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HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data

HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual I...

引用

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

作者： Yu, Qifan Li, Juncheng Wei, Longhui Pang, Liang Ye, Wentao Qin, Bosheng Tang, Siliang Tian, Qi Zhuang, Yueting Zhejiang Univ Hangzhou Peoples R China Huawei Cloud Suzhou Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9798350353006

Multi-modal Large Language Models (MLLMs) tuned on machine-generated instruction-following data have demonstrated remarkable performance in various multi-modal understanding and generation tasks. However, the hallucinations inherent in machine-generated data, which could lead to hallucinatory outputs in MLLMs, remain under-explored. This work aims to investigate various hallucinations (i.e., object, relation, attribute hallucinations) and mitigate those hallucinatory toxicities in large-scale machine-generated visual instruction datasets. Drawing on the human ability to identify factual errors, we present a novel hallucination detection and elimination framework, HalluciDoctor, based on the cross-checking paradigm. We use our framework to identify and eliminate hallucinations in the training data automatically. Interestingly, HalluciDoctor also indicates that spurious correlations arising from long-tail object cooccurrences contribute to hallucinations. Based on that, we execute counterfactual visual instruction expansion to balance data distribution, thereby enhancing MLLMs' resistance to hallucinations. Comprehensive experiments on hallucination evaluation benchmarks show that our method successfully mitigates 44.6% hallucinations relatively and maintains competitive performance compared to LLaVA. The data and code for this paper are publicly available.(1)

关键词： Hallucinations Multi-modal Language Model vision-language reasoning

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CLOVA: A Closed-LOop Visual Assistant with Tool Usage and Update

CLOVA: A Closed-LOop Visual Assistant with Tool Usage and Up...

引用

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

作者： Gao, Zhi Du, Yuntao Zhang, Xintong Ma, Xiaojian Han, Wenjuan Zhu, Song-Chun Li, Qing Peking Univ Sch Intelligence Sci & Technol Beijing Peoples R China BIGAI State Key Lab Gen Artificial Intelligence Beijing Peoples R China Beijing Jiaotong Univ Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China

ISBN: (纸本)9798350353006

Utilizing large language models (LLMs) to compose off-the-shelf visual tools represents a promising avenue of research for developing robust visual assistants capable of addressing diverse visual tasks. However, these methods often overlook the potential for continual learning, typically by freezing the utilized tools, thus limiting their adaptation to environments requiring new knowledge. To tackle this challenge, we propose CLOVA, a Closed-LOop Visual Assistant, which operates within a framework encompassing inference, reflection, and learning phases. During the inference phase, LLMs generate programs and execute cor responding tools to complete assigned tasks. In the reflection phase, a multimodal global-local reflection scheme analyzes human feedback to determine which tools require updating. Lastly, the learning phase employs three flexible approaches to automatically gather training data and introduces a novel prompt tuning scheme to update the tools, allowing CLOVA to efficiently acquire new knowledge. Experimental findings demonstrate that CLOVA surpasses existing tool-usage methods by 5% in visual question answering and multiple-image reasoning, by 10% in knowledge tagging, and by 20% in image editing. These results underscore the significance of the continual learning capability in general visual assistants.

关键词： Compositional Reasoning Large Language Models Multimodal Learning vision and Language Visual Assistants

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Holo-Relighting: Controllable Volumetric Portrait Relighting from a Single Image

Holo-Relighting: Controllable Volumetric Portrait Relighting...

引用

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

作者： Mei, Yiqun Zeng, Yu Zhang, He Shu, Zhixin Zhang, Xuaner Bi, Sai Zhang, Jianming Jung, HyunJoon Patel, Vishal M. Johns Hopkins Univ Baltimore MD 21218 USA Adobe Inc San Jose CA USA

ISBN: (纸本)9798350353013;9798350353006

At the core of portrait photography is the search for ideal lighting and viewpoint. The process often requires advanced knowledge in photography and an elaborate studio setup. In this work, we propose Holo-Relighting, a volumetric relighting method that is capable of synthesizing novel viewpoints, and novel lighting from a single image. Holo-Relighting leverages the pretrained 3D GAN (EG3D) to reconstruct geometry and appearance from an input portrait as a set of 3D-aware features. We design a relighting module conditioned on a given lighting to process these features, and predict a relit 3D representation in the form of a tri-plane, which can render to an arbitrary viewpoint through volume rendering. Besides viewpoint and lighting control, Holo-Relighting also takes the head pose as a condition to enable head-pose-dependent lighting effects. With these novel designs, Holo-Relighting can generate complex non-Lambertian lighting effects (e.g., specular highlights and cast shadows) without using any explicit physical lighting priors. We train Holo-Relighting with data captured with a light stage, and propose two data-rendering techniques to improve the data quality for training the volumetric relighting system. Through quantitative and qualitative experiments, we demonstrate Holo-Relighting can achieve state-of-the-arts relighting quality with better photorealism, 3D consistency and controllability.

