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Honeybee: Locality-enhanced Projector for Multimodal LLM

Honeybee: Locality-enhanced Projector for Multimodal LLM

引用

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

作者： Cha, Junbum Kang, Wooyoung Mun, Jonghwan Roh, Byungseok Kakao Brain Seongnam South Korea

ISBN: (纸本)9798350353006

In Multimodal Large Language Models (MLLMs), a visual projector plays a crucial role in bridging pre-trained vision encoders with LLMs, enabling profound visual understanding while harnessing the LLMs' robust capabilities. Despite the importance of the visual projector, it has been relatively less explored. In this study, we first identify two essential projector properties: (i) flexibility in managing the number of visual tokens, crucial for MLLMs' overall efficiency, and (ii) preservation of local context from visual features, vital for spatial understanding. Based on these findings, we propose a novel projector design that is both flexible and locality-enhanced, effectively satisfying the two desirable properties. Additionally, we present comprehensive strategies to effectively utilize multiple and multifaceted instruction datasets. Through extensive experiments, we examine the impact of individual design choices. Finally, our proposed MLLM, Honeybee, remarkably outperforms previous state-of-the-art methods across various benchmarks, including MME, MMBench, SEED-Bench, and LLaVA-Bench, achieving significantly higher efficiency. Code and models are available at https://github. com/kakaobrain/honeybee.

关键词： Multimodal LLM vision-Language

来源：评论

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Accurate Training Data for Occupancy Map Prediction in Automated Driving Using Evidence Theory

Accurate Training Data for Occupancy Map Prediction in Autom...

引用

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

作者： Kaelble, Jonas Wirges, Sascha Tatarchenko, Maxim Ilg, Eddy Bosch Ctr Artificial Intelligence Renningen Germany Saarland Univ Saarbrucken Germany

ISBN: (纸本)9798350353013;9798350353006

Automated driving fundamentally requires knowledge about the surrounding geometry of the scene. Modern approaches use only captured images to predict occupancy maps that represent the geometry. Training these approaches requires accurate data that may be acquired with the help of LiDAR scanners. We show that the techniques used for current benchmarks and training datasets to convert LiDAR scans into occupancy grid maps yield very low quality, and subsequently present a novel approach using evidence theory that yields more accurate reconstructions. We demonstrate that these are superior by a large margin, both qualitatively and quantitatively, and that we additionally obtain meaningful uncertainty estimates. When converting the occupancy maps back to depth estimates and comparing them with the raw LiDAR measurements, our method yields a MAE improvement of 30% to 52% on nuScenes and 53% on Waymo over other occupancy ground-truth data. Finally, we use the improved occupancy maps to train a state-of-the-art occupancy prediction method and demonstrate that it improves the MAE by 25% on nuScenes.

关键词： Automated Driving computer vision Occupancy Prediction

来源：评论

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NORPPA: NOvel Ringed seal re-identification by Pelage pattern Aggregation

NORPPA: NOvel Ringed seal re-identification by Pelage Patter...

引用

ieee/CVF Winter conference on Applications of computer vision (WACV)

作者： Nepovinnykh, Ekaterina Eerola, Tuomas Kalviainen, Heikki Chelak, Ilia Lappeenranta Lahti Univ Technol LUT Sch Engn Sci Dept Computat Engn Comp Vis & Pattern Recognit Lab CVPRL Lappeenranta Finland Univ Helsinki Dept Comp Sci Fac Sci Helsinki Finland

ISBN: (纸本)9798350370287;9798350370713

We propose a method for Saimaa ringed seal (Pusa hispida saimensis) re-identification. Access to large image volumes through camera trapping and crowdsourcing provides novel possibilities for animal conservation and monitoring and calls for automatic methods for analysis, in particular, when re-identifying individual animals from the images. The proposed method NOvel Ringed seal re-identification by Pelage pattern Aggregation (NORPPA) utilizes the permanent and unique pelage pattern of Saimaa ringed seals and content-based image retrieval techniques. First, the query image is preprocessed, and each seal instance is segmented. Next, the seal's pelage pattern is extracted using a U-net encoder-decoder based method. Then, CNN-based affine invariant features are embedded and aggregated into Fisher Vectors. Finally, the cosine distance between the Fisher Vectors is used to find the best match from a database of known individuals. We perform extensive experiments of various modifications of the method on challenging Saimaa ringed seals re-identification dataset. The proposed method is shown to produce the best re-identification accuracy on our dataset in comparisons with alternative approaches.

关键词： Animals

来源：评论

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Semantics-aware Motion Retargeting with vision-Language Models

Semantics-aware Motion Retargeting with Vision-Language Mode...

引用

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

作者： Zhang, Haodong Chen, Zhike Xu, Haocheng Hao, Lei Wu, Xiaofei Xu, Songcen Zhang, Zhensong Wang, Yue Xiong, Rong Zhejiang Univ Hangzhou Peoples R China Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (纸本)9798350353013;9798350353006

Capturing and preserving motion semantics is essential to motion retargeting between animation characters. However, most of the previous works neglect the semantic information or rely on human-designed joint-level representations. Here, we present a novel Semantics-aware Motion reTargeting (SMT) method with the advantage of vision-language models to extract and maintain meaningful motion semantics. We utilize a differentiable module to render 3D motions. Then the high-level motion semantics are incorporated into the motion retargeting process by feeding the vision-language model with the rendered images and aligning the extracted semantic embeddings. To ensure the preservation of fine-grained motion details and high-level semantics, we adopt a two-stage pipeline consisting of skeleton-aware pre-training and fine-tuning with semantics and geometry constraints. Experimental results show the effectiveness of the proposed method in producing high-quality motion retargeting results while accurately preserving motion semantics. Project page can be found at https://***/view/smtnet.

