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Referring Multi-Object Tracking

Referring Multi-Object Tracking

引用

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

作者： Wu, Dongming Han, Wencheng Wang, Tiancai Dong, Xingping Zhang, Xiangyu Shen, Jianbing Beijing Inst Technol Beijing Peoples R China Univ Macau SKL IOTSC CIS Macau Peoples R China MEGVII Technol Beijing Peoples R China Wuhan Univ Sch Comp Sci Wuhan Peoples R China Beijing Acad Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9798350301298

Existing referring understanding tasks tend to involve the detection of a single text-referred object. In this paper, we propose a new and general referring understanding task, termed referring multi-object tracking (RMOT). Its core idea is to employ a language expression as a semantic cue to guide the prediction of multi-object tracking. To the best of our knowledge, it is the first work to achieve an arbitrary number of referent object predictions in videos. To push forward RMOT, we construct one benchmark with scalable expressions based on KITTI, named Refer-KITTI. Specifically, it provides 18 videos with 818 expressions, and each expression in a video is annotated with an average of 10.7 objects. Further, we develop a transformer-based architecture TransRMOT to tackle the new task in an online manner, which achieves impressive detection performance and outperforms other counterparts. The Refer-KITTI dataset and the code are released at https://***.

关键词： and reasoning language vision

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You Are Catching My Attention: Are vision Transformers Bad Learners under Backdoor Attacks?

You Are Catching My Attention: Are Vision Transformers Bad L...

引用

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

作者： Yuan, Zenghui Zhou, Pan Zou, Kai Cheng, Yu Huazhong Univ Sci & Technol Hubei Engn Res Ctr Big Data Secur Sch Cyber Sci & Engn Hubei Key Lab Distributed Syst Secur Wuhan Peoples R China Protagolabs Inc Vienna Austria Microsoft Res Redmond WA USA

ISBN: (纸本)9798350301298

vision Transformers (ViTs), which made a splash in the field of computer vision (CV), have shaken the dominance of convolutional neural networks (CNNs). However, in the process of industrializing ViTs, backdoor attacks have brought severe challenges to security. The success of ViTs benefits from the self-attention mechanism. However, compared with CNNs, we find that this mechanism of capturing global information within patches makes ViTs more sensitive to patch-wise triggers. Under such observations, we delicately design a novel backdoor attack framework for ViTs, dubbed BadViT, which utilizes a universal patch-wise trigger to catch the model's attention from patches beneficial for classification to those with triggers, thereby manipulating the mechanism on which ViTs survive to confuse itself. Furthermore, we propose invisible variants of BadViT to increase the stealth of the attack by limiting the strength of the trigger perturbation. Through a large number of experiments, it is proved that BadViT is an efficient backdoor attack method against ViTs, which is less dependent on the number of poisons, with satisfactory convergence, and is transferable for downstream tasks. Furthermore, the risks inside of ViTs to backdoor attacks are also explored from the perspective of existing advanced defense schemes.

关键词： Machine learning (other than deep learning)

来源：评论

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Aligning Bag of Regions for Open-Vocabulary Object Detection

Aligning Bag of Regions for Open-Vocabulary Object Detection

引用

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

作者： Wu, Size Zhang, Wenwei Jin, Sheng Liu, Wentao Loy, Chen Change Nanyang Technol Univ S Lab Singapore Singapore Univ Hong Kong Hong Kong Peoples R China SenseTime Res & Tetras AI Shenzhen Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

Pre-trained vision-language models (VLMs) learn to align vision and language representations on large-scale datasets, where each image-text pair usually contains a bag of semantic concepts. However, existing open-vocabulary object detectors only align region embeddings individually with the corresponding features extracted from the VLMs. Such a design leaves the compositional structure of semantic concepts in a scene under-exploited, although the structure may be implicitly learned by the VLMs. In this work, we propose to align the embedding of bag of regions beyond individual regions. The proposed method groups contextually interrelated regions as a bag. The embeddings of regions in a bag are treated as embeddings of words in a sentence, and they are sent to the text encoder of a VLM to obtain the bag-of-regions embedding, which is learned to be aligned to the corresponding features extracted by a frozen VLM. Applied to the commonly used Faster R-CNN, our approach surpasses the previous best results by 4.6 box AP50 and 2.8 mask AP on novel categories of open-vocabulary COCO and LVIS benchmarks, respectively. Code and models are available at https://***/wusize/ovdet.

关键词： detection recognition: Categorization retrieval

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Task Navigator: Decomposing Complex Tasks for Multimodal Large Language Models

Task Navigator: Decomposing Complex Tasks for Multimodal Lar...

