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EgoGen: An Egocentric Synthetic Data Generator

EgoGen: An Egocentric Synthetic Data Generator

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

作者： Li, Gen Zhao, Kaifeng Zhang, Siwei Lyu, Xiaozhong Dusmanu, Mihai Zhang, Yan Pollefeys, Marc Tang, Siyu Swiss Fed Inst Technol Zurich Switzerland Microsoft Redmond WA USA

ISBN: (纸本)9798350353006

Understanding the world in first-person view is fundamental in Augmented Reality (AR). This immersive perspective brings dramatic visual changes and unique challenges compared to third-person views. Synthetic data has empowered third-person-view vision models, but its application to embodied egocentric perception tasks remains largely unexplored. A critical challenge lies in simulating natural human movements and behaviors that effectively steer the embodied cameras to capture a faithful egocentric representation of the 3D world. To address this challenge, we introduce EgoGen, a new synthetic data generator that can produce accurate and rich ground-truth training data for egocentric perception tasks. At the heart of EgoGen is a novel human motion synthesis model that directly leverages egocentric visual inputs of a virtual human to sense the 3D environment. Combined with collision-avoiding motion primitives and a two-stage reinforcement learning approach, our motion synthesis model offers a closed-loop solution where the embodied perception and movement of the virtual human are seamlessly coupled. Compared to previous works, our model eliminates the need for a predefined global path, and is directly applicable to dynamic environments. Combined with our easy-to-use and scalable data generation pipeline, we demonstrate EgoGen's efficacy in three tasks: mapping and localization for head-mounted cameras, egocentric camera tracking, and human mesh recovery from egocentric views. EgoGen will be fully open-sourced, offering a practical solution for creating realistic egocentric training data and aiming to serve as a useful tool for egocentric computer vision research.

关键词： autonomous virtual humans egocentric vision reinforcement learning synthetic data

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Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology

Masked Autoencoders for Microscopy are Scalable Learners of ...

引用

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

作者： Krauss, Oren Kenyon-Dean, Kian Saberian, Saber Fallah, Maryam McLean, Peter Leung, Jess Sharma, Vasudev Khan, Ayla Balakrishnan, Jia Celik, Safiye Beaini, Dominique Sypetkowski, Maciej Cheng, Chi Vicky Morsel, Kristen Makes, Maureen Mabey, Ben Earnshaw, Berton Recurs Salt Lake City UT 84101 USA Valence Labs Rajpura India

ISBN: (纸本)9798350353006

Featurizing microscopy images for use in biological research remains a significant challenge, especially for large-scale experiments spanning millions of images. This work explores the scaling properties of weakly supervised classifiers and self-supervised masked autoencoders (MAEs) when training with increasingly larger model backbones and microscopy datasets. Our results show that ViT-based MAEs outperform weakly supervised classifiers on a variety of tasks, achieving as much as a 11.5% relative improvement when recalling known biological relationships curated from public databases. Additionally, we develop a new channel-agnostic MAE architecture (CA-MAE) that allows for inputting images of different numbers and orders of channels at inference time. We demonstrate that CA-MAEs effectively generalize by inferring and evaluating on a microscopy image dataset (JUMP-CP) generated under different experimental conditions with a different channel structure than our pretraining data (RPI-93M). Our findings motivate continued research into scaling self-supervised learning on microscopy data in order to create powerful foundation models of cellular biology that have the potential to catalyze advancements in drug discovery and beyond. Relevant code and select models released with this work can be found at: https://***/recursionpharma/maes_microscopy.

关键词： cell biology cell morphology microscopy self-supervised learning SSL vision transformer ViT

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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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Gated Fields: Learning Scene Reconstruction from Gated Videos

Gated Fields: Learning Scene Reconstruction from Gated Video...

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

作者： Ramazzina, Andrea Walz, Stefanie Dahal, Pragyan Bijelic, Mario Heide, Felix Mercedes Benz Stuttgart Germany Saarland Univ Saarbrucken Germany Politecn Milan Milan Italy Torc Robot Blacksburg VA USA Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9798350353006

Reconstructing outdoor 3D scenes from temporal observations is a challenge that recent work on neural fields has offered a new avenue for. However, existing methods that recover scene properties, such as geometry, appearance, or radiance, solely from RGB captures often fail when handling poorly-lit or texture-deficient regions. Similarly, recovering scenes with scanning LiDAR sensors is also difficult due to their low angular sampling rate which makes recovering expansive real-world scenes difficult. Tackling these gaps, we introduce Gated Fields - a neural scene reconstruction method that utilizes active gated video sequences. To this end, we propose a neural rendering approach that seamlessly incorporates time-gated capture and illumination. Our method exploits the intrinsic depth cues in the gated videos, achieving precise and dense geometry reconstruction irrespective of ambient illumination conditions. We validate the method across day and night scenarios and find that Gated Fields compares favorably to RGB and LiDAR reconstruction methods. Our code and datasets are available here(1).

