This paper presents a generalizable CoSOD framework via mixed content-style modulation, termed CoMCS, to enhance the robustness of the model to unseen domains. The CoMCS, consisting of a mixed content modulator (MCM), a mixed style modulator (MSM), and a collaborative semantic contrast module (SCM), effectively extracts scene structure priors as well as augments the source domain styles to bridge the domain gap between the source and the unseen domains. Specifically, the CoMCS first utilizes the CLIP model to extract conceptual knowledge associated with the semantic classes in the whole scene, resulting in multi-class semantic embeddings that are domain-invariant. Subsequently, the MCM models the semantic relationships between the prototypes of co-salient objects and the multi-class semantic embeddings through the cross-attention mechanism, effectively capturing domain-invariant scene structure priors that aid in reducing scene distribution shift in unseen domains. Meanwhile, to alleviate domain perturbations encountered during testing, the MSM addresses the uncertainty associated with domain shifts by synthesizing feature statistics, such as mean and standard deviation, during training to simulate new stylistic characteristics, thus achieving data augmentation within the source domain. Finally, to reduce the ambiguity of the co-salient object representations within test data from unseen domains, the SCM employs a uniform loss function to ensure that the learned prototypes are uniformly distributed within the hyperspherical space, further enhancing the domain generalization capabilities of the framework. Besides, to further verify the generalization ability of the CoMCS to unseen domains, we construct an unseen-domain benchmark dataset (UND) that selects a variety of image groups with unseen classes from CoCA, CoSOD3k, CoSal2015. Extensive evaluations on the four benchmark datasets demonstrate favorable performance of our CoMCS to a variety of state-of-the-art methods.
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Nestwork: Conditional 3D Furnished House Layout Generation through Latent Heterogeneous Graph Diffusion
PDF ↗This paper introduces Nestwork, a unified latent-diffusion framework for conditional 3D furnished house layout generation using a heterogeneous graph of rooms and furniture. Designing reasonable and controllable 3D layouts that reflect the underlying semantic structure of a house is a key challenge in AI-assisted architectural design. Existing graph-based methods either produce unfurnished multi-room layouts or generate furnished scenes one room at a time, preventing joint reasoning over room structure and furniture placement. Nestwork represents an entire house as a heterogeneous graph with typed room and furniture nodes and multiple spatial relations. A single unconditional autoencoder based on a heterogeneous graph attention network embeds this graph into a compact latent space, and a low-rank relational field compensates for missing geometric edge information at test time. A diffusion denoiser is trained once using random masking, enabling the same model to operate under different conditioning strengths, from topology-only to fully annotated graphs. Multi-level conditioning combines masked node-level attention with graph-level embeddings to support flexible user control, including layouts specified through natural-language descriptions. Experiments on the 3D-FRONT dataset show that Nestwork achieves high fidelity, structural consistency, and diversity. Controlled ablations further validate the contributions of each component.
Visual localization, i.e., the problem of estimating the camera pose from which an image was taken, is an important part of applications such as augmented reality and autonomous robots. Many of these applications require a compact memory footprint. Thus, a considerable amount of work has been spent on designing memory-efficient scene representations for visual localization. In this paper, we focus on compressing the 3D structure of the scene by selecting a subset of points from a Structure-from-Motion (SfM) point cloud. In contrast to prior work, which aims to solve (complex) optimization problems, we propose a simple strategy that is almost trivial to implement. Our compression strategy is based on the idea of selecting triplets of points such that the camera pose of each database image (used to build the SfM point cloud) can be accurately estimated from these triplets. Despite its simplicity, our strategy performs similarly to or better than current state-of-the-art structure compression approaches. Combined with standard product quantization approaches to compress feature descriptors, our approach compares favorably with recent learning-based approaches for compact visual localization.
Model stitching, connecting early layers of one model (source) to later layers of another (target) via a light stitch layer, has served as a probe of representational compatibility. Prior work finds that models trained on the same dataset remain stitchable (negligible accuracy drop) despite different initializations or objectives. We revisit stitching for Vision Foundation Models (VFMs) that vary in objectives, data, and modality (e.g., CLIP, DINOv2, SigLIP2) and ask: Are heterogeneous VFMs stitchable? We introduce a systematic protocol spanning stitch positions, stitch layer families, training losses, and downstream tasks. Three findings emerge. (1) Stitch layer training matters: conventional approaches that match the intermediate features at the stitch position or optimize the task loss end-to-end struggle to retain accuracy, especially at shallow stitch positions. (2) With a simple feature-matching loss at the target model's penultimate layer, heterogeneous VFMs become reliably stitchable across vision tasks. (3) For deep stitch positions, the stitched model can significantly surpass either constituent model with a small inference overhead (for the stitch layer). Building on these findings, we further propose the VFM Stitch Tree (VST), which shares early layers across VFMs while retaining their later layers, yielding a controllable accuracy-latency trade-off for multimodal LLMs that often leverage multiple VFMs. Taken together, our study elevates stitching from a diagnostic probe to a practical recipe for integrating complementary VFM strengths and pinpointing where their representations align or diverge.
