Training robot policies within a learned world model is trending due to the inefficiency of real-world interactions. The established image-based world models and policies have shown prior success, but lack robust geometric information that requires consistent spatial and physical understanding of the three-dimensional world, even pre-trained on internet-scale video sources. To this end, we propose a novel branch of world model named Gaussian World Model (GWM) for robotic manipulation, which reconstructs the future state by inferring the propagation of Gaussian primitives under the effect of robot actions. At its core is a latent Diffusion Transformer (DiT) combined with a 3D variational autoencoder, enabling fine-grained scene-level future state reconstruction with Gaussian Splatting. GWM can not only enhance the visual representation for imitation learning agent by self-supervised future prediction training, but can serve as a neural simulator that supports model-based reinforcement learning. Both simulated and real-world experiments depict that GWM can precisely predict future scenes conditioned on diverse robot actions, and can be further utilized to train policies that outperform the state-of-the-art by impressive margins, showcasing the initial data scaling potential of 3D world model.
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Humans are undoubtedly the most important participants in computer vision, and the ability to detect any individual given a natural language description, a task we define as referring to any person, holds substantial practical value. However, we find that existing models generally fail to achieve real-world usability, and current benchmarks are limited by their focus on one-to-one referring, that hinder progress in this area. In this work, we revisit this task from three critical perspectives: task definition, dataset design, and model architecture. We first identify five aspects of referable entities and three distinctive characteristics of this task. Next, we introduce HumanRef, a novel dataset designed to tackle these challenges and better reflect real-world applications. From a model design perspective, we integrate a multimodal large language model with an object detection framework, constructing a robust referring model named RexSeek. Experimental results reveal that state-of-the-art models, which perform well on commonly used benchmarks like RefCOCO/+/g, struggle with HumanRef due to their inability to detect multiple individuals. In contrast, RexSeek not only excels in human referring but also generalizes effectively to common object referring, making it broadly applicable across various perception tasks.
SMP-Attack: Boosting the Transferability of Feature Importance-based Adversarial Attack with Semantics-aware Multi-granularity Patchout
PDF ↗Transfer-based attacks pose a significant security threat to deep neural networks (DNNs), due to their strong performance on unseen models in real-world black-box scenarios. Building on this, feature importance-based attacks further improve the transferability of adversarial examples by effectively suppressing model-specific feature patterns. However, existing methods primarily focus on single-granularity patch and single-stage training, leading to suboptimal solutions. To address these limitations, we propose a general multi-stage optimization framework based on Semantics-aware Multi-granularity Patchout, dubbed as SMP-Attack. Compared to the non-deformable/regular patch definition, we incorporate multi-granularity into the generation process of deformable/irregular patches, thereby enhancing the quality of the computed aggregate gradient. In contrast to conventional joint optimization of multi-layer losses, we introduce an effective multi-stage training strategy that systematically explores significant model-agnostic features from shallow to intermediate layers. Employing the ImageNet dataset, we conduct extensive experiments on undefended/defended CNNs and ViTs, which unequivocally demonstrate the superior performance of our proposed SMP-Attack over current state-of-the-art methods in black-box scenarios. Furthermore, we assess the compatibility of our multi-stage optimization, which supersedes single-stage training employed in existing feature-based methods, culminating in substantial performance improvement. Code is available at https://github.com/AdvML-Group/SMP-Attack.
Symmetry is a fundamental concept that has been extensively studied, yet detecting it in complex scenes remains a significant challenge in computer vision. Recent heatmap-based approaches can localize potential regions of symmetry axes but often lack precision in identifying individual axes. In this work, we propose a novel framework for axis-level detection of the two most common symmetry types--reflection and rotation--by representing them as explicit geometric primitives, i.e., lines and points. Our method employs a dual-branch architecture that is equivariant to the dihedral group, with each branch specialized to exploit the structure of dihedral group-equivariant features for its respective symmetry type. For reflection symmetry, we introduce \orientational anchors, aligned with group components, to enable orientation-specific detection, and a reflectional matching that measures similarity between patterns and their mirrored counterparts across candidate axes. For rotational symmetry, we propose a rotational matching that compares patterns at fixed angular intervals to identify rotational centers. Extensive experiments demonstrate that our method achieves state-of-the-art performance, outperforming existing approaches.
