mtybilly/Instruct2DS huggingface dataset:
Learning Visual Prompt for Gait Recognition
Authors: Ying Fu, Chunshui Cao, Saihui Hou, Yongzhen Huang, Dezhi Zheng Kang Ma
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Gait a prevalent and complex form of human motion plays a significant role in the field of long-range pedestrian retrieval due to the unique characteristics inherent in individual motion patterns. However gait recognition in real-world scenarios is challenging due to the limitations of capturing comprehensive cross-viewing and cross-clothing data. Additionally distractors such as occlusions directional changes and lingering movements further complicate the problem. The widespread application of deep learning techniques has led to the development of various potential gait recognition methods. However these methods utilize convolutional networks to extract shared information across different views and attire conditions. Once trained the parameters and non-linear function become constrained to fixed patterns limiting their adaptability to various distractors in real-world scenarios. In this paper we present a unified gait recognition framework to extract global motion patterns and develop a novel dynamic transformer to generate representative gait features. Specifically we develop a trainable part-based prompt pool with numerous key-value pairs that can dynamically select prompt templates to incorporate into the gait sequence thereby providing task-relevant shared knowledge information. Furthermore we specifically design dynamic attention to extract robust motion patterns and address the length generalization issue. Extensive experiments on four widely recognized gait datasets i.e. Gait3D GREW OUMVLP and CASIA-B reveal that the proposed method yields substantial improvements compared to current state-of-the-art approaches.
MLP Can Be A Good Transformer Learner
Authors: Pumeng Lyu, Dongrui Liu, Tao Tang, Xiaodan Liang, Andy Song, Xiaojun Chang Sihao Lin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Self-attention mechanism is the key of the Transformer but often criticized for its computation demands. Previous token pruning works motivate their methods from the view of computation redundancy but still need to load the full network and require same memory costs. This paper introduces a novel strategy that simplifies vision transformers and reduces computational load through the selective removal of non-essential attention layers guided by entropy considerations. We identify that regarding the attention layer in bottom blocks their subsequent MLP layers i.e. two feed-forward layers can elicit the same entropy quantity. Meanwhile the accompanied MLPs are under-exploited since they exhibit smaller feature entropy compared to those MLPs in the top blocks. Therefore we propose to integrate the uninformative attention layers into their subsequent counterparts by degenerating them into identical mapping yielding only MLP in certain transformer blocks. Experimental results on ImageNet-1k show that the proposed method can remove 40% attention layer of DeiT-B improving throughput and memory bound without performance compromise.
GraphDreamer: Compositional 3D Scene Synthesis from Scene Graphs
Authors: Weiyang Liu, Anpei Chen, Andreas Geiger, Bernhard Schölkopf Gege Gao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: As pretrained text-to-image diffusion models become increasingly powerful recent efforts have been made to distill knowledge from these text-to-image pretrained models for optimizing a text-guided 3D model. Most of the existing methods generate a holistic 3D model from a plain text input. This can be problematic when the text describes a complex scene with multiple objects because the vectorized text embeddings are inherently unable to capture a complex description with multiple entities and relationships. Holistic 3D modeling of the entire scene further prevents accurate grounding of text entities and concepts. To address this limitation we propose GraphDreamer a novel framework to generate compositional 3D scenes from scene graphs where objects are represented as nodes and their interactions as edges. By exploiting node and edge information in scene graphs our method makes better use of the pretrained text-to-image diffusion model and is able to fully disentangle different objects without image-level supervision. To facilitate modeling of object-wise relationships we use signed distance fields as representation and impose a constraint to avoid inter-penetration of objects. To avoid manual scene graph creation we design a text prompt for ChatGPT to generate scene graphs based on text inputs. We conduct both qualitative and quantitative experiments to validate the effectiveness of GraphDreamer in generating high-fidelity compositional 3D scenes with disentangled object entities.