Recently proposed pyramidal models decompose the conventional forward and backward diffusion processes into multiple stages operating at varying resolutions. These models handle inputs with higher noise levels at lower resolutions, while less noisy inputs are processed at higher resolutions. This hierarchical approach significantly reduces the computational cost of inference in multi-step denoising models. However, existing open-source pyramidal video models have been trained from scratch and tend to underperform compared to state-of-the-art systems in terms of visual plausibility.In this work, we present a pipeline that converts a pretrained diffusion model into a pyramidal one through low-cost finetuning, achieving this transformation without degradation in quality of output videos.Furthermore, we investigate and compare various strategies for step distillation within pyramidal models, aiming to further enhance the inference efficiency.
In this paper, we propose NeoVerse, a versatile 4D world model that is capable of 4D reconstruction, novel-trajectory video generation, and rich downstream applications. We first identify a common limitation of scalability in current 4D world modeling methods, caused either by expensive and specialized multi-view 4D data or by cumbersome training pre-processing. In contrast, our NeoVerse is built upon a core philosophy that makes the full pipeline scalable to diverse in-the-wild monocular videos. Specifically, NeoVerse features pose-free feed-forward 4D reconstruction, online monocular degradation pattern simulation, and other well-aligned techniques. These designs empower NeoVerse with versatility and generalization to various domains. Meanwhile, NeoVerse achieves state-of-the-art performance in standard reconstruction and generation benchmarks.
Foundational Vision Transformers (ViTs) have limited effectiveness in tasks requiring fine-grained spatial understanding, due to their fixed pre-training resolution and inherently coarse patch-level representations. These challenges are especially pronounced in dense prediction scenarios, such as open-vocabulary segmentation with ViT-based vision-language models, where high-resolution inputs are essential for accurate pixel-level reasoning. Existing approaches typically process large-resolution images using a sliding-window strategy at the pre-training resolution. While this improves accuracy through finer strides, it comes at a significant computational cost. We introduce SPAR: Single-Pass Any-Resolution ViT, a resolution-agnostic dense feature extractor designed for efficient high-resolution inference. We distill the spatial reasoning capabilities of a finely-strided, sliding-window teacher into a single-pass student using a feature regression loss, without requiring architectural changes or pixel-level supervision. Applied to open-vocabulary segmentation, SPAR improves single-pass baselines by up to 10.5 mIoU and even surpasses the teacher, demonstrating effectiveness in efficient, high-resolution reasoning. Code: https://github.com/naomikombol/SPAR
Federated Learning (FL) enables collaborative model training without sharing raw data, but client data are often Non-Independent and Identically Distributed (Non-IID), which often slow convergence and degrade global performance. Meanwhile, privacy preservation is also a critical concern in FL. To address these two issues, we propose FedAlign, a differentially private framework that aligns local data distributions via client-side statistical moment alignment. Clients upload perturbed distribution statistics, which the server aggregates to infer global distribution characteristics and guide local alignment, thereby reducing inter-client discrepancies. Experiments and theoretical analysis show that FedAlign accelerates convergence and improves accuracy under Non-IID settings while preserving rigorous privacy guarantees.
IVAAN: Instance-level Vision-Language Alignment via Attribute-Guided Text Prompts Generation for Nuclei Analysis
PDF ↗Nuclei instance segmentation and classification are fundamental but remain challenging in pathology due to severe class imbalance and organ- and stain-induced variability. While vision-language approaches can inject explicit semantic cues that reduce spurious contextual bias under imbalance, the absence of instance level textual annotations has limited their utility for nucleus-level analysis. We introduce an instance-level vision-language framework that derives attribute-guided textual descriptions from ground-truth masks. We then align visual representations with these semantic text anchors via contrastive learning, coupling morphology with semantics at the instance level. To capture intra-class variations while maintaining organ-consistent class semantics, we learn multiple class-specific tokens that act as prototypes representing diverse submodes within a class, summarizing morphologically similar nuclei. Our approach improves both segmentation and classification without manual text labels, indicating that language-guided instance alignment combined with prototype-based semantic feedback yields more discriminative and generalizable nuclei representations.
