GenAI4ELab/papercli-papers huggingface dataset:
COOL-CHIC: Coordinate-based Low Complexity Hierarchical Image Codec
Authors: Théo Ladune; Pierrick Philippe; Félix Henry; Gordon Clare; Thomas Leguay
Venue: ICCV 2023 (main)

Abstract: We introduce COOL-CHIC, a Coordinate-based Low Complexity Hierarchical Image Codec. It is a learned alternative to autoencoders with 629 parameters and 680 multiplications per decoded pixel. COOL-CHIC offers compression performance close to modern conventional MPEG codecs such as HEVC and is competitive with popular autoencoder-based systems. This method is inspired by Coordinate-based Neural Representations, where an image is represented as a learned function which maps pixel coordinates to RGB values. The parameters of the mapping function are then sent using entropy coding. At the receiver side, the compressed image is obtained by evaluating the mapping function for all pixel coordinates. COOL-CHIC implementation is made open-source.
Normalizing Flows for Human Pose Anomaly Detection
Authors: Or Hirschorn; Shai Avidan
Venue: ICCV 2023 (main)

Abstract: Video anomaly detection is an ill-posed problem because it relies on many parameters such as appearance, pose, camera angle, background, and more.
 We distill the problem to anomaly detection of human pose, thus decreasing the risk of nuisance parameters such as appearance affecting the result. Focusing on pose alone also has the side benefit of reducing bias against distinct minority groups.
 Our model works directly on human pose graph sequences and is exceptionally lightweight ( 1K parameters), capable of running on any machine able to run the pose estimation with negligible additional resources.
 We leverage the highly compact pose representation in a normalizing flows framework, which we extend to tackle the unique characteristics of spatio-temporal pose data and show its advantages in this use case.
 The algorithm is quite general and can handle training data of only normal examples as well as a supervised setting that consists of labeled normal and abnormal examples.
 We report state-of-the-art results on two anomaly detection benchmarks - the unsupervised ShanghaiTech dataset and the recent supervised UBnormal dataset. 
 Code available at https://github.com/orhir/STG-NF.
Reconstructing Groups of People with Hypergraph Relational Reasoning
Authors: Buzhen Huang; Jingyi Ju; Zhihao Li; Yangang Wang
Venue: ICCV 2023 (main)

Abstract: Due to the mutual occlusion, severe scale variation, and complex spatial distribution, the current multi-person mesh recovery methods cannot produce accurate absolute body poses and shapes in large-scale crowded scenes. To address the obstacles, we fully exploit crowd features for reconstructing groups of people from a monocular image. A novel hypergraph relational reasoning network is proposed to formulate the complex and high-order relation correlations among individuals and groups in the crowd. We first extract compact human features and location information from the original high-resolution image. By conducting the relational reasoning on the extracted individual features, the underlying crowd collectiveness and interaction relationship can provide additional group information for the reconstruction. Finally, the updated individual features and the localization information are used to regress human meshes in camera coordinates. To facilitate the network training, we further build pseudo ground-truth on two crowd datasets, which may also promote future research on pose estimation and human behavior understanding in crowded scenes. The experimental results show that our approach outperforms other baseline methods both in crowded and common scenarios. The code and datasets are publicly available at https://github.com/boycehbz/GroupRec.
PivotNet: Vectorized Pivot Learning for End-to-end HD Map Construction
Authors: Wenjie Ding; Limeng Qiao; Xi Qiu; Chi Zhang
Venue: ICCV 2023 (main)

Abstract: Vectorized high-definition map online construction has garnered considerable attention in the field of autonomous driving research. Most existing approaches model changeable map elements using a fixed number of points, or predict local maps in a two-stage autoregressive manner, which may miss essential details and lead to error accumulation. Towards precise map element learning, we propose a simple yet effective architecture named PivotNet, which adopts unified pivot-based map representations and is formulated as a direct set prediction paradigm. Concretely, we first propose a novel Point-to-Line Mask module to encode both the subordinate and geometrical point-line priors in the network. Then, a well-designed Pivot Dynamic Matching module is proposed to model the topology in dynamic point sequences by introducing the concept of sequence matching. Furthermore, to supervise the position and topology of the vectorized point predictions, we propose a Dynamic Vectorized Sequence loss. Extensive experiments and ablations show that PivotNet is remarkably superior to other SOTAs by 5.9 mAP at least. The code will be available soon.
Universal Domain Adaptation via Compressive Attention Matching
Authors: Didi Zhu; Yinchuan Li; Junkun Yuan; Zexi Li; Kun Kuang; Chao Wu
Venue: ICCV 2023 (main)

