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Back to 3D: Few-Shot 3D Keypoint Detection with Back-Projected 2D Features

Back to 3D: Few-Shot 3D Keypoint Detection with Back-Project...

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

作者： Wimmer, Thomas Wonka, Peter Ovsjanikov, Maks Ecole Polytech LIX Palaiseau France Tech Univ Munich Munich Germany KAUST Thuwal Saudi Arabia

ISBN: (纸本)9798350353013;9798350353006

With the immense growth of dataset sizes and computing resources in recent years, so-called foundation models have become popular in NLP and vision tasks. In this work, we propose to explore foundation models for the task of keypoint detection on 3D shapes. A unique characteristic of keypoint detection is that it requires semantic and geometric awareness while demanding high localization accuracy. To address this problem, we propose, first, to back-project features from large pre-trained 2D vision models onto 3D shapes and employ them for this task. We show that we obtain robust 3D features that contain rich semantic information and analyze multiple candidate features stemming from different 2D foundation models. Second, we employ a keypoint candidate optimization module which aims to match the average observed distribution of keypoints on the shape and is guided by the back-projected features. The resulting approach achieves a new state of the art for few-shot keypoint detection on the KeyPointNet dataset, almost doubling the performance of the previous best methods.

关键词： 3D vision Foundation Models Keypoint Detection Shape Analysis

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Gradient Reweighting: Towards Imbalanced Class-Incremental Learning

Gradient Reweighting: Towards Imbalanced Class-Incremental L...

引用

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

作者： He, Jiangpeng Purdue Univ Elmore Family Sch Elect & Comp Engn W Lafayette IN 47907 USA

ISBN: (纸本)9798350353006

Class-Incremental Learning (CIL) trains a model to continually recognize new classes from non-stationary data while retaining learned knowledge. A major challenge of CIL arises when applying to real-world data characterized by non-uniform distribution, which introduces a dual imbalance problem involving (i) disparities between stored exemplars of old tasks and new class data (inter-phase imbalance), and (ii) severe class imbalances within each individual task (intra-phase imbalance). We show that this dual imbalance issue causes skewed gradient updates with biased weights in FC layers, thus inducing over/under-fitting and catastrophic forgetting in CIL. Our method addresses it by reweighting the gradients towards balanced optimization and unbiased classifier learning. Additionally, we observe imbalanced forgetting where paradoxically the instance-rich classes suffer higher performance degradation during CIL due to a larger amount of training data becoming unavailable in subsequent learning phases. To tackle this, we further introduce a distribution-aware knowledge distillation loss to mitigate forgetting by aligning output logits proportionally with the distribution of lost training data. We validate our method on CIFAR-100, ImageNetSubset, and Food101 across various evaluation protocols and demonstrate consistent improvements compared to existing works, showing great potential to apply CIL in real-world scenarios with enhanced robustness and effectiveness.

关键词： computer vision continual learniing imbalanced gradient long-tailed learning

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Sieve: Multimodal Dataset Pruning Using Image Captioning Models

Sieve: Multimodal Dataset Pruning Using Image Captioning Mod...

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

作者： Mahmouc, Anas Elhoushi, Mostafa Abbass, Amro Yang, Yu Ardalani, Newsha Leather, Hugh Morcos, Art S. Meta FAIR Menlo Pk CA 94025 USA Univ Toronto Toronto ON Canada UC Los Angeles Los Angeles CA USA DatologyAI Redwood City CA USA

ISBN: (纸本)9798350353006

vision-Language Models (VLMs) are pretrained on large, diverse, and noisy web-crawled datasets. This underscores the critical need for dataset pruning, as the quality of these datasets is strongly correlated with the performance of VLMs on downstream tasks. Using CLIPScore from a pretrained model to only train models using highly-aligned samples is one of the most successful methods for pruning. We argue that this approach suffers from multiple limitations including: false positives and negatives due to CLIP's pretraining on noisy labels. We propose a pruning signal, Sieve, that employs synthetic captions generated by image-captioning models pretrained on small, diverse, and well-aligned image-text pairs to evaluate the alignment of noisy image-text pairs. To bridge the gap between the limited diversity of generated captions and the high diversity of alternative text (alt-text), we estimate the semantic textual similarity in the embedding space of a language model pretrained on unlabeled text corpus. Using DataComp, a multimodal dataset filtering benchmark, when evaluating on 38 downstream tasks, our pruning approach, surpasses CLIPScore by 2.6% and 1.7% on medium and large scale respectively. In addition, on retrieval tasks, Sieve leads to a significant improvement of 2.7% and 4.5% on medium and large scale respectively.

