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共 21179 条记录，以下是191-200 订阅

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ZeroShape: Regression-based Zero-shot Shape Reconstruction

ZeroShape: Regression-based Zero-shot Shape Reconstruction

引用

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

作者： Huang, Zixuan Stojanov, Stefan Thai, Anh Jampani, Varun Rehg, James M. Univ Illinois Champaign IL 61820 USA Georgia Inst Technol Atlanta GA 30332 USA Stabil AI London England

ISBN: (纸本)9798350353006

We study the problem of single-image zero-shot 3D shape reconstruction. Recent works learn zero-shot shape reconstruction through generative modeling of 3D assets, but these models are computationally expensive at train and inference time. In contrast, the traditional approach to this problem is regression-based, where deterministic models are trained to directly regress the object shape. Such regression methods possess much higher computational efficiency than generative methods. This raises a natural question: is generative modeling necessary for high performance, or conversely, are regression-based approaches still competitive? To answer this, we design a strong regression-based model, called ZeroShape, based on the converging findings in this field and a novel insight. We also curate a large real-world evaluation benchmark, with objects from three different real-world 3D datasets. This evaluation benchmark is more diverse and an order of magnitude larger than what prior works use to quantitatively evaluate their models, aiming at reducing the evaluation variance in our field. We show that ZeroShape not only achieves superior performance over state-of-the-art methods, but also demonstrates significantly higher computational and data efficiency.(1)

关键词： 3D generation 3D reconstruction 3D vision computer vision Deep Learning

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Finding Lottery Tickets in vision Models via Data-driven Spectral Foresight Pruning

Finding Lottery Tickets in Vision Models via Data-driven Spe...

引用

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

作者： Iurada, Leonardo Ciccone, Marco Tommasi, Tatiana Politecn Torino Turin Italy

ISBN: (纸本)9798350353006

Recent advances in neural network pruning have shown how it is possible to reduce the computational costs and memory demands of deep learning models before training. We focus on this framework and propose a new pruning at initialization algorithm that leverages the Neural Tangent Kernel (NTK) theory to align the training dynamics of the sparse network with that of the dense one. Specifically, we show how the usually neglected data-dependent component in the NTK's spectrum can be taken into account by providing an analytical upper bound to the NTK's trace obtained by decomposing neural networks into individual paths. This leads to our Path eXclusion (PX), a foresight pruning method designed to preserve the parameters that mostly influence the NTK's trace. PX is able to find lottery tickets (i.e. good paths) even at high sparsity levels and largely reduces the need for additional training. When applied to pre-trained models it extracts subnetworks directly usable for several downstream tasks, resulting in performance comparable to those of the dense counterpart but with substantial cost and computational savings. Code available at: https://***/iurada/px-ntk-pruning

关键词： Efficient computer vision Neural Network Pruning Neural Tangent Kernel Pruning-at-Initialization

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Compositional Chain-of-Thought Prompting for Large Multimodal Models

Compositional Chain-of-Thought Prompting for Large Multimoda...

引用

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

作者： Mitra, Chancharik Huang, Brandon Darrell, Trevor Herzig, Roei Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9798350353006

The combination of strong visual backbones and Large Language Model (LLM) reasoning has led to Large Multimodal Models (LMMs) becoming the current standard for a wide range of vision and language (VL) tasks. However, recent research has shown that even the most advanced LMMs still struggle to capture aspects of compositional visual reasoning, such as attributes and relationships between objects. One solution is to utilize scene graphs (SGs)-a formalization of objects and their relations and attributes that has been extensively used as a bridge between the visual and textual domains. Yet, scene graph data requires scene graph annotations, which are expensive to collect and thus not easily scalable. Moreover, finetuning an LMM based on SG data can lead to catastrophic forgetting of the pretraining objective. To overcome this, inspired by chain-of-thought methods, we propose Compositional Chain-of-Thought (CCoT), a novel zero-shot Chain-of-Thought prompting method that utilizes SG representations in order to extract compositional knowledge from an LMM. Specifically, we first generate an SG using the LMM, and then use that SG in the prompt to produce a response. Through extensive experiments, we find that the proposed CCoT approach not only improves LMM performance on several vision and language (VL) compositional benchmarks but also improves the performance of several popular LMMs on general multimodal benchmarks, without the need for fine-tuning or annotated ground-truth SGs. Code: https://***/chancharikmitra/CCoT.