Abstract: Universal domain adaptation (UniDA) aims to transfer knowledge from the source domain to the target domain without any prior knowledge about the label set. The challenge lies in how to determine whether the target samples belong to common categories. The mainstream methods make judgments based on the sample features, which overemphasizes global information while ignoring the most crucial local objects in the image, resulting in limited accuracy. To address this issue, we propose a Universal Attention Matching (UniAM) framework by exploiting the self-attention mechanism in vision transformer to capture the crucial object information. The proposed framework introduces a novel Compressive Attention Matching (CAM) approach to explore the core information by compressively representing attentions. Furthermore, CAM incorporates a residual-based measurement to determine the sample commonness. By utilizing the measurement, UniAM achieves domain-wise and category-wise Common Feature Alignment (CFA) and Target Class Separation (TCS). Notably, UniAM is the first method utilizing the attention in vision transformer directly to perform classification tasks. Extensive experiments show that UniAM outperforms the current state-of-the-art methods on various benchmark datasets.
Contactless Pulse Estimation Leveraging Pseudo Labels and Self-Supervision
Authors: Zhihua Li; Lijun Yin
Venue: ICCV 2023 (main)

Abstract: Remote photoplethysmography (rPPG) is a promising research area involving non-invasive monitoring of vital signs using cameras. While several supervised methods have been proposed, recent research has focused on contrastive-based self-supervised methods. However, these methods often collapse to learning irrelevant periodicities when dealing with interferences such as head motions, facial dynamics, and video compression. To address this limitation, firstly, we enhance the current self-supervised learning by introducing more reliable and explicit contrastive constraints. Secondly, we propose an innovative learning strategy that seamlessly integrates self-supervised constraints with pseudo-supervisory signals derived from traditional unsupervised methods. This is followed by a co-rectification technique designed to mitigate the adverse effects of noisy pseudo-labels. Experimental results demonstrate the superiority of our methodology over representative models when applied to small, high-quality datasets such as PURE and UBFC-rPPG. Importantly, on large-scale challenging datasets such as VIPL-HR and V4V, our method, with zero annotation cost, not only significantly surpasses prevailing self-supervised techniques but also showcases remarkable alignment with state-of-the-art supervised methods.
Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions
Authors: Ayaan Haque; Matthew Tancik; Alexei A. Efros; Aleksander Holynski; Angjoo Kanazawa
Venue: ICCV 2023 (main)

Abstract: We propose a method for editing NeRF scenes with text-instructions. Given a NeRF of a scene and the collection of images used to reconstruct it, our method uses an image-conditioned diffusion model (InstructPix2Pix) to iteratively edit the input images while optimizing the underlying scene, resulting in an optimized 3D scene that respects the edit instruction. We demonstrate that our proposed method is able to edit large-scale, real-world scenes, and is able to accomplish more realistic, targeted edits than prior work.
Point2Mask: Point-supervised Panoptic Segmentation via Optimal Transport
Authors: Wentong Li; Yuqian Yuan; Song Wang; Jianke Zhu; Jianshu Li; Jian Liu; Lei Zhang
Venue: ICCV 2023 (main)