关键词： vision + graphics

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3D Feature Tracking via Event Camera

3D Feature Tracking via Event Camera

引用

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

作者： Li, Siqi Zhou, Zhikuan Xue, Zhou Li, Yipeng Du, Shaoyi Gao, Yue Tsinghua Univ Sch Software BNRist THUIBCS Beijing Peoples R China Li Auto Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Natl Engn Res Ctr Visual Informat & Applicat Natl Key Lab Human Machine Hybrid Augmented Intel Xian Peoples R China

ISBN: (纸本)9798350353006

This paper presents the first 3D feature tracking method with the corresponding dataset. Our proposed method takes event streams from stereo event cameras as input to predict 3D trajectories of the target features with high-speed motion. To achieve this, our method leverages a joint framework to predict the 2D feature motion offsets and the 3D feature spatial position simultaneously. A motion compensation module is leveraged to overcome the feature deformation. A patch matching module based on bipolarity hypergraph modeling is proposed to robustly estimate the feature spatial position. Meanwhile, we collect the first 3D feature tracking dataset with high-speed moving objects and ground truth 3D feature trajectories at 250 FPS, named E-3DTrack, which can be used as the first high-speed 3D feature tracking benchmark. Our code and dataset could be found at: https:// github. com/ lisiqi19971013/E-3DTrack.

关键词： 3D vision Dataset Event Camera Feature Tracking

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Medusa: Universal Feature Learning via Attentional Multitasking

Medusa: Universal Feature Learning via Attentional Multitask...

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

作者： Spencer, Jaime Bowden, Richard Hadfield, Simon Univ Surrey Ctr Vis Speech & Signal Proc CVSSP Guildford Surrey England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent approaches to multi-task learning (MTL) have focused on modelling connections between tasks at the decoder level. This leads to a tight coupling between tasks, which need retraining if a new task is inserted or removed. We argue that MTL is a stepping stone towards universal feature learning (UFL), which is the ability to learn generic features that can be applied to new tasks without retraining. We propose Medusa to realize this goal, designing task heads with dual attention mechanisms. The shared feature attention masks relevant backbone features for each task, allowing it to learn a generic representation. Meanwhile, a novel Multi-Scale Attention head allows the network to better combine per-task features from different scales when making the final prediction. We show the effectiveness of Medusa in UFL (+13.18% improvement), while maintaining MTL performance and being 25% more efficient than previous approaches.

关键词： Representation learning Couplings computer vision conferences Multitasking pattern recognition Decoding

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Artistic Style Novel View Synthesis Based on A Single Image

Artistic Style Novel View Synthesis Based on A Single Image

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

作者： Tseng, Kuan-Wei Lee, Yao-Chih Chen, Chu-Song Natl Taiwan Univ Taipei Taiwan Tokyo Inst Technol Tokyo Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent progress in 3D display technologies has raised the demand for stylized 3D digital content. Previous approaches either perform style transfer on stereoscopic image pairs or reconstruct 3D environment with multiple view images. In this paper, we propose a novel view stylization framework that can convert a single 2D image into multiple stylized views. It is a two-stage solution that contains view synthesis and neural style transfer. We estimate dense optical flow between the source and novel views so that the style transfer model can produce consistent results. Experimental results show that our method significantly improves the consistency among views compared to the baseline method.

关键词： computer vision Three-dimensional displays Stereo image processing conferences Computational modeling pattern recognition Optical flow

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Model Inversion Robustness: Can Transfer Learning Help?

Model Inversion Robustness: Can Transfer Learning Help?

引用

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

作者： Ho, Sy-Tuyen Hao, Koh Jun Chandrasegaran, Keshigeyan Ngoc-Bao Nguyen Cheung, Ngai-Man Singapore Univ Technol & Design SUTD Singapore Singapore Stanford Univ Stanford CA 94305 USA SUTD Singapore Singapore

ISBN: (纸本)9798350353006

Model Inversion (MI) attacks aim to reconstruct private training data by abusing access to machine learning models. Contemporary MI attacks have achieved impressive attack performance, posing serious threats to privacy. Meanwhile, all existing MI defense methods rely on regularization that is in direct conflict with the training objective, resulting in noticeable degradation in model utility. In this work, we take a different perspective, and propose a novel and simple Transfer Learning-based Defense against Model Inversion (TL-DMI) to render MI-robust models. Particularly, by leveraging TL, we limit the number of layers encoding sensitive information from private training dataset, thereby degrading the performance of MI attack. We conduct an analysis using Fisher Information to justify our method. Our defense is remarkably simple to implement. Without bells and whistles, we show in extensive experiments that TL-DMI achieves state-of-the-art (SOTA) MI robustness. Our code, pre-trained models, demo and inverted data are available at: https://***/projects/TL-DMI

关键词： accountability ethics in vision fairness privacy Transparency

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Segment Any Event Streams via Weighted Adaptation of Pivotal Tokens

Segment Any Event Streams via Weighted Adaptation of Pivotal...