关键词： Compositionality Large Multimodal Models Multimodality Prompting Scene Graphs vision & Language

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DePT: Decoupled Prompt Tuning

DePT: Decoupled Prompt Tuning

引用

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

作者： Zhang, Ji Wu, Shihan Gao, Lianli Shen, Heng Tao Song, Jingkuan Univ Elect Sci & Technol China UESTC Chengdu Peoples R China UESTC Shenzhen Inst Adv Study Chengdu Peoples R China Tongji Univ Shanghai Peoples R China

ISBN: (纸本)9798350353006

This work breaks through the Base-New Tradeoff (BNT) dilemma in prompt tuning, i.e., the better the tuned model generalizes to the base (or target) task, the worse it generalizes to new tasks, and vice versa. Specifically, through an in-depth analysis of the learned features of the base and new tasks, we observe that the BNT stems from a channel bias issue - the vast majority of feature channels are occupied by base-specific knowledge, leading to the collapse of task-shared knowledge important to new tasks. To address this, we propose the Decoupled Prompt Tuning (DePT) framework, which decouples base-specific knowledge from feature channels into an isolated feature space during prompt tuning, so as to maximally preserve task-shared knowl-edge in the original feature space for achieving better zero-shot generalization on new tasks. Importantly, our DePT is orthogonal to existing prompt tuning approaches, and can enhance them with negligible additional computational cost. Extensive experiments on several datasets show the flexibility and effectiveness of DePT. Code is available at https://***/Koorye/DePT.

关键词： Feature decoupling Few-shot learning Prompt tuning vision and language

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Grounded Question-Answering in Long Egocentric Videos

Grounded Question-Answering in Long Egocentric Videos

引用

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

作者： Di, Shangzhe Xie, Weidi Shanghai Jiao Tong Univ CMIC Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350353006

Existing approaches to video understanding, mainly designed for short videos from a third-person perspective, are limited in their applicability in certain fields, such as robotics. In this paper, we delve into open-ended question-answering (QA) in long, egocentric videos, which allows individuals or robots to inquire about their own past visual experiences. This task presents unique challenges, including where did I put lettuce? Choices: (A) pantry (B) refrigerator (C) cupboard (D) draw 20-50s Answer: in the fridge / (B) refrigerator the complexity of temporally grounding queries within extensive video content, the high resource demands for precise data annotation, and the inherent difficulty of evaluating open-ended answers due to their ambiguous nature. Our proposed approach tackles these challenges by (i) integrating query grounding and answering within a unified model to reduce error propagation;(ii) employing large language models for efficient and scalable data synthesis;and (iii) introducing a close-ended QA task for evaluation, to manage answer ambiguity. Extensive experiments demonstrate the effectiveness of our method, which also achieves state-of-the-art performance on the QAEgo4D and Ego4D-NLQ benchmarks. Code, data, and models are open-sourced (1).

关键词： egocentric vision video grounding video question answering

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Flexible Biometrics recognition: Bridging the Multimodality Gap through Attention, Alignment and Prompt Tuning

Flexible Biometrics Recognition: Bridging the Multimodality ...

引用

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

作者： Tiong, Leslie Ching Ow Sigmund, Dick Chan, Chen-Hui Teoh, Andrew Beng Jin Samsung Elect Suwon South Korea AIDOT Inc Seoul South Korea Korea Inst Sci & Technol Seoul South Korea Yonsei Univ Seoul South Korea

ISBN: (纸本)9798350353013;9798350353006

Periocular and face are complementary biometrics for identity management, albeit with inherent limitations, notably in scenarios involving occlusion due to sunglasses or masks. In response to these challenges, we introduce Flexible Biometric recognition (FBR), a novel framework designed to advance conventional face, periocular, and multimodal face-periocular biometrics across both intra- and cross-modality recognition tasks. FBR strategically utilizes the Multimodal Fusion Attention (MFA) and Multimodal Prompt Tuning (MPT) mechanisms within the vision Transformer architecture. MFA facilitates the fusion of modalities, ensuring cohesive alignment between facial and periocular embeddings while incorporating soft-biometrics to enhance the model's ability to discriminate between individuals. The fusion of three modalities is pivotal in exploring interrelationships between different modalities. Additionally, MPT serves as a unifying bridge, intertwining inputs and promoting cross-modality interactions while preserving their distinctive characteristics. The collaborative synergy of MFA and MPT enhances the shared features of the face and periocular, with a specific emphasis on the ocular region, yielding exceptional performance in both intra- and cross-modality recognition tasks. Rigorous experimentation across four benchmark datasets validates the noteworthy performance of the FBR model. The source code is available at https://***/MIS-DevWorks/FBR.

关键词： cross-modality flexible biometrics recognition multimodal prompt tuning

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Stronger, Fewer, & Superior: Harnessing vision Foundation Models for Domain Generalized Semantic Segmentation

Stronger, Fewer, & Superior: Harnessing Vision Foundation Mo...

引用

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

作者： Wei, Zhixiang Chen, Lin Jin, Yi Ma, Xiaoxiao Liu, Tianle Ling, Pengyang Wang, Ben Chen, Huaian Zheng, Jinjin Univ Sci & Technol China Hefei Peoples R China Shanghai Ai Lab Shanghai Peoples R China

ISBN: (纸本)9798350353006

In this paper, we first assess and harness various vision Foundation Models (VFMs) in the context of Domain Generalized Semantic Segmentation (DGSS). Driven by the motivation that Leveraging Stronger pre-trained models and Fewer trainable parameters for Superior generalizability, we introduce a robust fine-tuning approach, namely "Rein", to parameter-efficiently harness VFMs for DGSS. Built upon a set of trainable tokens, each linked to distinct instances, Rein precisely refines and forwards the feature maps from each layer to the next layer within the backbone. This process produces diverse refinements for different categories within a single image. With fewer trainable parameters, Rein efficiently fine-tunes VFMs for DGSS tasks, surprisingly surpassing full parameter fine-tuning. Extensive experiments across various settings demonstrate that Rein significantly outperforms state-of-the-art methods. Remarkably, with just an extra 1% of trainable parameters within the frozen backbone, Rein achieves a mIoU of 78.4% on the Cityscapes, without accessing any real urban-scene datasets. Code is available at https://***/w1oves/***.