Visual-Augmented Dynamic Semantic Prototype for Generative Zero-Shot Learning
Authors: Shiming Chen, Shuhuang Chen, Ziming Hong, Yan Wang, Xuetao Feng, Salman Khan, Fahad Shahbaz Khan, Xinge You Wenjin Hou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Generative Zero-shot learning (ZSL) learns a generator to synthesize visual samples for unseen classes which is an effective way to advance ZSL. However existing generative methods rely on the conditions of Gaussian noise and the predefined semantic prototype which limit the generator only optimized on specific seen classes rather than characterizing each visual instance resulting in poor generalizations (e.g. overfitting to seen classes). To address this issue we propose a novel Visual-Augmented Dynamic Semantic prototype method (termed VADS) to boost the generator to learn accurate semantic-visual mapping by fully exploiting the visual-augmented knowledge into semantic conditions. In detail VADS consists of two modules: (1) Visual-aware Domain Knowledge Learning module (VDKL) learns the local bias and global prior of the visual features (referred to as domain visual knowledge) which replace pure Gaussian noise to provide richer prior noise information; (2) Vision-Oriented Semantic Updation module (VOSU) updates the semantic prototype according to the visual representations of the samples. Ultimately we concatenate their output as a dynamic semantic prototype which serves as the condition of the generator. Extensive experiments demonstrate that our VADS achieves superior CZSL and GZSL performances on three prominent datasets and outperforms other state-of-the-art methods with averaging increases by 6.4% 5.9% and 4.2% on SUN CUB and AWA2 respectively.
Dynamic Prompt Optimizing for Text-to-Image Generation
Authors: Tianyu Zhang, Yalong Bai, Bing Su, Ji-Rong Wen, Qing Yang Wenyi Mo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Text-to-image generative models specifically those based on diffusion models like Imagen and Stable Diffusion have made substantial advancements. Recently there has been a surge of interest in the delicate refinement of text prompts. Users assign weights or alter the injection time steps of certain words in the text prompts to improve the quality of generated images. However the success of fine-control prompts depends on the accuracy of the text prompts and the careful selection of weights and time steps which requires significant manual intervention. To address this we introduce the Prompt Auto-Editing (PAE) method. Besides refining the original prompts for image generation we further employ an online reinforcement learning strategy to explore the weights and injection time steps of each word leading to the dynamic fine-control prompts. The reward function during training encourages the model to consider aesthetic score semantic consistency and user preferences. Experimental results demonstrate that our proposed method effectively improves the original prompts generating visually more appealing images while maintaining semantic alignment. Code is available at \href https://github.com/Mowenyii/PAE  this https URL .
SC-GS: Sparse-Controlled Gaussian Splatting for Editable Dynamic Scenes
Authors: Yang-Tian Sun, Ziyi Yang, Xiaoyang Lyu, Yan-Pei Cao, Xiaojuan Qi Yi-Hua Huang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Novel view synthesis for dynamic scenes is still a challenging problem in computer vision and graphics. Recently Gaussian splatting has emerged as a robust technique to represent static scenes and enable high-quality and real-time novel view synthesis. Building upon this technique we propose a new representation that explicitly decomposes the motion and appearance of dynamic scenes into sparse control points and dense Gaussians respectively. Our key idea is to use sparse control points significantly fewer in number than the Gaussians to learn compact 6 DoF transformation bases which can be locally interpolated through learned interpolation weights to yield the motion field of 3D Gaussians. We employ a deformation MLP to predict time-varying 6 DoF transformations for each control point which reduces learning complexities enhances learning abilities and facilitates obtaining temporal and spatial coherent motion patterns. Then we jointly learn the 3D Gaussians the canonical space locations of control points and the deformation MLP to reconstruct the appearance geometry and dynamics of 3D scenes. During learning the location and number of control points are adaptively adjusted to accommodate varying motion complexities in different regions and an ARAP loss following the principle of as rigid as possible is developed to enforce spatial continuity and local rigidity of learned motions. Finally thanks to the explicit sparse motion representation and its decomposition from appearance our method can enable user-controlled motion editing while retaining high-fidelity appearances. Extensive experiments demonstrate that our approach outperforms existing approaches on novel view synthesis with a high rendering speed and enables novel appearance-preserved motion editing applications.
360Loc: A Dataset and Benchmark for Omnidirectional Visual Localization with Cross-device Queries
Authors: Changkun Liu, Yipeng Zhu, Hui Cheng, Tristan Braud, Sai-Kit Yeung Huajian Huang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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/.
