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arXiv 2404.13400cs.CV
HiVG：面向视觉定位的层级多模态细粒度调制
HiVG: Hierarchical Multimodal Fine-grained Modulation for Visual Grounding
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Pengcheng Laboratory（鹏城实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
Visual grounding, which aims to ground a visual region via natural language, is a task that heavily relies on cross-modal alignment. Existing works utilized uni-modal pre-trained models to transfer visual or linguistic knowledge separately while ignoring the multimodal corresponding information. Motivated by recent advancements in contrastive language-image pre-training and low-rank adaptation (LoRA) methods, we aim to solve the grounding task based on multimodal pre-training. However, there exists significant task gaps between pre-training and grounding. Therefore, to address these gaps, we propose a concise and efficient hierarchical multimodal fine-grained modulation framework, namely HiVG. Specifically, HiVG consists of a multi-layer adaptive cross-modal bridge and a hierarchical multimodal low-rank adaptation (HiLoRA) paradigm. The cross-modal bridge can address the inconsistency between visual features and those required for grounding, and establish a connection between multi-level visual and text features. HiLoRA prevents the accumulation of perceptual errors by adapting the cross-modal features from shallow to deep layers in a hierarchical manner. Experimental results on five datasets demonstrate the effectiveness of our approach and showcase the significant grounding capabilities as well as promising energy efficiency advantages. The project page: https://github.com/linhuixiao/HiVG.
EPIC-Bench: A Perception-Centric Benchmark for Fine-Grained Embodied Visual Grounding in Vision-Language Models
X-Humanoid；Fudan University（复旦大学）；University of Science and Technology of China（中国科学技术大学）；University of Manchester（曼彻斯特大学）；Monash University（墨尔本大学）；Celonis AI；University of New South Wales（新南威尔士大学）
VideoGLaMM: A Large Multimodal Model for Pixel-Level Visual Grounding in Videos
Mohamed bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）；Tianjin University（天津大学）；Carnegie Mellon University（卡内基梅隆大学）；Linköping University（林雪平大学）；Australian National University（澳大利亚国立大学）
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）；University of Science and Technology Beijing（北京科技大学）；Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学研究所）
Beihang University（北京航空航天大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Alibaba Group（阿里巴巴集团）
Recently proposed fine-grained 3D visual grounding is an essential and challenging task, whose goal is to identify the 3D object referred by a natural language sentence from other distractive objects of the same category. Existing works usually adopt dynamic graph networks to indirectly model the intra/inter-modal interactions, making the model difficult to distinguish the referred object from distractors due to the monolithic representations of visual and linguistic contents. In this work, we exploit Transformer for its natural suitability on permutation-invariant 3D point clouds data and pro…
Beyond the Global Scores: Fine-Grained Token Grounding as a Robust Detector of LVLM Hallucinations
Hanoi University of Science and Technology（河内科技大学）；Australian Institute for Machine Learning, University of Adelaide（澳大利亚机器学习研究所，阿德莱德大学）；Nanjing University（南京大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Hanoi-Amsterdam High School for the Gifted（河内阿姆斯特丹天才高中）
University of Electronic Science and Technology of China（电子科技大学）
Crowd understanding has aroused the widespread interest in vision domain due to its important practical significance. Unfortunately, there is no effort to explore crowd understanding in multi-modal domain that bridges natural language and computer vision. Referring expression comprehension (REF) is such a representative multi-modal task. Current REF studies focus more on grounding the target object from multiple distinctive categories in general scenarios. It is difficult to applied to complex real-world crowd understanding. To fill this gap, we propose a new challenging dataset, called RefCro…
From Objects to Anywhere: A Holistic Benchmark for Multi-level Visual Grounding in 3D Scenes
State Key Laboratory of General Artificial Intelligence, BIGAI（通用人工智能国家重点实验室，BIGAI）；Tsinghua University（清华大学）；Peking University（北京大学）；Beijing Institute of Technology（北京理工大学）
MedSG-Bench: A Benchmark for Medical Image Sequences Grounding
Beijing University of Posts and Telecommunications（北京邮电大学）；Tianjin University（天津大学）；South China Hospital, Medical School, Shenzhen University（深圳大学医学院南方医院）
EARL: Towards a Unified Analysis-Guided Reinforcement Learning Framework for Egocentric Interaction Reasoning and Pixel Grounding
Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong SAR（香港理工大学电子与电气工程系）；Division of Emerging Interdisciplinary Areas (EMIA), The Hong Kong University of Science and Technology, Hong Kong SAR（香港理工大学新兴跨学科领域研究中心）
Towards Unified Referring Expression Segmentation Across Omni-Level Visual Target Granularities
