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Rui Li, Mikhail Kudryashev, Artur Yakimovich

Optic deconvolution in light microscopy (LM) refers to recovering the object details from images, revealing the ground truth of samples. Traditional explicit methods in LM rely on the point spread function (PSF) during image acquisition. Yet, these approaches often fall short due to inaccurate PSF models and noise artifacts, hampering the overall restoration quality. In this paper, we approached the optic deconvolution as an inverse problem. Motivated by the nonstandard-form compression scheme introduced by Beylkin, Coifman, and Rokhlin (BCR), we proposed an innovative physics-informed neural network Multi-Stage Residual-BCR Net (m-rBCR) to approximate the optic deconvolution. We validated the m-rBCR model on four microscopy datasets - two simulated microscopy datasets from ImageNet and BioSR, real dSTORM microscopy images, and real widefield microscopy images. In contrast to the explicit deconvolution methods (e.g. Richardson-Lucy) and other state-of-the-art NN models (U-Net, DDPM, CARE, DnCNN, ESRGAN, RCAN, Noise2Noise, MPRNet, and MIMO-U-Net), the m-rBCR model demonstrates superior performance to other candidates by PSNR and SSIM in two real microscopy datasets and the simulated BioSR dataset. In the simulated ImageNet dataset, m-rBCR ranks the second-best place (right after MIMO-U-Net). With the backbone from the optical physics, m-rBCR exploits the trainable parameters with better performances (from ~30 times fewer than the benchmark MIMO-U-Net to ~210 times than ESRGAN). This enables m-rBCR to achieve a shorter runtime (from ~3 times faster than MIMO-U-Net to ~300 times faster than DDPM). To summarize, by leveraging physics constraints our model reduced potentially redundant parameters significantly in expertise-oriented NN candidates and achieved high efficiency with superior performance.

Jiyao Zhang, Weiyao Huang, Bo Peng, Mingdong Wu, Fei Hu, Zijian Chen, Bo Zhao, Hao Dong

6D Object Pose Estimation is a critical yet challenging task in the field of computer vision, distinguished from more traditional 2D tasks by its lack of large-scale datasets. This scarcity hampers comprehensive evaluation of model performance and consequently, limits research development while also restricting the applicability of research across diverse domains due to the limited number of instances or categories available. To address these issues and facilitate progress in this field, this paper introduces Omni6DPose, a substantial dataset characterized by its diversity in object categories, large scale, and variety in object materials. Omni6DPose is divided into three main components: ROPE (Real 6D Object Pose Estimation Dataset), which includes 270,000 images annotated with over one million annotations across 600 instances in 140 categories; SOPE(Simulated 6D Object Pose Estimation Dataset), consisting of 350,000 images created in a mixed reality setting with depth simulation, annotated with over one million annotations across 4,000 instances in the same 140 categories; and the manually aligned real scanned objects used in both ROPE and SOPE. Omni6DPose is inherently challenging due to the substantial variations and ambiguities. To address this challenge, we propose GenPose++, a novel framework incorporating a pre-trained 2D foundational model to enhance generalization capabilities and employing a diffusion-based generative approach to adeptly manage ambiguity issues. And, this paper provides a comprehensive benchmarking analysis to evaluate the performance of previous methods on this large-scale dataset in the realms of 6D object pose estimation and pose tracking.

Akshay Krishnan, Abhijit Kundu, Kevis Maninis, James Hays, Matthew Brown

We propose OmniNOCS: a large-scale monocular dataset with 3D Normalized Object Coordinate Space (NOCS), object masks, and 3D bounding box annotations for indoor and outdoor scenes. OmniNOCS has 20 times more object classes and 200 times more instances than existing NOCs datasets (NOCS-Real275, Wild6D). We use OmniNOCS to train a novel, transformer-based monocular NOCS prediction model (NOCSformer) that can predict accurate NOCS, instance masks and poses from 2D object detections across diverse classes. It is the first NOCS model that can generalize to a broad range of classes when prompted with 2D boxes. We evaluate our model on the task of 3D oriented bounding box prediction, where it achieves comparable results to state-of-the-art 3D detection methods such as Cube R-CNN. Unlike other 3D detection methods, our model also provides detailed and accurate 3D object shape and segmentation. We propose a novel benchmark for the task of NOCS prediction based on OmniNOCS, which we hope will serve as a useful baseline for future work in this area. Our dataset and code are at the project website: https://omninocs.github.io

