Cross-domain few-shot segmentation (CD-FSS) aims to segment objects of novel classes in new domains, which is often challenging due to the diverse characteristics of target domains and the limited availability of support data. Most CD-FSS methods redesign and retrain in-domain FSS models using abundant base data from the source domain, which are effective but costly to train. To address these issues, we propose adapting informative model structures of the well-trained FSS model for target domains by learning domain characteristics from few-shot labeled support samples during inference, thereby eliminating the need for source domain retraining. Specifically, we first adaptively identify domain-specific model structures by measuring parameter importance using a novel structure Fisher score in a data-dependent manner. Then, we progressively train the selected informative model structures with hierarchically constructed training samples, progressing from fewer to more support shots. The resulting Informative Structure Adaptation (ISA) method effectively addresses domain shifts and equips existing well-trained in-domain FSS models with flexible adaptation capabilities for new domains, eliminating the need to redesign or retrain CD-FSS models on base data. Extensive experiments validate the effectiveness of our method, demonstrating superior performance across multiple CD-FSS benchmarks. Codes are at https://github.com/fanq15/ISA
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LIRA: Inferring Segmentation in Large Multi-modal Models with Local Interleaved Region Assistance
PDF ↗While large multi-modal models (LMMs) demonstrate promising capabilities in segmentation and comprehension, they still struggle with two limitations: inaccurate segmentation and hallucinated comprehension. These challenges stem primarily from constraints in weak visual comprehension and a lack of fine-grained perception. To alleviate these limitations, we propose LIRA, a framework that capitalizes on the complementary relationship between visual comprehension and segmentation via two key components: (1) Semantic-Enhanced Feature Extractor (SEFE) improves object attribute inference by fusing semantic and pixel-level features, leading to more accurate segmentation; (2) Interleaved Local Visual Coupling (ILVC) autoregressively generates local descriptions after extracting local features based on segmentation masks, offering fine-grained supervision to mitigate hallucinations. Furthermore, we find that the precision of object segmentation is positively correlated with the latent related semantics of the <seg> token. To quantify this relationship and the model's potential semantic inferring ability, we introduce the Attributes Evaluation (AttrEval) dataset. Our experiments show that LIRA achieves state-of-the-art performance in both segmentation and comprehension tasks. Code will be available at https://github.com/echo840/LIRA.
We present OpenSubstance, a high-quality measured dataset with 2.4 million high-dynamic-range images of 187 objects with a wide variety in shape and appearance, captured under 270 camera views and 1,637 lighting conditions, including 1,620 one-light-at-a-time, 8 environment, 8 linear and 1 full-on illumination. For each image, the corresponding lighting condition, camera parameters and foreground segmentation mask are provided. High-precision 3D geometry is also acquired for rigid objects. It takes 1 hour on average to capture one object with our custom-built high-performance lightstage and a top-grade commercial 3D scanner. We perform comprehensive quantitative evaluation on state-of-the-art techniques across different tasks, including single- and multi-view photometric stereo, as well as relighting. The project is publicly available at https://opensubstance.github.io/
TrackVerse: A Large-Scale Object-Centric Video Dataset for Image-Level Representation Learning
PDF ↗Video data inherently captures rich, dynamic contexts that reveal objects in varying poses, interactions, and state transitions, offering rich potential for unsupervised object representation learning. However, most prior representation learning methods rely on static image datasets like ImageNet, which lack temporal cues and only provide high-level semantic supervision. Meanwhile, existing natural video datasets are not ideal for learning object-centric representations due to limited object focus and class diversity. To explore unsupervised object representation learning grounded in object dynamics--beyond static appearance--we introduce TrackVerse, a large-scale video dataset of 31.9 million object tracks spanning over 1,000 categories, each capturing the motion, appearance, and evolving states of an object over time. We further propose a variance-aware contrastive learning framework that adapts to data augmentations, encouraging the model to learn state-sensitive features. Extensive experiments demonstrate that representations learned from TrackVerse with variance-aware contrastive learning significantly outperform those from static image datasets and non-object-centric natural video across multiple downstream tasks including object/attributie recognition, action recognition and video instance segmentation, highlighting the rich semantic and state content in TrackVerse feature.