Robust depth estimation aims to maintain high-quality depths across diverse conditions. However, most existing methods estimate depth without taking into account the object-level information. As a result, the predicted depth may easily deviate within objects and become blurred under adverse conditions. To overcome this weakness, we propose RoSAMDepth, a novel framework that can assist robust self-supervised depth estimation in leveraging rich and diverse object-level priors from the Segment Anything Model (SAM). We focus on incorporating object-level information across three key aspects: a segment-guided representation contrasting method that injects object-level awareness into the feature representation space; an adaptive regional outlier masking strategy combined with a regional Gaussian likelihood loss that enforces regional depth smoothness; and an object-level reliability estimation strategy that mitigates the influence of unreliable supervision. Extensive experiments across multiple datasets and diverse weather conditions demonstrate that our method produces sharper, more accurate depth predictions, consistently outperforming state-of-the-art methods.
Assembling objects from parts requires understanding multimodal instructions, linking them to 3D components, and predicting physically plausible 6-DoF motions for each assembly step. Existing datasets focus on simplified scenarios, overlooking shape complexities and assembly trajectories in industrial assemblies. We introduce AssemblyBench, a synthetic dataset of 2,789 industrial objects with multimodal instruction manuals, corresponding 3D part models, and part assembly trajectories. We also propose a transformer-based model, AssemblyDyno, which uses the instructional manual and the 3D shape of each part to jointly predict assembly order and part assembly trajectories. AssemblyDyno outperforms prior works in both assembly pose estimation and trajectory feasibility, where the latter is evaluated by our physics-based simulations.
A Supervised Multi-task Framework for Joint cryo-ET Restoration Enabled by Generative Physical Simulation
PDF ↗Cryo-electron tomography (cryo-ET) enables in-situ visualization of cellular ultrastructure, but reconstructions are severely degraded by extremely low SNR and missing-wedge artifacts due to dose limits and restricted tilt angles. Existing learning-based approaches are further constrained by inaccurate noise modeling and the lack of reliable ground truth, limiting restoration quality. We propose cryoDeRec, a multi-task framework that jointly performs denoising and missing wedge recovery in a fully supervised manner. Our key contribution is a dual-objective training strategy that leverages synthetic noisy tomograms and their corresponding clean tomograms, encouraging structural fidelity while recovering missing information. To support training, we introduce an imaging simulation pipeline that captures authentic noise distributions and incorporates isotropic structural priors by simulating tomograms from real EMDB structures. Experiments on four realistic cryo-ET datasets and two extremely low-SNR simulated datasets (all reconstructed via WBP) show that cryoDeRec restores high-quality tomograms directly from raw inputs without preprocessing, consistently outperforming prior state of the art. Our findings show that training on a comprehensive simulated dataset, which captures realistic noise and structure, enables models to generalize effectively to real cryo-ET tomograms.
PRIMU: Uncertainty Estimation for Novel Views in Gaussian Splatting from Primitive-Based Representations of Error and Coverage
PDF ↗We introduce Primitive-based Representations of Uncertainty (PRIMU), a post-hoc uncertainty estimation (UE) framework for Gaussian Splatting (GS).Reliable UE is essential for deploying GS in safety-critical domains such as robotics and medicine.Existing approaches typically estimate Gaussian-primitive variances and rely on the rendering process to obtain pixel-wise uncertainties.In contrast, we construct primitive-level representations of error and visibility/coverage from training views, capturing interpretable uncertainty information. These representations are obtained by projecting view-dependent training errors and coverage statistics onto the primitives. Uncertainties for novel views are inferred by rendering these primitive-level representations, producing uncertainty feature maps, which are aggregate through pixel-wise regression on holdout data. We analyze combinations of uncertainty feature maps and regression models to understand how their interactions affect prediction accuracy and generalization.PRIMU also enables an effective active view selection strategy by directly leveraging these uncertainty feature maps.Additionally, we study the effect of separating splatting into foreground and background regions.Our estimates show strong correlations with true errors, outperforming state-of-the-art methods, especially for depth UE and foreground objects.Finally, our regression models show generalization capabilities to unseen scenes, enabling UE without additional holdout data.
