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Jiawen Zhu, Guansong Pang

This paper explores the problem of Generalist Anomaly Detection (GAD) aiming to train one single detection model that can generalize to detect anomalies in diverse datasets from different application domains without any further training on the target data. Some recent studies have shown that large pre-trained Visual-Language Models (VLMs) like CLIP have strong generalization capabilities on detecting industrial defects from various datasets but their methods rely heavily on handcrafted text prompts about defects making them difficult to generalize to anomalies in other applications e.g. medical image anomalies or semantic anomalies in natural images. In this work we propose to train a GAD model with few-shot normal images as sample prompts for AD on diverse datasets on the fly. To this end we introduce a novel approach that learns an in-context residual learning model for GAD termed InCTRL. It is trained on an auxiliary dataset to discriminate anomalies from normal samples based on a holistic evaluation of the residuals between query images and few-shot normal sample prompts. Regardless of the datasets per definition of anomaly larger residuals are expected for anomalies than normal samples thereby enabling InCTRL to generalize across different domains without further training. Comprehensive experiments on nine AD datasets are performed to establish a GAD benchmark that encapsulate the detection of industrial defect anomalies medical anomalies and semantic anomalies in both one-vs-all and multi-class setting on which InCTRL is the best performer and significantly outperforms state-of-the-art competing methods. Code is available at https://github.com/mala-lab/InCTRL.

Yunhan Zhao, Haoyu Ma, Shu Kong, Charless Fowlkes

Egocentric sensors such as AR/VR devices capture human-object interactions and offer the potential to provide task-assistance by recalling 3D locations of objects of interest in the surrounding environment. This capability requires instance tracking in real-world 3D scenes from egocentric videos (IT3DEgo). We explore this problem by first introducing a new benchmark dataset consisting of RGB and depth videos per-frame camera pose and instance-level annotations in both 2D camera and 3D world coordinates. We present an evaluation protocol which evaluates tracking performance in 3D coordinates with two settings for enrolling instances to track: (1) single-view online enrollment where an instance is specified on-the-fly based on the human wearer's interactions. and (2) multi-view pre-enrollment where images of an instance to be tracked are stored in memory ahead of time. To address IT3DEgo we first re-purpose methods from relevant areas e.g. single object tracking (SOT) -- running SOT methods to track instances in 2D frames and lifting them to 3D using camera pose and depth. We also present a simple method that leverages pretrained segmentation and detection models to generate proposals from RGB frames and match proposals with enrolled instance images. Our experiments show that our method (with no finetuning) significantly outperforms SOT-based approaches in the egocentric setting. We conclude by arguing that the problem of egocentric instance tracking is made easier by leveraging camera pose and using a 3D allocentric (world) coordinate representation.

Zhongcong Xu, Jianfeng Zhang, Jun Hao Liew, Hanshu Yan, Jia-Wei Liu, Chenxu Zhang, Jiashi Feng, Mike Zheng Shou

This paper studies the human image animation task which aims to generate a video of a certain reference identity following a particular motion sequence. Existing animation works typically employ the frame-warping technique to animate the reference image towards the target motion. Despite achieving reasonable results these approaches face challenges in maintaining temporal consistency throughout the animation due to the lack of temporal modeling and poor preservation of reference identity. In this work we introduce MagicAnimate a diffusion-based framework that aims at enhancing temporal consistency preserving reference image faithfully and improving animation fidelity. To achieve this we first develop a video diffusion model to encode temporal information. Second to maintain the appearance coherence across frames we introduce a novel appearance encoder to retain the intricate details of the reference image. Leveraging these two innovations we further employ a simple video fusion technique to encourage smooth transitions for long video animation. Empirical results demonstrate the superiority of our method over baseline approaches on two benchmarks. Notably our approach outperforms the strongest baseline by over 38% in terms of video fidelity on the challenging TikTok dancing dataset. Code and model will be made available at https://showlab.github.io/magicanimate.