Domain Gap Embeddings for Generative Dataset Augmentation
Authors: Younjoon Chung, Chen Henry Wu, Fernando De la Torre Yinong Oliver Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The performance of deep learning models is intrinsically tied to the quality volume and relevance of their training data. Gathering ample data for production scenarios often demands significant time and resources. Among various strategies data augmentation circumvents exhaustive data collection by generating new data points from existing ones. However traditional augmentation techniques can be less effective amidst a shift in training and testing distributions. This paper explores the potential of synthetic data by leveraging large pre-trained models for data augmentation especially when confronted with distribution shifts. Although recent advancements in generative models have enabled several prior works in cross-distribution data generation they require model fine-tuning and a complex setup. To bypass these shortcomings we introduce Domain Gap Embeddings (DoGE) a plug-and-play semantic data augmentation framework in a cross-distribution few-shot setting. Our method extracts disparities between source and desired data distributions in a latent form and subsequently steers a generative process to supplement the training set with endless diverse synthetic samples. Our evaluations conducted on a subpopulation shift and three domain adaptation scenarios under a few-shot paradigm reveal that our versatile method improves performance across tasks without needing hands-on intervention or intricate fine-tuning. DoGE paves the way to effortlessly generate realistic controllable synthetic datasets following the test distributions bolstering real-world efficacy for downstream task models.
Geometrically-driven Aggregation for Zero-shot 3D Point Cloud Understanding
Authors: Luigi Riz, Yiming Wang, Fabio Poiesi Guofeng Mei
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
Learning to Rank Patches for Unbiased Image Redundancy Reduction
Authors: Zhineng Chen, Peng Zhou, Zuxuan Wu, Xieping Gao, Yu-Gang Jiang Yang Luo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Images suffer from heavy spatial redundancy because pixels in neighboring regions are spatially correlated. Existing approaches strive to overcome this limitation by reducing less meaningful image regions. However current leading methods rely on supervisory signals. They may compel models to preserve content that aligns with labeled categories and discard content belonging to unlabeled categories. This categorical inductive bias makes these methods less effective in real-world scenarios. To address this issue we propose a self-supervised framework for image redundancy reduction called Learning to Rank Patches (LTRP). We observe that image reconstruction of masked image modeling models is sensitive to the removal of visible patches when the masking ratio is high (e.g. 90%). Building upon it we implement LTRP via two steps: inferring the semantic density score of each patch by quantifying variation between reconstructions with and without this patch and learning to rank the patches with the pseudo score. The entire process is self-supervised thus getting out of the dilemma of categorical inductive bias. We design extensive experiments on different datasets and tasks. The results demonstrate that LTRP outperforms both supervised and other self-supervised methods due to the fair assessment of image content.
Going Beyond Multi-Task Dense Prediction with Synergy Embedding Models
Authors: Yawen Huang, Lanfen Lin, Ruofeng Tong, Yen-Wei Chen, Hao Zheng, Yuexiang Li, Yefeng Zheng Huimin Huang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Multi-task visual scene understanding aims to leverage the relationships among a set of correlated tasks which are solved simultaneously by embedding them within a uni- fied network. However most existing methods give rise to two primary concerns from a task-level perspective: (1) the lack of task-independent correspondences for distinct tasks and (2) the neglect of explicit task-consensual dependencies among various tasks. To address these issues we propose a novel synergy embedding models (SEM) which goes be- yond multi-task dense prediction by leveraging two innova- tive designs: the intra-task hierarchy-adaptive module and the inter-task EM-interactive module. Specifically the con- structed intra-task module incorporates hierarchy-adaptive keys from multiple stages enabling the efficient learning of specialized visual patterns with an optimal trade-off. In ad- dition the developed inter-task module learns interactions from a compact set of mutual bases among various tasks benefiting from the expectation maximization (EM) algo- rithm. Extensive empirical evidence from two public bench- marks NYUD-v2 and PASCAL-Context demonstrates that SEM consistently outperforms state-of-the-art approaches across a range of metrics.