Vision-Language Navigation requires agents to act coherently over long horizons by understanding not only local visual context but also how far they have advanced within a multi-step instruction.However, recent Vision-Language-Action models focus on direct action prediction and earlier progress methods predict numeric achievements; both overlook the monotonic co-progression property of the observation and instruction sequences.Building on this insight, Progress-Think introduces semantic progress reasoning, predicting instruction-style progress from visual observations to enable more accurate navigation.To achieve this without annotations, we propose a three-stage framework.In the initial stage, Self-Aligned Progress Pretraining bootstraps a reasoning module via a novel differentiable alignment between visual history and instruction prefixes.Then, Progress-Guided Policy Pretraining injects learned progress states into the navigation context, guiding the policy toward consistent actions.Finally, Progress-Policy Co-Finetuning jointly optimizes both modules with tailored progress-aware reinforcement objectives.Experiments on R2R-CE and RxR-CE show substantial gains in success, efficiency, and interpretability, demonstrating semantic progress provides a more consistent and generalizable representation of navigation advancement.
Multi-period image collections are common in real-world applications. Cities are re-scanned for mapping, construction sites are revisited for progress tracking, and natural regions are monitored for environmental change. Such data form multi-period scenes, where geometry and appearance evolve. Reconstructing such scenes is an important yet underexplored problem. Existing pipelines rely on incompatible assumptions: static and in-the-wild methods enforce a single geometry, while dynamic ones assume smooth motion, both failing under long-term, discontinuous changes. To solve this problem, we introduce ChronoGS, a temporally modulated Gaussian representation that reconstructs all periods within a unified anchor scaffold. It's also designed to disentangle stable and evolving components, achieving temporally consistent reconstruction of multi-period scenes. To catalyze relevant research, we release ChronoScene dataset, a benchmark of real and synthetic multi-period scenes, capturing geometric and appearance variation. Experiments demonstrate that ChronoGS consistently outperforms baselines in reconstruction quality and temporal consistency. Our code and the ChronoScene dataset will be made publicly available.
Generating realistic human-human interactions is a challenging task that requires not only high-quality individual body and hand motions, but also coherent coordination among all interactants. Due to limitations in available data and increased learning complexity, previous methods tend to ignore hand motions, limiting the realism and expressivity of the interactions. Additionally, current diffusion-based approaches generate entire motion sequences simultaneously, limiting their ability to capture the reactive and adaptive nature of human interactions. To address these limitations, we introduce Interact2Ar, the first end-to-end text-conditioned autoregressive diffusion model for generating full-body, human-human interactions. Interact2Ar incorporates detailed hand kinematics through dedicated parallel branches, enabling high-fidelity full-body generation. Furthermore, we introduce an autoregressive pipeline coupled with a novel memory technique that facilitates adaptation to the inherent variability of human interactions using efficient large context windows. The adaptability of our model enables a series of downstream applications, including temporal motion composition, real-time adaptation to disturbances, and extension beyond dyadic to multi-person scenarios. To validate the generated motions, we introduce a set of robust evaluators and extended metrics designed specifically for assessing full-body interactions. Through quantitative and qualitative experiments, we demonstrate the state-of-the-art performance of Interact2Ar.
MMTIT-Bench: A Multilingual and Multi-Scenario Benchmark with Cognition-Perception-Reasoning Guided Text-Image Machine Translation
PDF ↗End-to-end text-image machine translation (TIMT), which directly translates textual content in images across languages, is crucial for real-world multilingual scene understanding. Despite advances in vision-language large models (VLLMs), robustness across diverse visual scenes and low-resource languages remains underexplored due to limited evaluation resources. We present MMTIT-Bench, a human-verified multilingual and multi-scenario benchmark with 1,400 images spanning fourteen non-English and non-Chinese languages and diverse settings such as documents, scenes, and web images, enabling rigorous assessment of end-to-end TIMT. Beyond benchmarking, we study how reasoning-oriented data design improves translation. Although recent VLLMs have begun to incorporate long Chain-of-Thought (CoT) reasoning, effective thinking paradigms for TIMT are still immature: existing designs either cascade parsing and translation in a sequential manner or focus on language-only reasoning, overlooking the visual cognition central to VLLMs. We propose Cognition-Perception-Reasoning for Translation (CPR-Trans), a data paradigm that integrates scene cognition, text perception, and translation reasoning within a unified reasoning process. Using a VLLM-driven data generation pipeline, CPR-Trans provides structured, interpretable supervision that aligns perception with reasoning. Experiments on 3B and 7B models show consistent gains in accuracy and interpretability. We will release MMTIT-Bench to promote the multilingual and multi-scenario TIMT research upon acceptance.
