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Ju-Hyeon Nam, Nur Suriza Syazwany, Su Jung Kim, Sang-Chul Lee

Generalizability in deep neural networks plays a pivotal role in medical image segmentation. However deep learning-based medical image analyses tend to overlook the importance of frequency variance which is critical element for achieving a model that is both modality-agnostic and domain-generalizable. Additionally various models fail to account for the potential information loss that can arise from multi-task learning under deep supervision a factor that can impair the model's representation ability. To address these challenges we propose a Modality-agnostic Domain Generalizable Network (MADGNet) for medical image segmentation which comprises two key components: a Multi-Frequency in Multi-Scale Attention (MFMSA) block and Ensemble Sub-Decoding Module (E-SDM). The MFMSA block refines the process of spatial feature extraction particularly in capturing boundary features by incorporating multi-frequency and multi-scale features thereby offering informative cues for tissue outline and anatomical structures. Moreover we propose E-SDM to mitigate information loss in multi-task learning with deep supervision especially during substantial upsampling from low resolution. We evaluate the segmentation performance of MADGNet across six modalities and fifteen datasets. Through extensive experiments we demonstrate that MADGNet consistently outperforms state-of-the-art models across various modalities showcasing superior segmentation performance. This affirms MADGNet as a robust solution for medical image segmentation that excels in diverse imaging scenarios. Our MADGNet code is available in GitHub Link.

Yuqian Yuan, Wentong Li, Jian Liu, Dongqi Tang, Xinjie Luo, Chi Qin, Lei Zhang, Jianke Zhu

Multimodal large language models (MLLMs) have recently achieved impressive general-purpose vision-language capabilities through visual instruction tuning. However current MLLMs primarily focus on image-level or box-level understanding falling short in achieving fine-grained vision-language alignment at pixel level. Besides the lack of mask-based instruction data limits their advancements. In this paper we propose Osprey a mask-text instruction tuning approach to extend MLLMs by incorporating fine-grained mask regions into language instruction aiming at achieving pixel-wise visual understanding. To achieve this goal we first meticulously curate a mask-based region-text dataset with 724K samples and then design a vision-language model by injecting pixel-level representation into LLM. Specifically Osprey adopts a convolutional CLIP backbone as the vision encoder and employs a mask-aware visual extractor to extract precise visual mask features from high resolution input. Experimental results demonstrate Osprey's superiority in various region understanding tasks showcasing its new capability for pixel-level instruction tuning. In particular Osprey can be integrated with Segment Anything Model (SAM) seamlessly to obtain multi-granularity semantics. The source code dataset and demo can be found at https://github.com/CircleRadon/Osprey.

Yuxin Guo, Siyang Sun, Shuailei Ma, Kecheng Zheng, Xiaoyi Bao, Shijie Ma, Wei Zou, Yun Zheng

Learning joint and coordinated features across modalities is essential for many audio-visual tasks. Existing pre-training methods primarily focus on global information neglecting fine-grained features and positions leading to suboptimal performance in dense prediction tasks. To address this issue we take a further step towards region-aware audio-visual pre-training and propose CrossMAE which excels in Cross-modality interaction and region alignment. Specifically we devise two masked autoencoding (MAE) pretext tasks at both pixel and embedding levels namely Cross-Conditioned Reconstruction and Cross-Embedding Reconstruction. Taking the visual modality as an example (the same goes for audio) in Cross-Conditioned Reconstruction the visual modality reconstructs the input image pixels conditioned on audio Attentive Tokens. As for the more challenging Cross-Embedding Reconstruction unmasked visual tokens reconstruct complete audio features under the guidance of learnable queries implying positional information which effectively enhances the interaction between modalities and exploits fine-grained semantics. Experimental results demonstrate that CrossMAE achieves state-of-the-art performance not only in classification and retrieval but also in dense prediction tasks. Furthermore we dive into the mechanism of modal interaction and region alignment of CrossMAE highlighting the effectiveness of the proposed components.

Eric Slyman, Stefan Lee, Scott Cohen, Kushal Kafle

Recent dataset deduplication techniques have demonstrated that content-aware dataset pruning can dramatically reduce the cost of training Vision-Language Pretrained (VLP) models without significant performance losses compared to training on the original dataset. These results have been based on pruning commonly used image-caption datasets collected from the web -- datasets that are known to harbor harmful social biases that may then be codified in trained models. In this work we evaluate how deduplication affects the prevalence of these biases in the resulting trained models and introduce an easy-to-implement modification to the recent SemDeDup algorithm that can reduce the negative effects that we observe. When examining CLIP-style models trained on deduplicated variants of LAION-400M we find our proposed FairDeDup algorithm consistently leads to improved fairness metrics over SemDeDup on the FairFace and FACET datasets while maintaining zero-shot performance on CLIP benchmarks.