引用

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

作者： Chen, Zhiwen Zhu, Zhiyu Zhang, Yifan Hou, Junhui Shi, Guangming Wu, Jinjian Xidian Univ Xian Shaanxi Peoples R China City Univ Hong Kong Hong Kong Peoples R China Pazhou Lab Guangzhou Peoples R China

ISBN: (纸本)9798350353013;9798350353006

In this paper, we delve into the nuanced challenge of tailoring the Segment Anything Models (SAMs) for integration with event data, with the overarching objective of attaining robust and universal object segmentation within the event-centric domain. One pivotal issue at the heart of this endeavor is the precise alignment and calibration of embeddings derived from event-centric data such that they harmoniously coincide with those originating from RGB imagery. Capitalizing on the vast repositories of datasets with paired events and RGB images, our proposition is to harness and extrapolate the profound knowledge encapsulated within the pretrained SAM framework. As a cornerstone to achieving this, we introduce a multi-scale feature distillation methodology. This methodology rigorously optimizes the alignment of token embeddings originating from event data with their RGB image counterparts, thereby preserving and enhancing the robustness of the overall architecture. Considering the distinct significance that token embeddings from intermediate layers hold for higher-level embeddings, our strategy is centered on accurately calibrating the pivotal token embeddings. This targeted calibration is aimed at effectively managing the discrepancies in high-level embeddings originating from both the event and image domains. Extensive experiments on different datasets demonstrate the effectiveness of the proposed distillation method. Code in https://***/happychenpipi/EventSAM.

关键词： cross-modal knowledge distillation event-based vision segmentation

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Segment and Caption Anything

Segment and Caption Anything

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

作者： Huang, Xiaoke Wang, Jianfeng Tang, Yansong Zhang, Zheng Hue, Han Lu, Jiwen Wang, Lijuan Liu, Zicheng Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China Microsoft Shanghai Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China Adv Micro Devices Inc Beijing Peoples R China

ISBN: (纸本)9798350353006

We propose a method to efficiently equip the Segment Anything Model ( SAM) with the ability to generate regional captions. SAM presents strong generalizability to segment anything while is short for semantic understanding. By introducing a lightweight query-based feature mixer, we align the region-specific features with the embedding space of language models for later caption generation. As the number of trainable parameters is small (typically in the order of tens of millions), it costs less computation, less memory usage, and less communication bandwidth, resulting in both fast and scalable training. To address the scarcity problem of regional caption data, we propose to first pretrain our model on objection detection and segmentation tasks. We call this step weak supervision pretraining since the pretraining data only contains category names instead of full-sentence descriptions. The weak supervision pretraining allows us to leverage many publicly available object detection and segmentation datasets. We conduct extensive experiments to demonstrate the superiority of our method and validate each design choice. This work serves as a stepping stone towards scaling up regional captioning data and sheds light on exploring efficient ways to augment SAM with regional semantics. The project page, along with the associated code, can be accessed via the following link.

关键词： Image Captioning Parameter-Efficient Fine-Tuning Regional Captioning Segmentation vision Language Learning

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Constrained Layout Generation with Factor Graphs

Constrained Layout Generation with Factor Graphs

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

作者： Dupty, Mohammed Haroon Dong, Yanfei Leng, Sicong Fu, Guoji Goh, Yong Liang Lu, Wei Lee, Wee Sun Natl Univ Singapore Singapore Singapore Paypal San Jose CA USA Singapore Univ Technol & Design Singapore Singapore

ISBN: (纸本)9798350353006

This paper addresses the challenge of object-centric layout generation under spatial constraints, seen in multiple domains including floorplan design process. The design process typically involves specifying a set of spatial constraints that include object attributes like size and inter-object relations such as relative positioning. Existing works, which typically represent objects as single nodes, lack the granularity to accurately model complex interactions between objects. For instance, often only certain parts of an object, like a room's right wall, interact with adjacent objects. To address this gap, we introduce a factor graph based approach with four latent variable nodes for each room, and a factor node for each constraint. The factor nodes represent dependencies among the variables to which they are connected, effectively capturing constraints that are potentially of a higher order. We then develop message-passing on the bipartite graph, forming a factor graph neural network that is trained to produce a floorplan that aligns with the desired requirements. Our approach is simple and generates layouts faithful to the user requirements, demonstrated by a large improvement in IOU scores over existing methods. Additionally, our approach, being inferential and accurate, is well-suited to the practical human-in-the-loop design process where specifications evolve iteratively, offering a practical and powerful tool for AI-guided design.

关键词： AI guided design Factor graph neural networks Floorplan generation Layout generation vision applications

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