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Harnessing Large Language Models for Training-free Video Anomaly Detection

Harnessing Large Language Models for Training-free Video Ano...

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

作者： Zanella, Luca Menapace, Willi Mancini, Massimiliano Wang, Yiming Ricci, Elisa Univ Trento Trenton NJ 38122 USA Fdn Bruno Kessler Trenton NJ USA

ISBN: (纸本)9798350353006

Video anomaly detection (VAD) aims to temporally locate abnormal events in a video. Existing works mostly rely on training deep models to learn the distribution of normality with either video-level supervision, one-class supervision, or in an unsupervised setting. Training-based methods are prone to be domain-specific, thus being costly for practical deployment as any domain change will involve data collection and model training. In this paper, we radically depart from previous efforts and propose LAnguage-based VAD (LAVAD), a method tackling VAD in a novel, training-free paradigm, exploiting the capabilities of pre-trained large language models (LLMs) and existing vision-language models (VLMs). We leverage VLM-based captioning models to generate textual descriptions for each frame of any test video. With the textual scene description, we then devise a prompting mechanism to unlock the capability of LLMs in terms of temporal aggregation and anomaly score estimation, turning LLMs into an effective video anomaly detector. We further leverage modality-aligned VLMs and propose effective techniques based on cross-modal similarity for cleaning noisy captions and refining the LLM-based anomaly scores. We evaluate LAVAD on two large datasets featuring real-world surveillance scenarios (UCF-Crime and XD-Violence), showing that it outperforms both unsupervised and one-class methods without requiring any training or data collection.

关键词： large language models video anomaly detection vision-language models

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SPIN: Simultaneous Perception, Interaction and Navigation

SPIN: Simultaneous Perception, Interaction and Navigation

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

作者： Uppal, Shagun Agarwal, Ananye Xiong, Haoyu Shaw, Kenneth Pathak, Deepak Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798350353006

While there has been remarkable progress recently in the fields of manipulation and locomotion, mobile manipulation remains a long-standing challenge. Compared to locomotion or static manipulation, a mobile system must make a diverse range of long-horizon tasks feasible in unstructured and dynamic environments. While the applications are broad and interesting, there are a plethora of challenges in developing these systems such as coordination between the base and arm, reliance on onboard perception for perceiving and interacting with the environment, and most importantly, simultaneously integrating all these parts together. Prior works approach the problem using disentangled modular skills for mobility and manipulation that are trivially tied together. This causes several limitations such as compounding errors, delays in decision-making, and no whole-body coordination. In this work, we present a reactive mobile manipulation framework that uses an active visual system to consciously perceive and react to its environment. Similar to how humans leverage whole-body and hand-eye coordination, we develop a mobile manipulator that exploits its ability to move and see, more specifically - to move in order to see and to see in order to move. This allows it to not only move around and interact with its environment but also, choose "when" to perceive "what" using an active visual system. We observe that such an agent learns to navigate around complex cluttered scenarios while displaying agile whole-body coordination using only ego-vision without needing to create environment maps. Videos are available at https://***

关键词： Active vision End-to-end learning Mobile Manipulation Whole-body coordination

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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...

引用

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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Enhancing Emotion recognition with Pre-trained Masked Autoencoders and Sequential Learning

Enhancing Emotion Recognition with Pre-trained Masked Autoen...

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

作者： Zhou, Weiwei Lu, Jiada Ling, Chenkun Wang, Weifeng Liu, Shaowei Chinatelecom Cloud Chengdu Peoples R China

ISBN: (纸本)9798350365474

Human emotion recognition plays a pivotal role in facilitating seamless interactions between humans and computers. This paper delineates our methodology in tackling the Valence-Arousal (VA) Estimation Challenge, Expression (Expr) recognition Challenge, and Action Unit (AU) Detection Challenge within the ambit of the 6th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW). Our study advocates a novel approach aimed at refining continuous emotion recognition. We achieve this by first pre-training with Masked Autoencoders (MAE) on facial datasets and then fine-tuning the model on the affwild2 dataset, which is annotated with expression (Expr) labels. The pre-trained model serves as an adept visual feature extractor, thereby enhancing the model's robustness. Furthermore, we bolster the performance of continuous emotion recognition by integrating Temporal Convolutional Network (TCN) modules and Transformer Encoder modules into our framework. Our model excels beyond baseline performance, securing a commendable 3rd place in the Valence-Arousal Estimation Challenge, while also achieving an impressive 2nd place in both the Expression recognition Challenge and the Action Unit Detection Challenge.

