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AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving

AIDE: An Automatic Data Engine for Object Detection in Auton...

引用

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

作者： Liang, Mingfu Sue, Jong-Chyi Schulter, Samuel Garg, Sparsh Zhao, Shiyu Wu, Ying Chandraker, Manmohan Northwestern Univ Evanston IL 60208 USA NEC Labs Amer Princeton NJ USA Rutgers State Univ New Brunswick NJ USA Univ Calif San Diego San Diego CA USA

ISBN: (纸本)9798350353006

Autonomous vehicle (AV) systems rely on robust perception models as a cornerstone of safety assurance. However, objects encountered on the road exhibit a long-tailed distribution, with rare or unseen categories posing challenges to a deployed perception model. This necessitates an expensive process of continuously curating and annotating data with significant human effort. We propose to leverage recent advances in vision-language and large language models to design an Automatic Data Engine (AIDE) that automatically identifies issues, efficiently curates data, improves the model through auto-labeling, and verifies the model through generation of diverse scenarios. This process operates iteratively, allowing for continuous self-improvement of the model. We further establish a benchmark for open-world detection on AV datasets to comprehensively evaluate various learning paradigms, demonstrating our method's superior performance at a reduced cost.

关键词： Automatic Data Engine Autonomous Driving Continual Training Large Language Model Novel Object Detection vision Language Model

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Intrinsic Image Diffusion for Indoor Single-view Material Estimation

Intrinsic Image Diffusion for Indoor Single-view Material Es...

引用

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

作者： Kocsis, Peter Sitzmann, Vincent Niessner, Matthias Tech Univ Munich Munich Germany MIT EECS Cambridge MA 02139 USA

ISBN: (纸本)9798350353013;9798350353006

We present Intrinsic Image Diffusion, a generative model for appearance decomposition of indoor scenes. Given a single input view, we sample multiple possible material explanations represented as albedo, roughness, and metallic maps. Appearance decomposition poses a considerable challenge in computer vision due to the inherent ambiguity between lighting and material properties and the lack of real datasets. To address this issue, we advocate for a probabilistic formulation, where instead of attempting to directly predict the true material properties, we employ a conditional generative model to sample from the solution space. Furthermore, we show that utilizing the strong learned prior of recent diffusion models trained on large-scale real-world images can be adapted to material estimation and highly improves the generalization to real images. Our method produces significantly sharper, more consistent, and more detailed materials, outperforming state-of-the-art methods by 1.5dB on PSNR and by 45% better FID score on albedo prediction. We demonstrate the effectiveness of our approach through experiments on both synthetic and real-world datasets.

关键词： Appearance Decompostion computer vision Deep Learning Diffusion Graphics Lighting Estimation Material Estimation

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Solving Masked Jigsaw Puzzles with Diffusion vision Transformers

Solving Masked Jigsaw Puzzles with Diffusion Vision Transfor...

引用

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

作者： Liu, Jinyang Teshome, Wondmgezahu Ghimire, Sandesh Sznaier, Mario Camps, Octavia Northeastern Univ Boston MA 02115 USA Qualcomm San Diego CA USA

ISBN: (纸本)9798350353006

Solving image and video jigsaw puzzles poses the challenging task of rearranging image fragments or video frames from unordered sequences to restore meaningful images and video sequences. Existing approaches often hinge on discriminative models tasked with predicting either the absolute positions of puzzle elements or the permutation actions applied to the original data. Unfortunately, these methods face limitations in effectively solving puzzles with a large number of elements. In this paper, we propose JPDVT, an innovative approach that harnesses diffusion transformers to address this challenge. Specifically, we generate positional information for image patches or video frames, conditioned on their underlying visual content. This information is then employed to accurately assemble the puzzle pieces in their correct positions, even in scenarios involving missing pieces. Our method achieves state-of-the-art performance on several datasets.

关键词： data imputation diffusion models Solving puzzles

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BIOCLIP: A vision Foundation Model for the Tree of Life

BIOCLIP: A Vision Foundation Model for the Tree of Life

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

作者： Stevens, Samuel Wu, Jiaman Thompson, Matthew J. Campolongo, Elizabeth G. Song, Chan Hee Carlyle, David Edward Dong, Li Dahdul, Wasila M. Stewart, Charles Berger-Wolf, Tanya Chao, Wei-Lun Su, Yu Ohio State Univ Columbus OH 43210 USA Microsoft Res Mountain View CA USA Univ Calif Irvine Irvine CA USA Rensselaer Polytech Inst Troy NY USA