Bo Li, Haoke Xiao, Lv Tang

In the evolving landscape of computer vision foundation models have emerged as pivotal tools exhibiting exceptional adaptability to a myriad of tasks. Among these the Segment Anything Model (SAM) by Meta AI has distinguished itself in image segmentation. However SAM like its counterparts encounters limitations in specific niche applications prompting a quest for enhancement strategies that do not compromise its inherent capabilities. This paper introduces ASAM a novel methodology that amplifies SAM's performance through adversarial tuning. We harness the potential of natural adversarial examples inspired by their successful implementation in natural language processing. By utilizing a stable diffusion model we augment a subset (1%) of the SA-1B dataset generating adversarial instances that are more representative of natural variations rather than conventional imperceptible perturbations. Our approach maintains the photorealism of adversarial examples and ensures alignment with original mask annotations thereby preserving the integrity of the segmentation task. The fine-tuned ASAM demonstrates significant improvements across a diverse range of segmentation tasks without necessitating additional data or architectural modifications. The results of our extensive evaluations confirm that ASAM establishes new benchmarks in segmentation tasks thereby contributing to the advancement of foundational models in computer vision. Our project page is in https://asam2024.github.io/.

Jiaming Li, Jiacheng Zhang, Jichang Li, Ge Li, Si Liu, Liang Lin, Guanbin Li

Open vocabulary object detection (OVD) aims at seeking an optimal object detector capable of recognizing objects from both base and novel categories. Recent advances leverage knowledge distillation to transfer insightful knowledge from pre-trained large-scale vision-language models to the task of object detection significantly generalizing the powerful capabilities of the detector to identify more unknown object categories. However these methods face significant challenges in background interpretation and model overfitting and thus often result in the loss of crucial background knowledge giving rise to sub-optimal inference performance of the detector. To mitigate these issues we present a novel OVD framework termed LBP to propose learning background prompts to harness explored implicit background knowledge thus enhancing the detection performance w.r.t. base and novel categories. Specifically we devise three modules: Background Category-specific Prompt Background Object Discovery and Inference Probability Rectification to empower the detector to discover represent and leverage implicit object knowledge explored from background proposals. Evaluation on two benchmark datasets OV-COCO and OV-LVIS demonstrates the superiority of our proposed method over existing state-of-the-art approaches in handling the OVD tasks.

Shubham Parashar, Zhiqiu Lin, Tian Liu, Xiangjue Dong, Yanan Li, Deva Ramanan, James Caverlee, Shu Kong

Vision-language models (VLMs) excel in zero-shot recognition but their performance varies greatly across different visual concepts. For example although CLIP achieves impressive accuracy on ImageNet (60-80%) its performance drops below 10% for more than ten concepts like night snake presumably due to their limited presence in the pretraining data. However measuring the frequency of concepts in VLMs' large-scale datasets is challenging. We address this by using large language models (LLMs) to count the number of pretraining texts that contain synonyms of these concepts. Our analysis confirms that popular datasets such as LAION exhibit a long-tailed concept distribution yielding biased performance in VLMs. We also find that downstream applications of VLMs including visual chatbots (e.g. GPT-4V) and text-to-image models (e.g. Stable Diffusion) often fail to recognize or generate images of rare concepts identified by our method. To mitigate the imbalanced performance of zero-shot VLMs we propose REtrieval-Augmented Learning (REAL). First instead of prompting VLMs using the original class names REAL uses their most frequent synonyms found in pretraining texts. This simple change already outperforms costly human-engineered and LLM-enriched prompts over nine benchmark datasets. Second REAL trains a linear classifier on a small yet balanced set of pretraining data retrieved using concept synonyms. REAL surpasses the previous zero-shot SOTA using 400x less storage and 10000x less training time!