Recent efforts in video reasoning segmentation (VRS) integrate large language models (LLMs) with perception models to localize and track objects via textual instructions, achieving barely satisfactory results in simple scenarios. However, they struggled to discriminate and deduce the objects from user queries in more real-world scenes featured by long durations, multiple objects, rapid motion, and heavy occlusions. In this work, we analyze the underlying causes of these limitations, and present **ViLLa**: **Vi**deo reasoning segmentation with **L**arge **La**nguage Model. Remarkably, our ViLLa manages to tackle these challenges through multiple core innovations: (1) a context synthesizer that dynamically encodes the user intent with video contexts for accurate reasoning, resolving ambiguities in complex queries, and (2) a hierarchical temporal synchronizer that disentangles multi-object interactions across complex temporal scenarios by modelling multi-object interactions at local and global temporal scales. To enable efficient processing of long videos, ViLLa incorporates (3) a key segment sampler that adaptively partitions long videos into shorter but semantically dense segments for less redundancy. What's more, to promote research in this unexplored area, we construct a VRS benchmark, **VideoReasonSeg**, featuring different complex scenarios. Our model also exhibits impressive state-of-the-art results on VideoReasonSeg, Ref-YouTube-VOS, Ref-DAVIS17, MeViS, and ReVOS. Both quantitative and qualitative experiments demonstrate that our method effectively enhances video reasoning segmentation capabilities for multimodal LLMs.
Lidar point cloud synthesis based on generative models offers a promising solution to augment deep learning pipelines, particularly when real-world data is scarce or lacks diversity. By enabling flexible object manipulation, this synthesis approach can significantly enrich training datasets and enhance discriminative models. However, existing methods focus on unconditional lidar point cloud generation, overlooking their potential for real-world applications. In this paper, we propose SG-LDM, a Semantic-Guided Lidar Diffusion Model that employs latent alignment to enable robust semantic-to-lidar synthesis. By directly operating in the native lidar space and leveraging explicit semantic conditioning, SG-LDM achieves state-of-the-art performance in generating high-fidelity lidar point clouds guided by semantic labels. Moreover, we propose the first diffusion-based lidar translation framework based on SG-LDM, which enables cross-domain translation as a domain adaptation strategy to enhance downstream perception performance. Systematic experiments demonstrate that SG-LDM significantly outperforms existing lidar diffusion models and the proposed lidar translation framework further improves data augmentation performance in the downstream lidar segmentation task.
Long video understanding poses unique challenges due to its temporal complexity and low information density. Recent works address this task by sampling numerous frames or incorporating auxiliary tools using LLMs, both of which result in high computational costs. In this work, we introduce a curiosity-driven video agent with self-exploration capability, dubbed as "VCA". Built upon VLMs, VCA autonomously navigates video segments and efficiently builds a comprehensive understanding of complex video sequences.Instead of directly sampling frames, VCA employs a tree-search structure to explore video segments and collect frames. Rather than relying on external feedback or reward, VCA leverages VLM's self-generated intrinsic reward to guide its exploration, enabling it to capture the most crucial information for reasoning. Experimental results on multiple long video benchmarks demonstrate our approach's superior effectiveness and efficiency.
We formulate the motor system of an interactive avatar as a generative motion model that can drive the body to move through 3D space in a perpetual, realistic, controllable, and responsive manner. Although human motion generation has been extensively studied, many existing methods lack the responsiveness and realism of real human movements. Inspired by recent advances in foundation models, we propose PRIMAL, which is learned with a two-stage paradigm. In the pretraining stage, the model learns body movements from a large number of sub-second motion segments, providing a generative foundation from which more complex motions are built. This training is fully unsupervised without annotations. Given a single-frame initial state during inference, the pretrained model not only generates unbounded, realistic, and controllable motion, but also enables the avatar to be responsive to induced impulses in real time. In the adaptation phase, we employ a novel ControlNet-like adaptor to fine-tune the base model efficiently, adapting it to new tasks such as few-shot personalized action generation and spatial target reaching. Evaluations show that our proposed method outperforms state-of-the-art baselines. We leverage the model to create a real-time character animation system in Unreal Engine that feels highly responsive and natural.