关键词： 3D vision Gated Camera Neural Radiance Fields Neural Rendering

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Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Mitigating Object Dependencies: Improving Point Cloud Self-S...

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

作者： Wu, Yanhao Zhang, Tong Ke, Wei Qi, Congpei Susstrunk, Sabine Salzmann, Mathieu Xi An Jiao Tong Univ Sch Software Engn Xian Peoples R China Ecole Polytech Fed Lausanne Sch Comp & Commun Sci Lausanne Switzerland

ISBN: (纸本)9798350353006

In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object cor-relations. This can create a tendency for neural networks to exploit these strong dependencies, bypassing the individual object patterns. To address this challenge, we introduce a novel self-supervised learning (SSL) strategy. Our approach leverages both object patterns and contextual cues to produce robust features. It begins with the formulation of an object-exchanging strategy, where pairs of objects with comparable sizes are exchanged across different scenes, effectively disentangling the strong contextual dependencies. Subsequently, we introduce a context-aware feature learning strategy, which encodes object patterns without relying on their specific context by aggregating object features across various scenes. Our extensive experiments demonstrate the superiority of our method over existing SSL techniques, further showing its better robustness to environmen tal changes. Moreover, we showcase the applicability of our approach by transferring pre-trained models to diverse point cloud datasets.

关键词： 3D vision self-supervised learning

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MMA: Multi-Modal Adapter for vision-Language Models

MMA: Multi-Modal Adapter for Vision-Language Models

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

作者： Yang, Lingxiao Zhang, Ru-Yuan Wang, Yanchen Xie, Xiaohua Sun Yat Sen Univ Guangzhou Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Stanford Univ Stanford CA USA

ISBN: (纸本)9798350353006

Pre-trained vision-Language Models (VLMs) have served as excellent foundation models for transfer learning in diverse downstream tasks. However, tuning VLMs for few-shot generalization tasks faces a discrimination - generalization dilemma, i.e., general knowledge should be preserved and task-specific knowledge should be fine-tuned. How to precisely identify these two types of representations remains a challenge. In this paper, we propose a Multi-Modal Adapter (MMA) for VLMs to improve the alignment between representations from text and vision branches. MMA aggregates features from different branches into a shared feature space so that gradients can be communicated across branches. To determine how to incorporate MMA, we systematically analyze the discriminability and generalizability of features across diverse datasets in both the vision and language branches, and find that (1) higher layers contain discriminable dataset-specific knowledge, while lower layers contain more generalizable knowledge, and (2) language features are more discriminable than visual features, and there are large semantic gaps between the features of the two modalities, especially in the lower layers. Therefore, we only incorporate MMA to a few higher layers of transformers to achieve an optimal balance between discrimination and generalization. We evaluate the effectiveness of our approach on three tasks: generalization to novel classes, novel target datasets, and domain generalization. Compared to many state-of-the-art methods, our MMA achieves leading performance in all evaluations. Code is at https://***/ZjjConan/Multi-Modal-Adapter

关键词： Adapter Multi-Modal vision-Language Models

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SC-Tune: Unleashing Self-Consistent Referential Comprehension in Large vision Language Models

SC-Tune: Unleashing Self-Consistent Referential Comprehensio...

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

作者： Yue, Tongtian Cheng, Jie Guo, Longteng Dai, Xingyuan Zhao, Zijia He, Xingjian Xiong, Gang Lv, Yisheng Liu, Jing CASIA Lab Cognit & Decis Intelligence Complex Syst Beijing Peoples R China CASIA State Key Lab Multimodal Artificial Intelligence Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9798350353006

Recent trends in Large vision Language Models (LVLMs) research have been increasingly focusing on advancing beyond general image understanding towards more nuanced, object-level referential comprehension. In this paper, we present and delve into the self-consistency capability of LVLMs, a crucial aspect that reflects the models' ability to both generate informative captions for specific objects and subsequently utilize these captions to accurately re-identify the objects in a closed-loop process. This capability significantly mirrors the precision and reliability of fine-grained visual-language understanding. Our findings reveal that the self-consistency level of existing LVLMs falls short of expectations, posing limitations on their practical applicability and potential. To address this gap, we introduce a novel fine-tuning paradigm named Self-Consistency Tuning (SC-Tune). It features the synergistic learning of a cyclic describer-locator system. This paradigm is not only data-efficient but also exhibits generalizability across multiple LVLMs. Through extensive experiments, we demonstrate that SC-Tune significantly elevates performance across a spectrum of object-level vision-language benchmarks and maintains competitive or improved performance on image-level vision-language benchmarks. Both our model and code will be publicly available at https://***/ivattyue/SC-Tune.

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Revisiting Counterfactual Problems in Referring Expression Comprehension

Revisiting Counterfactual Problems in Referring Expression C...