Kilichbek Haydarov, Aashiq Muhamed, Xiaoqian Shen, Jovana Lazarevic, Ivan Skorokhodov, Chamuditha Jayanga Galappaththige, Mohamed Elhoseiny

Existing GAN-based text-to-image models treat images as 2D pixel arrays. In this paper we approach the text-to-image task from a different perspective where a 2D image is represented as an implicit neural representation (INR). We show that straightforward conditioning of the unconditional INR-based GAN method on text inputs is not enough to achieve good performance. We propose a word-level attention-based weight modulation operator that controls the generation process of INR-GAN based on hypernetworks. Our experiments on benchmark datasets show that HyperCGAN achieves competitive performance to existing pixel-based methods and retains the properties of continuous generative models.

Jiawei Yao, Qi Qian, Juhua Hu

Multiple clustering has gained significant attention in recent years due to its potential to reveal multiple hidden structures of data from different perspectives. The advent of deep multiple clustering techniques has notably advanced the performance by uncovering complex patterns and relationships within large datasets. However a major challenge arises as users often do not need all the clusterings that algorithms generate and figuring out the one needed requires a substantial understanding of each clustering result. Traditionally aligning a user's brief keyword of interest with the corresponding vision components was challenging but the emergence of multi-modal and large language models (LLMs) has begun to bridge this gap. In response given unlabeled target visual data we propose Multi-Map a novel method employing a multi-modal proxy learning process. It leverages CLIP encoders to extract coherent text and image embeddings with GPT-4 integrating users' interests to formulate effective textual contexts. Moreover reference word constraint and concept-level constraint are designed to learn the optimal text proxy according to the user's interest. Multi-Map not only adeptly captures a user's interest via a keyword but also facilitates identifying relevant clusterings. Our extensive experiments show that Multi-Map consistently outperforms state-of-the-art methods in all benchmark multi-clustering vision tasks. Our code is available at https://github.com/Alexander-Yao/Multi-MaP.

Yaqing Ding, Jonathan Astermark, Magnus Oskarsson, Viktor Larsson

Two-view homography estimation is a classic and fundamental problem in computer vision. While conceptually simple the problem quickly becomes challenging when multiple planes are visible in the image pair. Even with correct matches each individual plane (homography) might have a very low number of inliers when comparing to the set of all correspondences. In practice this requires a large number of RANSAC iterations to generate a good model hypothesis. The current state-of-the-art methods therefore seek to reduce the sample size from four point correspondences originally by including additional information such as keypoint orientation/angles or local affine information. In this work we continue in this direction and propose a novel one-point solver that leverages different approximate constraints derived from the same auxiliary information. In experiments we obtain state-of-the-art results with execution time speed-ups on large benchmark datasets and show that it is more beneficial for the solver to be sample efficient compared to generating more accurate homographies.

Eunsu Baek, Keondo Park, Jiyoon Kim, Hyung-Sin Kim

Computer vision applications predict on digital images acquired by a camera from physical scenes through light. However conventional robustness benchmarks rely on perturbations in digitized images diverging from distribution shifts occurring in the image acquisition process. To bridge this gap we introduce a new distribution shift dataset ImageNet-ES comprising variations in environmental and camera sensor factors by directly capturing 202k images with a real camera in a controllable testbed. With the new dataset we evaluate out-of-distribution (OOD) detection and model robustness. We find that existing OOD detection methods do not cope with the covariate shifts in ImageNet-ES implying that the definition and detection of OOD should be revisited to embrace real-world distribution shifts. We also observe that the model becomes more robust in both ImageNet-C and -ES by learning environment and sensor variations in addition to existing digital augmentations. Lastly our results suggest that effective shift mitigation via camera sensor control can significantly improve performance without increasing model size. With these findings our benchmark may aid future research on robustness OOD and camera sensor control for computer vision. Our code and dataset are available at https://github.com/Edw2n/ImageNet-ES.

