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Improving Visual Grounding by Encouraging Consistent Gradient-based Explanations

Improving Visual Grounding by Encouraging Consistent Gradien...

引用

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

作者： Yang, Ziyan Kafle, Kushal Dernoncourt, Franck Ordonez, Vicente Rice Univ Houston TX 77005 USA Adobe Res San Francisco CA USA

ISBN: (纸本)9798350301298

We propose a margin-based loss for tuning joint vision-language models so that their gradient-based explanations are consistent with region-level annotations provided by humans for relatively smaller grounding datasets. We refer to this objective as Attention Mask Consistency (AMC) and demonstrate that it produces superior visual grounding results than previous methods that rely on using vision-language models to score the outputs of object detectors. Particularly, a model trained with AMC on top of standard vision-language modeling objectives obtains a state-of-the-art accuracy of 86.49% in the Flickr30k visual grounding benchmark, an absolute improvement of 5.38% when compared to the best previous model trained under the same level of supervision. Our approach also performs exceedingly well on established benchmarks for referring expression comprehension where it obtains 80.34% accuracy in the easy test of RefCOCO+, and 64.55% in the difficult split. AMC is effective, easy to implement, and is general as it can be adopted by any vision-language model, and can use any type of region annotations.

关键词： language reasoning vision

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OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation

OPERA: Alleviating Hallucination in Multi-Modal Large Langua...

引用

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

作者： Hu, Qidong Dong, Xiaoyi Zhang, Pan Wang, Bin He, Conghui Wang, Jiaqi Lin, Dahua Zhang, Weiming Yu, Nenghai Univ Sci & Technol China Anhui Prov Key Lab Digital Secur Hefei Peoples R China Shanghai AI Lab Shanghai Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350353006

Hallucination, posed as a pervasive challenge of multi-modal large language models (MLLMs), has significantly impeded their real-world usage that demands precise judgment. Existing methods mitigate this issue with either training with specific designed data or inferencing with external knowledge from other sources, incurring inevitable additional costs. In this paper, we present OPERA, a novel MLLM decoding method grounded in an Overtrust Penalty and a Retrospection-Allocation strategy, serving as a nearly free lunch to alleviate the hallucination issue without additional data, knowledge, or training. Our approach begins with an interesting observation that, most hallucinations are closely tied to the knowledge aggregation patterns manifested in the self-attention matrix, i.e., MLLMs tend to generate new tokens by focusing on a few summary tokens, but not all the previous tokens. Such partial over-trust inclination results in the neglecting of image tokens and describes the image content with hallucination. Based on the observation, OPERA introduces a penalty term on the model logits during the beam-search decoding to mitigate the over-trust issue, along with a rollback strategy that retrospects the presence of summary tokens in the previ-ously generated tokens, and re-allocate the token selection if necessary. With extensive experiments, OPERA shows significant hallucination-mitigating performance on differ-ent MLLMs and metrics, proving its effectiveness and gen-erality. Our code is at: https://github. com/shikiw/OPERA.

关键词： Hallucination Large vision-language model LLM Multimodal LLM

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Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network

Towards Scalable 3D Anomaly Detection and Localization: A Be...

引用

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

作者： Li, Wenqiao Xu, Xiaohao Gu, Yao Zheng, Bozhong Gao, Shenghua Wu, Yingna ShanghaiTech Univ Shanghai Peoples R China Univ Michigan Ann Arbor MI USA

