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SPIN: Simultaneous Perception, Interaction and Navigation

SPIN: Simultaneous Perception, Interaction and Navigation

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

作者： Uppal, Shagun Agarwal, Ananye Xiong, Haoyu Shaw, Kenneth Pathak, Deepak Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798350353006

While there has been remarkable progress recently in the fields of manipulation and locomotion, mobile manipulation remains a long-standing challenge. Compared to locomotion or static manipulation, a mobile system must make a diverse range of long-horizon tasks feasible in unstructured and dynamic environments. While the applications are broad and interesting, there are a plethora of challenges in developing these systems such as coordination between the base and arm, reliance on onboard perception for perceiving and interacting with the environment, and most importantly, simultaneously integrating all these parts together. Prior works approach the problem using disentangled modular skills for mobility and manipulation that are trivially tied together. This causes several limitations such as compounding errors, delays in decision-making, and no whole-body coordination. In this work, we present a reactive mobile manipulation framework that uses an active visual system to consciously perceive and react to its environment. Similar to how humans leverage whole-body and hand-eye coordination, we develop a mobile manipulator that exploits its ability to move and see, more specifically - to move in order to see and to see in order to move. This allows it to not only move around and interact with its environment but also, choose "when" to perceive "what" using an active visual system. We observe that such an agent learns to navigate around complex cluttered scenarios while displaying agile whole-body coordination using only ego-vision without needing to create environment maps. Videos are available at https://***

关键词： Active vision End-to-end learning Mobile Manipulation Whole-body coordination

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Leveraging Cross-Modal Neighbor Representation for Improved CLIP Classification

Leveraging Cross-Modal Neighbor Representation for Improved ...

引用

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

作者： Yi, Chao Ren, Lu Zhan, De-Chuan Ye, Han-Jia Nanjing Univ Natl Key Lab Novel Software Technol Nanjing Peoples R China Nanjing Univ Sch Artificial Intelligence Nanjing Peoples R China

ISBN: (纸本)9798350353006

CLIP showcases exceptional cross-modal matching capabilities due to its training on image-text contrastive learning tasks. However, without specific optimization for unimodal scenarios, its performance in single-modality feature extraction might be suboptimal. Despite this, some studies have directly used CLIP's image encoder for tasks like few-shot classification, introducing a misalignment between its pre-training objectives and feature extraction methods. This inconsistency can diminish the quality of the image's feature representation, adversely affecting CLIP's effectiveness in target tasks. In this paper, we view text features as precise neighbors of image features in CLIP's space and present a novel CrOss-moDal nEighbor Representation (CODER) based on the distance structure between images and their neighbor texts. This feature extraction method aligns better with CLIP's pre-training objectives, thereby fully leveraging CLIP's robust cross-modal capabilities. The key to construct a high-quality CODER lies in how to create a vast amount of high-quality and diverse texts to match with images. We introduce the Auto Text Generator (ATG) to automatically generate the required texts in a data-free and training-free manner. We apply CODER to CLIP's zero-shot and few-shot image classification tasks. Experiment results across various datasets and models confirm CODER's effectiveness. Code is available at: https://***/YCaigogogo/cvpr24-CODER.

关键词： Multimodal Learning vision-Language Model

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PEM: Prototype-based Efficient MaskFormer for Image Segmentation

PEM: Prototype-based Efficient MaskFormer for Image Segmenta...

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

作者： Cavagnero, Niccolo Rosi, Gabriele Cuttano, Claudia Pistilli, Francesca Ciccone, Marco Averta, Giuseppe Cermelli, Fabio Politecn Torino Turin Italy Focoos AI Rome Italy