Yiwei Bao, Feng Lu

Annotating gaze is an expensive and time-consuming endeavor requiring costly eye-trackers or complex geometric calibration procedures. Although some eye-based unsupervised gaze representation learning methods have been proposed the quality of gaze representation extracted by these methods degrades severely when the head pose is large. In this paper we present the Multi-View Dual-Encoder (MV-DE) a framework designed to learn gaze representations from unlabeled multi-view face images. Through the proposed Dual-Encoder architecture and the multi-view gaze representation swapping strategy the MV-DE successfully disentangles gaze from general facial information and derives gaze representations closely tied to the subject's eyeball rotation without gaze label. Experimental results illustrate that the gaze representations learned by the MV-DE can be used in downstream tasks including gaze estimation and redirection. Gaze estimation results indicates that the proposed MV-DE displays notably higher robustness to uncontrolled head movements when compared to state-of-the-art (SOTA) unsupervised learning methods.

Hyejin Park, Jeongyeon Hwang, Sunung Mun, Sangdon Park, Jungseul Ok

Test-time adaptation (TTA) has emerged as a promising solution to address performance decay due to unforeseen distribution shifts between training and test data. While recent TTA methods excel in adapting to test data variations such adaptability exposes a model to vulnerability against malicious examples an aspect that has received limited attention. Previous studies have uncovered security vulnerabilities within TTA even when a small proportion of the test batch is maliciously manipulated. In response to the emerging threat we propose median batch normalization (MedBN) leveraging the robustness of the median for statistics estimation within the batch normalization layer during test-time inference. Our method is algorithm-agnostic thus allowing seamless integration with existing TTA frameworks. Our experimental results on benchmark datasets including CIFAR10-C CIFAR100-C and ImageNet-C consistently demonstrate that MedBN outperforms existing approaches in maintaining robust performance across different attack scenarios encompassing both instant and cumulative attacks. Through extensive experiments we show that our approach sustains the performance even in the absence of attacks achieving a practical balance between robustness and performance.

Yunsheng Ma, Can Cui, Xu Cao, Wenqian Ye, Peiran Liu, Juanwu Lu, Amr Abdelraouf, Rohit Gupta, Kyungtae Han, Aniket Bera 等

Autonomous driving (AD) has made significant strides in recent years. However existing frameworks struggle to interpret and execute spontaneous user instructions such as "overtake the car ahead." Large Language Models (LLMs) have demonstrated impressive reasoning capabilities showing potential to bridge this gap. In this paper we present LaMPilot a novel framework that integrates LLMs into AD systems enabling them to follow user instructions by generating code that leverages established functional primitives. We also introduce LaMPilot-Bench the first benchmark dataset specifically designed to quantitatively evaluate the efficacy of language model programs in AD. Adopting the LaMPilot framework we conduct extensive experiments to assess the performance of off-the-shelf LLMs on LaMPilot-Bench. Our results demonstrate the potential of LLMs in handling diverse driving scenarios and following user instructions in driving. To facilitate further research in this area we release our code and data at GitHub.com/PurdueDigitalTwin/LaMPilot.

Yuxi Xiao, Qianqian Wang, Shangzhan Zhang, Nan Xue, Sida Peng, Yujun Shen, Xiaowei Zhou

Recovering dense and long-range pixel motion in videos is a challenging problem. Part of the difficulty arises from the 3D-to-2D projection process leading to occlusions and discontinuities in the 2D motion domain. While 2D motion can be intricate we posit that the underlying 3D motion can often be simple and low-dimensional. In this work we propose to estimate point trajectories in 3D space to mitigate the issues caused by image projection. Our method named SpatialTracker lifts 2D pixels to 3D using monocular depth estimators represents the 3D content of each frame efficiently using a triplane representation and performs iterative updates using a transformer to estimate 3D trajectories. Tracking in 3D allows us to leverage as-rigid-as possible(ARAP) constraints while simultaneously learning a rigidity embedding that clusters pixels into different rigid parts. Extensive evaluation shows that our approach achieves state-of-the-art tracking performance both qualitatively and quantitatively particularly in chal- lenging scenarios such as out-of-plane rotation. And our project page is available at https://henry123-boy.github.io/SpaTracker/.

