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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"

共 11891 条记录，以下是1311-1320 订阅

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From Images to Textual Prompts: Zero-shot Visual Question Answering with Frozen Large Language Models

From Images to Textual Prompts: Zero-shot Visual Question An...

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

作者： Guo, Jiaxian Li, Junnan Li, Dongxu Tiong, Anthony Meng Huat Li, Boyang Tao, Dacheng Hoi, Steven Univ Sydney Sydney NSW Australia Salesforce Res San Francisco CA USA Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9798350301298

Large language models (LLMs) have demonstrated excellent zero-shot generalization to new language tasks. However, effective utilization of LLMs for zero-shot visual question-answering (VQA) remains challenging, primarily due to the modality disconnect and task disconnect between the LLM and VQA tasks. End-to-end training on multimodal data may bridge the disconnects, but is inflexible and computationally expensive. To address this issue, we propose Img2LLM, a plug-and-play module that provides LLM prompts to enable LLMs to perform zero-shot VQA tasks without end-to-end training. We develop LLM-agnostic models describe image content as exemplar question-answer pairs, which prove to be effective LLM prompts. Img2LLM offers the following benefits: 1) It achieves comparable or better performance than methods relying on end-to-end training. For example, we outperform Flamingo [3] by 5.6% on VQAv2. On the challenging A-OKVQA dataset, our method outperforms few-shot methods by as much as 20%. 2) It flexibly interfaces with a wide range of LLMs to perform VQA. 3) It eliminates the need to specialize LLMs using end-to-end finetuning and serve highly specialized LLMs to end users, thereby reducing cost. Code is available via the LAVIS [28] framework at https://***/salesforce/LAVIS/ tree/main/projects/img2llm-vqa.

关键词： language reasoning vision

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S³C: Semi-Supervised VQA Natural Language Explanation via Self-Critical Learning

S<SUP>3</SUP>C: Semi-Supervised VQA Natural Language Explana...

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

作者： Suo, Wei Sun, Mengyang Liu, Weisong Gao, Yiqi Wang, Peng Zhang, Yanning Wu, Qi Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Northwestern Polytech Univ Ningbo Inst Xian Peoples R China Northwestern Polytech Univ Sch Cybersecur Xian Peoples R China Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9798350301298

VQA Natural Language Explanation (VQA-NLE) task aims to explain the decision-making process of VQA models in natural language. Unlike traditional attention or gradient analysis, free-text rationales can be easier to understand and gain users' trust. Existing methods mostly use post-hoc or self-rationalization models to obtain a plausible explanation. However, these frameworks are bottlenecked by the following challenges: 1) the reasoning process cannot be faithfully responded to and suffer from the problem of logical inconsistency. 2) Human-annotated explanations are expensive and time-consuming to collect. In this paper, we propose a new Semi-Supervised VQA-NLE via Self-Critical Learning ((SC)-C-3), which evaluates the candidate explanations by answering rewards to improve the logical consistency between answers and rationales. With a semi-supervised learning framework, the (SC)-C-3 can benefit from a tremendous amount of samples without human-annotated explanations. A large number of automatic measures and human evaluations all show the effectiveness of our method. Meanwhile, the framework achieves a new state-of-the-art performance on the two VQA-NLE datasets.

关键词： and reasoning language vision

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PlaneDepth: Self-supervised Depth Estimation via Orthogonal Planes

PlaneDepth: Self-supervised Depth Estimation via Orthogonal ...

引用

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

作者： Wang, Ruoyu Yu, Zehao Gao, Shenghua ShanghaiTech Univ Shanghai Peoples R China Univ Tubingen Tubingen Germany Shanghai Engn Res Ctr Intelligent Vision & Imagin Shanghai Peoples R China Shanghai Engn Res Ctr Energy Efficient & Custom I Shanghai Peoples R China