Recent advancements in multimodal foundation models have yielded significant progress in vision-language understanding. Initial attempts have also explored the potential of multimodal large language models for visual content generation. However, existing approaches face a trade-off between generation diversity and controllability, struggling to meet the varying granularity demands of different image generation tasks within a unified MLLM framework. In this work, we propose PUMA, emPowering Unified MLLM with Multi-grAnular visual generation, a novel paradigm that tackles the diversity-controllability trade-off. PUMA achieves this by unifying multi-granular visual features as both inputs and outputs of MLLMs, thus effectively meeting the distinct granularity needs for diverse generation and precise manipulation within a single framework. Following multimodal pretraining and instruction tuning, PUMA demonstrates remarkable capabilities in a wide range of multimodal tasks, including image understanding, diverse text-to-image generation, editing, inpainting, colorization, and conditional generation. This work marks a significant stride towards realizing truly unified MLLMs capable of seamlessly adapting to the diverse granularity demands and task requirements inherent in various visual tasks. The code and model will be released upon acceptance.
In video Multimodal Large Language Models (video MLLMs), the visual encapsulation process plays a pivotal role in converting video contents into representative tokens for LLM input. While linear projectors are widely employed for encapsulation, they introduce semantic indistinctness and temporal incoherence when applied to videos. Conversely, the structure of resamplers shows promise in tackling these challenges, but an effective solution remains unexplored. Drawing inspiration from resampler structures, we introduce DisCo, a novel visual encapsulation method designed to yield semantically distinct and temporally coherent visual tokens for video MLLMs. DisCo integrates two key components: (1) A Visual Concept Discriminator (VCD) module, assigning unique semantics for visual tokens by associating them in pair with discriminative concepts in the video. (2) A Temporal Focus Calibrator (TFC) module, ensuring consistent temporal focus of visual tokens to video elements across every video frame. Through extensive experiments on multiple video MLLM frameworks, we demonstrate that DisCo remarkably outperforms previous state-of-the-art methods across a variety of video understanding benchmarks, while also achieving higher token efficiency thanks to the reduction of semantic indistinctness.
We introduce alignment techniques from reasoning large language models (LLMs) to the task of generating engineering sketch constraints in computer-aided design (CAD) models. Engineering sketches are composed of geometric primitives (such as points and lines) connected by constraints (such as perpendicularity and tangency) that define their relationships. For a design to remain easily editable, these constraints must accurately capture design intent, ensuring that geometry updates predictably as parameters change. While current methods can generate CAD designs, aligning model outputs with design intent--what we call "design alignment"--remains an open challenge. A crucial first step is to generate constraints that fully constrain all geometric primitives without over-constraining or distorting the sketch geometry. By training an existing constraint generation model with alignment techniques and feedback from a constraint solver, we achieve a 93% rate of fully-constrained sketches, compared to 34% using a naive supervised fine-tuning (SFT) baseline and only 8.9% without SFT. Our approach is model-agnostic and paves the way for further research bridging alignment strategies between language and design domains.
Diffusion models (DMs) have achieved state-of-the-art generative performance but suffer from high sampling latency due to their sequential denoising nature. Existing solver-based acceleration methods often face image quality degradation under a low-latency budget. In this paper, we propose the Ensemble Parallel Direction solver (dubbed as EPD-Solver), a novel ODE solver that mitigates truncation errors by incorporating multiple parallel gradient evaluations in each ODE step. Importantly, since the additional gradient computations are independent, they can be fully parallelized, preserving low-latency sampling. Our method optimizes a small set of learnable parameters in a distillation fashion, ensuring minimal training overhead. In addition, our method can serve as a plugin to improve existing ODE samplers. Extensive experiments on various image synthesis benchmarks demonstrate the effectiveness of our EPD-Solver in achieving high-quality and low-latency sampling. For example, at the same latency level of 5 NFE, EPD achieves an FID of 4.47 on CIFAR-10, 7.97 on FFHQ, 8.17 on ImageNet, and 8.26 on LSUN Bedroom, surpassing existing learning-based solvers by a significant margin. Codes are available in https://github.com/BeierZhu/EPD.