关键词： data distillation pruning vision-language

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Active Prompt Learning in vision Language Models

Active Prompt Learning in Vision Language Models

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

作者： Bang, Jihwan Ahn, Sumyeong Lee, Jae-Gil Korea Adv Inst Sci & Technol Daejeon South Korea Michigan State Univ E Lansing MI USA

ISBN: (纸本)9798350353006

Pre-trained vision Language Models (VLMs) have demonstrated notable progress in various zero-shot tasks, such as classification and retrieval. Despite their performance, because improving performance on new tasks requires task-specific knowledge, their adaptation is essential. While labels are needed for the adaptation, acquiring them is typically expensive. To overcome this challenge, active learning, a method of achieving a high performance by obtaining labels for a small number of samples from experts, has been studied. Active learning primarily focuses on selecting unlabeled samples for labeling and leveraging them to train models. In this study, we pose the question, "how can the pre-trained VLMs be adapted under the active learning framework?" In response to this inquiry, we observe that (1) simply applying a conventional active learning framework to pre-trained VLMs even may degrade performance compared to random selection because of the class imbalance in labeling candidates, and (2) the knowledge of VLMs can provide hints for achieving the balance before labeling. Based on these observations, we devise a novel active learning framework for VLMs, denoted as PCB. To assess the effectiveness of our approach, we conduct experiments on seven different real-world datasets, and the results demonstrate that PCB surpasses conventional active learning and random sampling methods. Code is available at https://***/kaist-dmlab/pcb.

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Multiscale vision Transformers meet Bipartite Matching for efficient single-stage Action Localization

Multiscale Vision Transformers meet Bipartite Matching for e...

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

作者： Ntinou, Ioanna Sanchez, Enrique Tzimiropoulos, Georgios Queen Mary Univ London London England Samsung AI Ctr Cambridge Cambridge England

ISBN: (纸本)9798350353006

Action Localization is a challenging problem that combines detection and recognition tasks, which are often addressed separately. State-of-the-art methods rely on off-the-shelf bounding box detections pre-computed at high resolution, and propose transformer models that focus on the classification task alone. Such two-stage solutions are prohibitive for real-time deployment. On the other hand, single-stage methods target both tasks by devoting part of the network (generally the backbone) to sharing the majority of the workload, compromising performance for speed. These methods build on adding a DETR head with learnable queries that after cross- and self-attention can be sent to corresponding MLPs for detecting a person's bounding box and action. However, DETR-like architectures are challenging to train and can incur in big complexity. In this paper, we observe that a straight bipartite matching loss can be applied to the output tokens of a vision transformer. This results in a backbone + MLP architecture that can do both tasks without the need of an extra encoder-decoder head and learnable queries. We show that a single MViTv2-S architecture trained with bipartite matching to perform both tasks surpasses the same MViTv2-S when trained with RoI align on pre-computed bounding boxes. With a careful design of token pooling and the proposed training pipeline, our Bipartite-Matching vision Transformer model, BMViT, achieves +3 mAP on AVA2.2. w.r.t. the two-stage MViTv2-S counterpart. Code is available at https://***/IoannaNti/BMViT

关键词： Signal encoding

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LowRankOcc: Tensor Decomposition and Low-Rank Recovery for vision-based 3D Semantic Occupancy Prediction

LowRankOcc: Tensor Decomposition and Low-Rank Recovery for V...