ISBN: (纸本)9798350301298

Multiple near frontal-parallel planes based depth representation demonstrated impressive results in self-supervised monocular depth estimation (MDE). Whereas, such a representation would cause the discontinuity of the ground as it is perpendicular to the frontal-parallel planes, which is detrimental to the identification of drivable space in autonomous driving. In this paper, we propose the PlaneDepth, a novel orthogonal planes based presentation, including vertical planes and ground planes. PlaneDepth estimates the depth distribution using a Laplacian Mixture Model based on orthogonal planes for an input image. These planes are used to synthesize a reference view to provide the self-supervision signal. Further, we find that the widely used resizing and cropping data augmentation breaks the orthogonality assumptions, leading to inferior plane predictions. We address this problem by explicitly constructing the resizing cropping transformation to rectify the predefined planes and predicted camera pose. Moreover, we propose an augmented self-distillation loss supervised with a bilateral occlusion mask to boost the robustness of orthogonal planes representation for occlusions. Thanks to our orthogonal planes representation, we can extract the ground plane in an unsupervised manner, which is important for autonomous driving. Extensive experiments on the KITTI dataset demonstrate the effectiveness and efficiency of our method. The code is available at https://***/svip-lab/PlaneDepth.

关键词： 3D from multi-view and sensors

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All-in-one Image Restoration for Unknown Degradations Using Adaptive Discriminative Filters for Specific Degradations

All-in-one Image Restoration for Unknown Degradations Using ...

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

作者： Park, Dongwon Lee, Byung Hyun Chun, Se Young Seoul Natl Univ INMC Seoul South Korea Seoul Natl Univ Dept ECE Seoul South Korea Seoul Natl Univ IPAI Seoul South Korea UNIST Dept EE Ulsan South Korea

ISBN: (纸本)9798350301298

Image restorations for single degradations have been widely studied, demonstrating excellent performance for each degradation, but can not reflect unpredictable realistic environments with unknown multiple degradations, which may change over time. To mitigate this issue, image restorations for known and unknown multiple degradations have recently been investigated, showing promising results, but require large networks or have sub-optimal architectures for potential interference among different degradations. Here, inspired by the filter attribution integrated gradients (FAIG), we propose an adaptive discriminative filter-based model for specific degradations (ADMS) to restore images with unknown degradations. Our method allows the network to contain degradation-dedicated filters only for about 3% of all network parameters per each degradation and to apply them adaptively via degradation classification (DC) to explicitly disentangle the network for multiple degradations. Our proposed method has demonstrated its effectiveness in comparison studies and achieved state-of-the-art performance in all-in-one image restoration benchmark datasets of both Rain-Noise-Blur and Rain-Snow-Haze.

关键词： Low-level vision

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Cross-Modal Implicit Relation Reasoning and Aligning for Text-to-Image Person Retrieval

Cross-Modal Implicit Relation Reasoning and Aligning for Tex...

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

作者： Jiang, Ding Ye, Mang Wuhan Univ Inst Artificial Intelligence Hubei Key Lab Multimedia & Network Commun Natl Engn Res Ctr Multimedia SoftwareSch Comp Sc Wuhan Peoples R China Hubei Luojia Lab Wuhan Peoples R China

ISBN: (纸本)9798350301298

Text-to-image person retrieval aims to identify the target person based on a given textual description query. The primary challenge is to learn the mapping of visual and textual modalities into a common latent space. Prior works have attempted to address this challenge by leveraging separately pre-trained unimodal models to extract visual and textual features. However, these approaches lack the necessary underlying alignment capabilities required to match multimodal data effectively. Besides, these works use prior information to explore explicit part alignments, which may lead to the distortion of intra-modality information. To alleviate these issues, we present IRRA: a cross-modal Implicit Relation Reasoning and Aligning framework that learns relations between local visual-textual tokens and enhances global image-text matching without requiring additional prior supervision. Specifically, we first design an Implicit Relation Reasoning module in a masked language modeling paradigm. This achieves cross-modal interaction by integrating the visual cues into the textual tokens with a cross-modal multimodal interaction encoder. Secondly, to globally align the visual and textual embeddings, Similarity Distribution Matching is proposed to minimize the KL divergence between image-text similarity distributions and the normalized label matching distributions. The proposed method achieves new state-of-the-art results on all three public datasets, with a notable margin of about 3%-9% for Rank-1 accuracy compared to prior methods.