ISBN: (纸本)9798350353006

Images of the natural world, collected by a variety of cameras, from drones to individual phones, are increasingly abundant sources of biological information. There is an explosion of computational methods and tools, particularly computer vision, for extracting biologically relevant information from images for science and conservation. Yet most of these are bespoke approaches designed for a specific task and are not easily adaptable or extendable to new questions, contexts, and datasets. A vision model for general organismal biology questions on images is of timely need. To approach this, we curate and release TREEOFLIFE-10M, the largest and most diverse ML-ready dataset of biology images. We then develop BIOCLIP, a foundation model for the tree of life, leveraging the unique properties of biology captured by TREEOFLIFE-10M, namely the abundance and variety of images of plants, animals, and fungi, together with the availability of rich structured biological knowledge. We rigorously benchmark our approach on diverse fine-grained biology classification tasks and find that BIOCLIP consistently and substantially outperforms existing baselines (by 16% to 17% absolute). Intrinsic evaluation reveals that BIOCLIP has learned a hierarchical representation conforming to the tree of life, shedding light on its strong generalizability.(1)

关键词： computer vision evolutionary biology & ecology imageomics machine learning

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MAFA: Managing False Negatives for vision-Language Pre-training

MAFA: Managing False Negatives for Vision-Language Pre-train...

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

作者： Byun, Jaeseok Kim, Dohoon Moon, Taesup Seoul Natl Univ Dept ECE Seoul South Korea Seoul Natl Univ Dept ASRI INMC IPAI AIIS Seoul South Korea

ISBN: (纸本)9798350353006

We consider a critical issue of false negatives in vision-Language Pre-training (VLP), a challenge that arises from the inherent many-to-many correspondence of image-text pairs in large-scale web-crawled datasets. The presence of false negatives can impede achieving optimal performance and even lead to a significant performance drop. To address this challenge, we propose MAFA (MAnaging FAlse negatives), which consists of two pivotal components building upon the recently developed GRouped mIni-baTch sampling (GRIT) strategy: 1) an efficient connection mining process that identifies and converts false negatives into positives, and 2) label smoothing for the image-text contrastive (ITC) loss. Our comprehensive experiments verify the effectiveness of MAFA across multiple downstream tasks, emphasizing the crucial role of addressing false negatives in VLP, potentially even surpassing the importance of addressing false positives. In addition, the compatibility of MAFA with the recent BLIP-family model is also demonstrated. Code is available at https://***/jaeseokbyun/MAFA.

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VicTR: Video-conditioned Text Representations for Activity recognition

VicTR: Video-conditioned Text Representations for Activity R...

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

作者： Kahatapitiya, Kumara Arnab, Anurag Nagrani, Arsha Ryoo, Michael S. SUNY Stony Brook Stony Brook NY 11794 USA Google Res Mountain View CA USA

ISBN: (纸本)9798350353006

vision-Language models (VLMs) have excelled in the image-domain- especially in zero-shot settings- thanks to the availability of vast pretraining data (i.e., paired image-text samples). However for videos, such paired data is not as abundant. Therefore, video- VLMs are usually designed by adapting pretrained image- VLMs to the video-domain, instead of training from scratch. All such recipes rely on aug-menting visual embeddings with temporal information (i.e., image -+ video), often keeping text embeddings unchanged or even being discarded. In this paper, we argue the contrary, that better video- VLMs can be designed by focusing more on augmenting text, rather than visual information. More specifically, we introduce Video-conditioned Text Representations (Vi c TR): a form of text embeddings optimized w.r.t. vi-sual embeddings, creating a more-flexible contrastive latent space. Our model canfurther make use offreely-available semantic information, in the form of visually- grounded aux-iliary text (e.g. object or scene information). We evaluate our model on few-shot, zero-shot (HMDB-51, UCF-10l), short-form (Kinetics-400) and long-form (Charades) activ-ity recognition benchmarks, showing strong performance among video-VLMs.

关键词： Activity recognition Video Understanding Video-conditioned Text vision-language models

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StyleCineGAN: Landscape Cinemagraph Generation using a Pre-trained StyleGAN

StyleCineGAN: Landscape Cinemagraph Generation using a Pre-t...

