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Hyper-MD: Mesh Denoising with Customized Parameters Aware of Noise Intensity and Geometric Characteristics

Hyper-MD: Mesh Denoising with Customized Parameters Aware of...

引用

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

作者： Wang, Xingtao Wei, Hongliang Fan, Xiaopeng Zhao, Debin Harbin Inst Technol Harbin Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Mesh denoising (MD) is a critical task in geometry processing, as meshes from scanning or AIGC techniques are susceptible to noise contamination. The challenge of MD lies in the diverse nature of mesh facets in terms of geometric characteristics and noise distributions. Despite recent advancements in deep learning-based MD methods, existing MD networks typically neglect the consideration of geometric characteristics and noise distributions. In this paper, we propose Hyper-MD, a hyper-network-based approach that addresses this limitation by dynamically customizing denoising parameters for each facet based on its noise intensity and geometric characteristics. Specifically, HyperMD is composed of a hyper-network and an MD network. For each noisy facet, the hyper-network takes two angles as input to customize parameters for the MD network. These two angles are specially defined to reveal the noise intensity and geometric characteristics of the current facet, respectively. The MD network receives a facet patch as input, and outputs the denoised normal using the customized parameters. Experimental results on synthetic and real-scanned meshes demonstrate that Hyper-MD outperforms state-of-the-art mesh denoising methods.

关键词： computer graphics hyper-network Mesh denoising

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Distilling vision-Language Models on Millions of Videos

Distilling Vision-Language Models on Millions of Videos

引用

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

作者： Zhao, Yue Zhao, Long Zhou, Xingyi Wu, Jialin Chu, Chun-Te Mia, Hui Schroff, Florian Adam, Hartwig Liu, Ting Gong, Boqing Krahenbuhl, Philipp Yuan, Liangzhe Google Res Mountain View CA 94043 USA Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9798350353006

The recent advance in vision-language models is largely attributed to the abundance of image-text data. We aim to replicate this success for video-language models, but there simply is not enough human- curated video-text data available. We thus resort to fine-tuning a video-language model from a strong image-language baseline with synthesized instructional data. The resulting video model by video-instruction-tuning (VIIT) is then used to auto-label millions of videos to generate high-quality captions. We show the adapted video-language model performs well on a wide range of video-language benchmarks. For instance, it surpasses the best prior result on open-ended NExT-QA by 2.8%. Besides, our model generates detailed descriptions for previously unseen videos, which provide better textual supervision than existing methods. Experiments show that a video-language dual-encoder model contrastively trained on these auto-generated captions is 3.8% better than the strongest baseline that also leverages vision-language models. Our best model outperforms state-of-the-art methods on MSR-VTT zero-shot text-to-video retrieval by 6%. As a side product, we generate the largest video capation dataset to date.

关键词： Video analysis

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GROUNDHOG : Grounding Large Language Models to Holistic Segmentation

GROUNDHOG : Grounding Large Language Models to Holistic Segm...

引用

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

作者： Zhang, Yichi Qiao, Zhiqiao Gao, Xiaofeng Shakiah, Suhaila Gao, Qiaozi Chai, Joyce Univ Michigan Ann Arbor MI 48109 USA Amazon AGI Seattle WA USA

ISBN: (纸本)9798350353006

Most multimodal large language models (MLLMs) learn language-to-object grounding through causal language modeling where grounded objects are captured by bounding boxes as sequences of location tokens. This paradigm lacks pixel-level representations that are important for fine-grained visual understanding and diagnosis. In this work, we introduce GROUNDHOG, an MLLM developed by grounding Large Language Models to holistic segmentation. GROUNDHOG incorporates a masked feature extractor and converts extracted features into visual entity tokens for the MLLM backbone, which then connectsgroundable phrases to unified grounding masks by retrieving and merging the entity masks. To train GROUNDHOG, we carefully curated M3G2, a grounded visual instruction tuning dataset with Multi-Modal Multi-Grained Grounding, by harvesting a collection of segmentation-grounded datasets with rich annotations. Our experimental results show that GROUNDHOG achieves superior performance on various language grounding tasks without task-specific fine-tuning, and significantly reduces object hallucination. GROUNDHOG also demonstrates better grounding towards complex forms of visual input and provides easy-to-understand diagnosis in failure cases.

关键词： Language Grounding Multi-Modal vision-Language Model

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Proceedings - 2021 ieee/cvf conference on computer vision and pattern recognition, CVPR 2021

Proceedings - 2021 IEEE/CVF Conference on Computer Vision an...

引用

2021 ieee/cvf conference on computer vision and pattern recognition, CVPR 2021

ISBN: (纸本)9781665445092

The proceedings contain 1658 papers. The topics discussed include: single-stage instance shadow detection with bidirectional relation learning;learning Delaunay surface elements for mesh reconstruction;fusing the old with the new: learning relative camera pose with geometry-guided uncertainty;uncertainty guided collaborative training for weakly supervised temporal action detection;privacy-preserving collaborative learning with automatic transformation search;rethinking and improving the robustness of image style transfer;style-aware normalized loss for improving arbitrary style transfer;faster meta update strategy for noise-robust deep learning;a hyperbolic-to-hyperbolic graph convolutional network;training networks in null space of feature covariance for continual learning;and exponential moving average normalization for self-supervised and semi-supervised learning.

