Chat-UniVi.

关键词： Image Understanding Large Language Models Video Understanding vision Language Model

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LAFS: Landmark-based Facial Self-supervised Learning for Face recognition

LAFS: Landmark-based Facial Self-supervised Learning for Fac...

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

作者： Sun, Zhonglin Feng, Chen Patras, Ioannis Tzimiropoulos, Georgios Queen Mary Univ London London England

ISBN: (纸本)9798350353013;9798350353006

In this work we focus on learning facial representations that can be adapted to train effective face recognition models, particularly in the absence of labels. Firstly, compared with existing labelled face datasets, a vastly larger magnitude of unlabeled faces exists in the real world. We explore the learning strategy of these unlabeled facial images through self-supervised pretraining to transfer generalized face recognition performance. Moreover, motivated by one recent finding, that is, the face saliency area is critical for face recognition, in contrast to utilizing random cropped blocks of images for constructing augmentations in pretraining, we utilize patches localized by extracted facial landmarks. This enables our method - namely LAndmark-based Facial Self-supervised learning (LAFS), to learn key representation that is more critical for face recognition. We also incorporate two landmark-specific augmentations which introduce more diversity of landmark information to further regularize the learning. With learned landmark-based facial representations, we further adapt the representation for face recognition with regularization mitigating variations in landmark positions. Our method achieves significant improvement over the state-of-the-art on multiple face recognition benchmarks, especially on more challenging few-shot scenarios. The code is available at https://***/szlbiubiubiulLAFS_cvpr2024.

关键词： Face recognition Facial Parts Few-shot Self-supervised

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