Yuchen Che, Ryo Furukawa, Asako Kanezaki

Category-level articulated object pose estimation task focuses on the pose estimation of unknown articulated objects within known categories. Despite its significance, this task remains challenging due to the varying shapes and poses of objects, expensive dataset annotation costs, and complex real-world environments. In this paper, we propose a novel self-supervised approach that leverages a single-frame point cloud to accomplish the aforementioned task. Our model consistently generates canonical reconstruction with a canonical pose and joint state for the entire input object, and it estimates object-level pose that reduce overall pose variance and part-level poses that align each part of the input with its corresponding part of the reconstruction. Experimental results demonstrate our approach achieves state-of-the-art performance to other self-supervised methods and comparable performance to other supervised methods. To assess the performance of our model in real-world scenarios, we also introduce a novel real-world articulated object benchmark dataset.

KILICHBEK HAYDAROV, Xiaoqian Shen, Avinash Madasu, Mahmoud Salem, Li-Jia Li, Gamaleldin F Elsayed, Mohamed Elhoseiny

We introduce Affective Visual Dialog , an emotion explanation and reasoning task as a testbed for research on understanding constructed emotions in response to visually grounded conversations. The task involves three skills: (1) Dialog-based Question Answering (2) Dialog-based Emotion Prediction and (3) Affective explanation generation based on the dialog. Our key contribution is the collection of a large-scale dataset, dubbed AffectVisDial, consisting of 50K 10-turn visually grounded dialogs as well as concluding emotion attributions and dialog-informed textual emotion explanations, resulting in a total of 27,180 working hours. Notably, the dataset spans a broad range of visual stimuli, covering human heritage and contemporary life, with an average per-turn answer length of about 12 words — 5 times that of the VisDial dataset — and explanations exceeding 28 words on average. We explain our determining design decisions in collecting the dataset, data inclusion and exclusion criteria starting from over 100K dialogs for quality control, and introduce the questioner and answerer tasks that are associated with the participants in the conversation. We propose and demonstrate solid Affective Visual Dialog baselines adapted from state-of-the-art multimodal models. Remarkably, the responses generated by our models show promising emotional reasoning abilities in response to visually grounded conversations. The dataset and code will be publicly available

Dilxat Muhtar, Zhenshi Li, Feng Gu, Xueliang Zhang, Pengfeng Xiao

The revolutionary capabilities of large language models (LLMs) have paved the way for multimodal large language models (MLLMs) and fostered diverse applications across various specialized domains. In the remote sensing (RS) field, however, the diverse geographical landscapes and varied objects in RS imagery are not adequately considered in recent MLLM endeavors. To bridge this gap, we construct a large-scale RS image-text dataset, LHRS-Align, and an informative RS-specific instruction dataset, LHRS-Instruct, leveraging the extensive volunteered geographic information (VGI) and globally available RS images. Building on this foundation, we introduce LHRS-Bot, an MLLM tailored for RS image understanding through a novel multi-level vision-language alignment strategy and a curriculum learning method. Additionally, we introduce LHRS-Bench, a benchmark for thoroughly evaluating MLLMs’ abilities in RS image understanding. Comprehensive experiments demonstrate that LHRS-Bot exhibits a profound understanding of RS images and the ability to perform nuanced reasoning within the RS domain.