关键词： Domain Generalized Semantic Segmentation parameter-efficient fine-tuning Semantic Segmentation vision Foundation Models

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DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D Reassembly

DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D ...

引用

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

作者： Scarpellini, Gianluca Fiorini, Stefano Giuliari, Francesco Morerio, Pietro Del Bue, Alessio Ist Italiano Tecnol IIT Pattern Anal & Comp Vis PAVIS Genoa Italy

ISBN: (纸本)9798350353006

Reassembly tasks play a fundamental role in many fields and multiple approaches exist to solve specific reassembly problems. In this context, we posit that a general unified model can effectively address them all, irrespective of the input data type (images, 3D, etc.). We introduce DiffAssemble, a Graph Neural Network (GNN)-based architecture that learns to solve reassembly tasks using a diffusion model formulation. Our method treats the elements of a set, whether pieces of 2D patch or 3D object fragments, as nodes of a spatial graph. Training is performed by introducing noise into the position and rotation of the elements and iteratively denoising them to reconstruct the coherent initial pose. DiffAssemble achieves state-of-the-art (SOTA) results in most 2D and 3D reassembly tasks and is the first learning-based approach that solves 2D puzzles for both rotation and translation. Furthermore, we highlight its remarkable reduction in run-time, performing 11 times faster than the quickest optimization-based method for puzzle solving. Code available at https:// ***/IITPAVIS/DiffAssemble

关键词： diffusion model graph neural network puzzle reassembly

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Boosting Continual Learning of vision-Language Models via Mixture-of-Experts Adapters

Boosting Continual Learning of Vision-Language Models via Mi...

引用

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

作者： Yu, Jiazuo Zhuge, Yunzhi Zhang, Lu Hu, Ping Wang, Dong Lu, Huchuan He, You Dalian Univ Technol Dalian Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350353006

Continual learning can empower vision-language models to continuously acquire new knowledge, without the need for access to the entire historical dataset. However, mitigating the performance degradation in large-scale models is non-trivial due to (i) parameter shifts throughout life-long learning and (ii) significant computational burdens associated with full-model tuning. In this work, we present a parameter-efficient continual learning framework to alleviate long-term forgetting in incremental learning with vision-language models. Our approach involves the dynamic expansion of a pre-trained CLIP model, through the integration of Mixture-of-Experts (MoE) adapters in response to new tasks. To preserve the zero-shot recognition capability of vision-language models, we further introduce a Distribution Discriminative Auto-Selector (DDAS) that automatically routes in-distribution and out-of-distribution inputs to the MoE Adapter and the original CLIP, respectively. Through extensive experiments across various settings, our proposed method consistently outperforms previous state-of-the-art approaches while concurrently reducing parameter training burdens by 60%. Our code locates at https://***/JiazuoYu/MoE-Adapters4CL

关键词： class incremental learning continual learning task incremental learning vision-Language Model

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DGC-GNN: Leveraging Geometry and Color Cues for Visual Descriptor-Free 2D-3D Matching

DGC-GNN: Leveraging Geometry and Color Cues for Visual Descr...

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

作者： Wang, Shuzhe Kannala, Juho Baratht, Daniel Aalto Univ Dept Comp Sci Espoo Finland Swiss Fed Inst Technol Comp Vision & Geometry Grp Zurich Switzerland

ISBN: (纸本)9798350353006

Matching 2D keypoints in an image to a sparse 3D point cloud of the scene without requiring visual descriptors has garnered increased interest due to its low memory requirements, inherent privacy preservation, and reduced need for expensive 3D model maintenance compared to visual descriptor-based methods. However, existing algorithms of-ten compromise on performance, resulting in a significant deterioration compared to their descriptor-based counterparts. In this paper, we introduce DGC-GNN, a novel algorithm that employs a global-to-local Graph Neural Network (GNN) that progressively exploits geometric and color cues to represent keypoints, thereby improving matching accuracy. Our procedure encodes both Euclidean and angular relations at a coarse level, forming the geometric embedding to guide the point matching. We evaluate DGC-GNN on both indoor and outdoor datasets, demonstrating that it not only doubles the accuracy of the state-of-the-art visual descriptor-free algorithm but also substantially narrows the performance gap between descriptor-based and descriptor-free methods.

关键词： 2D-3D Matching Global-to-local GNN privacy preservation Visual Descriptor-Free

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