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

作者： Yu, Zhihan Li, Ruifan Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9798350353006

Traditional referring expression comprehension (REC) aims to locate the target referent in an image guided by a text query. Several previous methods have studied on the Counterfactual problem in REC (C-REC) where the objects for a given query cannot be found in the image. However, these methods focus on the overall image-text or specific attribute mismatch only. In this paper, we address the C-REC problem from a deep perspective of fine-grained attributes. To this aim, we first propose a fine-grained counterfactual sample generation method to construct C-REC datasets. Specifically, we leverage pre-trained language model such as BERT to modify the attribute words in the queries, obtaining the corresponding counterfactual samples. Furthermore, we propose a C-REC framework. We first adopt three encoders to extract image, text and attribute features. Then, our dual-branch attentive fusion module fuses these crossmodal features with two branches by an attention mechanism. At last, two prediction heads generate a bounding box and a counterfactual label, respectively. In addition, we incorporate contrastive learning with the generated counterfactual samples as negatives to enhance the counterfactual perception. Extensive experiments show that our framework achieves promising performance on both public REC datasets RefCOCO/+/g and our constructed C-REC datasets C-RefCOCO/+/g. The code and data are available at https://***/Glacier0012/CREC.

关键词： Counterfactual Generation Referring Expression Comprehension vision and Language

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Improving Image Restoration through Removing Degradations in Textual Representations

Improving Image Restoration through Removing Degradations in...

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

作者： Lin, Jingbo Zhang, Zhilu Wei, Yuxiang Ren, Dongwei Jiang, Dongsheng Tian, Qi Zuo, Wangmeng Harbin Inst Technol Harbin Peoples R China Huawei Cloud Comp Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9798350353013;9798350353006

In this paper, we introduce a new perspective for improving image restoration by removing degradation in the textual representations of a given degraded image. Intuitively, restoration is much easier on text modality than image one. For example, it can be easily conducted by removing degradation-related words while keeping the content-aware words. Hence, we combine the advantages of images in detail description and ones of text in degradation removal to perform restoration. To address the cross-modal assistance, we propose to map the degraded images into textual representations for removing the degradations, and then convert the restored textual representations into a guidance image for assisting image restoration. In particular, We ingeniously embed an image-to-text mapper and text restoration module into CLIP-equipped text-to-image models to generate the guidance. Then, we adopt a simple coarse-to-fine approach to dynamically inject multi-scale information from guidance to image restoration networks. Extensive experiments are conducted on various image restoration tasks, including deblurring, dehazing, deraining, and denoising, and all-in-one image restoration. The results showcase that our method outperforms state-of-the-art ones across all these tasks. The codes and models are available at https://***/mrluin/TextualDegRemoval.

关键词： image restoration low-level vision

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TransNeXt: Robust Foveal Visual Perception for vision Transformers

TransNeXt: Robust Foveal Visual Perception for Vision Transf...

引用

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

作者： Shi, Dai

ISBN: (纸本)9798350353006

Due to the depth degradation effect in residual connections, many efficient vision Transformers models that rely on stacking layers for information exchange often fail to form sufficient information mixing, leading to unnatural visual perception. To address this issue, in this paper, we propose Aggregated Attention, a biomimetic design-based token mixer that simulates biological foveal vision and continuous eye movement while enabling each token on the feature map to have a global perception. Furthermore, we incorporate learnable tokens that interact with conventional queries and keys, which further diversifies the generation of affinity matrices beyond merely relying on the similarity between queries and keys. Our approach does not rely on stacking for information exchange, thus effectively avoiding depth degradation and achieving natural visual perception. Additionally, we propose Convolutional GLU, a channel mixer that bridges the gap between GLU and SE mechanism, which empowers each token to have channel attention based on its nearest neighbor image features, enhancing local modeling capability and model robustness. We combine aggregated attention and convolutional GLU to create a new visual backbone called TransNeXt. Extensive experiments demonstrate that our TransNeXt achieves state-of-the-art performance across multiple model sizes. At a resolution of 224(2), TransNeXt-Tiny attains an ImageNet accuracy of 84.0%, surpassing ConvNeXt-B with 69% fewer parameters. Our TransNeXt-Base achieves an ImageNet accuracy of 86.2% and an ImageNet-A accuracy of 61.6% at a resolution of 384(2), a COCO object detection mAP of 57.1, and an ADE20K semantic segmentation mIoU of 54.7.

关键词： Aggregated Attention Biomimetic vision Design Convolutional GLU Efficient Transformer Foveal Visual Perception Image Classification Image Segmentation ImageNet-1K ImageNet-Adversarial Large-Kernel Convolution Length-Scaled Cosine Attention Multi-Scale Extrapolation Object Detection Perceptual Artifacts Pixel-focused Attention Robustness Self-Attention vision Transformer Visual Backbone

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