ISBN: (纸本)9798350353006

Recently, 3D anomaly detection, a crucial problem involving fine-grained geometry discrimination, is getting more attention. However, the lack of abundant real 3D anomaly data limits the scalability of current models. To enable scalable anomaly data collection, we propose a 3D anomaly synthesis pipeline to adapt existing large-scale 3D models for 3D anomaly detection. Specifically, we construct a synthetic dataset, i.e., Anomaly-ShapeNet, based on ShapeNet. Anomaly-ShapeNet consists of 1600 point cloud samples under 40 categories, which provides a rich and varied collection of data, enabling efficient training and enhancing adaptability to industrial scenarios. Meanwhile, to enable scalable representation learning for 3D anomaly localization, we propose a self-supervised method, i.e., Iterative Mask Reconstruction Network (IMRNet). During training, we propose a geometry-aware sample module to preserve potentially anomalous local regions during point cloud down-sampling. Then, we randomly mask out point patches and sent the visible patches to a transformer for reconstruction-based self-supervision. During testing, the point cloud repeatedly goes through the Mask Reconstruction Network, with each iteration's output becoming the next input. By merging and contrasting the final reconstructed point cloud with the initial input, our method successfully locates anomalies. Experiments show that IMRNet outperforms previous state-of-the-art methods, achieving 66.1% in I-AUC on our Anomaly-ShapeNet dataset and 72.5% in I-AUC on Real3D-AD dataset. Our benchmark will be released at https://***/Chopper-233/Anomaly-ShapeNet.

关键词： Anomaly Detection 3D vision Self-Supervised

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Video Frame Interpolation via Direct Synthesis with the Event-based Reference

Video Frame Interpolation via Direct Synthesis with the Even...

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

作者： Liu, Yuhan Deng, Yongjian Chen, Hao Yang, Hen Beijing Univ Technol Coll Comp Sci Beijing Peoples R China Southeast Univ Key Lab Comp Network & Informat Integrat Dhaka Bangladesh

ISBN: (纸本)9798350353013;9798350353006

Video Frame Interpolation (VFI) has witnessed a surge in popularity due to its abundant downstream applications. Event-based VFI (E-VFI) has recently propelled the advancement of VFI. Thanks to the high temporal resolution benefits, event cameras can bridge the informational void present between successive video frames. Most state-of-the-art E-VFI methodologies follow the conventional VFI paradigm, which pivots on motion estimation between consecutive frames to generate intermediate frames through a process of warping and refinement. However, this reliance engenders a heavy dependency on the quality and consistency of keyframes, rendering these methods susceptible to challenges in extreme real-world scenarios, such as missing moving objects and severe occlusion dilemmas. This study proposes a novel E-VFI framework that directly synthesize intermediate frames leveraging event-based reference, obviating the necessity for explicit motion estimation and substantially enhancing the capacity to handle motion occlusion. Given the sparse and inherently noisy nature of event data, we prioritize the reliability of the event-based reference, leading to the development of an innovative event-aware reconstruction strategy for accurate reference generation. Besides, we implement a bi-directional event- guided alignment from keyframes to the reference using the introduced E-PCD module. Finally, a transformer-based decoder is adopted for prediction refinement. Comprehensive experimental evaluations on both synthetic and real- world datasets underscore the superiority of our approach and its potential to execute high-quality VFI tasks.

关键词： Event-Based vision Video Frame Interpolation Video Synthesis

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Multi-modal Aerial View Object Classification Challenge Results - PBVS 2022

Multi-modal Aerial View Object Classification Challenge Resu...

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

作者： Low, Spencer Nina, Oliver Sappa, Angel D. Blasch, Erik Brigham Young Univ Provo UT 84602 USA Air Force Res Lab Dayton OH USA ESPOL Polytech Univ Ecuador Comp Vision Ctr Guayaquil Ecuador Air Force Off Sci Res Arlington VA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper details the results and main findings of the second iteration of the Multi-modal Aerial View Object Classification (MAVOC) challenge. The primary goal of both MAVOC challenges is to inspire research into methods for building recognition models that utilize both synthetic aperture radar (SAR) and electro-optical (EO) imagery. Teams are encouraged to develop multi-modal approaches that incorporate complementary information from both domains. While the 2021 challenge showed a proof of concept that both modalities could be used together, the 2022 challenge focuses on the detailed multi-modal methods. The 2022 challenge uses the same UNIfied COincident Optical and Radar for recognition (UNICORN) dataset and competition format that was used in 2021. Specifically, the challenge focuses on two tasks, (1) SAR classification and (2) SAR + EO classification. The bulk of this document is dedicated to discussing the top performing methods and describing their performance on our blind test set. Notably, all of the top ten teams outperform a Resnet-18 baseline. For SAR classification, the top team showed a 129% improvement over baseline and an 8% average improvement from the 2021 winner. The top team for SAR + EO classification shows a 165% improvement with a 32% average improvement over 2021.

