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共 11889 条记录，以下是121-130 订阅

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Dense vision Transformer Compression with Few Samples

Dense Vision Transformer Compression with Few Samples

引用

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

作者： Zhang, Hanxiao Zhou, Yifan Wang, Guo-Hua Nanjing Univ Natl Key Lab Novel Software Technol Nanjing Peoples R China Nanjing Univ Sch Artificial Intelligence Nanjing Peoples R China

ISBN: (纸本)9798350353006

Few-shot model compression aims to compress a large model into a more compact one with only a tiny training set (even without labels). Block-level pruning has recently emerged as a leading technique in achieving high accuracy and low latency in few-shot CNN compression. But, few-shot compression for vision Transformers (ViT) remains largely unexplored, which presents a new challenge. In particular, the issue of sparse compression exists in traditional CNN few-shot methods, which can only produce very few compressed models of different model sizes. This paper proposes a novel framework for few-shot ViT compression named DC-ViT. Instead of dropping the entire block, DC-ViT selectively eliminates the attention module while retaining and reusing portions of the MLP module. DC-ViT enables dense compression, which outputs numerous compressed models that densely populate the range of model complexity. DC-ViT outperforms state-of-the-art few-shot compression methods by a significant margin of 10 percentage points, along with lower latency in the compression of ViT and its variants.

关键词： Few-shot Model Compression Pruning vision Transformer

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PoNQ: a Neural QEM-based Mesh Representation

PoNQ: a Neural QEM-based Mesh Representation

引用

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

作者： Maruani, Nissim Ovsjanikov, Maks Alliez, Pierre Desbrun, Mathieu Univ Cote Azur INRIA Nice France IP Paris Ecole Polytech LIX Paris France Ecole Polytech Inria Saclay Paris France

ISBN: (纸本)9798350353013;9798350353006

Although polygon meshes have been a standard representation in geometry processing, their irregular and combinatorial nature hinders their suitability for learning-based applications. In this work, we introduce a novel learnable mesh representation through a set of local 3D sample Points and their associated Normals and Quadric error metrics (QEM) w.r.t. the underlying shape, which we denote PoNQ. A global mesh is directly derived from PoNQ by efficiently leveraging the knowledge of the local quadric errors. Besides marking the first use of QEM within a neural shape representation, our contribution guarantees both topological and geometrical properties by ensuring that a PoNQ mesh does not self-intersect and is always the boundary of a volume. Notably, our representation does not rely on a regular grid, is supervised directly by the target surface alone, and also handles open surfaces with boundaries and/or sharp features. We demonstrate the efficacy of PoNQ through a learning-based mesh prediction from SDF grids and show that our method surpasses recent state-of-the-art techniques in terms of both surface and edge-based metrics.

关键词： 3D Mesh Computational Geometry computer vision

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JRDB-Social: A Multifaceted Robotic Dataset for Understanding of Context and Dynamics of Human Interactions Within Social Groups

JRDB-Social: A Multifaceted Robotic Dataset for Understandin...

引用

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

作者： Jahangard, Simindokht Cai, Zhixi Wen, Shiki Rezatofighi, Hamid Monash Univ Clayton Vic Australia

ISBN: (纸本)9798350353006

Understanding human social behaviour is crucial in computer vision and robotics. Micro-level observations like individual actions fall short, necessitating a comprehensive approach that considers individual behaviour, intra-group dynamics, and social group levels for a thorough understanding. To address dataset limitations, this paper introduces JRDB-Social, an extension of JRDB [2]. Designed to fill gaps in human understanding across diverse indoor and outdoor social contexts, JRDB-Social provides annotations at three levels: individual attributes, intra-group interactions, and social group context. This dataset aims to enhance our grasp of human social dynamics for robotic applications. Utilizing the recent cutting-edge multi-modal large language models, we evaluated our benchmark to explore their capacity to decipher social human behaviour.

关键词： dataset human attributes human human interaction human social behaviour understanding interaction large language model multifaceted robotic dataset social group social robot vision language model visual question answering visual reasoning

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Generative Rendering: Controllable 4D-Guided Video Generation with 2D Diffusion Models

Generative Rendering: Controllable 4D-Guided Video Generatio...

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

作者： Cai, Shengqu Ceylan, Duygu Gadelha, Matheus Huang, Chun-Hao Paul Wang, Tuanfeng Yang Wetzstein, Gordon Stanford Univ Stanford CA 94305 USA Adobe Res San Francisco CA USA

ISBN: (纸本)9798350353013;9798350353006

Traditional 3D content creation tools empower users to bring their imagination to life by giving them direct control over a scene's geometry, appearance, motion, and camera path. Creating computer-generated videos, however, is a tedious manual process, which can be automated by emerging text-to-video diffusion models. Despite great promise, video diffusion models are difficult to control, hindering a user to apply their own creativity rather than amplifying it. To address this challenge, we present a novel approach that combines the controllability of dynamic 3D meshes with the expressivity and editability of emerging diffusion models. For this purpose, our approach takes an animated, low-fidelity rendered mesh as input and injects the ground truth correspondence information obtained from the dynamic mesh into various stages of a pre-trained text-to-image generation model to output high-quality and temporally consistent frames. We demonstrate our approach on various examples where motion can be obtained by animating rigged assets or changing the camera path. Project page: ***/generative_rendering.