Multi-modal object tracking has emerged as a significant research focus in computer vision due to its robustness in complex environments, such as exposure variations, blur, and occlusions. Despite existing studies integrating supplementary modal information into pre-trained RGB trackers through visual prompt mechanisms, this approach exhibits a critical limitation: it inherently prioritizes RGB information as the dominant modality, thereby underutilizing the complementary information of alternative modalities. To address this fundamental limitation, we present SMSTracker, an innovative tri-path score mask sigma fusion framework for multi-modal tracking, including three key modules. Firstly, we design a tri-path Score Mask Fusion (SMF) module to evaluate and quantify the reliability of each modality, allowing optimal exploitation of complementary features between modalities. Secondly, we introduce a pioneering Sigma Interaction (SGI) module to facilitate a sophisticated fusion of modal features across tri-branches. Furthermore, we advance a Drop Key Fine-tuning (DKF) strategy to address the inherent challenge of unequal data contribution in multi-modal learning scenarios, thereby enhancing the model's capacity for comprehensive multi-modal information processing. Finally, extensive experiments on RGB+Thermal, RGB+Depth, and RGB+Event datasets demonstrate the significant performance improvements achieved by SMSTracker over existing state-of-the-art methods. Code and model are available at https://github.com/Leezed525/SMSTracker.
Hierarchy UGP: Hierarchy Unified Gaussian Primitive for Large-Scale Dynamic Scene Reconstruction
PDF ↗Recent advances in differentiable rendering have significantly improved dynamic street scene reconstruction. However, the complexity of large-scale scenarios and dynamic elements, such as vehicles and pedestrians, remains a substantial challenge. Existing methods often struggle to scale to large scenes or accurately model arbitrary dynamics. To address these limitations, we propose Hierarchy UGP, which constructs a hierarchical structure consisting of a root level, sub-scenes level, and primitive level, using Unified Gaussian Primitive (UGP) defined in 4D space as the representation. The root level serves as the entry point to the hierarchy. At the sub-scenes level, the scene is spatially divided into multiple sub-scenes, with various elements extracted. At the primitive level, each element is modeled with UGPs, and its global pose is controlled by a motion prior related to time. This hierarchical design greatly enhances the model's capacity, enabling it to model large-scale scenes. Additionally, our UGP allows for the reconstruction of both rigid and non-rigid dynamics. We conducted experiments on Dynamic City, our proprietary large-scale dynamic street scene dataset, as well as the public Waymo dataset. Experimental results demonstrate that our method achieves state-of-the-art performance. We plan to release the accompanying code and the Dynamic City dataset as open resources to further research within the community.
Foundation medical segmentation models, with MedSAM being the most popular, have achieved promising performance across organs and lesions. However, MedSAM still suffers from compromised performance on specific lesions with intricate structures and appearance, as well as bounding box prompt-induced perturbations. Although current test-time adaptation (TTA) methods for medical image segmentation may tackle this issue, partial (e.g., batch normalization) or whole parametric updates restrict their effectiveness due to limited update signals or catastrophic forgetting in large models. Meanwhile, these approaches ignore the computational complexity during adaptation, which is particularly significant for modern foundation models. To this end, our theoretical analyses reveal that directly refining image embeddings is feasible to approach the same goal as parametric updates under the MedSAM architecture, which enables us to realize high computational efficiency and segmentation performance without the risk of catastrophic forgetting. Under this framework, we propose to encourage maximizing factorized conditional probabilities of the posterior prediction probability using a proposed distribution-approximated latent conditional random field loss combined with an entropy minimization loss. Experiments show that we achieve about 3% Dice score improvements across three datasets while reducing computational complexity by over 7 times.
Multi-face deepfake videos are becoming increasingly prevalent, often appearing in natural social settings that challenge existing detection methods. Most current approaches excel at single-face detection but struggle in multi-face scenarios, due to a lack of awareness of crucial contextual cues. In this work, we develop a novel approach that leverages human cognition to analyze and defend against multi-face deepfake videos. Through a series of human studies, we systematically examine how people detect deepfake faces in social settings. Our quantitative analysis reveals four key cues humans rely on: scene-motion coherence, inter-face appearance compatibility, interpersonal gaze alignment, and face-body consistency. Guided by these insights, we introduce \textsf HICOM , a novel framework designed to detect every fake face in multi-face scenarios. Extensive experiments on benchmark datasets show that \textsf HICOM improves average accuracy by 3.3% in in-dataset detection and 2.8% under real-world perturbations. Moreover, it outperforms existing methods by 5.8% on unseen datasets, demonstrating the generalization of human-inspired cues. \textsf HICOM further enhances interpretability by incorporating an LLM to provide human-readable explanations, making detection results more transparent and convincing. Our work sheds light on involving human factors to enhance defense against deepfakes.