引用

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

作者： Ma, Feipeng Zhou, Yizhou Zhang, Yueyi Wu, Siying Zhang, Zheyu He, Zilong Rao, Fengyun Sun, Xiaoyan Univ Sci & Technol China Hefei Peoples R China Tencent Inc WeChat Shenzhen Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China

ISBN: (纸本)9798350365474

Inspired by the remarkable progress achieved by recent Large Language Models (LLMs), Multimodal Large Language Models (MLLMs) take LLMs as their brains, and have achieved surprising results in many downstream tasks by training on a large amount of task-specific data. However, when faced with complex tasks that require the collaboration of multiple capabilities, existing MLLMs recollect training data and retrain the model, ignoring the systematic utilization of LLMs and their possessed capabilities learned in downstream tasks. Inspired by the way humans tackle complex questions, in this paper, we propose a novel framework called Task Navigator. In our framework, LLMs act as navigators to chart a viable path for solving complex tasks and guide MLLMs through the process step by step. Specifically, LLMs iteratively break down sub-problems and refine them to be more reasonable and answerable, which are subsequently resolved by MLLMs to obtain relevant subanswers, until the LLMs have collected enough information to answer the initial question. Task Navigator provides an effective way to extend MLLMs to tackle complex tasks, thus broadening MLLMs' applicability. To evaluate the performance of the proposed framework, we have curated a carefully designed benchmark called VersaChallenge. Experiments on VersaChallenge demonstrate the effectiveness of our proposed method.

关键词： Language and vision Multi-modal vision

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Multivariate, Multi-frequency and Multimodal: Rethinking Graph Neural Networks for Emotion recognition in Conversation

Multivariate, Multi-frequency and Multimodal: Rethinking Gra...

引用

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

作者： Chen, Feiyu Shao, Jie Zhu, Shuyuan Shen, Heng Tao Univ Elect Sci & Technol China Chengdu Peoples R China Sichuan Artificial Intelligence Res Inst Yibin Peoples R China

ISBN: (纸本)9798350301298

Complex relationships of high arity across modality and context dimensions is a critical challenge in the Emotion recognition in Conversation (ERC) task. Yet, previous works tend to encode multimodal and contextual relationships in a loosely-coupled manner, which may harm relationship modelling. Recently, Graph Neural Networks (GNN) which show advantages in capturing data relations, offer a new solution for ERC. However, existing GNN-based ERC models fail to address some general limits of GNNs, including assuming pairwise formulation and erasing high-frequency signals, which may be trivial for many applications but crucial for the ERC task. In this paper, we propose a GNN-based model that explores multivariate relationships and captures the varying importance of emotion discrepancy and commonality by valuing multi-frequency signals. We empower GNNs to better capture the inherent relationships among utterances and deliver more sufficient multimodal and contextual modelling. Experimental results show that our proposed method outperforms previous state-of-the-art works on two popular multimodal ERC datasets.

关键词： Multi-modal learning

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Learning to Render Novel Views from Wide-Baseline Stereo Pairs

Learning to Render Novel Views from Wide-Baseline Stereo Pai...

引用

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

作者： Du, Yilun Smith, Cameron Tewari, Ayush Sitzmann, Vincent MIT CSAIL Cambridge MA 02139 USA

ISBN: (纸本)9798350301298

We introduce a method for novel view synthesis given only a single wide-baseline stereo image pair. In this challenging regime, 3D scene points are regularly observed only once, requiring prior-based reconstruction of scene geometry and appearance. We find that existing approaches to novel view synthesis from sparse observations fail due to recovering incorrect 3D geometry and due to the high cost of differentiable rendering that precludes their scaling to large-scale training. We take a step towards resolving these shortcomings by formulating a multi-view transformer encoder, proposing an efficient, image-space epipolar line sampling scheme to assemble image features for a target ray, and a lightweight cross-attention-based renderer. Our contributions enable training of our method on a large-scale real-world dataset of indoor and outdoor scenes. We demonstrate that our method learns powerful multi-view geometry priors while reducing the rendering time. We conduct extensive comparisons on held-out test scenes across two real-world datasets, significantly outperforming prior work on novel view synthesis from sparse image observations and achieving multi-view-consistent novel view synthesis.

关键词： vision + graphics

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ViTA: An Efficient Video-to-Text Algorithm using VLM for RAG-based Video Analysis System

ViTA: An Efficient Video-to-Text Algorithm using VLM for RAG...