关键词： Animation computer Graphics computer vision Generative Models Video Generation Video Synthesis

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Adaptive Hyper-graph Aggregation for Modality-Agnostic Federated Learning

Adaptive Hyper-graph Aggregation for Modality-Agnostic Feder...

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

作者： Qi, Fan Li, Shuai Tianjin Univ Technol Tianjin Peoples R China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353006

In Federated Learning (FL), the issue of statistical data heterogeneity has been a significant challenge to the field's ongoing development. This problem is further exacerbated when clients' data vary in modalities. In response to these issues of statistical heterogeneity and modality incompatibility, we propose the Adaptive Hyper-graph Aggregation framework, a novel solution for Modality-Agnostic Federated Learning. We design a Modular Architecture for Local Model with single modality, setting the stage for efficient intra-modality sharing and inter-modality complementarity. An innovative Global Consensus Prototype Enhancer is crafted to assimilate and broadcast global consensus knowledge within the network. At the core of our approach lies the Adaptive Hyper-graph Learning Strategy, which effectively tackles the inherent challenges of modality incompatibility and statistical heterogeneity within federated learning environments, accomplishing this adaptively even without the server being aware of the clients' modalities. Our approach, tested on three multimodal benchmark datasets, demonstrated strong performance across diverse data distributions, affirming its effectiveness in multimodal federated learning.

关键词： Knowledge engineering computer vision Adaptation models Federated learning Prototypes computer architecture Benchmark testing

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Prompt Learning via Meta-Regularization

Prompt Learning via Meta-Regularization

引用

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

作者： Park, Jinyoung Ko, Juyeon Kim, Hyunwoo J. Korea Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9798350353006

Pre-trained vision-language models have shown impressive success on various computer vision tasks with their zero-shot generalizability. Recently, prompt learning approaches have been explored to efficiently and effectively adapt the vision-language models to a variety of down-stream tasks. However, most existing prompt learning methods suffer from task overfitting since the general knowledge of the pre-trained vision language models is forgotten while the prompts are finetuned on a small data set from a specific target task. To address this issue, we propose a Prompt Meta-Regularization (ProMetaR) to improve the generalizability of prompt learning for vision-language models. Specifically, ProMetaR meta-learns both the regularizer and the soft prompts to harness the task-specific knowledge from the downstream tasks and task-agnostic general knowledge from the vision-language models. Further, ProMetaR augments the task to generate multiple virtual tasks to alleviate the meta-overfitting. In addition, we provide the analysis to comprehend how ProMetaR improves the generalizability of prompt tuning in the perspective of the gradient alignment. Our extensive experiments demonstrate that our ProMetaR improves the generalizability of conventional prompt learning methods under base-to-base/base-to-new and domain generalization settings. The code of ProMetaR is available at https://***/mlvlab/ProMetaR.

关键词： Meta-learning Prompt tuning vision-Language Model

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AM-RADIO: Agglomerative vision Foundation Model Reduce All Domains Into One

AM-RADIO: Agglomerative Vision Foundation Model Reduce All D...

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

作者： Ranzinger, Mike Heinrich, Greg Kautz, Jan Molchanov, Pavlo NVIDIA Santa Clara CA 95051 USA

ISBN: (纸本)9798350353006

A handful of visual foundation models (VFMs) have recently emerged as the backbones for numerous downstream tasks. VFMs like CLIP, DINOv2, SAM are trained with distinct objectives, exhibiting unique characteristics for various downstream tasks. We find that despite their conceptual differences, these models can be effectively merged into a unified model through multi-teacher distillation. We name this approach AM-RADIO (Agglomerative Model - Reduce All Domains Into One). This integrative approach not only surpasses the performance of individual teacher models but also amalgamates their distinctive features, such as zero-shot vision-language comprehension, detailed pixel-level understanding, and open vocabulary segmentation capabilities. Additionally, in pursuit of the most hardware-efficient backbone, we evaluated numerous architectures in our multi-teacher distillation pipeline using the same training recipe. This led to the development of a novel architecture (E-RADIO) that exceeds the performance of its predecessors and is at least 6x faster than the teacher models at matched resolution. Our comprehensive benchmarking process covers downstream tasks including ImageNet classification, semantic segmentation linear probing, COCO object detection and integration into LLaVa-1.5. Code: https://***/NVlabs/RADIO.