Abstract: Weakly-supervised image segmentation has recently attracted increasing research attentions, aiming to avoid the expensive pixel-wise labeling. In this paper, we present an effective method, namely Point2Mask, to achieve high-quality panoptic prediction using only a single random point annotation per target for training. Specifically, we formulate the panoptic pseudo-mask generation as an Optimal Transport (OT) problem, where each ground-truth (gt) point label and pixel sample are defined as the label supplier and consumer, respectively. The transportation cost is calculated by the introduced task-oriented maps, which focus on the category-wise and instance-wise differences among the various thing and stuff targets. Furthermore, a centroid-based scheme is proposed to set the accurate unit number for each gt point supplier. Hence, the pseudo-mask generation is converted into finding the optimal transport plan at a globally minimal transportation cost, which can be solved via the Sinkhorn-Knopp Iteration. Experimental results on Pascal VOC and COCO demonstrate the promising performance of our proposed Point2Mask approach to point-supervised panoptic segmentation. Source code is available at: https://github.com/LiWentomng/Point2Mask.
Multi-Task Learning with Knowledge Distillation for Dense Prediction
Authors: Yangyang Xu; Yibo Yang; Lefei Zhang
Venue: ICCV 2023 (main)

Abstract: While multi-task learning (MTL) has become an attractive topic, its training usually poses more difficulties than the single-task case. How to successfully apply knowledge distillation into MTL to improve training efficiency and model performance is still a challenging problem. In this paper, we introduce a new knowledge distillation procedure with an alternative match for MTL of dense prediction based on two simple design principles. First, for memory and training efficiency, we use a single strong multi-task model as a teacher during training instead of multiple teachers, as widely adopted in existing studies. Second, we employ a less sensitive Cauchy-Schwarz (CS) divergence instead of the Kullback-Leibler (KL) divergence and propose a CS distillation loss accordingly. With the less sensitive divergence, our knowledge distillation with an alternative match is applied for capturing inter-task and intra-task information between the teacher model and the student model of each task, thereby learning more "dark knowledge" for effective distillation. We conducted extensive experiments on dense prediction datasets, including NYUD-v2 and PASCAL-Context, for multiple vision tasks, such as semantic segmentation, human parts segmentation, depth estimation, surface normal estimation, and boundary detection. The results show that our proposed method decidedly improves model performance and the practical inference efficiency.
What Does a Platypus Look Like? Generating Customized Prompts for Zero-Shot Image Classification
Authors: Sarah Pratt; Ian Covert; Rosanne Liu; Ali Farhadi
Venue: ICCV 2023 (main)

Abstract: Open-vocabulary models are a promising new paradigm for image classification. Unlike traditional classification models, open-vocabulary models classify among any arbitrary set of categories specified with natural language during inference. This natural language, called "prompts", typically consists of a set of hand-written templates (e.g., "a photo of a   ") which are completed with each of the category names. This work introduces a simple method to generate higher accuracy prompts, without relying on any explicit knowledge of the task domain and with far fewer hand-constructed sentences. To achieve this, we combine open-vocabulary models with large language models (LLMs) to create Customized Prompts via Language models (CuPL, pronounced "couple"). In particular, we leverage the knowledge contained in LLMs in order to generate many descriptive sentences that contain important discriminating characteristics of the image categories. This allows the model to place a greater importance on these regions in the image when making predictions. We find that this straightforward and general approach improves accuracy on a range of zero-shot image classification benchmarks, including over one percentage point gain on ImageNet. Finally, this simple baseline requires no additional training and remains completely zero-shot. Code available at https://github.com/sarahpratt/CuPL.
Scene as Occupancy
Authors: Wenwen Tong; Chonghao Sima; Tai Wang; Li Chen; Silei Wu; Hanming Deng; Yi Gu; Lewei Lu; Ping Luo; Dahua Lin; Hongyang Li
Venue: ICCV 2023 (main)

Abstract: Human driver can easily describe the complex traffic scene by visual system. Such an ability of precise perception is essential for driver's planning. To achieve this, a geometry-aware representation that quantizes the physical 3D scene into structured grid map with semantic labels per cell, termed as 3D Occupancy, would be desirable. Compared to the form of bounding box, a key insight behind occupancy is that it could capture the fine-grained details of critical obstacles in the scene, and thereby facilitate subsequent tasks. Prior or concurrent literature mainly concentrate on a single scene completion task, where we might argue that the potential of this occupancy representation might obsess broader impact. In this paper, we propose OccNet, a multi-view vision centric pipeline with a cascade and temporal voxel decoder to reconstruct 3D occupancy. At the core of OccNet is a general occupancy embedding to represent 3D physical world. Such a descriptor could be applied towards a wide span of driving tasks, including detection, segmentation and planning. To validate the effectiveness of this new representation and our proposed algorithm, we propose OpenOcc, the first dense high-quality 3D occupancy benchmark built on top of nuScenes. Empirical experiments show that there are evident performance gain across multiple tasks, e.g., motion planning could witness a collision rate reduction by 15%-58%, demonstrating the superiority of our method.
U-RED: Unsupervised 3D Shape Retrieval and Deformation for Partial Point Clouds
Authors: Yan Di; Chenyangguang Zhang; Ruida Zhang; Fabian Manhardt; Yongzhi Su; Jason Rambach; Didier Stricker; Xiangyang Ji; Federico Tombari
Venue: ICCV 2023 (main)