Gabriele Berton, Lorenz Junglas, Riccardo Zaccone, Thomas Pollok, Barbara Caputo, Carlo Masone

Mesh-based scene representation offers a promising direction for simplifying large-scale hierarchical visual localization pipelines combining a visual place recognition step based on global features (retrieval) and a visual localization step based on local features. While existing work demonstrates the viability of meshes for visual localization, the impact of using synthetic databases rendered from them in visual place recognition remains largely unexplored. In this work we investigate using dense 3D textured meshes for large-scale Visual Place Recognition (VPR) and identify a significant performance drop when using synthetic mesh-based databases compared to real-world images for retrieval. To address this, we propose MeshVPR, a novel VPR pipeline that utilizes a lightweight features alignment framework to bridge the gap between real-world and synthetic domains. MeshVPR leverages pre-trained VPR models and it is efficient and scalable for city-wide deployments. We introduce novel datasets with freely available 3D meshes and manually collected queries from Berlin, Paris, and Melbourne. Extensive evaluations demonstrate that MeshVPR achieves competitive performance with standard VPR pipelines, paving the way for mesh-based localization systems. Our contributions include the new task of citywide mesh-based VPR, the new benchmark datasets, MeshVPR, and a thorough analysis of open challenges.

Silvio Galesso, Philipp Schröppel, Hssan Driss, Thomas Brox

In recent years, research on out-of-distribution (OoD) detection for semantic segmentation has mainly focused on road scenes -- a domain with a constrained amount of semantic diversity. In this work, we challenge this constraint and extend the domain of this task to general natural images. To this end, we introduce: 1. the ADE-OoD benchmark, which is based on the ADE20k dataset and includes images from diverse domains with a high semantic diversity, and 2. a novel approach that uses Diffusion score matching for OoD detection (DOoD) and is robust to the increased semantic diversity. ADE-OoD features indoor and outdoor images, defines 150 semantic categories as in-distribution, and contains a variety of OoD objects. For DOoD, we train a diffusion model with an MLP architecture on semantic in-distribution embeddings and build on the score matching interpretation to compute pixel-wise OoD scores at inference time. On common road scene OoD benchmarks, DOoD performs on par or better than the state of the art, without using outliers for training or making assumptions about the data domain. On ADE-OoD, DOoD outperforms previous approaches, but leaves much room for future improvements.

Xixi Liu, Christopher Zach

Out-of-distribution (OOD) detection has been extensively studied for the reliable deployment of deep-learning models. Despite great progress in this research direction, most works focus on discriminative classifiers and perform OOD detection based on single-modal representations that consist of either visual or textual features. Moreover, they rely on training with in-distribution (ID) data. The emergence of vision-language models (e.g. \CLIPc) allows to perform zero-shot OOD detection by leveraging multi-modal feature embeddings and therefore only rely on labels defining ID data. Several approaches have been devised but these either need a given OOD label set, which might deviate from real OOD data, or fine-tune CLIP, which potentially has to be done for different ID datasets. In this paper, we first adapt various OOD scores developed for discriminative classifiers to \CLIP. Further, we propose an enhanced method named \emph{TAG} based on Text prompt AuGmentation to amplify the separation between ID and OOD data, which is simple but effective, and can be applied on various score functions. Its performance is demonstrated on CIFAR-100 and large-scale ImageNet-1k OOD detection benchmarks. It consistently improves AUROC and FPR95 on CIFAR-100 across five commonly used architectures over four baseline OOD scores. The average AUROC and FPR95 improvements are 6.35 % and 10.67 %, respectively. The results for ImageNet-1k follow a similar, but less pronounced pattern.

Prakash Chandra Chhipa, Meenakshi Subhash Chippa, Kanjar De, Rajkumar Saini, Marcus Liwicki, Shah Mubarak

Perspective distortion (PD) causes unprecedented changes in shape, size, orientation, angles, and other spatial relationships of visual concepts in images. Precisely estimating camera intrinsic and extrinsic parameters is a challenging task that prevents synthesizing perspective distortion. Non-availability of dedicated training data poses a critical barrier to developing robust computer vision methods. Additionally, distortion correction methods make other computer vision tasks a multi-step approach and lack performance. In this work, we propose mitigating perspective distortion (MPD) by employing a fine-grained parameter control on a specific family of Möbius transform to model real-world distortion without estimating camera intrinsic and extrinsic parameters and without the need for actual distorted data. Also, we present a dedicated perspectively distorted benchmark dataset, ImageNet-PD, to benchmark the robustness of deep learning models against this new dataset. The proposed method outperforms on existing benchmarks, ImageNet-E and ImageNet-X. Additionally, it significantly improves performance on ImageNet-PD while consistently performing on standard data distribution. Further, our method shows improved performance on three PD-affected real-world applications: crowd counting, fisheye image recognition, and person re-identification. We will release source code, dataset, and models for foster further research.