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

作者： Choi, Jongwoo Seo, Kwanggyoon Ashtari, Amirsaman Noh, Junyong Korea Adv Inst Sci & Technol Visual Media Lab Daejeon South Korea

ISBN: (纸本)9798350353013;9798350353006

We propose a method that can generate cinemagraphs automatically from a still landscape image using a pre-trained StyleGAN. Inspired by the success of recent unconditional video generation, we leverage a powerful pre-trained image generator to synthesize high-quality cinemagraphs. Unlike previous approaches that mainly utilize the latent space of a pre-trained StyleGAN, our approach utilizes its deep feature space for both GAN inversion and cinemagraph generation. Specifically, we propose multi-scale deep feature warping (MSDFW), which warps the intermediate features of a pre-trained StyleGAN at different resolutions. By using MSDFW, the generated cinemagraphs are of high resolution and exhibit plausible looping animation. We demonstrate the superiority of our method through user studies and quantitative comparisons with state-of-the-art cinemagraph generation methods and a video generation method that uses a pre-trained StyleGAN.

关键词： Image and video synthesis and generation vision + graphics vision applications and systems

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Attention-Propagation Network for Egocentric Heatmap to 3D Pose Lifting

Attention-Propagation Network for Egocentric Heatmap to 3D P...

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

作者： Kang, Taeho Lee, Youngki Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9798350353013;9798350353006

We present EgoTAP, a heatmap-to-3D pose lifting method for highly accurate stereo egocentric 3D pose estimation. Severe self-occlusion and out-of-view limbs in egocentric camera views make accurate pose estimation a challenging problem. To address the challenge, prior methods employ joint heatmaps-probabilistic 2D representations of the body pose, but heatmap-to-3D pose conversion still remains an inaccurate process. We propose a novel heatmap-to-3D lifting method composed of the Grid ViT Encoder and the Propagation Network. The Grid ViT Encoder summarizes joint heatmaps into effective feature embedding using self-attention. Then, the Propagation Network estimates the 3D pose by utilizing skeletal information to better estimate the position of obscure joints. Our method significantly outperforms the previous state-of-the-art qualitatively and quantitatively demonstrated by a 23.9% reduction of error in an MPJPE metric. Our source code is available on GitHub (1).

关键词： 3D pose estimation egocentric vision stereo vision

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Resource-Efficient Transformer Pruning for Finetuning of Large Models

Resource-Efficient Transformer Pruning for Finetuning of Lar...

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

作者： Ilhan, Fatih Su, Gong Tekin, Selim Furkan Huang, Tiansheng Hu, Sihao Liu, Ling Georgia Inst Technol Atlanta GA 30332 USA IBM Res Yorktown Hts NY USA

ISBN: (纸本)9798350353006

With the recent advances in vision transformers and large language models (LLMs), finetuning costly large models on downstream learning tasks poses significant challenges under limited computational resources. This paper presents a REsource and ComputAtion-efficient Pruning framework (RECAP) for the finetuning of transformer-based large models. RECAP by design bridges the gap between efficiency and performance through an iterative process cycling between pruning, finetuning, and updating stages to explore different chunks of the given large-scale model. At each iteration, we first prune the model with Taylor-approximation-based importance estimation and then only update a subset of the pruned model weights based on the Fisher-information criterion. In this way, RECAP achieves two synergistic and yet conflicting goals: reducing the GPU memory footprint while maintaining model performance, unlike most existing pruning methods that require the model to be finetuned beforehand for better preservation of model performance. We perform extensive experiments with a wide range of large transformer-based architectures on various computer vision and natural language understanding tasks. Compared to recent pruning techniques, we demonstrate that RECAP offers significant improvements in GPU memory efficiency, capable of reducing the footprint by up to 65%.

关键词： efficient finetuning pruning vision transformers

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Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation

Animate Anyone: Consistent and Controllable Image-to-Video S...

引用

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

作者： Hu, Li Alibaba Grp Inst Intelligent Comp Hangzhou Peoples R China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013;9798350353006

Character Animation aims to generating character videos from still images through driving signals. Currently, diffusion models have become the mainstream in visual generation research, owing to their robust generative capabilities. However, challenges persist in the realm of image-to-video, especially in character animation, where temporally maintaining consistency with detailed information from character remains a formidable problem. In this paper, we leverage the power of diffusion models and propose a novel framework tailored for character animation. To preserve consistency of intricate appearance features from reference image, we design ReferenceNet to merge detail features via spatial attention. To ensure controllability and continuity, we introduce an efficient pose guider to direct character's movements and employ an effective temporal modeling approach to ensure smooth inter-frame transitions between video frames. By expanding the training data, our approach can animate arbitrary characters, yielding superior results in character animation compared to other image-to-video methods. Furthermore, we evaluate our method on image animation benchmarks, achieving state-of-the-art results.

关键词： Visualization computer vision Training data Benchmark testing Animation Diffusion models Controllability

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