关键词： and reasoning language vision

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Fusing Pre-trained Language Models with Multimodal Prompts through Reinforcement Learning

Fusing Pre-trained Language Models with Multimodal Prompts t...

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

作者： Yu, Youngjae Chung, Jiwan Yun, Heeseung Hessel, Jack Park, Jae Sung Lu, Ximing Zellers, Rowan Ammanabrolu, Prithviraj Le Bras, Ronan Kim, Gunhee Choi, Yejin Allen Inst Artificial Intelligence Seattle WA USA OpenAI Seattle WA USA Yonsei Univ Dept Artificial Intelligence Seoul South Korea Seoul Natl Univ Dept Comp Sci & Engn Seoul South Korea Univ Washington Paul G Allen Sch Comp Sci Seattle WA 98195 USA

ISBN: (纸本)9798350301298

Language models are capable of commonsense reasoning: while domain-specific models can learn from explicit knowledge (e.g. commonsense graphs [6], ethical norms [25]), and larger models like GPT-3 [7] manifest broad commonsense reasoning capacity. Can their knowledge be extended to multimodal inputs such as images and audio without paired domain data? In this work, we propose ESPER (Extending Sensory PErception with Reinforcement learning) which enables text-only pretrained models to address multimodal tasks such as visual commonsense reasoning. Our key novelty is to use reinforcement learning to align multimodal inputs to language model generations without direct supervision: for example, our reward optimization relies only on cosine similarity derived from CLIP [52] and requires no additional paired (image, text) data. Experiments demonstrate that ESPER outperforms baselines and prior work on a variety of multimodal text generation tasks ranging from captioning to commonsense reasoning;these include a new benchmark we collect and release, the ESP dataset, which tasks models with generating the text of several different domains for each image. Our code and data are publicly released at https://***/JiwanChung/esper.

关键词： and reasoning language vision

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LINe: Out-of-Distribution Detection by Leveraging Important Neurons

LINe: Out-of-Distribution Detection by Leveraging Important ...

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

作者： Ahn, Yong Hyun Park, Gyeong-Moon Kim, Seong Tae Kyung Hee Univ Dept Artificial Intelligence Seoul South Korea Kyung Hee Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9798350301298

It is important to quantify the uncertainty of input samples, especially in mission-critical domains such as autonomous driving and healthcare, where failure predictions on out-of-distribution (OOD) data are likely to cause big problems. OOD detection problem fundamentally begins in that the model cannot express what it is not aware of Post-hoc OOD detection approaches are widely explored because they do not require an additional re-training process which might degrade the model's performance and increase the training cost. In this study, from the perspective of neurons in the deep layer of the model representing high-level features, we introduce a new aspect for analyzing the difference in model outputs between in-distribution data and OOD data. We propose a novel method, Leveraging Important Neurons (LINe), for post-hoc Out of distribution detection. Shapley value-based pruning reduces the effects of noisy outputs by selecting only high-contribution neurons for predicting specific classes of input data and masking the rest. Activation clipping fixes all values above a certain threshold into the same value, allowing LINe to treat all the class-specificfeatures equally and just consider the difference between the number of activated feature differences between in-distribution and OOD data. Comprehensive experiments verify the effectiveness of the proposed method by outperforming state-of-the-art post-hoc OOD detection methods on CIFAR-10, CIFAR-100, and imageNet datasets. Code is available on https://***/LINe-OOD

关键词： detection recognition: Categorization retrieval

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Query-Dependent Video Representation for Moment Retrieval and Highlight Detection

Query-Dependent Video Representation for Moment Retrieval an...