Disentangled Pre-training for Human-Object Interaction Detection
Authors: Xingao Li, Changxing Ding, Xiangmin Xu Zhuolong Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Detecting human-object interaction (HOI) has long been limited by the amount of supervised data available. Recent approaches address this issue by pre-training according to pseudo-labels which align object regions with HOI triplets parsed from image captions. However pseudo-labeling is tricky and noisy making HOI pre-training a complex process. Therefore we propose an efficient disentangled pre-training method for HOI detection (DP-HOI) to address this problem. First DP-HOI utilizes object detection and action recognition datasets to pre-train the detection and interaction decoder layers respectively. Then we arrange these decoder layers so that the pre-training architecture is consistent with the downstream HOI detection task. This facilitates efficient knowledge transfer. Specifically the detection decoder identifies reliable human instances in each action recognition dataset image generates one corresponding query and feeds it into the interaction decoder for verb classification. Next we combine the human instance verb predictions in the same image and impose image-level supervision. The DP-HOI structure can be easily adapted to the HOI detection task enabling effective model parameter initialization. Therefore it significantly enhances the performance of existing HOI detection models on a broad range of rare categories. The code and pre-trained weight are available at https://github.com/xingaoli/DP-HOI.
Light the Night: A Multi-Condition Diffusion Framework for Unpaired Low-Light Enhancement in Autonomous Driving
Authors: Baolu Li, Zhengzhong Tu, Xinyu Liu, Qing Guo, Felix Juefei-Xu, Runsheng Xu, Hongkai Yu Jinlong Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Vision-centric perception systems for autonomous driving have gained considerable attention recently due to their cost-effectiveness and scalability especially compared to LiDAR-based systems. However these systems often struggle in low-light conditions potentially compromising their performance and safety. To address this our paper introduces LightDiff a domain-tailored framework designed to enhance the low-light image quality for autonomous driving applications. Specifically we employ a multi-condition controlled diffusion model. LightDiff works without any human-collected paired data leveraging a dynamic data degradation process instead. It incorporates a novel multi-condition adapter that adaptively controls the input weights from different modalities including depth maps RGB images and text captions to effectively illuminate dark scenes while maintaining context consistency. Furthermore to align the enhanced images with the detection model's knowledge LightDiff employs perception-specific scores as rewards to guide the diffusion training process through reinforcement learning. Extensive experiments on the nuScenes datasets demonstrate that LightDiff can significantly improve the performance of several state-of-the-art 3D detectors in night-time conditions while achieving high visual quality scores highlighting its potential to safeguard autonomous driving.
MetaCloak: Preventing Unauthorized Subject-driven Text-to-image Diffusion-based Synthesis via Meta-learning
Authors: Chenrui Fan, Yutong Dai, Xun Chen, Pan Zhou, Lichao Sun Yixin Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Text-to-image diffusion models allow seamless generation of personalized images from scant reference photos. Yet these tools in the wrong hands can fabricate misleading or harmful content endangering individuals. To address this problem existing poisoning-based approaches perturb user images in an imperceptible way to render them "unlearnable" from malicious uses. We identify two limitations of these defending approaches: i) sub-optimal due to the hand-crafted heuristics for solving the intractable bilevel optimization and ii) lack of robustness against simple data transformations like Gaussian filtering. To solve these challenges we propose MetaCloak which solves the bi-level poisoning problem with a meta-learning framework with an additional transformation sampling process to craft transferable and robust perturbation. Specifically we employ a pool of surrogate diffusion models to craft transferable and model-agnostic perturbation. Furthermore by incorporating an additional transformation process we design a simple denoising-error maximization loss that is sufficient for causing transformation-robust semantic distortion and degradation in a personalized generation. Extensive experiments on the VGGFace2 and CelebA-HQ datasets show that MetaCloak outperforms existing approaches. Notably MetaCloak can successfully fool online training services like Replicate in a black-box manner demonstrating the effectiveness of MetaCloak in real-world scenarios.
Neural Modes: Self-supervised Learning of Nonlinear Modal Subspaces
Authors: Yinwei Du, Stelian Coros, Bernhard Thomaszewski Jiahong Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We propose a self-supervised approach for learning physics-based subspaces for real-time simulation. Existing learning-based methods construct subspaces by approximating pre-defined simulation data in a purely geometric way. However this approach tends to produce high-energy configurations leads to entangled latent space dimensions and generalizes poorly beyond the training set. To overcome these limitations we propose a self-supervised approach that directly minimizes the system's mechanical energy during training. We show that our method leads to learned subspaces that reflect physical equilibrium constraints resolve overfitting issues of previous methods and offer interpretable latent space parameters.