ISBN: (纸本)9798350353006

Recent transformer-based architectures have shown impressive results in the field of image segmentation. Thanks to their flexibility, they obtain outstanding performance in multiple segmentation tasks, such as semantic and panoptic, under a single unified framework. To achieve such impressive performance, these architectures employ intensive operations and require substantial computational resources, which are often not available, especially on edge devices. To fill this gap, we propose Prototype-based Efficient MaskFormer (PEM), an efficient transformer-based architecture that can operate in multiple segmentation tasks. PEM proposes a novel prototype-based cross-attention which leverages the redundancy of visual features to restrict the computation and improve the efficiency without harming the performance. In addition, PEM introduces an efficient multi-scale feature pyramid network, capable of extracting features that have high semantic content in an efficient way, thanks to the combination of deformable convolutions and context-based self-modulation. We benchmark the proposed PEM architecture on two tasks, semantic and panoptic segmentation, evaluated on two different datasets, Cityscapes and ADE20K. PEM demonstrates outstanding performance on every task and dataset, outperforming task-specific architectures while being comparable and even better than computationally expensive baselines. Code is available at https://***/NiccoloCavagnero/PEM.

关键词： computer vision Efficient Neural Network Image Segmentation

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Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs

Eyes Wide Shut? Exploring the Visual Shortcomings of Multimo...

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

作者： Tong, Shengbang Liu, Zhuang Zhai, Yuexiang Ma, Yi Lecun, Yann Xie, Saining NYU New York NY 10003 USA Meta FAIR Menlo Pk CA 94025 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (纸本)9798350353006

Is vision good enough for language? Recent advancements in multimodal models primarily stem from the powerful reasoning abilities of large language models (LLMs). However, the visual component typically depends only on the instance-level contrastive language-image pre-training ( CLIP). Our research reveals that the visual capabilities in recent MultiModal LLMs (MLLMs) still exhibit systematic shortcomings. To understand the roots of these errors, we explore the gap between the visual embedding space of CLIP and vision-only self-supervised learning. We identify "CLIP-blind pairs" - images that CLIP perceives as similar despite their clear visual differences. With these pairs, we construct the Multimodal Visual patterns (MMVP) benchmark. MMVP exposes areas where state-of-the-art systems, including GPT-4V, struggle with straightforward questions across nine basic visual patterns, often providing incorrect answers and hallucinated explanations. We further evaluate various CLIP-based vision-and-language models and found a notable correlation between visual patterns that challenge CLIP models and those problematic for multimodal LLMs. As an initial effort to address these issues, we propose a Mixture of Features (MoF) approach, demonstrating that integrating vision self-supervised learning features with MLLMs can significantly enhance their visual grounding capabilities. Together, our research suggests visual representation learning remains an open challenge, and accurate visual grounding is crucial for future successful multimodal systems.

关键词： Multimodal LLMs vision Language Model

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UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio, Video, Point Cloud, Time-Series and Image recognition

UniRepLKNet: A Universal Perception Large-Kernel ConvNet for...

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

作者： Ding, Xiaohan Zhang, Yiyuan Ge, Yixiao Zhao, Sijie Song, Lin Yue, Xiangyu Shan, Ying Tencent AI Lab Shenzhen Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Large-kernel convolutional neural networks (ConvNets) have recently received extensive research attention, but two unresolved and critical issues demand further investigation. 1) The architectures of existing large-kernel ConvNets largely follow the design principles of conventional ConvNets or transformers, while the architectural design for large-kernel ConvNets remains under-addressed. 2) As transformers have dominated multiple modalities, it remains to be investigated whether ConvNets also have a strong universal perception ability in domains beyond vision. In this paper, we contribute from two aspects. 1) We propose four architectural guidelines for designing large-kernel ConvNets, the core of which is to exploit the essential characteristics of large kernels that distinguish them from small kernels - they can see wide without going deep. Following such guidelines, our proposed large-kernel ConvNet shows leading performance in image recognition (ImageNet accuracy of 88.0%, ADE20K mIoU of 55.6%, and COCO box AP of 56.4%), demonstrating better performance and higher speed than the recent powerful competitors. 2) We discover large kernels are the key to unlocking the exceptional performance of ConvNets in domains where they were originally not proficient. With certain modality-related pre-processing approaches, the proposed model achieves state-of-the-art performance on time-series forecasting and audio recognition tasks even without modality-specific customization to the architecture. All the code and models are publicly available on GitHub and Huggingface.