Yuxuan Zhou, Xudong Yan, Zhi-Qi Cheng, Yan Yan, Qi Dai, Xian-Sheng Hua

Graph Convolutional Networks (GCNs) have long set the state-of-the-art in skeleton-based action recognition leveraging their ability to unravel the complex dynamics of human joint topology through the graph's adjacency matrix. However an inherent flaw has come to light in these cutting-edge models: they tend to optimize the adjacency matrix jointly with the model weights. This process while seemingly efficient causes a gradual decay of bone connectivity data resulting in a model indifferent to the very topology it sought to represent. To remedy this we propose a two-fold strategy: (1) We introduce an innovative approach that encodes bone connectivity by harnessing the power of graph distances to describe the physical topology; we further incorporate action-specific topological representation via persistent homology analysis to depict systemic dynamics. This preserves the vital topological nuances often lost in conventional GCNs. (2) Our investigation also reveals the redundancy in existing GCNs for multi-relational modeling which we address by proposing an efficient refinement to Graph Convolutions (GC) - the BlockGC. This significantly reduces parameters while improving performance beyond original GCNs. Our full model BlockGCN establishes new benchmarks in skeleton-based action recognition across all model categories. Its high accuracy and lightweight design most notably on the large-scale NTU RGB+D 120 dataset stand as strong validation of the efficacy of BlockGCN.

Zhuofan Xia, Dongchen Han, Yizeng Han, Xuran Pan, Shiji Song, Gao Huang

Generalized Referring Expression Segmentation (GRES) extends the scope of classic RES to refer to multiple objects in one expression or identify the empty targets absent in the image. GRES poses challenges in modeling the complex spatial relationships of the instances in the image and identifying non-existing referents. Multimodal Large Language Models (MLLMs) have recently shown tremendous progress in these complicated vision-language tasks. Connecting Large Language Models (LLMs) and vision models MLLMs are proficient in understanding contexts with visual inputs. Among them LISA as a representative adopts a special [SEG] token to prompt a segmentation mask decoder e.g. SAM to enable MLLMs in the RES task. However existing solutions to GRES remain unsatisfactory since current segmentation MLLMs cannot correctly handle the cases where users might reference multiple subjects in a singular prompt or provide descriptions incongruent with any image target. In this paper we propose Generalized Segmentation Vision Assistant (GSVA) to address this gap. Specifically GSVA reuses the [SEG] token to prompt the segmentation model towards supporting multiple mask references simultaneously and innovatively learns to generate a [REJ] token to reject the targets explicitly. Experiments validate GSVA's efficacy in resolving the GRES issue marking a notable enhancement and setting a new record on the GRES benchmark gRefCOCO dataset. GSVA also proves effective across various classic referring segmentation and comprehension tasks.

Qixuan Zheng, Ming Zhang, Hong Yan

To achieve greater accuracy hypergraph matching algorithms require exponential increases in computational resources. Recent kd-tree-based approximate nearest neighbor (ANN) methods despite the sparsity of their compatibility tensor still require exhaustive calculations for large-scale graph matching. This work utilizes CUR tensor decomposition and introduces a novel cascaded second and third-order hypergraph matching framework (CURSOR) for efficient hypergraph matching. A CUR-based second-order graph matching algorithm is used to provide a rough match and then the core of CURSOR a fiber-CUR-based tensor generation method directly calculates entries of the compatibility tensor by leveraging the initial second-order match result. This significantly decreases the time complexity and tensor density. A probability relaxation labeling (PRL)-based matching algorithm especially suitable for sparse tensors is developed. Experiment results on large-scale synthetic datasets and widely-adopted benchmark sets demonstrate the superiority of CURSOR over existing methods. The tensor generation method in CURSOR can be integrated seamlessly into existing hypergraph matching methods to improve their performance and lower their computational costs.