关键词： Training Laser radar Image recognition Radar imaging Optical imaging pattern recognition Task analysis

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Semi-Supervised Few-Shot Learning from A Dependency-Discriminant Perspective

Semi-Supervised Few-Shot Learning from A Dependency-Discrimi...

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

作者： Hou, Zejiang Kung, Sun-Yuan Princeton Univ Princeton NJ 08544 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We study the few-shot learning (FSL) problem, where a model learns to recognize new objects with extremely few labeled training data per category. Most of previous FSL approaches resort to the meta-learning paradigm, where the model accumulates inductive bias through learning from many training tasks, in order to solve new unseen few-shot tasks. In contrast, we propose a simple semi-supervised FSL approach to exploit unlabeled data accompanying the few-shot task to improve FSL performance. More exactly, to train a classifier, we propose a Dependency Maximization loss based on the Hilbert-Schmidt norm of the cross-covariance operator, which maximizes the statistical dependency between the embedded feature of the unlabeled data and their label predictions, together with the supervised loss over the support set. The obtained classifier is used to infer the pseudo-labels of the unlabeled data. Furthermore, we propose an Instance Discriminant Analysis to evaluate the credibility of the pseudo-labeled examples and select the faithful ones into an augmented support set, which is used to retrain the classifier. We iterate the process until the pseudo-labels of the unlabeled data becomes stable. Through extensive experiments on four widely used few-shot classification benchmarks, including mini-ImageNet, tiered-ImageNet, CUB, and CIFARFS, the proposed method outperforms previous state-of-the-art FSL methods.

关键词： Training computer vision conferences Training data Benchmark testing Data models pattern recognition

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Faster, Lighter, Robuster: A Weakly-Supervised Crowd Analysis Enhancement Network and A Generic Feature Extraction Framework

Faster, Lighter, Robuster: A Weakly-Supervised Crowd Analysi...

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

作者： Wu, Shaokai Liu, Zhaogeng Pei, Wencheng Hong, Jianbo Li, Zhanshan Jilin Univ Coll Comp Sci & Technol Changchun Peoples R China Jilin Univ Sch Artificial Intelligence Changchun Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

With bounding box labels needed for training, object detection is viewed unfavorably in terms of crowd analysis, due to the intensive labor for labeling and the unsatisfactory performance in clutters and severe occlusions. Another feasible method, density-based regression, despite its proficiency in counting and only point-level labels used for training, cannot get the location of each person, and the time and space consumption is relatively high. In this paper, we propose a generic feature extraction framework, Adaptive Pyramid Score (APS), based on object detection and designed specifically for extracting quantitative and spatial-semantic features. Moreover, as an intuitive and feasible solution regarding crowd analysis, we propose the weakly-supervised Confidence-Threshold-Foresight Network (CTFNet) under our APS feature extraction framework, which only needs count-level labels for training and improves the performance of various methods dramatically. Our system realizes the triple enhancement of counting, localization, and detection, which is also proved to be faster than advanced crowd analysis methods, lighter to be transplanted to various object detection methods, and robuster to tackle tasks of extreme scenes. Furthermore, the weakly-supervised paradigm leverage the intensive labor for labeling profoundly.

关键词： Training Location awareness computer vision conferences Object detection Feature extraction pattern recognition

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TAMM: TriAdapter Multi-Modal Learning for 3D Shape Understanding

TAMM: TriAdapter Multi-Modal Learning for 3D Shape Understan...