Yawen Lu, Dongfang Liu, Qifan Wang, Cheng Han, Yiming Cui, Zhiwen Cao, Xueling Zhang, Yingjie Victor Chen, Heng Fan

In this work we introduce ProMotion a unified prototypical transformer-based framework engineered to model fundamental motion tasks. ProMotion offers a range of compelling attributes that set it apart from current task-specific paradigms. 1. We adopt a prototypical perspective establishing a unified paradigm that harmonizes disparate motion learning approaches. This novel paradigm streamlines the architectural design enabling the simultaneous assimilation of diverse motion information. 2. We capitalize on a dual mechanism involving the feature denoiser and the prototypical learner to decipher the intricacies of motion. This approach effectively circumvents the pitfalls of ambiguity in pixel-wise feature matching significantly bolstering the robustness of motion representation. We demonstrate a profound degree of transferability across distinct motion patterns. This inherent versatility reverberates robustly across a comprehensive spectrum of both 2D and 3D downstream tasks. Empirical results demonstrate that outperforms various well-known specialized architectures achieving 0.54 and 0.054 Abs Rel error on the Sintel and KITTI depth datasets 1.04 and 2.01 average endpoint error on the clean and final pass of Sintel flow benchmark and 4.30 F1-all error on the KITTI flow benchmark. For its efficacy we hope our work can catalyze a paradigm shift in universal models in computer vision.

Junwen He, Yifan Wang, Lijun Wang, Huchuan Lu, Jun-Yan He, Jin-Peng Lan, Bin Luo, Xuansong Xie

Multimodal Large Language Model (MLLMs) leverages Large Language Models as a cognitive framework for diverse visual-language tasks. Recent efforts have been made to equip MLLMs with visual perceiving and grounding capabilities. However there still remains a gap in providing fine-grained pixel-level perceptions and extending interactions beyond text-specific inputs. In this work we propose \bf AnyRef a general MLLM model that can generate pixel-wise object perceptions and natural language descriptions from multi-modality references such as texts boxes images or audio. This innovation empowers users with greater flexibility to engage with the model beyond textual and regional prompts without modality-specific designs. Through our proposed refocusing mechanism the generated grounding output is guided to better focus on the referenced object implicitly incorporating additional pixel-level supervision. This simple modification utilizes attention scores generated during the inference of LLM eliminating the need for extra computations while exhibiting performance enhancements in both grounding masks and referring expressions. With only publicly available training data our model achieves state-of-the-art results across multiple benchmarks including diverse modality referring segmentation and region-level referring expression generation. Code and models are available at https://github.com/jwh97nn/AnyRef

Rui Zhu, Yingwei Pan, Yehao Li, Ting Yao, Zhenglong Sun, Tao Mei, Chang Wen Chen

Diffusion Transformer (DiT) has emerged as the new trend of generative diffusion models on image generation. In view of extremely slow convergence in typical DiT recent breakthroughs have been driven by mask strategy that significantly improves the training efficiency of DiT with additional intra-image contextual learning. Despite this progress mask strategy still suffers from two inherent limitations: (a) training-inference discrepancy and (b) fuzzy relations between mask reconstruction & generative diffusion process resulting in sub-optimal training of DiT. In this work we address these limitations by novelly unleashing the self-supervised discrimination knowledge to boost DiT training. Technically we frame our DiT in a teacher-student manner. The teacher-student discriminative pairs are built on the diffusion noises along the same Probability Flow Ordinary Differential Equation (PF-ODE). Instead of applying mask reconstruction loss over both DiT encoder and decoder we decouple DiT encoder and decoder to separately tackle discriminative and generative objectives. In particular by encoding discriminative pairs with student and teacher DiT encoders a new discriminative loss is designed to encourage the inter-image alignment in the self-supervised embedding space. After that student samples are fed into student DiT decoder to perform the typical generative diffusion task. Extensive experiments are conducted on ImageNet dataset and our method achieves a competitive balance between training cost and generative capacity.

Huajian Huang, Longwei Li, Hui Cheng, Sai-Kit Yeung

The integration of neural rendering and the SLAM system recently showed promising results in joint localization and photorealistic view reconstruction. However existing methods fully relying on implicit representations are so resource-hungry that they cannot run on portable devices which deviates from the original intention of SLAM. In this paper we present Photo-SLAM a novel SLAM framework with a hyper primitives map. Specifically we simultaneously exploit explicit geometric features for localization and learn implicit photometric features to represent the texture information of the observed environment. In addition to actively densifying hyper primitives based on geometric features we further introduce a Gaussian-Pyramid-based training method to progressively learn multi-level features enhancing photorealistic mapping performance. The extensive experiments with monocular stereo and RGB-D datasets prove that our proposed system Photo-SLAM significantly outperforms current state-of-the-art SLAM systems for online photorealistic mapping e.g. PSNR is 30% higher and rendering speed is hundreds of times faster in the Replica dataset. Moreover the Photo-SLAM can run at real-time speed using an embedded platform such as Jetson AGX Orin showing the potential of robotics applications. Project Page and code: https://huajianup.github.io/research/Photo-SLAM/.