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SpatialVLM: Endowing vision-Language Models with Spatial Reasoning Capabilities

SpatialVLM: Endowing Vision-Language Models with Spatial Rea...

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

作者： Chen, Boyuan Xu, Zhuo Kirman, Sean Ichter, Brian Sadigh, Dorsa Guibas, Leonidas Xia, Fei Google DeepMind London England Google Res Mountain View CA USA MIT 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9798350353006

Understanding and reasoning about spatial relationships is a fundamental capability for Visual Question Answering (VQA) and robotics. While vision Language Models (VLM) have demonstrated remarkable performance in certain VQA benchmarks, they still lack capabilities in 3D spatial reasoning, such as recognizing quantitative relationships of physical objects like distances or size difference. We hypothesize that VLMs' limited spatial reasoning capability is due to the lack of 3D spatial knowledge in training data and aim to solve this problem by training VLMs with Internet-scale spatial reasoning data. To this end, we present a system to facilitate this approach. We first develop an automatic 3D spatial VQA data generation framework that scales up to 2 billion VQA examples on 10 million real-world images. We then investigate various factors in training recipe including data quality, training pipeline and VLM architecture. Our work features the first Internet-scale 3D spatial reasoning dataset in metric space. By training a VLM on such data, we significantly enhance its ability on both qualitative and quantitative spatial VQA. Finally, we demonstrate that this VLM unlocks novel downstream applications in chain-of-thought spatial reasoning and robotics due to its quantitative estimation capability. Website: https://***/

关键词： large language model multimodal spatial reasoning vision language model

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Synthesize, Diagnose, and Optimize: Towards Fine-Grained vision-Language Understanding

Synthesize, Diagnose, and Optimize: Towards Fine-Grained Vis...

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

作者： Peng, Wujian Xi, Sicheng You, Zuyao Lan, Shiyi Wu, Zuxuan Fudan Univ Sch CS Shanghai Key Lab Intell Info Proc Shanghai Peoples R China Shanghai Collaborat Innovat Ctr Intelligent Visua Shanghai Peoples R China NVIDIA Shenzhen Guangdong Peoples R China

ISBN: (纸本)9798350353006

vision language models (VLM) have demonstrated remarkable performance across various downstream tasks. However, understanding fine-grained visual-linguistic concepts, such as attributes and inter-object relationships, remains a significant challenge. While several benchmarks aim to evaluate VLMs in finer granularity, their primary focus remains on the linguistic aspect, neglecting the visual dimension. Here, we highlight the importance of evaluating VLMs from both a textual and visual perspective. We introduce a progressive pipeline to synthesize images that vary in a specific attribute while ensuring consistency in all other aspects. Utilizing this data engine, we carefully design a benchmark, SPEC, to diagnose the comprehension of object size, position, existence, and count. Subsequently, we conduct a thorough evaluation of four leading VLMs on SPEC. Surprisingly, their performance is close to random guess, revealing significant limitations. With this in mind, we propose a simple yet effective approach to optimize VLMs in fine-grained understanding, achieving significant improvements on SPEC without compromising the zero-shot performance. Results on two additional fine-grained benchmarks also show consistent improvements, further validating the transferability of our approach. Code and data are available at https://***/wjpoom/SPEC.

关键词： Fine-grained understdanding vision language model

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BigGait: Learning Gait Representation You Want by Large vision Models

BigGait: Learning Gait Representation You Want by Large Visi...

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

作者： Ye, Dingqiang Fan, Chao Ma, Jingzhe Liu, Xiaoming Yu, Shiqi Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China Michigan State Univ E Lansing MI USA

ISBN: (纸本)9798350353013;9798350353006

Gait recognition stands as one of the most pivotal remote identification technologies and progressively expands across research and industry communities. However, existing gait recognition methods heavily rely on task-specific upstream driven by supervised learning to provide explicit gait representations like silhouette sequences, which inevitably introduce expensive annotation costs and potential error accumulation. Escaping from this trend, this work explores effective gait representations based on the all-purpose knowledge produced by task-agnostic Large vision Models (LVMs) and proposes a simple yet efficient gait framework, termed BigGait. Specifically, the Gait Representation Extractor (GRE) within BigGait draws upon design principles from established gait representations, effectively transforming all-purpose knowledge into implicit gait representations without requiring third-party supervision signals. Experiments on CCPG, CAISA-B* and SUSTech1K indicate that BigGait significantly outperforms the previous methods in both within-domain and cross-domain tasks in most cases, and provides a more practical paradigm for learning the next-generation gait representation. Finally, we delve into prospective challenges and promising directions in LVMs-based gait recognition, aiming to inspire future work in this emerging topic. The source code is available at https://github. com/ShiqiYu/OpenGait.