Marko Savic, Guoying Zhao

Remote photoplethysmography (rPPG) is an emerging technology that can detect the pulse rate remotely from face videos. However, it is easily influenced by the recording environment, as robustness to noise is still an open problem. This vulnerability can therefore be exploited to inject fake signals or impair predictions physically. In this study we propose the first dataset containing a wide set of physical domain attack scenarios divided in three categories (illumination, movement, concealment) that directly target the main weaknesses of rPPG. We propose the rPPG Physical Domain Attacks Database (RPDAD) as a benchmark for evaluation of robustness to physical attacks. We perform extensive experiments on conventional hand-crafted and deep learning (end-to-end, non-end-to-end, CNN, transformer, self-supervised) methods and study their susceptibility to the attacks. We conclude by discussing the most critical vulnerabilities discovered and stress the importance of designing more secure solutions.

Amrin Kareem, Jean Lahoud, Hisham Cholakkal

Recent advancements in 3D perception systems have significantly improved their ability to perform visual recognition tasks such as segmentation. However, these systems still heavily rely on explicit human instruction to identify target objects or categories, lacking the capability to actively reason and comprehend implicit user intentions. We introduce a novel segmentation task known as reasoning part segmentation for 3D objects, aiming to output a segmentation mask based on complex and implicit textual queries about specific parts of a 3D object. To facilitate evaluation and benchmarking, we present a large 3D dataset comprising over 60k instructions paired with corresponding ground-truth part segmentation annotations specifically curated for reasoning-based 3D part segmentation. We propose a model that is capable of segmenting parts of 3D objects based on implicit textual queries and generating natural language explanations corresponding to 3D object segmentation requests. Experiments show that our method achieves competitive performance to models that use explicit queries, with the additional abilities to identify part concepts, reason about them, and complement them with world knowledge. Our source code, dataset, and trained models are publicly available.

Camillo Quattrocchi, Antonino Furnari, Daniele Di Mauro, Mario Valerio Giuffrida, Giovanni Maria Farinella

We consider the problem of transferring a temporal action segmentation system initially designed for exocentric (fixed) cameras to an egocentric scenario, where wearable cameras capture video data. The conventional supervised approach requires the collection and labeling of a new set of egocentric videos to adapt the model, which is costly and time-consuming. Instead, we propose a novel methodology which performs the adaptation leveraging existing labeled exocentric videos and a new set of unlabeled, synchronized exocentric-egocentric video pairs, for which temporal action segmentation annotations do not need to be collected. We implement the proposed methodology with an approach based on knowledge distillation, which we investigate both at the feature and model level. To evaluate our approach, we introduce a new benchmark based on the Assembly101 dataset. Results demonstrate the feasibility and effectiveness of the proposed method against classic unsupervised domain adaptation and temporal sequence alignment approaches. Remarkably, without bells and whistles, our best model performs on par with supervised approaches trained on labeled egocentric data, without ever seeing a single egocentric label, achieving a +15.99% (28.59% vs 12.60%) improvement in the edit score on the Assembly101 dataset compared to a baseline model trained solely on exocentric data.