引用

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

作者： Arefeen, Md Adnan Debnath, Biplob Uddin, Md Yusuf Sarwar Chakradhar, Srimat NEC Labs Amer Princeton NJ 08540 USA Univ Missouri Kansas City MO 64110 USA

ISBN: (纸本)9798350365474

Retrieval-augmented generation (RAG) is used in natural language processing (NLP) to provide query-relevant information in enterprise documents to large language models (LLMs). Such enterprise context enables the LLMs to generate more informed and accurate responses. When enterprise data is primarily videos, AI models like vision language models (VLMs) are necessary to convert information in videos into text. While essential, this conversion is a bottleneck, especially for large corpus of videos. It delays the timely use of enterprise videos to generate useful responses. We propose ViTA, a novel method that leverages two unique characteristics of VLMs to expedite the conversion process. As VLMs output more text tokens, they incur higher latency. In addition, large (heavyweight) VLMs can extract intricate details from images and videos, but they incur much higher latency per output token when compared to smaller (lightweight) VLMs that may miss details. To expedite conversion, ViTA first employs a lightweight VLM to quickly understand the gist or overview of an image or a video clip, and directs a heavyweight VLM (through prompt engineering) to extract additional details by using only a few (preset number of) output tokens. Our experimental results show that ViTA expedites the conversion time by as much as 43%, without compromising the accuracy of responses when compared to a baseline system that only uses a heavyweight VLM.

关键词： Large Language Models (LLMs) Natural Language Processing Retrieval Augmented Generation (RAG) Video Analytics vision Language Models (VLMs)

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Feature Representation Learning with Adaptive Displacement Generation and Transformer Fusion for Micro-Expression recognition

Feature Representation Learning with Adaptive Displacement G...

引用

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

作者： Zhai, Zhijun Zhao, Jianhui Long, Chengjiang Xu, Wenju He, Shuangjiang Zhao, Huijuan Wuhan Univ Sch Comp Sci Wuhan Hubei Peoples R China Meta Real Labs Burlingame CA USA InnoPeak Technol Inc OPPO US Res Ctr Palo Alto CA USA FiberHome Telecommun Technol Co Ltd Wuhan Hubei Peoples R China

ISBN: (纸本)9798350301298

Micro-expressions are spontaneous, rapid and subtle facial movements that can neither be forged nor suppressed. They are very important nonverbal communication clues, but are transient and of low intensity thus difficult to recognize. Recently deep learning based methods have been developed for micro-expression (ME) recognition using feature extraction and fusion techniques, however, targeted feature learning and efficient feature fusion still lack further study according to the ME characteristics. To address these issues, we propose a novel framework Feature Representation Learning with adaptive Displacement Generation and Transformer fusion (FRL-DGT), in which a convolutional Displacement Generation Module (DGM) with self-supervised learning is used to extract dynamic features from onset/apex frames targeted to the subsequent ME recognition task, and a well-designed Transformer Fusion mechanism composed of three Transformer-based fusion modules (local, global fusions based on AU regions and full-face fusion) is applied to extract the multi-level informative features after DGM for the final ME prediction. The extensive experiments with solid leave-one-subject-out (LOSO) evaluation results have demonstrated the superiority of our proposed FRL-DGT to state-of-the-art methods.

关键词： body gesture Humans: Face movement pose

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Deep Factorized Metric Learning

Deep Factorized Metric Learning

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

作者： Wang, Chengkun Zheng, Wenzhao Li, Junlong Zhou, Jie Lu, Jiwen Tsinghua Univ Dept Automat Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China

ISBN: (纸本)9798350301298

Learning a generalizable and comprehensive similarity metric to depict the semantic discrepancies between images is the foundation of many computer vision tasks. While existing methods approach this goal by learning an ensemble of embeddings with diverse objectives, the backbone network still receives a mix of all the training signals. Differently, we propose a deep factorized metric learning (DFML) method to factorize the training signal and employ different samples to train various components of the backbone network. We factorize the network to different sub-blocks and devise a learnable router to adaptively allocate the training samples to each sub-block with the objective to capture the most information. The metric model trained by DFML capture different characteristics with different sub-blocks and constitutes a generalizable metric when using all the subblocks. The proposed DFML achieves state-of-the-art performance on all three benchmarks for deep metric learning including CUB-200-2011, Cars196, and Stanford Online Products. We also generalize DFML to the image classification task on ImageNet-1K and observe consistent improvement in accuracy/computation trade-off. Specifically, we improve the performance of ViT-B on ImageNet (+0.2% accuracy) with less computation load (-24% FLOPs). (1)

关键词： detection recognition: Categorization retrieval

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Universal Guidance for Diffusion Models

Universal Guidance for Diffusion Models

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

作者： Bansal, Arpit Chu, Hong-Min Schwarzschild, Avi Sengupta, Soumyadip Goldblum, Micah Geiping, Jonas Goldstein, Tom Univ Maryland College Pk MD 20742 USA Univ North Carolina Chapel Hill Chapel Hill NC USA NYU New York NY USA

ISBN: (纸本)9798350302493

Typical diffusion models are trained to accept a particular form of conditioning, most commonly text, and cannot be conditioned on other modalities without retraining. In this work, we propose a universal guidance algorithm that enables diffusion models to be controlled by arbitrary guidance modalities without the need to retrain any use-specific components. We show that our algorithm successfully generates quality images with guidance functions including segmentation, face recognition, object detection, and classifier signals. Code is available at ***/arpitbansal297/UniversalGuided-Diffusion.

关键词： Face recognition

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