How to Train Neural Field Representations: A Comprehensive Study and Benchmark
Authors: Riccardo Valperga, David Knigge, Miltiadis Kofinas, Phillip Lippe, Jan-Jakob Sonke, Efstratios Gavves Samuele Papa
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
Delving into the Trajectory Long-tail Distribution for Muti-object Tracking
Authors: En Yu, Jinyang Li, Wenbing Tao Sijia Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Multiple Object Tracking (MOT) is a critical area within computer vision with a broad spectrum of practical implementations. Current research has primarily focused on the development of tracking algorithms and enhancement of post-processing techniques. Yet there has been a lack of thorough examination concerning the nature of tracking data it self. In this study we pioneer an exploration into the distribution patterns of tracking data and identify a pronounced long-tail distribution issue within existing MOT datasets. We note a significant imbalance in the distribution of trajectory lengths across different pedestrians a phenomenon we refer to as "pedestrians trajectory long-tail distribution". Addressing this challenge we introduce a bespoke strategy designed to mitigate the effects of this skewed distribution. Specifically we propose two data augmentation strategies including Stationary Camera View Data Augmentation (SVA) and Dynamic Camera View Data Augmentation (DVA) designed for viewpoint states and the Group Softmax (GS) module for Re-ID. SVA is to backtrack and predict the pedestrian trajectory of tail classes and DVA is to use diffusion model to change the background of the scene. GS divides the pedestrians into unrelated groups and performs softmax operation on each group individually. Our proposed strategies can be integrated into numerous existing tracking systems and extensive experimentation validates the efficacy of our method in reducing the influence of long-tail distribution on multi-object tracking performance. The code is available at https://github.com/chen-si-jia/Trajectory-Long-tail-Distribution-for-MOT.
Tri-Modal Motion Retrieval by Learning a Joint Embedding Space
Authors: Shihao Zou, Yuxuan Ge, Zheng Tian Kangning Yin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Text-to-motion tasks have been the focus of recent advancements in the human motion domain. However the performance of text-to-motion tasks have not reached its potential primarily due to the lack of motion datasets and the pronounced gap between the text and motion modalities. To mitigate this challenge we introduce VLMA a novel Video-Language-Motion Alignment method. This approach leverages human-centric videos as an intermediary modality effectively bridging the divide between text and motion. By employing contrastive learning we construct a cohesive embedding space across the three modalities. Furthermore we incorporate a motion reconstruction branch ensuring that the resulting motion remains closely aligned with its original trajectory. Experimental evaluations on the HumanML3D and KIT-ML datasets demonstrate the superiority of our method in comparison to existing approaches. Furthermore we introduce a novel task termed video-to-motion retrieval designed to facilitate the seamlessxt eraction of corresponding 3D motions from an RGB video. Supplementary experiments demonstrate that our model is extensible to real-world human-centric videos offering a valuable complement to the pose estimation task.
Seg2Reg: Differentiable 2D Segmentation to 1D Regression Rendering for 360 Room Layout Reconstruction
Authors: Wei-En Tai, Yu-Lin Shih, Kuan-Wei Chen, Yong-Jing Syu, Kent Selwyn The, Yu-Chiang Frank Wang, Hwann-Tzong Chen Cheng Sun
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: State-of-the-art single-view 360 room layout reconstruction methods formulate the problem as a high-level 1D (per-column) regression task. On the other hand traditional low-level 2D layout segmentation is simpler to learn and can represent occluded regions but it requires complex post-processing for the targeting layout polygon and sacrifices accuracy. We present Seg2Reg to render 1D layout depth regression from the 2D segmentation map in a differentiable and occlusion-aware way marrying the merits of both sides. Specifically our model predicts floor-plan density for the input equirectangular 360 image. Formulating the 2D layout representation as a density field enables us to employ 'flattened' volume rendering to form 1D layout depth regression. In addition we propose a novel 3D warping augmentation on layout to improve generalization. Finally we re-implement recent room layout reconstruction methods into our codebase for benchmarking and explore modern backbones and training techniques to serve as the strong baseline. The code is at https: //PanoLayoutStudio.github.io .