Ming Chang, Xishan Zhang, Rui Zhang, Zhipeng Zhao, Guanhua He, Shaoli Liu

Long-term temporal fusion is frequently employed in camera-based Bird’s-Eye-View (BEV) 3D object detection to improve detection of occluded objects. Existing methods can be divided into two categories, parallel fusion and recurrent fusion. Recurrent fusion reduces inference latency and memory consumption but fails to exploit the long-term information as well as parallel fusion. In this paper, we first find two shortcomings of recurrent fusion paradigm: (1) Gradients of previous BEV features cannot directly contribute to the fusion module. (2) Semantic ambiguity are caused by coarse granularity of the BEV grids during aligning BEV features. Then based on the above analysis, we propose RecurrentBEV, a novel recurrent temporal fusion method for BEV based 3D object detector. By adopting RNN-style back-propagation and new-designed inner grid transformation, RecurrentBEV improves the long-term fusion ability while still enjoying efficient inference latency and memory consumption during inference. Extensive experiments on the nuScenes benchmark demonstrate its effectiveness, achieving a new state-of-the-art performance of 57.4 mAP and 65.1 NDS on the test set. The real-time version (25.6 FPS) achieves 44.5 mAP and 54.9 NDS without external dataset, outperforming the previous best method StreamPETR by 1.3 mAP and 0.9 NDS.

Rosario Leonardi, Antonino Furnari, Francesco Ragusa, Giovanni Maria Farinella

In this study, we investigate the effectiveness of synthetic data in enhancing egocentric hand-object interaction detection. Via extensive experiments and comparative analyses on three egocentric datasets, VISOR, EgoHOS, and ENIGMA-51, our findings reveal how to exploit synthetic data for the HOI detection task when real labeled data are scarce or unavailable. Specifically, by leveraging only 10% of real labeled data, we achieve improvements in Overall AP compared to baselines trained exclusively on real data of: +5.67% on EPIC-KITCHENS VISOR, +8.24% on EgoHOS, and +11.69% on ENIGMA-51. Our analysis is supported by a novel data generation pipeline and the newly introduced HOI-Synth benchmark which augments existing datasets with synthetic images of hand-object interactions automatically labeled with hand-object contact states, bounding boxes, and pixel-wise segmentation masks. Data, code, and data generation tools to support future research are released at: https://fpv-iplab.github.io/HOI-Synth/.

jiazhou zhou, Xu Zheng, Yuanhuiyi Lyu, LIN WANG

In this paper, we propose EventBind, a novel and effective framework that unleashes the potential of vision-language models (VLMs) for event-based recognition to compensate for the lack of large-scale event-based datasets. In particular, due to the distinct modality gap with the image-text data and the lack of large-scale datasets, learning a common representation space for images, texts, and events is non-trivial. Intuitively, we need to address two key challenges: 1) how to generalize CLIP's visual encoder to event data while fully leveraging events' unique properties, e.g., sparsity and high temporal resolution; 2) how to effectively align the multi-modal embeddings, i.e., image, text, and events. Accordingly, we first introduce a novel event encoder that subtly models the temporal information from events and meanwhile generates event prompts for modality bridging. We then design a text encoder that generates content prompts and utilizes hybrid text prompts to enhance EventBind's generalization ability across diverse datasets. With the proposed event encoder, text encoder, and image encoder, a novel Hierarchical Triple Contrastive Alignment (HTCA}) module is introduced to jointly optimize the correlation and enable efficient knowledge transfer among the three modalities. We evaluate various settings, including fine-tuning and few-shot on three benchmarks and our EventBind achieves new state-of-art accuracy compared with the previous methods, such as N-Caltech101 (+5.34% and +1.70%) and N-Imagenet (+5.65% and +1.99%) with fine-tuning and 20-shot settings respectively. Moreover, our EventBind can be flexibly extended to the event retrieval task using text or image queries, showing plausible performance. Our project code will be made publicly available.

Hyunsin Park, Sungrack Yun

We propose a prototype-based federated learning method tailored for embedding networks in classification or verification tasks. Our focus lies in scenarios where each client possesses data from only one class. The central challenge arises from the need to learn an embedding network capable of discriminating between different classes while respecting privacy constraints. Sharing true class prototypes with the server or other clients could potentially compromise sensitive information. To address this, we introduce a proxy class prototype that can be safely shared among clients. Our approach involves generating proxy class prototypes by linearly combining them with their nearest neighbors. This technique conceals the true class prototype while enabling clients to learn discriminative embedding networks. We compare our method against alternative techniques, including random Gaussian noise addition and random selection with cosine similarity constraints. Additionally, we evaluate the robustness of our approach against gradient inversion attacks and introduce a prototype leakage measure to quantify the extent of private information revealed when sharing the proposed proxy class prototype. Furthermore, we provide a theoretical convergence analysis of our approach. Empirical results on three benchmark datasets CIFAR-100, VoxCeleb1, and VGGFace2 demonstrate the effectiveness of our proposed method for federated learning from scratch.