Abstract: In this paper, we propose U-RED, an Unsupervised shape REtrieval and Deformation pipeline that takes an arbitrary object observation as input, typically captured by RGB images or scans, and jointly retrieves and deforms the geometrically similar CAD models from a pre-established database to tightly match the target. Considering existing methods typically fail to handle noisy partial observations, U-RED is designed to address this issue from two aspects. First, since one partial shape may correspond to multiple potential full shapes, the retrieval method must allow such an ambiguous one-to-many relationship. Thereby U-RED learns to project all possible full shapes of a partial target onto the surface of a unit sphere. Then during inference, each sampling on the sphere will yield a feasible retrieval. Second, since real-world partial observations usually contain noticeable noise, a reliable learned metric that measures the similarity between shapes is necessary for stable retrieval. In U-RED, we design a novel point-wise residual-guided metric that allows noise-robust comparison. Extensive experiments on the synthetic datasets PartNet, ComplementMe and the real-world dataset Scan2CAD demonstrate that U-RED surpasses existing state-of-the-art approaches by 47.3%, 16.7% and 31.6% respectively under Chamfer Distance. Codes and trained models will be released soon.
RFLA: A Stealthy Reflected Light Adversarial Attack in the Physical World
Authors: Donghua Wang; Wen Yao; Tingsong Jiang; Chao Li; Xiaoqian Chen
Venue: ICCV 2023 (main)

Abstract: Physical adversarial attacks against deep neural networks (DNNs) have recently gained increasing attention. The current mainstream physical attacks use printed adversarial patches or camouflage to alter the appearance of the target object. However, these approaches generate conspicuous adversarial patterns that show poor stealthiness. Another physical deployable attack is the optical attack, featuring stealthiness while exhibiting weakly in the daytime with sunlight. In this paper, we propose a novel Reflected Light Attack (RFLA), featuring effective and stealthy in both the digital and physical world, which is implemented by placing the color transparent plastic sheet and a paper cut of a specific shape in front of the mirror to create different colored geometries on the target object. To achieve these goals, we devise a general framework based on the circle to model the reflected light on the target object. Specifically, we optimize a circle (composed of a coordinate and radius) to carry various geometrical shapes determined by the optimized angle. The fill color of the geometry shape and its corresponding transparency are also optimized. We extensively evaluate the effectiveness of RFLA on different datasets and models. Experiment results suggest that the proposed method achieves over 99% success rate on different datasets and models in the digital world. Additionally, we verify the effectiveness of the proposed method in different physical environments by using sunlight or a flashlight.
Nearest Neighbor Guidance for Out-of-Distribution Detection
Authors: Jaewoo Park; Yoon Gyo Jung; Andrew Beng Jin Teoh
Venue: ICCV 2023 (main)

Abstract: Detecting out-of-distribution (OOD) samples are crucial for machine learning models deployed in open-world environments. Classifier-based scores are a standard approach for OOD detection due to their fine-grained detection capability. However, these scores often suffer from overconfidence issues, misclassifying OOD samples distant from the in-distribution region. To address this challenge, we propose a method called Nearest Neighbor Guidance (NNGuide) that guides the classifier-based score to respect the boundary geometry of the data manifold. NNGuide reduces the overconfidence of OOD samples while preserving the fine-grained capability of the classifier-based score. We conduct extensive experiments on ImageNet OOD detection benchmarks under diverse settings, including a scenario where the ID data undergoes natural distribution shift. Our results demonstrate that NNGuide provides a significant performance improvement on the base detection scores, achieving state-of-the-art results on both AUROC, FPR95, and AUPR metrics.
PatchCT: Aligning Patch Set and Label Set with Conditional Transport for Multi-Label Image Classification
Authors: Miaoge Li; Dongsheng Wang; Xinyang Liu; Zequn Zeng; Ruiying Lu; Bo Chen; Mingyuan Zhou
Venue: ICCV 2023 (main)