关键词： Distillation Knowledge Distillation Semantic Segmentation Visual Foundation Model Visual Large Language Models Zero Shot Classification

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Collaborating Foundation Models for Domain Generalized Semantic Segmentation

Collaborating Foundation Models for Domain Generalized Seman...

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

作者： Benigmim, Yasser Roy, Subhankar Essid, Slim Kalogeiton, Vicky Lathuiliere, Stephane Inst Polytech Paris Telecom Paris LTCI Palaiseau France Inst Polytech Paris CNRS Ecole Polytech LIX Palaiseau France Univ Aberdeen Aberdeen Scotland

ISBN: (纸本)9798350353013;9798350353006

Domain Generalized Semantic Segmentation (DGSS) deals with training a model on a labeled source domain with the aim of generalizing to unseen domains during inference. Existing DGSS methods typically effectuate robust features by means of Domain Randomization (DR). Such an approach is often limited as it can only account for style diversification and not content. In this work, we take an orthogonal approach to DGSS and propose to use an assembly of CoLlaborative FOUndation models for Domain Generalized Semantic Segmentation (CLOUDS). In detail, CLOUDS is a framework that integrates Foundation Models of various kinds: (i) CLIP backbone for its robust feature representation, (ii) Diffusion Model to diversify the content, thereby covering various modes of the possible target distribution, and (iii) Segment Anything Model (SAM) for iteratively refining the predictions of the segmentation model. Extensive experiments show that our CLOUDS excels in adapting from synthetic to real DGSS benchmarks and under varying weather conditions, notably outperforming prior methods by 5.6% and 6.7% on averaged mIoU, respectively. Our code is available at https://***/yasserben/CLOUDS

关键词： computer vision Deep Learning Domain Adaptation Domain Generalization Foundation Models Semantic Segmentation

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A&B BNN: Add&Bit-Operation-Only Hardware-Friendly Binary Neural Network

A&B BNN: Add&Bit-Operation-Only Hardware-Friendly Binary Neu...

引用

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

作者： Ma, Ruichen Qiao, Guanchao Liu, Yian Meng, Liwei Ning, Ning Liu, Yang Hu, Shaogang Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Binary neural networks utilize 1-bit quantized weights and activations to reduce both the model's storage demands and computational burden. However, advanced binary architectures still incorporate millions of inefficient and non-hardware-friendly full-precision multiplication operations. A&B BNN is proposed to directly remove part of the multiplication operations in a traditional BNN and replace the rest with an equal number of bit operations, introducing the mask layer and the quantized RPReLU structure based on the normalizer-free network architecture. The mask layer can be removed during inference by leveraging the intrinsic characteristics of BNN with straightforward mathematical transformations to avoid the associated multiplication operations. The quantized RPReLU structure enables more efficient bit operations by constraining its slope to be integer powers of 2. Experimental results achieved 92.30%, 69.35%, and 66.89% on the CIFAR-10, CIFAR-100, and ImageNet datasets, respectively, which are competitive with the state-of-the-art. Ablation studies have verified the efficacy of the quantized RPReLU structure, leading to a 1.14% enhancement on the ImageNet compared to using a fixed slope RLeakyReLU. The proposed add&bit-operation-only BNN offers an innovative approach for hardware-friendly network architecture.

关键词： Binary neural networks Hardware-friendly structure Multiplication-free structure pattern recognition

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Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation

Repurposing Diffusion-Based Image Generators for Monocular D...

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

作者： Ke, Bingxin Obukhov, Anton Huang, Shengyu Metzger, Nando Daudt, Rodrigo Caye Schindler, Konrad Swiss Fed Inst Technol Photogrammetry & Remote Sensing Zurich Switzerland

ISBN: (纸本)9798350353006

Monocular depth estimation is a fundamental computer vision task. Recovering 3D depth from a single image is geometrically ill-posed and requires scene understanding, so it is not surprising that the rise of deep learning has led to a breakthrough. The impressive progress of monocular depth estimators has mirrored the growth in model capacity, from relatively modest CNNs to large Transformer architectures. Still, monocular depth estimators tend to struggle when presented with images with unfamiliar content and layout, since their knowledge of the visual world is restricted by the data seen during training, and challenged by zero-shot generalization to new domains. This motivates us to explore whether the extensive priors captured in recent generative diffusion models can enable better, more generalizable depth estimation. We introduce Marigold, a method for affine-invariant monocular depth estimation that is derived from Stable Diffusion and retains its rich prior knowledge. The estimator can be fine-tuned in a couple of days on a single GPU using only synthetic training data. It delivers state-of-the-art performance across a wide range of datasets, including over 20% performance gains in specific cases. Project page: https://***.

关键词： ddim ddpm depth estimation diffusion generative LDM vision

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