Florin-Alexandru Vasluianu, Tim Seizinger, Zongwei Wu, Rakesh Ranjan, Radu Timofte

Lighting normalization is a crucial but underexplored restoration task with broad applications. However, existing works often simplify this task within the context of shadow removal, limiting the light sources to one and oversimplifying the scene, thus excluding complex self-shadows and restricting surface classes to smooth ones. Although promising, such simplifications hinder generalizability to more realistic settings encountered in daily use. In this paper, we propose a new challenging task termed Ambient Lighting Normalization (ALN), which enables the study of interactions between shadows, unifying image restoration and shadow removal in a broader context. To address the lack of appropriate datasets for ALN, we introduce the large-scale high-resolution dataset Ambient6K, comprising samples obtained from multiple light sources and including self-shadows resulting from complex geometries, which is the first of its kind. For benchmarking, we select various mainstream methods and rigorously evaluate them on Ambient6K. Additionally, we propose IFBlend, a novel strong baseline that maximizes Image-Frequency joint entropy to selectively restore local areas under different lighting conditions, without relying on shadow localization priors. Experiments show that IFBlend achieves SOTA scores on Ambient6K and exhibits competitive performance on conventional shadow removal benchmarks compared to shadow-specific models with mask priors. The dataset, benchmark, and code are available at https://github.com/fvasluianu97/IFBlend.

Shicheng Li, Lei Li, Yi Liu, Shuhuai Ren, Yuanxin Liu, Rundong Gao, Xu Sun, Lu Hou

The ability to perceive how objects change over time is a crucial ingredient in human intelligence. However, current benchmarks cannot faithfully reflect the temporal understanding abilities of video-language models (VidLMs) due to the existence of static visual shortcuts. To remedy this issue, we present VITATECS, a diagnostic VIdeo-Text dAtaset for the evaluation of TEmporal Concept underStanding. Specifically, we first introduce a fine-grained taxonomy of temporal concepts in natural language in order to diagnose the capability of VidLMs to comprehend different temporal aspects. Furthermore, to disentangle the correlation between static and temporal information, we generate counterfactual video descriptions that differ from the original one only in the specified temporal aspect. We employ a semi-automatic data collection framework using large language models and human-in-the-loop annotation to obtain high-quality counterfactual descriptions efficiently. Evaluation of representative video-language understanding models confirms their deficiency in temporal understanding, revealing the need for greater emphasis on the temporal elements in video-language research. Our dataset is publicly available at https://github.com/lscpku/VITATECS.

Jiarui Duan, Haoling Li, Haofei Zhang, Hao Jiang, Mengqi Xue, Li Sun, Mingli Song, Jie Song

Attribution-based explanations are garnering increasing attention recently and have emerged as the predominant approach towards \textit{eXplanable Artificial Intelligence}~(XAI). However, the absence of consistent configurations and systematic investigations in prior literature impedes comprehensive evaluations of existing methodologies. In this work, we introduce {Meta-Rank}, an open platform for benchmarking attribution methods in the image domain. Presently, Meta-Rank assesses eight exemplary attribution methods using six renowned model architectures on four diverse datasets, employing both the \textit{Most Relevant First} (MoRF) and \textit{Least Relevant First} (LeRF) evaluation protocols.Through extensive experimentation, our benchmark reveals three insights in attribution evaluation endeavors: 1) evaluating attribution methods under disparate settings can yield divergent performance rankings; 2) although inconsistent across numerous cases, the performance rankings exhibit remarkable consistency across distinct checkpoints along the same training trajectory; 3) prior attempts at consistent evaluation fare no better than baselines when extended to more heterogeneous models and datasets. Our findings underscore the necessity for future research in this domain to conduct rigorous evaluations encompassing a broader range of models and datasets, and to reassess the assumptions underlying the empirical success of different attribution methods. Code, models, and datasets will be made publicly available in the near future.