Abstract: Multi-label image classification is a prediction task that aims to identify more than one label from a given image. This paper considers the semantic consistency of the latent space between the visual patch and linguistic label domains and introduces the conditional transport (CT) theory to bridge the acknowledged gap. While recent cross-modal attention-based studies have attempted to align such two representations and achieved impressive performance, they required carefully-designed alignment modules and extra complex operations in the attention computation. We find that by formulating the multi-label classification as a CT problem, we can exploit the interactions between the image and label efficiently by minimizing the bidirectional CT cost. Specifically, after feeding the images and textual labels into the modality-specific encoders, we view each image as a mixture of patch embeddings and a mixture of label embeddings, which capture the local region features and the class prototypes, respectively. CT is then employed to learn and align those two semantic sets by defining the forward and backward navigators. Importantly, the defined navigators in CT distance model the similarities between patches and labels, which provides an interpretable tool to visualize the learned prototypes. Extensive experiments on three public image benchmarks show that the proposed model consistently outperforms the previous methods.
VI-Net: Boosting Category-level 6D Object Pose Estimation via Learning Decoupled Rotations on the Spherical Representations
Authors: Jiehong Lin; Zewei Wei; Yabin Zhang; Kui Jia
Venue: ICCV 2023 (main)

Abstract: Rotation estimation of high precision from an RGB-D object observation is a huge challenge in 6D object pose estimation, due to the difficulty of learning in the non-linear space of SO(3). In this paper, we propose a novel rotation estimation network, termed as VI-Net, to make the task easier by decoupling the rotation as the combination of a viewpoint rotation and an in-plane rotation. More specifically, VI-Net bases the feature learning on the sphere with two individual branches for the estimates of two factorized rotations, where a V-Branch is employed to learn the viewpoint rotation via binary classification on the spherical signals, while another I-Branch is used to estimate the in-plane rotation by transforming the signals to view from the zenith direction. To process the spherical signals, a Spherical Feature Pyramid Network is constructed based on a novel design of SPAtial Spherical Convolution (SPA-SConv), which settles the boundary problem of spherical signals via feature padding and realizesviewpoint-equivariant feature extraction by symmetric convolutional operations. We apply the proposed VI-Net to the challenging task of category-level 6D object pose estimation for predicting the poses of unknown objects without available CAD models; experiments on the benchmarking datasets confirm the efficacy of our method, which outperforms the existing ones with a large margin in the regime of high precision.
ICD-Face: Intra-class Compactness Distillation for Face Recognition
Authors: Zhipeng Yu; Jiaheng Liu; Haoyu Qin; Yichao Wu; Kun Hu; Jiayi Tian; Ding Liang
Venue: ICCV 2023 (main)

Abstract: Knowledge distillation is an effective model compression method to improve the performance of a lightweight student model by transferring the knowledge of a well-performed teacher model, which has been widely adopted in many computer vision tasks, including face recognition (FR). The current FR distillation methods usually utilize the Feature Consistency Distillation (FCD) (e.g., L2 distance) on the learned embeddings extracted by the teacher and student models. However, after using FCD, we observe that the intra-class similarities of the student model
 are lower than the intra-class similarities of the teacher model a lot. Therefore, we propose an effective FR distillation method called ICD-Face by introducing intra-class compactness distillation into the existing distillation framework. Specifically, in ICD-Face, we first propose to calculate the similarity distributions of the teacher and student models, where the feature banks are introduced to construct
 sufficient and high-quality positive pairs. Then, we estimate the probability distributions of the teacher and student models and introduce the Similarity Distribution Consistency (SDC) loss to improve the intra-class compactness of
 the student model. Extensive experimental results on multiple benchmark datasets demonstrate the effectiveness of our proposed ICD-Face for face recognition.