Strong Transferable Adversarial Attacks via Ensembled Asymptotically Normal Distribution Learning
Authors: Rui Wang, Tao Huang, Liping Jing Zhengwei Fang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Strong adversarial examples are crucial for evaluating and enhancing the robustness of deep neural networks. However the performance of popular attacks is usually sensitive for instance to minor image transformations stemming from limited information -- typically only one input example a handful of white-box source models and undefined defense strategies. Hence the crafted adversarial examples are prone to overfit the source model which hampers their transferability to unknown architectures. In this paper we propose an approach named Multiple Asymptotically Normal Distribution Attacks (MultiANDA) which explicitly characterize adversarial perturbations from a learned distribution. Specifically we approximate the posterior distribution over the perturbations by taking advantage of the asymptotic normality property of stochastic gradient ascent (SGA) then employ the deep ensemble strategy as an effective proxy for Bayesian marginalization in this process aiming to estimate a mixture of Gaussians that facilitates a more thorough exploration of the potential optimization space. The approximated posterior essentially describes the stationary distribution of SGA iterations which captures the geometric information around the local optimum. Thus MultiANDA allows drawing an unlimited number of adversarial perturbations for each input and reliably maintains the transferability. Our proposed method outperforms ten state-of-the-art black-box attacks on deep learning models with or without defenses through extensive experiments on seven normally trained and seven defense models.
Spanning Training Progress: Temporal Dual-Depth Scoring (TDDS) for Enhanced Dataset Pruning
Authors: Jiawei Du, Yunsong Li, Weiying Xie, Joey Tianyi Zhou Xin Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Dataset pruning aims to construct a coreset capable of achieving performance comparable to the original full dataset. Most existing dataset pruning methods rely on snapshot-based criteria to identify representative samples often resulting in poor generalization across various pruning and cross-architecture scenarios. Recent studies have addressed this issue by expanding the scope of training dynamics considered including factors such as forgetting event and probability change typically using an averaging approach. However these works struggle to integrate a broader range of training dynamics without overlooking well-generalized samples which may not be sufficiently highlighted in an averaging manner. In this study we propose a novel dataset pruning method termed as Temporal Dual-Depth Scoring (TDDS) to tackle this problem. TDDS utilizes a dual-depth strategy to achieve a balance between incorporating extensive training dynamics and identifying representative samples for dataset pruning. In the first depth we estimate the series of each sample's individual contributions spanning the training progress ensuring comprehensive integration of training dynamics. In the second depth we focus on the variability of the sample-wise contributions identified in the first depth to highlight well-generalized samples. Extensive experiments conducted on CIFAR and ImageNet datasets verify the superiority of TDDS over previous SOTA methods. Specifically on CIFAR-100 our method achieves 54.51% accuracy with only 10% training data surpassing baselines methods by more than 12.69%. Our codes are available at https://github.com/zhangxin-xd/Dataset-Pruning-TDDS.
UniMix: Towards Domain Adaptive and Generalizable LiDAR Semantic Segmentation in Adverse Weather
Authors: Jing Zhang, Zhuo Chen, Shanshan Zhao, Dacheng Tao Haimei Zhao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: LiDAR semantic segmentation (LSS) is a critical task in autonomous driving and has achieved promising progress. However prior LSS methods are conventionally investigated and evaluated on datasets within the same domain in clear weather. The robustness of LSS models in unseen scenes and all weather conditions is crucial for ensuring safety and reliability in real applications. To this end we propose UniMix a universal method that enhances the adaptability and generalizability of LSS models. UniMix first leverages physically valid adverse weather simulation to construct a Bridge Domain which serves to bridge the domain gap between the clear weather scenes and the adverse weather scenes. Then a Universal Mixing operator is defined regarding spatial intensity and semantic distributions to create the intermediate domain with mixed samples from given domains. Integrating the proposed two techniques into a teacher-student framework UniMix efficiently mitigates the domain gap and enables LSS models to learn weather-robust and domain-invariant representations. We devote UniMix to two main setups: 1) unsupervised domain adaption adapting the model from the clear weather source domain to the adverse weather target domain; 2) domain generalization learning a model that generalizes well to unseen scenes in adverse weather. Extensive experiments validate the effectiveness of UniMix across different tasks and datasets all achieving superior performance over state-of-the-art methods. The code will be released.