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；University of Science and Technology Beijing（北京科技大学）；School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机与信息技术学院）
KG-ViP: Bridging Knowledge Grounding and Visual Perception in Multi-modal LLMs for Visual Question Answering
University of Science and Technology of China（中国科学技术大学）；Data Darkness Lab, MIRACLE Center, USTC（数据黑暗实验室，MIRACLE中心，中国科学技术大学）；School of Computer Science and Technology, Xidian University（西安电子科技大学计算机科学与技术学院）
Universidad de los Andes（安第斯大学）；Google Research（谷歌研究院）
This paper proposes Panoptic Narrative Grounding, a spatially fine and general formulation of the natural language visual grounding problem. We establish an experimental framework for the study of this new task, including new ground truth and metrics, and we propose a strong baseline method to serve as stepping stone for future work. We exploit the intrinsic semantic richness in an image by including panoptic categories, and we approach visual grounding at a fine-grained level by using segmentations. In terms of ground truth, we propose an algorithm to automatically transfer Localized Narrativ…
Training-Free Open-Vocabulary Visual Grounding for Remote Sensing Images and Videos
School of Computer Science and Technology, Xidian University（西安电子科技大学计算机科学与技术学院）；Interdisciplinary Institute of Artificial Intelligence, Xidian University（西安电子科技大学跨学科人工智能研究院）；School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）；Northwest Institute of Nuclear Technology（西北核技术研究所）；School of Physics and Information Engineering, Fuzhou University（福州大学物理与信息工程学院）
ResVG: Enhancing Relation and Semantic Understanding in Multiple Instances for Visual Grounding
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；National Institute of Health Data Science, Peking University（北京大学健康数据科学国家研究院）；State Key Laboratory of General Artificial Intelligence（通用人工智能国家重点实验室）
Hefei University of Technology（合肥工业大学）；University of Science and Technology of China（中国科学技术大学）；Nanyang Technological University（南洋理工大学）
Grounding natural language in images, such as localizing "the black dog on the left of the tree", is one of the core problems in artificial intelligence, as it needs to comprehend the fine-grained and compositional language space. However, existing solutions rely on the association between the holistic language features and visual features, while neglect the nature of compositional reasoning implied in the language. In this paper, we propose a natural language grounding model that can automatically compose a binary tree structure for parsing the language and then perform visual reasoning along…
Seeing Clearly without Training: Mitigating Hallucinations in Multimodal LLMs for Remote Sensing
School of Computer Science, Wuhan University, China（武汉大学计算机学院）；Zhongguancun Academy, China（中关村学院）；School of Computer Science, Chongqing University, China（重庆大学计算机学院）
Talk to Parallel LiDARs: A Human-LiDAR Interaction Method Based on 3D Visual Grounding
Institute of Automation（自动化研究所）；Chinese Academy of Sciences（中国科学院）；College of Electronic Engineering（电子工程学院）；South China Agricultural University（华南农业大学）；School of Computer Science（计算机学院）；Beijing Institute of Technology（北京理工大学）
Benchmarking Large Vision-Language Models on Fine-Grained Image Tasks: From Evaluation to Diagnosis
School of Computer Science and Engineering, Southeast University, China（东南大学计算机科学与工程学院，中国）；Alibaba Group（阿里巴巴集团）；School of Computer Science and Engineering, School of Intelligence Science and Engineering, and Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, Southeast University, China（东南大学计算机科学与工程学院、智能科学与工程学院以及新一代人工智能技术及其交叉应用关键实验室，中国）；Wangxuan Institute of Computer Technology, National Key Laboratory for Multimedia Information Processing, Peking University, China（北京大学王轩计算机技术研究所、多媒体信息处理国家重点实验室，中国）；University of Copenhagen, Denmark（丹麦哥本哈根大学）
Northeastern University（东北大学）；NEC Labs America（NEC美国实验室）；UC San Diego（加州大学圣地亚哥分校）
Self-supervised vision-language pretraining from pure images and text with a contrastive loss is effective, but ignores fine-grained alignment due to a dual-stream architecture that aligns image and text representations only on a global level. Earlier, supervised, non-contrastive methods were capable of finer-grained alignment, but required dense annotations that were not scalable. We propose a single stream architecture that aligns images and language at multiple levels: global, fine-grained patch-token, and conceptual/semantic, using two novel tasks: symmetric cross-modality reconstruction (…
Beijing Institute of Technology（北京理工大学）；JD AI Research（京东人工智能研究院）；Australian National University（澳大利亚国立大学）
Multimodal named entity recognition (MNER) is a critical step in information extraction, which aims to detect entity spans and classify them to corresponding entity types given a sentence-image pair. Existing methods either (1) obtain named entities with coarse-grained visual clues from attention mechanisms, or (2) first detect fine-grained visual regions with toolkits and then recognize named entities. However, they suffer from improper alignment between entity types and visual regions or error propagation in the two-stage manner, which finally imports irrelevant visual information into texts…