Silvia Zuffi, Ylva Mellbin, Ci Li, Markus Hoeschle, Hedvig Kjellström, Senya Polikovsky, Elin Hernlund, Michael J. Black

Data-driven three-dimensional parametric shape models of the human body have gained enormous popularity both for the analysis of visual data and for the generation of synthetic humans. Following a similar approach for animals does not scale to the multitude of existing animal species not to mention the difficulty of accessing subjects to scan in 3D. However we argue that for domestic species of great importance like the horse it is a highly valuable investment to put effort into gathering a large dataset of real 3D scans and learn a realistic 3D articulated shape model. We introduce VAREN a novel 3D articulated parametric shape model learned from 3D scans of many real horses. VAREN bridges synthesis and analysis tasks as the generated model instances have unprecedented realism while being able to represent horses of different sizes and shapes. Differently from previous body models VAREN has two resolutions an anatomical skeleton and interpretable learned pose-dependent deformations which are related to the body muscles. We show with experiments that this formulation has superior performance with respect to previous strategies for modeling pose-dependent deformations in the human body case while also being more compact and allowing an analysis of the relationship between articulation and muscle deformation during articulated motion.

Linzhan Mou, Jun-Kun Chen, Yu-Xiong Wang

This paper proposes Instruct 4D-to-4D that achieves 4D awareness and spatial-temporal consistency for 2D diffusion models to generate high-quality instruction-guided dynamic scene editing results. Traditional applications of 2D diffusion models in dynamic scene editing often result in inconsistency primarily due to their inherent frame-by-frame editing methodology. Addressing the complexities of extending instruction-guided editing to 4D our key insight is to treat a 4D scene as a pseudo-3D scene decoupled into two sub-problems: achieving temporal consistency in video editing and applying these edits to the pseudo-3D scene. Following this we first enhance the Instruct-Pix2Pix (IP2P) model with an anchor-aware attention module for batch processing and consistent editing. Additionally we integrate optical flow-guided appearance propagation in a sliding window fashion for more precise frame-to-frame editing and incorporate depth-based projection to manage the extensive data of pseudo-3D scenes followed by iterative editing to achieve convergence. We extensively evaluate our approach in various scenes and editing instructions and demonstrate that it achieves spatially and temporally consistent editing results with significantly enhanced detail and sharpness over the prior art. Notably Instruct 4D-to-4D is general and applicable to both monocular and challenging multi-camera scenes.

Dongsu Zhang, Francis Williams, Zan Gojcic, Karsten Kreis, Sanja Fidler, Young Min Kim, Amlan Kar

We aim to generate fine-grained 3D geometry from large-scale sparse LiDAR scans abundantly captured by autonomous vehicles (AV). Contrary to prior work on AV scene completion we aim to extrapolate fine geometry from unlabeled and beyond spatial limits of LiDAR scans taking a step towards generating realistic high-resolution simulation-ready 3D street environments. We propose hierarchical Generative Cellular Automata (hGCA) a spatially scalable conditional 3D generative model which grows geometry recursively with local kernels following GCAs in a coarse-to-fine manner equipped with a light-weight planner to induce global consistency. Experiments on synthetic scenes show that hGCA generates plausible scene geometry with higher fidelity and completeness compared to state-of-the-art baselines. Our model generalizes strongly from sim-to-real qualitatively outperforming baselines on the Waymo-open dataset. We also show anecdotal evidence of the ability to create novel objects from real-world geometric cues even when trained on limited synthetic content.

Ximiao Zhang, Min Xu, Xiuzhuang Zhou

Self-supervised feature reconstruction methods have shown promising advances in industrial image anomaly detection and localization. Despite this progress these methods still face challenges in synthesizing realistic and diverse anomaly samples as well as addressing the feature redundancy and pre-training bias of pre-trained feature. In this work we introduce RealNet a feature reconstruction network with realistic synthetic anomaly and adaptive feature selection. It is incorporated with three key innovations: First we propose Strength-controllable Diffusion Anomaly Synthesis (SDAS) a diffusion process-based synthesis strategy capable of generating samples with varying anomaly strengths that mimic the distribution of real anomalous samples. Second we develop Anomaly-aware Features Selection (AFS) a method for selecting representative and discriminative pre-trained feature subsets to improve anomaly detection performance while controlling computational costs. Third we introduce Reconstruction Residuals Selection (RRS) a strategy that adaptively selects discriminative residuals for comprehensive identification of anomalous regions across multiple levels of granularity. We assess RealNet on four benchmark datasets and our results demonstrate significant improvements in both Image AUROC and Pixel AUROC compared to the current state-of-the-art methods. The code data and models are available at https://github.com/cnulab/RealNet.