关键词： Gait recognition Large vision Models Person Re-Identification

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ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts

ViP-LLaVA: Making Large Multimodal Models Understand Arbitra...

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

作者： Cai, Mu Liu, Haotian Mustikovela, Siva Karthik Meyer, Gregory P. Chai, Yuning Park, Dennis Lee, Yong Jae Univ Wisconsin Madison WI 53706 USA Cruise LLC San Francisco CA USA

ISBN: (纸本)9798350353006

While existing large vision-language multimodal models focus on whole image understanding, there is a prominent gap in achieving region-specific comprehension. Current approaches that use textual coordinates or spatial encodings often fail to provide a user-friendly interface for visual prompting. To address this challenge, we introduce a novel multimodal model capable of decoding arbitrary (free-form) visual prompts. This allows users to intuitively mark images and interact with the model using natural cues like a "red bounding box" or "pointed arrow". Our simple design directly overlays visual markers onto the RGB image, eliminating the need for complex region encodings, yet achieves state-of-the-art performance on region-understanding tasks like Visual7W, PointQA, and Visual Commonsense Reasoning benchmark. Furthermore, we present ViP-Bench, a comprehensive benchmark to assess the capability of models in understanding visual prompts across multiple dimensions, enabling future research in this domain. Code, data, and model are publicly available.

关键词： Large Language Models Large Multimodal Models Multimodal Benchmark Region-level Understanding vision-language models Visual Commonsense Reasoning Visual Prompts

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Language-aware Visual Semantic Distillation for Video Question Answering

Language-aware Visual Semantic Distillation for Video Questi...

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

作者： Zou, Bo Yang, Chao Qiao, Yu Quan, Chengbin Zhao, Youjian Tsinghua Univ Beijing Peoples R China Shanghai AI Lab Shanghai Peoples R China Zhongguancun Lab Beijing Peoples R China

ISBN: (纸本)9798350353006

Significant progress in video question answering (VideoQA) have been made thanks to thriving large image-language pretraining frameworks. Although image-language models can efficiently represent both video and language branches, they typically employ goal-free vision perception and do not interact vision with language well during the answer generation, thus omitting crucial visual cues. In this paper, we are inspired by the human recognition and learning pattern and propose VideoDistill, a framework with language-aware (i.e., goal-driven) behavior in both vision perception and answer generation. VideoDistill generates answers only from question-related visual embeddings and follows a thinking-observing-answering approach that closely resembles human behavior, distinguishing it from previous research. Specifically, we develop a language-aware gating mechanism to replace the standard cross-attention, avoiding language's direct fusion into visual representations. We incorporate this mechanism into two key components of the entire framework. The first component is a differentiable sparse sampling module, which selects frames containing the necessary dynamics and semantics relevant to the questions. The second component is a vision refinement module that merges existing spatial-temporal attention layers to ensure extracting multi-grained visual semantics associated with the questions. We conduct evaluations on various challenging video question-answering benchmarks, and VideoDistill achieves state-of-the-art performance in both general and long-form VideoQA datasets. In Addition, we verify that VideoDistill can effectively alleviate the utilization of language shortcut solutions in the EgoTaskQA dataset.

关键词： Multi-modal Fusion Question-Answering Video Understanding VideoQA vision-Language

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SyncMask: Synchronized Attentional Masking for Fashion-centric vision-Language Pretraining

SyncMask: Synchronized Attentional Masking for Fashion-centr...

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

作者： Song, Chull Hwan Hwang, Taebaek Yoon, Jooyoung Choi, Shunghyun Gu, Yeong Hyeon Dealicious Inc Seoul South Korea Sejong Univ Seoul South Korea

ISBN: (纸本)9798350353006

vision-language models (VLMs) have made significant strides in cross-modal understanding through large-scale paired datasets. However, in fashion domain, datasets of-en exhibit a disparity between the information conveyed in image and text. This issue stems from datasets containing multiple images of a single fashion item all paired with one text, leading to cases where some textual details are not visible in individual images. This mismatch, particularly when non-co-occurring elements are masked, undermines the training of conventional VLM objectives like Masked Language Modeling and Masked Image Modeling, thereby hindering the model's ability to accurately align fine-grained visual and textual features. Addressing this problem, we pro-pose Synchronized attentional Masking (SyncMask), which generate masks that pinpoint the image patches and word tokens where the information co-occur in both image and text. This synchronization is accomplished by harnessing cross-attentional features obtained from a momentum model, ensuring a precise alignment between the two modalities. Additionally, we enhance grouped batch sampling with semi-hard negatives, effectively mitigating false negative issues in Image-Text Matching and Image-Text Contrastive learning objectives within fashion datasets. Our experiments demonstrate the effectiveness of the proposed approach, outperforming existing methods in three downstream tasks.

关键词： Visual languages

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