Visual Delta Generator with Large Multi-modal Models for Semi-supervised Composed Image Retrieval
Authors: Donghyun Kim, Zihang Meng, Dat Huynh, Ser-Nam Lim Young Kyun Jang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Composed Image Retrieval (CIR) is a task that retrieves images similar to a query based on a provided textual modification. Current techniques rely on supervised learning for CIR models using labeled triplets of the <reference image text target image>. These specific triplets are not as commonly available as simple image-text pairs limiting the widespread use of CIR and its scalability. On the other hand zero-shot CIR can be relatively easily trained with image-caption pairs without considering the image-to-image relation but this approach tends to yield lower accuracy. We propose a new semi-supervised CIR approach where we search for a reference and its related target images in auxiliary data and learn our large language model-based Visual Delta Generator (VDG) to generate text describing the visual difference (i.e. visual delta) between the two. VDG equipped with fluent language knowledge and being model agnostic can generate pseudo triplets to boost the performance of CIR models. Our approach significantly improves the existing supervised learning approaches and achieves state-of-the-art results on the CIR benchmarks.
Selective Interpretable and Motion Consistent Privacy Attribute Obfuscation for Action Recognition
Authors: He Zhao, Thomas Pock, Richard P. Wildes Filip Ilic
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Concerns for the privacy of individuals captured in public imagery have led to privacy-preserving action recognition. Existing approaches often suffer from issues arising through obfuscation being applied globally and a lack of interpretability. Global obfuscation hides privacy sensitive regions but also contextual regions important for action recognition. Lack of interpretability erodes trust in these new technologies. We highlight the limitations of current paradigms and propose a solution: Human selected privacy templates that yield interpretability by design an obfuscation scheme that selectively hides attributes and also induces temporal consistency which is important in action recognition. Our approach is architecture agnostic and directly modifies input imagery while existing approaches generally require architecture training. Our approach offers more flexibility as no retraining is required and outperforms alternatives on three widely used datasets.
HiPose: Hierarchical Binary Surface Encoding and Correspondence Pruning for RGB-D 6DoF Object Pose Estimation
Authors: Yongzhi Su, Praveen Nathan, Sandeep Inuganti, Yan Di, Martin Sundermeyer, Fabian Manhardt, Didier Stricker, Jason Rambach, Yu Zhang Yongliang Lin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In this work we present a novel dense-correspondence method for 6DoF object pose estimation from a single RGB-D image. While many existing data-driven methods achieve impressive performance they tend to be time-consuming due to their reliance on rendering-based refinement approaches. To circumvent this limitation we present HiPose which establishes 3D-3D correspondences in a coarse-to-fine manner with a hierarchical binary surface encoding. Unlike previous dense-correspondence methods we estimate the correspondence surface by employing point-to-surface matching and iteratively constricting the surface until it becomes a correspondence point while gradually removing outliers. Extensive experiments on public benchmarks LM-O YCB-V and T-Less demonstrate that our method surpasses all refinement-free methods and is even on par with expensive refinement-based approaches. Crucially our approach is computationally efficient and enables real-time critical applications with high accuracy requirements.
DiffForensics: Leveraging Diffusion Prior to Image Forgery Detection and Localization
Authors: Jiangqun Ni, Yuzhen Lin, Haoyi Deng, Bin Li Zeqin Yu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: As manipulating images may lead to misinterpretation of the visual content addressing the image forgery detection and localization (IFDL) problem has drawn serious public concerns. In this work we propose a simple assumption that the effective forensic method should focus on the mesoscopic properties of images. Based on the assumption a novel two-stage self-supervised framework leveraging the diffusion model for IFDL task i.e. DiffForensics is proposed in this paper. The DiffForensics begins with self-supervised denoising diffusion paradigm equipped with the module of encoder-decoder structure by freezing the pre-trained encoder (e.g. in ADE-20K) to inherit macroscopic features for general image characteristics while encouraging the decoder to learn microscopic feature representation of images enforcing the whole model to focus the mesoscopic representations. The pre-trained model as a prior is then further fine-tuned for IFDL task with the customized Edge Cue Enhancement Module (ECEM) which progressively highlights the boundary features within the manipulated regions thereby refining tampered area localization with better precision. Extensive experiments on several public challenging datasets demonstrate the effectiveness of the proposed method compared with other state-of-the-art methods. The proposed DiffForensics could significantly improve the model's capabilities for both accurate tamper detection and precise tamper localization while concurrently elevating its generalization and robustness.