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

作者： Moon, WonJun Hyun, Sangeek Park, SangUk Park, Dongchan Heo, Jae-Pil Sungkyunkwan Univ Seoul South Korea Pyler Seoul South Korea

ISBN: (纸本)9798350301298

Recently, video moment retrieval and highlight detection (MR/HD) are being spotlighted as the demand for video understanding is drastically increased. The key objective of MR/HD is to localize the moment and estimate clip-wise accordance level, i.e., saliency score, to the given text query. Although the recent transformer-based models brought some advances, we found that these methods do not fully exploit the information of a given query. For example, the relevance between text query and video contents is sometimes neglected when predicting the moment and its saliency. To tackle this issue, we introduce Query-Dependent DETR (QD-DETR), a detection transformer tailored for MR/HD. As we observe the insignificant role of a given query in transformer architectures, our encoding module starts with cross-attention layers to explicitly inject the context of text query into video representation. Then, to enhance the model's capability of exploiting the query information, we manipulate the video-query pairs to produce irrelevant pairs. Such negative (irrelevant) video-query pairs are trained to yield low saliency scores, which in turn, encourages the model to estimate precise accordance between query-video pairs. Lastly, we present an input-adaptive saliency predictor which adaptively defines the criterion of saliency scores for the given video-query pairs. Our extensive studies verify the importance of building the query-dependent representation for MR/HD. Specifically, QD-DETR outperforms state-of-the-art methods on QVHighlights, TVSum, and Charades-STA datasets. Codes are available at ***/wjun0830/QD-DETR.

关键词： detection recognition: Categorization retrieval

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Auto-CARD: Efficient and Robust Codec Avatar Driving for Real-time Mobile Telepresence

Auto-CARD: Efficient and Robust Codec Avatar Driving for Rea...

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

作者： Fu, Yonggan Li, Yuecheng Li, Chenghui Saragih, Jason Zhang, Peizhao Dai, Xiaoliang Lin, Yingyan (Celine) Georgia Inst Technol Atlanta GA 30332 USA Meta Pittsburgh PA USA

ISBN: (纸本)9798350301298

Real-time and robust photorealistic avatars for telepresence in AR/VR have been highly desired for enabling immersive photorealistic telepresence. However, there still exists one key bottleneck: the considerable computational expense needed to accurately infer facial expressions captured from headset-mounted cameras with a quality level that can match the realism of the avatar's human appearance. To this end, we propose a framework called Auto-CARD, which for the first time enables real-time and robust driving of Codec Avatars when exclusively using merely on-device computing resources. This is achieved by minimizing two sources of redundancy. First, we develop a dedicated neural architecture search technique called AVE-NAS for avatar encoding in AR/VR, which explicitly boosts both the searched architectures' robustness in the presence of extreme facial expressions and hardware friendliness on fast evolving AR/VR headsets. Second, we leverage the temporal redundancy in consecutively captured images during continuous rendering and develop a mechanism dubbed LATEX to skip the computation of redundant frames. Specifically, we first identify an opportunity from the linearity of the latent space derived by the avatar decoder and then propose to perform adaptive latent extrapolation for redundant frames. For evaluation, we demonstrate the efficacy of our Auto-CARD framework in real-time Codec Avatar driving settings, where we achieve a 5.05x speed-up on Meta Quest 2 while maintaining a comparable or even better animation quality than state-of-the-art avatar encoder designs.

关键词： vision applications and systems

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Learning Weather-General and Weather-Specific Features for Image Restoration Under Multiple Adverse Weather Conditions

Learning Weather-General and Weather-Specific Features for I...

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

作者： Zhu, Yurui Wang, Tianyu Fu, Xueyang Yang, Xuanyu Guo, Xin Dai, Jifeng Qiao, Yu Hu, Xiaowei Univ Sci & Technol China Langfang Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

Image restoration under multiple adverse weather conditions aims to remove weather-related artifacts by using a single set of network parameters. In this paper, we find that image degradations under different weather conditions contain general characteristics as well as their specific characteristics. Inspired by this observation, we design an efficient unified framework with a two-stage training strategy to explore the weather-general and weather-specific features. The first training stage aims to learn the weather-general features by taking the images under various weather conditions as inputs and outputting the coarsely restored results. The second training stage aims to learn to adaptively expand the specific parameters for each weather type in the deep model, where the requisite positions for expanding weather-specific parameters are automatically learned. Hence, we can obtain an efficient and unified model for image restoration under multiple adverse weather conditions. Moreover, we build the first real-world benchmark dataset with multiple weather conditions to better deal with real-world weather scenarios. Experimental results show that our method achieves superior performance on all the synthetic and real-world benchmarks. Codes and datasets are available at this repository.

关键词： Low-level vision

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