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

作者： Zhang, Zhihao Cao, Shengcao Wang, Yu-Xiong Xi An Jiao Tong Univ Xian Peoples R China Univ Illinois Champaign IL USA

ISBN: (纸本)9798350353006

The limited scale of current 3D shape datasets hinders the advancements in 3D shape understanding, and motivates multi-modal learning approaches which transfer learned knowledge from data-abundant 2D image and language modalities to 3D shapes. However, even though the image and language representations have been aligned by cross-modal models like CLIP, we find that the image modality fails to contribute as much as the language in existing multi-modal 3D representation learning methods. This is attributed to the domain shift in the 2D images and the distinct focus of each modality. To more effectively leverage both modalities in the pre-training, we introduce TriAdapter Multi-Modal Learning (TAMM) - a novel two-stage learning approach based on three synergistic adapters. First, our CLIP Image Adapter mitigates the domain gap between 3D-rendered images and natural images, by adapting the visual representations of CLIP for synthetic image-text pairs. Subsequently, our Dual Adapters decouple the 3D shape representation space into two complementary sub-spaces: one focusing on visual attributes and the other for semantic understanding, which ensure a more comprehensive and effective multi-modal pre-training. Extensive experiments demonstrate that TAMM consistently enhances 3D representations for a wide range of 3D encoder architectures, pre-training datasets, and downstream tasks. Notably, we boost the zero-shot classification accuracy on Objaverse-LVIS from 46.8% to 50.7%, and improve the 5-way 10-shot linear probing classification accuracy on ModelNet40 from 96.1% to 99.0%. Project page: https://***/tamm-page.

关键词： 3D shape classification 3D vision multi-modal learning

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Cell Selection-based Data Reduction Pipeline for Whole Slide Image Analysis of Acute Myeloid Leukemia

Cell Selection-based Data Reduction Pipeline for Whole Slide...

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

作者： Kockwelp, Jacqueline Thiele, Sebastian Kockwelp, Pascal Bartsch, Jannis Schliemann, Christoph Angenendt, Linus Risse, Benjamin Univ Munster Munster Germany Univ Med Ctr Munster Munster Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

computer-aided analyses of cells in Whole Slide Images (WSIs) have become an important topic in digital pathology. Despite the recent success of deep learning in biomedical research, these methods are still difficult to apply to multi-gigabyte WSIs. To overcome this difficulty, a variety of patch-based solutions have been introduced, which however all suffer from certain limitations compared to manual examinations and often fail to meet the specificities of cytological inspections. Here we introduce an alternative scheme which incorporates clinical expertise in the selection process to automatically identify the clinically relevant areas. By using a bone marrow smear dataset containing 22-gigapixel images of 153 patients, we introduce a novel pipeline combining unsupervised and supervised methodologies to gradually select the most appropriate single-cell regions, which are subsequently used in multiple medically crucial Acute Myeloid Leukemia (AML) predictions. Our approach is capable of dealing with a variety of common WSI challenges, massively limits the manual annotation effort, reduces the data by a factor of up to 99.9% and achieves super-human performance on the final cytological prediction tasks.

关键词： Deep learning Pathology computer vision Image analysis conferences Pipelines Manuals

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Material Swapping for 3D Scenes using a Learnt Material Similarity Measure

Material Swapping for 3D Scenes using a Learnt Material Simi...

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

作者： Perroni-Scharf, Maxine Sunkavalli, Kalyan Eisenmann, Jonathan Hold-Geoffroy, Yannick Princeton Univ Princeton NJ 08544 USA Adobe Res San Jose CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We present a method for augmenting photo-realistic 3D scene assets by automatically recognizing, matching, and swapping their materials. Our method proposes a material matching pipeline for the efficient replacement of unknown materials with perceptually similar PBR materials from a database, enabling the quick creation of many variations of a given 3D synthetic scene. At the heart of this method is a novel material similarity feature that is learnt, in conjunction with optimal lighting conditions, by fine-tuning a deep neural network on a material classification task using our proposed dataset. Our evaluation demonstrates that lighting optimization improves CNN-based texture feature extraction methods and better estimates material properties. We conduct a series of experiments showing our method's ability to augment photo-realistic indoor scenes using both standard and procedurally generated PBR materials.

关键词： Training Three-dimensional displays Pipelines Neural networks Lighting Feature extraction Rendering (computer graphics)

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