关键词： Emotion recognition

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SelfOcc: Self-Supervised vision-Based 3D Occupancy Prediction

SelfOcc: Self-Supervised Vision-Based 3D Occupancy Predictio...

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

作者： Huang, Yuanhui Zheng, Wenzhao Zhang, Borui Zhou, Jie Lu, Jiwen Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Automat Beijing Peoples R China

ISBN: (纸本)9798350353006

3D occupancy prediction is an important task for the robustness of vision-centric autonomous driving, which aims to predict whether each point is occupied in the surrounding 3D space. Existing methods usually require 3D occupancy labels to produce meaningful results. However, it is very laborious to annotate the occupancy status of each voxel. In this paper, we propose SelfOcc to explore a self-supervised way to learn 3D occupancy using only video sequences. We first transform the images into the 3D space (e.g., bird's eye view) to obtain 3D representation of the scene. We directly impose constraints on the 3D representations by treating them as signed distance fields. We can then render 2D images of previous and future frames as self-supervision signals to learn the 3D representations. We propose an MVS-embedded strategy to directly optimize the SDF-induced weights with multiple depth proposals. Our SelfOcc outperforms the previous best method SceneRF by 58.7% using a single frame as input on SemanticKITTI and is the first self-supervised work that produces reasonable 3D occupancy for surround cameras on nuScenes. SelfOcc produces high-quality depth and achieves state-of-the-art results on novel depth synthesis, monocular depth estimation, and surround-view depth estimation on the SemanticKITTI, KITTI-2015, and nuScenes, respectively. Code: https://***/huang-yh/SelfOcc.

关键词： 3D reconstruction autonomous driving NeRF occupancy self-supervised

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Fashion Outfit Complementary Item Retrieval

Fashion Outfit Complementary Item Retrieval

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

作者： Lin, Yen-Liang Tran, Son Davis, Larry S. Amazon Seattle WA 98109 USA

ISBN: (纸本)9781728171685

Complementary fashion item recommendation is critical for fashion outfit completion. Existing methods mainly focus on outfit compatibility prediction but not in a retrieval setting. We propose a new framework for outfit complementary item retrieval. Specifically, a category-based subspace attention network is presented, which is a scalable approach for learning the subspace attentions. In addition, we introduce an outfit ranking loss that better models the item relationships of an entire outfit. We evaluate our method on the outfit compatibility, FITB and new retrieval tasks. Experimental results demonstrate that our approach outperforms state-of-the-art methods in both compatibility prediction and complementary item retrieval.

关键词： computer vision

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VideoGrounding-DINO: Towards Open-Vocabulary Spatio-Temporal Video Grounding

VideoGrounding-DINO: Towards Open-Vocabulary Spatio-Temporal...

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

作者： Wasim, Syed Talal Naseer, Muzammal Khan, Salman Yang, Ming-Hsuan Khan, Fahad Shahbaz Mohamed Bin Zayed Univ AI Abu Dhabi U Arab Emirates Australian Natl Univ Canberra Australia Univ Calif Merced Merced CA USA Google Res Mountain View CA USA Linkoping Univ Linkoping Sweden