PFGNet: A Fully Convolutional Frequency-Guided Peripheral Gating Network for Efficient Spatiotemporal Predictive Learning
PDF ↗Spatiotemporal predictive learning (STPL) aims to forecast future frames from past observations and is essential across a wide range of applications. Compared with recurrent or hybrid architectures, pure convolutional models offer superior efficiency and full parallelism, yet their fixed receptive fields limit their ability to adaptively capture spatially varying motion patterns. Inspired by biological center-surround organization and frequency-selective signal processing, we propose PFGNet, a fully convolutional framework that dynamically modulates receptive fields through pixel-wise frequency-guided gating. The core Peripheral Frequency Gating (PFG) block extracts localized spectral cues and adaptively fuses multi-scale large-kernel peripheral responses with learnable center suppression, effectively forming spatially adaptive band-pass filters. To maintain efficiency, all large kernels are decomposed into separable 1D convolutions (1xk followed by kx1), reducing per-channel computational cost from O(k2) to O(2k). PFGNet enables structure-aware spatiotemporal modeling without recurrence or attention. Experiments on Moving MNIST, TaxiBJ, Human3.6M, and KTH show that PFGNet delivers SOTA or near-SOTA forecasting performance with substantially fewer parameters and FLOPs. Our code is available at https://github.com/fhjdqaq/PFGNet.
The devil is in the details: Enhancing Video Virtual Try-On via Keyframe-Driven Details Injection
PDF ↗Although diffusion transformer (DiT)-based video virtual try-on (VVT) has made significant progress in synthesizing realistic videos, existing methods still struggle to capture fine-grained garment dynamics and preserve background integrity across video frames. They also incur high computational costs due to additional interaction modules introduced into DiTs, while the limited scale and quality of existing public datasets also restrict model generalization and effective training. To address these challenges, we propose a novel framework, KeyTailor, along with a large-scale, high-definition dataset, ViT-HD. The core idea of KeyTailor is a keyframe-driven details injection strategy, motivated by the fact that keyframes inherently contain both foreground dynamics and background consistency. Specifically, KeyTailor adopts an instruction-guided keyframe sampling strategy to filter informative frames from the input video. Subsequently, two tailored keyframe-driven modules--the garment details enhancement module and the collaborative background optimization module--are employed to distill garment dynamics into garment-related latents and to optimize the integrity of background latents, both guided by keyframes. These enriched details are then injected into standard DiT blocks together with pose, mask, and noise latents, enabling efficient and realistic try-on video synthesis. This design ensures consistency without explicitly modifying the DiT architecture, while simultaneously avoiding additional complexity. In addition, our dataset ViT-HD comprises 15,070 high-quality video samples at a resolution of 810 x 1080, covering diverse garments. Extensive experiments demonstrate that KeyTailor outperforms state-of-the-art baselines in terms of garment fidelity and background integrity across both dynamic and static scenarios. The dataset and code will be publicly released.
Rethinking SNN Online Training and Deployment: Gradient-Coherent Learning via Hybrid-Driven LIF Model
PDF ↗Spiking Neural Networks (SNNs) are considered to have enormous potential in the future development of Artificial Intelligence due to their brain-inspired and energy-efficient properties. Compared to vanilla Spatial-Temporal Back-propagation (STBP) training methods, online training can effectively avoid the risk of GPU memory explosion. However, current online learning frameworks cannot tackle the gradient discrepancy problem between the forward and backward process, merely aiming to optimize the GPU memory, resulting in no performance advantages compared to the STBP-based models in the inference stage. To address the aforementioned challenges, we propose Hybrid-Driven Leaky Integrate-and-Fire (HD-LIF) model family for efficient online learning, which respectively adopt different spiking calculation mechanism in the upper-region and lower-region of the firing threshold. We theoretically point out that our learning framework can effectively separate temporal gradients and address the misalignment problem of surrogate gradients, as well as achieving full-stage optimization towards learning precision, memory footprint and power consumption. Experimental results have demonstrated that our scheme is enable to achieve state-of-the-art performance for multiple evaluation metrics, breaking through the traditional paradigm of SNN online training and deployment. Code is available at https://github.com/hzc1208/HD_LIF.