关键词： Animation Motion Retargeting vision-Language Model

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Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation

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

引用

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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GSNeRF: Generalizable Semantic Neural Radiance Fields with Enhanced 3D Scene Understanding

GSNeRF: Generalizable Semantic Neural Radiance Fields with E...

引用

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

作者： Chou, Zi-Ting Huang, Sheng-Yu Liu, I-Jieh Wang, Yu-Chiang Frank Natl Taiwan Univ Grad Inst Commun Engn Taipei Taiwan NVIDIA Taipei Taiwan

ISBN: (纸本)9798350353006

Utilizing multi-view inputs to synthesize novel-view images, Neural Radiance Fields (NeRF) have emerged as a popular research topic in 3D vision. In this work, we introduce a Generalizable Semantic Neural Radiance Fields ( GSNeRF), which uniquely takes image semantics into the synthesis process so that both novel view image and the associated semantic maps can be produced for unseen scenes. Our GSNeRF is composed of two stages: Semantic GeoReasoning and Depth-Guided Visual rendering. The former is able to observe multi- view image inputs to extract semantic and geometry features from a scene. Guided by the resulting image geometry information, the latter performs both image and semantic rendering with improved performances. Our experiments not only confirm that GSNeRF performs favorably against prior works on both novel-view image and semantic segmentation synthesis but the effectiveness of our sampling strategy for visual rendering is further verified.

关键词： 3D computer vision generalizable nerf NeRF segmentation

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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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Continuous Hand Gesture recognition for Human-Robot Collaborative Assembly

Continuous Hand Gesture Recognition for Human-Robot Collabor...

引用

ieee/CVF International conference on computer vision (ICCV)

作者： Kwolek, Bogdan AGH Univ Krakow 30 Mickiewicza Av PL-30059 Krakow Poland

ISBN: (纸本)9798350307443

In this work, we present a framework for dynamic hand gesture recognition on RGB images acquired by an overhead camera. The recognition is realized for Methods Time Measurement-based planning of human-robot collaborative workspace. The 3D hand posture is estimated by MediaPipe. The recognition is done by a neural network in which a layer-wise feature combination takes place. We combine features extracted by basic blocks of Spatio-Temporal Adaptive Graph Convolutional Neural Network and by basic spatio-temporal self-attention blocks. We recorded and manually annotated 12 videos consisting of 54,659 RGB images with five basic motion sequences: grasp, move, position, release, and reach. We demonstrate experimentally that results of our networks are superior to results achieved by RNNs, ST-GCN, ST-AGCN, and CTR-GCN networks.

关键词： Assistive computer vision Continuous Hand Gesture recognition Self Attention ST GCN

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ArGue: Attribute-Guided Prompt Tuning for vision-Language Models

ArGue: Attribute-Guided Prompt Tuning for Vision-Language Mo...

引用

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

作者： Tian, Xinyu Zou, Shu Yang, Zhaoyuan Zhang, Jing Australian Natl Univ Canberra ACT Australia GE Res Niskayuna NY USA

ISBN: (纸本)9798350353006

Although soft prompt tuning is effective in efficiently adapting vision-Language (V&L) models for downstream tasks, it shows limitations in dealing with distribution shifts. We address this issue with Attribute-Guided Prompt Tuning (ArGue), making three key contributions. 1) In contrast to the conventional approach of directly appending soft prompts preceding class names, we align the model with primitive visual attributes generated by Large Language Models (LLMs). We posit that a model's ability to express high confidence in these attributes signifies its capacity to discern the correct class rationales. 2) We introduce attribute sampling to eliminate disadvantageous attributes, thus only semantically meaningful attributes are preserved. 3) We propose negative prompting, explicitly enumerating class-agnostic attributes to activate spurious correlations and encourage the model to generate highly orthogonal probability distributions in relation to these negative features. In experiments, our method significantly outperforms current state-of-the-art prompt tuning methods on both novel class prediction and out-of-distribution generalization tasks. The code is available https://***/Liam-Tian/ArGue.

关键词： few-shot adaptation prompt tuning vision-language model

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A Theory of Joint Light and Heat Transport for Lambertian Scenes

A Theory of Joint Light and Heat Transport for Lambertian Sc...

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

作者： Ramanagopal, Mani Narayanan, Sriram Sankaranarayanan, Aswin C. Narasimhan, Srinivasa G. Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798350353006

We present a novel theory that establishes the relationship between light transport in visible and thermal infrared, and heat transport in solids. We show that heat generated due to light absorption can be estimated by modeling heat transport using a thermal camera. For situations where heat conduction is negligible, we analytically solve the heat transport equation to derive a simple expression relating the change in thermal image intensity to the absorbed light intensity and heat capacity of the material. Next, we prove that intrinsic image decomposition for Lambertian scenes becomes a well-posed problem if one has access to the absorbed light. Our theory generalizes to arbitrary shapes and unstructured illumination. Our theory is based on applying energy conservation principle at each pixel independently. We validate our theory using real-world experiments on diffuse objects made of different materials that exhibit both direct and global components (inter-reflections) of light transport under unknown complex lighting.

关键词： heat transport intrinsic image decomposition light transport physics-based vision

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