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

作者： Zhao, Linqing Xu, Xiuwei Wang, Ziwei Zhang, Yunpeng Zhang, Borui Zheng, Wenzhao Du, Dalong Zhou, Jie Lu, Jiwen Tsinghua Univ Dept Automat Beijing Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China PhiGent Robot Beijing Peoples R China

ISBN: (纸本)9798350353006

In this paper, we present a tensor decomposition and low-rank recovery approach (LowRankOcc) for vision-based 3D semantic occupancy prediction. Conventional methods model outdoor scenes with fine-grained 3D grids, but the sparsity of non-empty voxels introduces considerable spatial redundancy, leading to potential overfitting risks. In contrast, our approach leverages the intrinsic low-rank property of 3D occupancy data, factorizing voxel representations into low-rank components to efficiently mitigate spatial redundancy without sacrificing performance. Specifically, we present the Vertical-Horizontal (VH) decomposition block factorizes 3D tensors into vertical vectors and horizontal matrices. With our "decomposition-encoding recovery" framework, we encode 3D contexts with only 1/2D convolutions and poolings, and subsequently recover the encoded compact yet informative context features back to voxel representations. Experimental results demonstrate that LowRankOcc achieves state-of-the-art performances in semantic scene completion on the SemanticKITTI dataset and 3D occupancy prediction on the nuScenes dataset.

关键词： 3D semantic occupancy tensor decomposition

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USE: Universal Segment Embeddings for Open-Vocabulary Image Segmentation

USE: Universal Segment Embeddings for Open-Vocabulary Image ...

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

作者： Wang, Xiaoqi He, Wenbin Xuan, Xiwei Sebastian, Clint Ono, Jorge Piazentin Li, Xin Behpour, Sima Thang Doan Gou, Liang Shen, Han-Wei Ren, Liu Bosch Res North Amer Oak Brook Terrace IL 60181 USA Bosch Ctr Artificial Intelligence Baden Baden Germany Ohio State Univ Columbus OH 43210 USA Univ Calif Davis Davis CA 95616 USA

ISBN: (纸本)9798350353013;9798350353006

The open-vocabulary image segmentation task involves partitioning images into semantically meaningful segments and classifying them with flexible text-defined categories. The recent vision-based foundation models such as the Segment Anything Model (SAM) have shown superior performance in generating class-agnostic image segments. The main challenge in open-vocabulary image segmentation now lies in accurately classifying these segments into text-defined categories. In this paper, we introduce the Universal Segment Embedding (USE) framework to address this challenge. This framework is comprised of two key components: 1) a data pipeline designed to efficiently curate a large amount of segment-text pairs at various granularities, and 2) a universal segment embedding model that enables precise segment classification into a vast range of text-defined categories. The USE model can not only help open-vocabulary image segmentation but also facilitate other downstream tasks (e.g., querying and ranking). Through comprehensive experimental studies on semantic segmentation and part segmentation benchmarks, we demonstrate that the USE framework outperforms state-of-the-art open-vocabulary segmentation methods.

关键词： foundation model open-vocabulary image segmentation vision-language model

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Collaborating Foundation Models for Domain Generalized Semantic Segmentation

Collaborating Foundation Models for Domain Generalized Seman...

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

作者： Benigmim, Yasser Roy, Subhankar Essid, Slim Kalogeiton, Vicky Lathuiliere, Stephane Inst Polytech Paris Telecom Paris LTCI Palaiseau France Inst Polytech Paris CNRS Ecole Polytech LIX Palaiseau France Univ Aberdeen Aberdeen Scotland

ISBN: (纸本)9798350353013;9798350353006

Domain Generalized Semantic Segmentation (DGSS) deals with training a model on a labeled source domain with the aim of generalizing to unseen domains during inference. Existing DGSS methods typically effectuate robust features by means of Domain Randomization (DR). Such an approach is often limited as it can only account for style diversification and not content. In this work, we take an orthogonal approach to DGSS and propose to use an assembly of CoLlaborative FOUndation models for Domain Generalized Semantic Segmentation (CLOUDS). In detail, CLOUDS is a framework that integrates Foundation Models of various kinds: (i) CLIP backbone for its robust feature representation, (ii) Diffusion Model to diversify the content, thereby covering various modes of the possible target distribution, and (iii) Segment Anything Model (SAM) for iteratively refining the predictions of the segmentation model. Extensive experiments show that our CLOUDS excels in adapting from synthetic to real DGSS benchmarks and under varying weather conditions, notably outperforming prior methods by 5.6% and 6.7% on averaged mIoU, respectively. Our code is available at https://***/yasserben/CLOUDS