ISBN: (纸本)9798350353006

Video grounding aims to localize a spatio-temporal section in a video corresponding to an input text query. This paper addresses a critical limitation in current video grounding methodologies by introducing an Open-Vocabulary Spatio-Temporal Video Grounding task. Unlike prevalent closed-set approaches that struggle with open-vocabulary scenarios due to limited training data and pre-defined vocabularies, our model leverages pre-trained representations from foundational spatial grounding models. This empowers it to effectively bridge the semantic gap between natural language and diverse visual content, achieving strong performance in closed-set and open-vocabulary settings. Our contributions include a novel spatio-temporal video grounding model, surpassing state-of-the-art results in closed-set evaluations on multiple datasets and demonstrating superior performance in open-vocabulary scenarios. Notably, the proposed model outperforms state-of-the-art methods in closed-set settings on VidSTG (Declarative and Interrogative) and HC-STVG (V1 and V2) datasets. Furthermore, in open-vocabulary evaluations on HC-STVG V1 and YouCook-Interactions, our model surpasses the recent best-performing models by 4.88 m vIoU and 1.83% accuracy, demonstrating its efficacy in handling diverse linguistic and visual concepts for improved video understanding. Our codes will be publicly released.

关键词： MultiModal Open Vocabulary Video Grounding vision Language

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MM-Narrator: Narrating Long-form Videos with Multimodal In-Context Learning

MM-Narrator: Narrating Long-form Videos with Multimodal In-C...

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

作者： Zh, Chaoyi Lin, Kevin Yang, Zhengyuan Wang, Jianfeng Li, Linjie Lin, Chung-Ching Liu, Zicheng Wang, Lijuan Univ Sydney Sydney NSW Australia Microsoft Corp Redmond WA 98052 USA Adv Micro Devices Inc Santa Clara CA USA

ISBN: (纸本)9798350353006

We present MM-Narrator, a novel system leveraging GPT-4 with multimodal in-context learning for the generation of audio descriptions (AD). Unlike previous methods that primarily focused on downstream fine-tuning with short video clips, MM-Narrator excels in generating precise audio descriptions for videos of extensive lengths, even beyond hours, in an autoregressive manner. This capability is made possible by the proposed memory-augmented generation process, which effectively utilizes both the short-term textual context and long-term visual memory through an efficient register-and-recall mechanism. These contextual memories compile pertinent past information, including storylines and character identities, ensuring an accurate tracking and depicting of story-coherent and character-centric audio descriptions. Maintaining the training-free design of MM-Narrator, we further propose a complexity-based demonstration selection strategy to largely enhance its multi-step reasoning capability via few-shot multimodal in-context learning (MM-ICL). Experimental results on MAD-eval dataset demonstrate that MM-Narrator consistently outperforms both the existing fine-tuning-based approaches and LLM-based approaches in most scenarios, as measured by standard evaluation metrics. Additionally, we introduce the first segment-based evaluator for recurrent text generation. Empowered by GPT-4, this evaluator comprehensively reasons and marks AD generation performance in various extendable dimensions.

关键词： audio description in-context learning LLM multimodal retrieval-augmented generation video understanding vision-and-language

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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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An End-to-End Approach for Handwriting recognition: From Handwritten Text Lines to Complete Pages

An End-to-End Approach for Handwriting Recognition: From Han...

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

作者： Castro, Dayvid Bezerra, Byron Leite Dantas Zanchettin, Cleber Univ Fed Pernambuco Recife PE Brazil

ISBN: (纸本)9798350365474

Handwritten Document recognition (HDR) has emerged as a challenging task integrating text and layout information recognition to tackle manuscripts end-to-end. Despite advancements, the computational efficiency of processing entire documents remains a critical challenge, limiting the practical applicability of these models. This paper presents the Document Attention Network for Computationally Efficient recognition (DANCER). The model differs from existing approaches with its unique encoder-decoder structure, where the encoder reduces spatial redundancy and enhances spatial attention, and the decoder, comprising transformer layers, efficiently decodes the text using optimized attention operations. This design results in a fast, memory-efficient model capable of effectively transcribing and understanding complex manuscript layouts. We evaluated DANCER's efficacy on the ICFHR 2016 READ competition dataset, focusing on recognizing single and doublepage historical documents. We demonstrate how DANCER can triple the training batch size compared to prior models within the same memory limits and reduce memory usage by up to 65% without compromising recognition quality. The proposed approach sets new standards in efficiency and accuracy for HDR solutions, paving the way for practical and scalable applications in diverse contexts.

关键词： Attention Networks Full-Page recognition Handwriting recognition Handwritten Document recognition Transformer Network

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