DiffRefine: Diffusion-based Proposal Specific Point Cloud Densification for Cross-Domain Object Detection
PDF ↗The robustness of 3D object detection in large-scale outdoor point clouds degrades significantly when deployed in an unseen environment due to domain shifts. To minimize the domain gap, existing works on domain adaptive detection focuses on several factors, including point density, object shape and sizes, to reduce the false negative detections. However, the adaptation results indicate that there are still remaining challenges. We argue that this is due to the challenge in recognizing comparably less distinctive region on object surface due to sparsity, occlusion, etc. In this work, we aim to reinforce those features by generating points on object surface to make them straightforwardly recognizable. We draw our motivation from a common observation that detection proposals already contain the accurate bounding boxes, but with relatively low objectness score predictions, which lead to false negatives. Given these box proposals, we densify sparse object points with a diffusion approach. As a result, our model DiffRefine can act as a simple additional module before second-stage refinement, where most existing detection models for two-stage detection can use. Experimental results on domain adaptive detection show competitive performance, especially on vanishing points due to distance on various detection architectures.
Low-Rank Adaptation (LoRA) has emerged as a powerful and popular technique for personalization, enabling efficient adaptation of pre-trained image generation models for specific tasks without comprehensive retraining. While employing individual pre-trained LoRA models excels at representing single concepts, such as those representing a specific dog or a cat, utilizing multiple LoRA models to capture a variety of concepts in a single image still poses a significant challenge. Existing methods often fall short, primarily because the attention mechanisms within different LoRA models overlap, leading to scenarios where one concept may be completely ignored (e.g., omitting the dog) or where concepts are incorrectly combined (e.g., producing an image of two cats instead of one cat and one dog). We introduce CLoRA, a training-free approach that addresses these limitations by updating the attention maps of multiple LoRA models at test-time, and leveraging the attention maps to create semantic masks for fusing latent representations. This enables the generation of composite images that accurately reflect the characteristics of each LoRA. Our comprehensive qualitative and quantitative evaluations demonstrate that CLoRA significantly outperforms existing methods in multi-concept image generation using LoRAs.
Academic emotion analysis plays a crucial role in evaluating students' engagement and cognitive states during the learning process. This paper addresses the challenge of automatically recognizing academic emotions through facial expressions in real-world learning environments. While significant progress has been made in facial expression recognition for basic emotions, academic emotion recognition remains underexplored, largely due to the scarcity of publicly available datasets. To bridge this gap, we introduce RAER, a novel dataset comprising approximately 2,700 video clips collected from around 140 students in diverse, natural learning contexts such as classrooms, libraries, laboratories, and dormitories, covering both classroom sessions and individual study. Each clip was annotated independently by approximately ten annotators using two distinct sets of academic emotion labels with varying granularity, enhancing annotation consistency and reliability. To our knowledge, RAER is the first dataset capturing diverse natural learning scenarios. Observing that annotators naturally consider context cues--such as whether a student is looking at a phone or reading a book--alongside facial expressions, we propose CLIP-CAER (CLIP-based Context-aware Academic Emotion Recognition). Our method utilizes learnable text prompts within the vision-language model CLIP to effectively integrate facial expression and context cues from videos. Experimental results demonstrate that CLIP-CAER substantially outperforms state-of-the-art video-based facial expression recognition methods, which are primarily designed for basic emotions, emphasizing the crucial role of context in accurately recognizing academic emotions. Project page: https://zgsfer.github.io/CAER
Zero-shot human-object interaction (HOI) detection remains a challenging task, particularly in generalizing to unseen actions. Existing methods address this challenge by tapping Vision-Language Models (VLMs) to access knowledge beyond the training data. However, they either struggle to distinguish actions involving the same object or demonstrate limited generalization to unseen classes. In this paper, we introduce HOLa (Zero-Shot HOI Detection with Low-Rank Decomposed VLM Feature Adaptation), a novel approach that both enhances generalization to unseen classes and improves action distinction. In training, HOLa decomposes VLM text features for given HOI classes via low-rank factorization, producing class-shared basis features and adaptable weights. These features and weights form a compact HOI representation that preserves shared information across classes, enhancing generalization to unseen classes. Subsequently, we refine action distinction by adapting weights for each HOI class and introducing human-object tokens to enrich visual interaction representations. To further distinguish unseen actions, we guide the weight adaptation with LLM-derived action regularization. Experimental results show that our method sets a new state-of-the-art across zero-shot HOI settings on HICO-DET, achieving an unseen-class mAP of 27.91 in the unseen-verb setting. Our code is available at https://github.com/ChelsieLei/HOLa.