Philipp Schröppel, Christopher Wewer, Jan Eric Lenssen, Eddy Ilg, Thomas Brox

Controllable generation of 3D assets is important for many practical applications like content creation in movies games and engineering as well as in AR/VR. Recently diffusion models have shown remarkable results in generation quality of 3D objects. However none of the existing models enable disentangled generation to control the shape and appearance separately. For the first time we present a suitable representation for 3D diffusion models to enable such disentanglement by introducing a hybrid point cloud and neural radiance field approach. We model a diffusion process over point positions jointly with a high-dimensional feature space for a local density and radiance decoder. While the point positions represent the coarse shape of the object the point features allow modeling the geometry and appearance details. This disentanglement enables us to sample both independently and therefore to control both separately. Our approach sets a new state of the art in generation compared to previous disentanglement-capable methods by reduced FID scores of 30-90% and is on-par with other non-disentanglement-capable state-of-the art methods.

Hyelin Nam, Gihyun Kwon, Geon Yeong Park, Jong Chul Ye

With the remarkable advent of text-to-image diffusion models image editing methods have become more diverse and continue to evolve. A promising recent approach in this realm is Delta Denoising Score (DDS) - an image editing technique based on Score Distillation Sampling (SDS) framework that leverages the rich generative prior of text-to-image diffusion models. However relying solely on the difference between scoring functions is insufficient for preserving specific structural elements from the original image a crucial aspect of image editing. To address this here we present an embarrassingly simple yet very powerful modification of DDS called Contrastive Denoising Score (CDS) for latent diffusion models (LDM). Inspired by the similarities and differences between DDS and the contrastive learning for unpaired image-to-image translation(CUT) we introduce a straightforward approach using CUT loss within the DDS framework. Rather than employing auxiliary networks as in the original CUT approach we leverage the intermediate features of LDM specifically those from the self-attention layers which possesses rich spatial information. Our approach enables zero-shot image-to-image translation and neural radiance field (NeRF) editing achieving structural correspondence between the input and output while maintaining content controllability. Qualitative results and comparisons demonstrates the effectiveness of our proposed method.

Lizhe Liu, Bohua Wang, Hongwei Xie, Daqi Liu, Li Liu, Zhiqiang Tian, Kuiyuan Yang, Bing Wang

Vision-centric 3D environment understanding is both vital and challenging for autonomous driving systems. Recently object-free methods have attracted considerable attention. Such methods perceive the world by predicting the semantics of discrete voxel grids but fail to construct continuous and accurate obstacle surfaces. To this end in this paper we propose SurroundSDF to implicitly predict the signed distance field (SDF) and semantic field for the continuous perception from surround images. Specifically we introduce a query-based approach and utilize SDF constrained by the Eikonal formulation to accurately describe the surfaces of obstacles. Furthermore considering the absence of precise SDF ground truth we propose a novel weakly supervised paradigm for SDF referred to as the Sandwich Eikonal formulation which emphasizes applying correct and dense constraints on both sides of the surface thereby enhancing the perceptual accuracy of the surface. Experiments suggest that our method achieves SOTA for both occupancy prediction and 3D scene reconstruction tasks on the nuScenes dataset.

Sungjune Kim, Hyung-gun Chi, Hyerin Lim, Karthik Ramani, Jinkyu Kim, Sangpil Kim

Social relations have substantial impacts on the potential trajectories of each individual. Modeling these dynamics has been a central solution for more precise and accurate trajectory forecasting. However previous works ignore the importance of `social depth' meaning the influences flowing from different degrees of social relations. In this work we propose HighGraph a graph-based pedestrian relational reasoning method that captures the higher-order dynamics of social interactions. First we construct a collision-aware relation graph based on the agents' observed trajectories. Upon this graph structure we build our core module that aggregates the agent features from diverse social distances. As a result the network is able to model complex social relations thereby yielding more accurate and socially acceptable trajectories. Our HighGraph is a plug-and-play module that can be easily applied to any current trajectory predictors. Extensive experiments with ETH/UCY and SDD datasets demonstrate that our HighGraph noticeably improves the previous state-of-the-art baselines both quantitatively and qualitatively.