关键词： Large-Kernel ConvNets Multimodal Learning Network Architecture

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MULTIFLOW: Shifting Towards Task-Agnostic vision-Language Pruning

MULTIFLOW: Shifting Towards Task-Agnostic Vision-Language Pr...

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

作者： Farina, Matteo Mancini, Massimiliano Cunegatti, Elia Liu, Gaowen Iacca, Giovanni Ricci, Elisa Univ Trento Trento Italy Cisco Res Res Triangle Pk NC USA Fdn Bruno Kessler Povo Italy

ISBN: (纸本)9798350353006

While excellent in transfer learning, vision-Language models (VLMs) come with high computational costs due to their large number of parameters. To address this issue, removing parameters via model pruning is a viable solution. However, existing techniques for VLMs are task-specific, and thus require pruning the network from scratch for each new task of interest. In this work, we explore a new direction: Task-Agnostic vision-Language Pruning (TA-VLP). Given a pretrained VLM, the goal is to find a unique pruned counterpart transferable to multiple unknown downstream tasks. In this challenging setting, the transferable representations already encoded in the pretrained model are a key aspect to preserve. Thus, we propose Multimodal Flow Pruning (MULTIFLOW), a first, gradient-free, pruning framework for TA-VLP where: (i) the importance of a parameter is expressed in terms of its magnitude and its information flow, by incorporating the saliency of the neu-rons it connects;and (ii) pruning is driven by the emergent (multimodal) distribution of the VLM parameters after pretraining. We benchmark eight state-of-the-art pruning algorithms in the context of TA-VLP, experimenting with two VLMs, three vision-language tasks, and three pruning ratios. Our experimental results show that MULTIFLOW outperforms recent sophisticated, combinatorial competitors in the vast majority of the cases, paving the way towards addressing TA-VLP. The code is publicly available at https://***/FarinaMatteo/multiflow.

关键词： multimodal learning neural network pruning sparse neural networks transfer learning vision-language models

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Jack of All Tasks, Master of Many: Designing General-purpose Coarse-to-Fine vision-Language Model

Jack of All Tasks, Master of Many: Designing General-purpose...

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

作者： Pramanick, Shraman Han, Guangxing Hou, Rui Nag, Sayan Lim, Ser-Nam Ballas, Nicolas Wang, Qifan Chellappa, Rama Almahairi, Amjad Johns Hopkins Univ Baltimore MD 21218 USA Meta New York NY 10003 USA Univ Toronto Toronto ON Canada Univ Cent Florida Orlando FL 32816 USA

ISBN: (纸本)9798350353006

The ability of large language models (LLMs) to process visual inputs has given rise to general-purpose vision systems, unifying various vision-language (VL) tasks by instruction tuning. However, due to the enormous diversity in input-output formats in the vision domain, existing general-purpose models fail to successfully integrate segmentation and multi-image inputs with coarse-level tasks into a single framework. In this work, we introduce VistaLLM, a powerful visual system that addresses coarse- and fine-grained VL tasks over single and multiple input images using a unified framework. VistaLLM utilizes an instruction-guided image tokenizer that filters global embeddings using task descriptions to extract compressed and refined features from numerous images. Moreover, VistaLLM employs a gradient-aware adaptive sampling technique to represent binary segmentation masks as sequences, significantly improving over previously used uniform sampling. To bolster the desired capability of VistaLLM, we curate CoinIt, a comprehensive coarse-to-fine instruction tuning dataset with 6.8M samples. We also address the lack of multi-image grounding datasets by introducing a novel task, AttCoSeg (Attribute-level Co-Segmentation), which boosts the model's reasoning and grounding capability over multiple input images. Extensive experiments on a wide range of V- and VL tasks demonstrate the effectiveness of VistaLLM by achieving consistent state-of-the-art performance over strong base-lines across many downstream tasks. Our project page can be found at https://***/VistaLLM/.

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Emergent Open-Vocabulary Semantic Segmentation from Off-the-shelf vision-Language Models

Emergent Open-Vocabulary Semantic Segmentation from Off-the-...