Seeing the Unseen: A Semantic Alignment and Context-Aware Prompt Framework for Open-Vocabulary Camouflaged Object Segmentation
PDF ↗Open-Vocabulary Camouflaged Object Segmentation (OVCOS) aims to segment camouflaged objects of any category based on text descriptions. Despite existing open-vocabulary methods exhibit strong segmentation capabilities, they still have a major limitation in camouflaged scenarios: semantic confusion, which leads to incomplete segmentation and class shift in the model. To mitigate the above limitation, we propose a framework for OVCOS, named SuCLIP. Specifically, we design a context-aware prompt scheme that leverages the internal knowledge of the CLIP visual encoder to enrich the text prompt and align it with local visual features, thereby enhancing the text prompt. To better align the visual semantic space and the text semantic space, we design a class-aware feature selection module to dynamically adjust text and visual embeddings, making them more matched with camouflaged object. Meanwhile, we introduce a semantic consistency loss to mitigate the semantic deviation between the text prompt and visual features, ensuring semantic consistency between the segmentation results and the text prompt. Finally, we design a text query decoder that precisely maps textual semantics to pixel-level segmentation results, thereby achieving semantic-spatial consistent decoding. Experimental results show that SuCLIP significantly outperforms the advanced method OVCoser on the OVCamo dataset.
The use of CLIP embeddings to assess the fidelity of samples produced by text-to-image generative models has been extensively explored in the literature. While the widely adopted CLIPScore, derived from the cosine similarity of text and image embeddings, effectively measures the alignment of a generated image, it does not quantify the diversity of images generated by a text-to-image model. In this work, we extend the application of CLIP embeddings to quantify and interpret the intrinsic diversity of text-to-image models, which are responsible for generating diverse images from similar text prompts, which we refer to as prompt-aware diversity. To achieve this, we propose a decomposition of the CLIP-based kernel covariance matrix of image data into text-based and non-text-based components. Using the Schur complement of the joint image-text kernel covariance matrix, we perform this decomposition and define the matrix-based entropy of the decomposed component as the Schur Complement ENtopy DIversity (Scendi) score, as a measure of the prompt-aware diversity for prompt-guided generative models. Additionally, we discuss the application of the Schur complement-based decomposition to nullify the influence of a given prompt on the CLIP embedding of an image, enabling focus or defocus of the embedded vectors on specific objects. We present several numerical results that apply our proposed Scendi score to evaluate text-to-image and LLM (text-to-text) models. Our numerical results indicate the success of the Scendi score in capturing the intrinsic diversity of prompt-guided generative models. The codebase is available at https://github.com/aziksh-ospanov/scendi-score.
Reconstruction from multi-view images is a fundamental challenge in computer vision that has been extensively studied over the past decades. Recently, neural radiance fields have driven significant advancements, especially through methods using implicit functions and volume rendering, achieving high levels of accuracy. A core component of these methods is the mapping that transforms an implicit function's output into corresponding volume densities. Despite its critical role, this mapping has received limited attention in existing literature. In this paper, we provide a comprehensive and systematic study of mapping functions, examining their properties and representations. We first outline the necessary conditions for the mapping function and propose a family of functions that meet these criteria, to ensure first-order unbiasedness. We further demonstrate that the mappings employed by NeuS and VolSDF, two representative neural implicit surface techniques, are special cases within this broader family. Building on our theoretical framework, we introduce several new mapping functions and evaluate their effectiveness through numerical experiments. Our approach offers a fresh perspective on this well-established problem, opening avenues for the development of new techniques in the field.
The temporal interpolation task for 4D medical imaging, plays a crucial role in clinical practice of respiratory motion modeling. Following the simplified linear-motion hypothesis, existing approaches adopt optical flow-based models to interpolate intermediate frames. However, realistic respiratory motions should be nonlinear and quasi-periodic with specific frequencies. Intuited by this property, we resolve the temporal interpolation task from the frequency perspective, and propose a Fourier Basis-guided Diffusion model, termed FB-Diff. Specifically, due to the regular motion discipline of respiration, physiological motion priors are introduced to describe general characteristics of temporal data distributions. Then a Fourier motion operator is elaborately devised to extract Fourier bases by incorporating physiological motion priors and case-specific spectral information in the feature space of Variational Autoencoder. Well-learned Fourier bases can better simulate respiratory motions with motion patterns of specific frequencies. Conditioned on starting and ending frames, the diffusion model further leverages well-learned Fourier bases via the basis interaction operator, which promotes the temporal interpolation task in a generative manner. Extensive results demonstrate that FB-Diff achieves state-of-the-art (SOTA) perceptual performance with better temporal consistency while maintaining promising reconstruction metrics. Codes are available at https://github.com/AlexYouXin/FB-Diff.