CoSeR: Bridging Image and Language for Cognitive Super-Resolution
Authors: Wenbo Li, Jianzhuang Liu, Haoyu Chen, Renjing Pei, Xueyi Zou, Youliang Yan, Yujiu Yang Haoze Sun
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Existing super-resolution (SR) models primarily focus on restoring local texture details often neglecting the global semantic information within the scene. This oversight can lead to the omission of crucial semantic details or the introduction of inaccurate textures during the recovery process. In our work we introduce the Cognitive Super-Resolution (CoSeR) framework empowering SR models with the capacity to comprehend low-resolution images. We achieve this by marrying image appearance and language understanding to generate a cognitive embedding which not only activates prior information from large text-to-image diffusion models but also facilitates the generation of high-quality reference images to optimize the SR process. To further improve image fidelity we propose a novel condition injection scheme called "All-in-Attention" consolidating all conditional information into a single module. Consequently our method successfully restores semantically correct and photorealistic details demonstrating state-of-the-art performance across multiple benchmarks. Project page: https://coser-main.github.io/
Geometry-aware Reconstruction and Fusion-refined Rendering for Generalizable Neural Radiance Fields
Authors: Xinyi Ye, Min Shi, Zihao Huang, Zhiyu Pan, Zhan Peng, Zhiguo Cao Tianqi Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Generalizable NeRF aims to synthesize novel views for unseen scenes. Common practices involve constructing variance-based cost volumes for geometry reconstruction and encoding 3D descriptors for decoding novel views. However existing methods show limited generalization ability in challenging conditions due to inaccurate geometry sub-optimal descriptors and decoding strategies. We address these issues point by point. First we find the variance-based cost volume exhibits failure patterns as the features of pixels corresponding to the same point can be inconsistent across different views due to occlusions or reflections. We introduce an Adaptive Cost Aggregation (ACA) approach to amplify the contribution of consistent pixel pairs and suppress inconsistent ones. Unlike previous methods that solely fuse 2D features into descriptors our approach introduces a Spatial-View Aggregator (SVA) to incorporate 3D context into descriptors through spatial and inter-view interaction. When decoding the descriptors we observe the two existing decoding strategies excel in different areas which are complementary. A Consistency-Aware Fusion (CAF) strategy is proposed to leverage the advantages of both. We incorporate the above ACA SVA and CAF into a coarse-to-fine framework termed Geometry-aware Reconstruction and Fusion-refined Rendering (GeFu). GeFu attains state-of-the-art performance across multiple datasets.
Boosting Self-Supervision for Single-View Scene Completion via Knowledge Distillation
Authors: Dominik Muhle, Felix Wimbauer, Daniel Cremers Keonhee Han
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Inferring scene geometry from images via Structure from Motion is a long-standing and fundamental problem in computer vision. While classical approaches and more recently depth map predictions only focus on the visible parts of a scene the task of scene completion aims to reason about geometry even in occluded regions. With the popularity of NeRF implicit representations also became popular for scene completion by predicting so-called density fields. Unlike explicit approaches e.g. voxel-based methods density fields also allow for accurate depth prediction and novel-view synthesis via image-based rendering. In this work we propose to fuse the scene reconstruction from multiple images and distill this knowledge into a more accurate single-view scene reconstruction. To this end we propose MVBTS to fuse density fields from multiple posed images trained fully self-supervised only from image data. Using knowledge distillation we use MVBTS to train a single-view scene completion network via direct supervision called KDBTS. It achieves state-of-the-art performance on occupancy prediction especially in occluded regions.