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

作者： Luo, Jiayun Khandelwal, Siddhesh Sigal, Leonid Li, Boyang Nanyang Technol Univ Singapore Singapore Univ British Columbia Vector Inst AI Vancouver BC Canada

ISBN: (纸本)9798350353013;9798350353006

From image-text pairs, large-scale vision-language models (VLMs) learn to implicitly associate image regions with words, which prove effective for tasks like visual question answering. However, leveraging the learned association for open-vocabulary semantic segmentation remains a challenge. In this paper, we propose a simple, yet extremely effective, training-free technique, Plug-and-Play Open-Vocabulary Semantic Segmentation (PnP-OVSS) for this task. PnP-OVSS leverages a VLM with direct text-to-image cross-attention and an image-text matching loss. To balance between over-segmentation and under-segmentation, we introduce Salience Dropout;by iteratively dropping patches that the model is most attentive to, we are able to better resolve the entire extent of the segmentation mask. PnP-OVSS does not require any neural network training and performs hyperparameter tuning without the need for any segmentation annotations, even for a validation set. PnP-OVSS demonstrates substantial improvements over comparable baselines (+29.4% mIoU on Pascal VOC, +13.2% mIoU on Pascal Context, +14.0% mIoU on MS COCO, +2.4% mIoU on COCO Stuff) and even outperforms most baselines that conduct additional network training on top of pretrained VLMs. Our codebase is at https://***/letitiabanana/PnP-OVSS.

关键词： open-vocabulary semantic segmentation training-free

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RegionPLC: Regional Point-Language Contrastive Learning for Open-World 3D Scene Understanding

RegionPLC: Regional Point-Language Contrastive Learning for ...

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

作者： Yang, Jihan Ding, Runyu Deng, Weipeng Wang, Zhe Qi, Xiaojuan Univ Hong Kong Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China

ISBN: (纸本)9798350353006

We propose a lightweight and scalable Regional Point-Language Contrastive learning framework, namely RegionPLC, for open-world 3D scene understanding, aiming to identify and recognize open-set objects and categories. Specifically, based on our empirical studies, we introduce a 3D-aware SFusion strategy that fuses 3D vision-language pairs derived from multiple 2D foundation models, yielding high-quality, dense region-level language descriptions without human 3D annotations. Subsequently, we devise a region-aware point-discriminative contrastive learning objective to enable robust and effective 3D learning from dense regional language supervision. We carry out extensive experiments on ScanNet, ScanNet200, and nuScenes datasets, and our model outperforms prior 3D open-world scene understanding approaches by an average of 17.2% and 9.1% for semantic and instance segmentation, respectively, while maintaining greater scalability and lower resource demands. Furthermore, our method has the flexibility to be effortlessly integrated with language models to enable open-ended grounded 3D reasoning without extra task-specific training. Code will be released at github.

关键词： 3D vision embodied AI Open-world

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Unravelling Robustness of Deep Face recognition Networks Against Illicit Drug Abuse Images

Unravelling Robustness of Deep Face Recognition Networks Aga...

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

作者： Dhake, Hruturaj Agarwal, Akshay IISER Bhopal Data Sci & Engn Bhopal India

ISBN: (纸本)9798350365474

Alteration in facial features can lead to a significant drop in recognition performance. These alterations can be due to several factors: one such prominent and less explored factor is illicit drug abuse. To advance the understanding of how drug abuse faces affect the performance of state-of-the-art deep face recognition (DFR) networks, in this study, we have utilized clean and illicit drug abuse faces. Extensive studies are performed on deep face recognition and soft biometric identification, such as gender, ethnicity, and expression recognition. It is observed that illicit drug abuse not only impacts the identity recognition performance but also degrades the soft biometrics identification accuracy. Therefore, to advance the integrity of DFR, we have performed the detection of illicit drug abuse as a potential solution to its mitigation. In the end, the robustness of the drug abuse face detector is evaluated under the prominent use of social-media filters on face images.

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