Jianjun Xu, Yuxin Wang, Hongtao Xie, Yongdong Zhang

In this paper we propose a novel framework to fully exploit the potential of a single vector for scene text recognition (STR). Different from previous sequence-to-sequence methods that rely on a sequence of visual tokens to represent scene text images we prove that just one token is enough to characterize the entire text image and achieve accurate text recognition. Based on this insight we introduce a new paradigm for STR called One Token rEcognizer (OTE). Specifically we implement an image-to-vector encoder to extract the fine-grained global semantics eliminating the need for sequential features. Furthermore an elegant yet potent vector-to-sequence decoder is designed to adaptively diffuse global semantics to corresponding character locations enabling both autoregressive and non-autoregressive decoding schemes. By executing decoding within a high-level representational space our vector-to-sequence (V2S) approach avoids the alignment issues between visual tokens and character embeddings prevalent in traditional sequence-to-sequence methods. Remarkably due to introducing character-wise fine-grained information such global tokens also boost the performance of scene text retrieval tasks. Extensive experiments on synthetic and real datasets demonstrate the effectiveness of our method by achieving new state-of-the-art results on various public STR benchmarks. Our code is available at https://github.com/Xu-Jianjun/OTE.

Shunli Wang, Shuaibing Wang, Dingkang Yang, Mingcheng Li, Haopeng Kuang, Xiao Zhao, Liuzhen Su, Peng Zhai, Lihua Zhang

Fine-grained medical action analysis plays a vital role in improving medical skill training efficiency but it faces the problems of data and algorithm shortage. Cardiopulmonary Resuscitation (CPR) is an essential skill in emergency treatment. Currently the assessment of CPR skills mainly depends on dummies and trainers leading to high training costs and low efficiency. For the first time this paper constructs a vision-based system to complete error action recognition and skill assessment in CPR. Specifically we define 13 types of single-error actions and 74 types of composite error actions during external cardiac compression and then develop a video dataset named CPR-Coach. By taking the CPR-Coach as a benchmark this paper investigates and compares the performance of existing action recognition models based on different data modalities. To solve the unavoidable "Single-class Training & Multi-class Testing" problem we propose a human-cognition-inspired framework named ImagineNet to improve the model's multi-error recognition performance under restricted supervision. Extensive comparison and actual deployment experiments verify the effectiveness of the framework. We hope this work could bring new inspiration to the computer vision and medical skills training communities simultaneously. The dataset and the code are publicly available on https://github.com/Shunli-Wang/CPR-Coach.

Samuele Papa, Riccardo Valperga, David Knigge, Miltiadis Kofinas, Phillip Lippe, Jan-Jakob Sonke, Efstratios Gavves

Neural fields (NeFs) have recently emerged as a versatile method for modeling signals of various modalities including images shapes and scenes. Subsequently a number of works have explored the use of NeFs as representations for downstream tasks e.g. classifying an image based on the parameters of a NeF that has been fit to it. However the impact of the NeF hyperparameters on their quality as downstream representation is scarcely understood and remains largely unexplored. This is in part caused by the large amount of time required to fit datasets of neural fields.In this work we propose a JAX-based library that leverages parallelization to enable fast optimization of large-scale NeF datasets resulting in a significant speed-up. With this library we perform a comprehensive study that investigates the effects of different hyperparameters on fitting NeFs for downstream tasks. In particular we explore the use of a shared initialization the effects of overtraining and the expressiveness of the network architectures used. Our study provides valuable insights on how to train NeFs and offers guidance for optimizing their effectiveness in downstream applications. Finally based on the proposed library and our analysis we propose Neural Field Arena a benchmark consisting of neural field variants of popular vision datasets including MNIST CIFAR variants of ImageNet and ShapeNetv2. Our library and the Neural Field Arena will be open-sourced to introduce standardized benchmarking and promote further research on neural fields.