关键词： computer vision Deep Learning Domain Adaptation Domain Generalization Foundation Models Semantic Segmentation

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Boosting Adversarial Transferability by Block Shuffle and Rotation

Boosting Adversarial Transferability by Block Shuffle and Ro...

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

作者： Wang, Kunyu He, Xuanran Wang, Wenxuan Wang, Xiaosen Chinese Univ Hong Kong Hong Kong Peoples R China Nanyang Technol Univ Singapore Singapore Huawei Singular Secur Lab Beijing Peoples R China

ISBN: (纸本)9798350353006

Adversarial examples mislead deep neural networks with imperceptible perturbations and have brought significant threats to deep learning. An important aspect is their transferability, which refers to their ability to deceive other models, thus enabling attacks in the black-box setting. Though various methods have been proposed to boost transferability, the performance still falls short compared with white-box attacks. In this work, we observe that existing input transformation based attacks, one of the mainstream transfer-based attacks, result in different attention heatmaps on various models, which might limit the transferability. We also find that breaking the intrinsic relation of the image can disrupt the attention heatmap of the original image. Based on this finding, we propose a novel input transformation based attack called block shuffle and rotation (BSR). Specifically, BSR splits the input image into several blocks, then randomly shuffles and rotates these blocks to construct a set of new images for gradient calculation. Empirical evaluations on the ImageNet dataset demonstrate that BSR could achieve significantly better transferability than the existing input transformation based methods under single-model and ensemble-model settings. Combining BSR with the current input transformation method can further improve the transferability, which significantly outperforms the state-of-the-art methods. Code is available at https://***/Trustworthy-AI-Group/BSR.

关键词： adversarial attack computer vision

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ViTamin: Designing Scalable vision Models in the vision-Language Era

ViTamin: Designing Scalable Vision Models in the Vision-Lang...

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

作者： Chen, Jieneng Yu, Qihang Shen, Xiaohui Yuille, Alan Chen, Liang-Chieh Johns Hopkins Univ Baltimore MD 21218 USA ByteDance Beijing Peoples R China

ISBN: (纸本)9798350353006

Recent breakthroughs in vision-language models (VLMs) start a new page in the vision community. The VLMs provide stronger and more generalizable feature embeddings compared to those from ImageNet-pretrained models, thanks to the training on the large-scale Internet image-text pairs. However, despite the amazing achievement from the VLMs, vanilla vision Transformers (ViTs) remain the default choice for the image encoder. Although pure transformer proves its effectiveness in the text encoding area, it remains questionable whether it is also the case for image encoding, especially considering that various types of networks are proposed on the ImageNet benchmark, which, unfortunately, are rarely studied in VLMs. Due to small data/model scale, the original conclusions of model design on ImageNet can be limited and biased. In this paper, we aim at building an evaluation protocol of vision models in the vision-language era under the contrastive language-image pretraining (CLIP) framework. We provide a comprehensive way to benchmark different vision models, covering their zero-shot performance and scalability in both model and training data sizes. To this end, we introduce ViTamin, a new vision models tailored for VLMs. ViTamin-L significantly outperforms ViT-L by 2.0% ImageNet zero-shot accuracy, when using the same publicly available DataComp-1B dataset and the same OpenCLIP training scheme. ViTamin-L presents promising results on 60 diverse benchmarks, including classification, retrieval, open-vocabulary detection and segmentation, and large multi-modal models. When further scaling up the model size, our ViTamin-XL with only 436M parameters attains 82.9% ImageNet zero-shot accuracy, surpassing 82.0% achieved by EVA-E that has ten times more parameters (4.4B).

关键词： Architectural Design vision-Language Models

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