Guofeng Mei, Luigi Riz, Yiming Wang, Fabio Poiesi

Zero-shot 3D point cloud understanding can be achieved via 2D Vision-Language Models (VLMs). Existing strategies directly map VLM representations from 2D pixels of rendered or captured views to 3D points overlooking the inherent and expressible point cloud geometric structure. Geometrically similar or close regions can be exploited for bolstering point cloud understanding as they are likely to share semantic information. To this end we introduce the first training-free aggregation technique that leverages the point cloud's 3D geometric structure to improve the quality of the transferred VLM representation. Our approach operates iteratively performing local-to-global aggregation based on geometric and semantic point-level reasoning. We benchmark our approach on three downstream tasks including classification part segmentation and semantic segmentation with a variety of datasets representing both synthetic/real-world and indoor/outdoor scenarios. Our approach achieves new state-of-the-art results in all benchmarks.

Huajian Huang, Changkun Liu, Yipeng Zhu, Hui Cheng, Tristan Braud, Sai-Kit Yeung

Portable 360^\circ cameras are becoming a cheap and efficient tool to establish large visual databases. By capturing omnidirectional views of a scene these cameras could expedite building environment models that are essential for visual localization. However such an advantage is often overlooked due to the lack of valuable datasets. This paper introduces a new benchmark dataset 360Loc composed of 360^\circ images with ground truth poses for visual localization. We present a practical implementation of 360^\circ mapping combining 360^\circ images with lidar data to generate the ground truth 6DoF poses. 360Loc is the first dataset and benchmark that explores the challenge of cross-device visual positioning involving 360^\circ reference frames and query frames from pinhole ultra-wide FoV fisheye and 360^\circ cameras. We propose a virtual camera approach to generate lower-FoV query frames from 360^\circ images which ensures a fair comparison of performance among different query types in visual localization tasks. We also extend this virtual camera approach to feature matching-based and pose regression-based methods to alleviate the performance loss caused by the cross-device domain gap and evaluate its effectiveness against state-of-the-art baselines. We demonstrate that omnidirectional visual localization is more robust in challenging large-scale scenes with symmetries and repetitive structures. These results provide new insights into 360-camera mapping and omnidirectional visual localization with cross-device queries. Project Page and dataset: https://huajianup.github.io/research/360Loc/.

HyunJun Jung, Shun-Cheng Wu, Patrick Ruhkamp, Guangyao Zhai, Hannah Schieber, Giulia Rizzoli, Pengyuan Wang, Hongcheng Zhao, Lorenzo Garattoni, Sven Meier 等

Estimating 6D object poses is a major challenge in 3D computer vision. Building on successful instance-level approaches research is shifting towards category-level pose estimation for practical applications. Current category-level datasets however fall short in annotation quality and pose variety. Addressing this we introduce HouseCat6D a new category-level 6D pose dataset. It features 1) multi-modality with Polarimetric RGB and Depth (RGBD+P) 2) encompasses 194 diverse objects across 10 household categories including two photometrically challenging ones and 3) provides high-quality pose annotations with an error range of only 1.35 mm to 1.74 mm. The dataset also includes 4) 41 large-scale scenes with comprehensive viewpoint and occlusion coverage 5) a checkerboard-free environment and 6. dense 6D parallel-jaw robotic grasp annotations. Additionally we present benchmark results for leading category-level pose estimation networks.

Sabarinath Mahadevan, Idil Esen Zulfikar, Paul Voigtlaender, Bastian Leibe

Current state-of-the-art Video Object Segmentation (VOS) methods rely on dense per-object mask annotations both during training and testing. This requires time-consuming and costly video annotation mechanisms. We propose a novel Point-VOS task with a spatio-temporally sparse point-wise annotation scheme that substantially reduces the annotation effort. We apply our annotation scheme to two large-scale video datasets with text descriptions and annotate over 19M points across 133K objects in 32K videos. Based on our annotations we propose a new Point-VOS benchmark and a corresponding point-based training mechanism which we use to establish strong baseline results. We show that existing VOS methods can easily be adapted to leverage our point annotations during training and can achieve results close to the fully-supervised performance when trained on pseudo-masks generated from these points. In addition we show that our data can be used to improve models that connect vision and language by evaluating it on the Video Narrative Grounding (VNG) task. We will make our code and annotations available at https://pointvos.github.io.