3D semantic segmentation provides high-level scene understanding for applications in robotics, autonomous systems, etc. Traditional methods adapt exclusively to either task-specific goals (open-vocabulary segmentation) or scene content (unsupervised semantic segmentation). We propose DiSCO-3D, the first method addressing the broader problem of 3D Open-Vocabulary Sub-concepts Discovery, which aims to provide a 3D semantic segmentation that adapts to both the scene and user queries. We build DiSCO-3D on Neural Fields representations, combining unsupervised segmentation with weak open-vocabulary guidance. Our evaluations demonstrate that DiSCO-3D achieves effective performance in Open-Vocabulary Sub-concepts Discovery and exhibits state-of-the-art results in the edge cases of both open-vocabulary and unsupervised segmentation.
Segment anything model (SAM) has shown impressive general-purpose segmentation performance on natural images, but its performance on camouflaged object detection (COD) is unsatisfactory. In this paper, we propose SAM-DSA that performs COD for RGB-D inputs via Dual Stream Adapters. While keeping the SAM architecture intact, dual stream adapters are expanded on the image encoder to learn potential complementary information from RGB images and depth images, and fine-tune the mask decoder and its depth-aware replica to perform dual-stream mask prediction. In practice, the dual stream adapters are embedded into the attention block of the image encoder in a parallel manner to facilitate the refinement and correction of the two types of image embeddings. To mitigate channel discrepancies arising from dual stream embeddings that do not directly interact with each other, we augment the association of dual stream embeddings using bidirectional knowledge distillation including a model distiller and a modal distiller. In addition, to predict the masks for RGB and depth attention maps, we integrate the two types of image embeddings which are jointly learned with the prompt embeddings to update the initial prompt, and then feed them into the mask decoders to synchronize the consistency of image embeddings and prompt embeddings. Experimental results on four COD benchmarks show that our SAM-DSA achieves excellent detection performance gains over SAM and achieves state-of-the-art results with a given fine-tuning paradigm.
Although deep neural networks have achieved remarkable success in various computer vision tasks, they face significant challenges in degraded image understanding due to domain shifts caused by quality variations. Drawing biological inspiration from the human visual system (HVS), which dynamically adjusts perception strategies through contrast gain control and selective attention to salient regions, we propose Quality-Adaptive Normalization (Q-Norm) - a novel normalization method that learns adaptive parameters guided by image quality features. Our approach addresses two critical limitations of conventional normalization techniques: 1) Domain Covariance Shift: Existing methods fail to align feature distributions across different quality domains. Q-Norm implicitly achieves cross-domain alignment through quality-aware parameter adaptation without explicit loss functions. 2) Biological Plausibility: By mimicking HVS's contrast normalization mechanisms and attention-based feature selection, Q-Norm dynamically adjusts the mean and variance parameters using a pre-trained quality assessment model, ensuring robustness to image degradation. Extensive experiments across multiple tasks (image classification, semantic segmentation, object detection) demonstrate that Q-Norm consistently outperforms baseline methods on low-quality images. Code has been released at https://github.com/IIP-Lab-XDU/Q-Norm.
Domain generalization in 3D segmentation is a critical challenge in deploying models to unseen environments. Current methods mitigate the domain shift by augmenting the data distribution of point clouds. However, the model learns global geometric patterns in point clouds while ignoring the category-level distribution and alignment. In this paper, a category-level geometry learning framework is proposed to explore the domain-invariant geometric features for domain generalized 3D semantic segmentation. Specifically, Category-level Geometry Embedding (CGE) is proposed to perceive the fine-grained geometric properties of point cloud features, which constructs the geometric properties of each class and couples geometric embedding to semantic learning. Secondly, Geometric Consistent Learning (GCL) is proposed to simulate the latent 3D distribution and align the category-level geometric embeddings, allowing the model to focus on the geometric invariant information to improve generalization. Experimental results verify the effectiveness of the proposed method, which has very competitive segmentation accuracy compared with the state-of-the-art domain generalized point cloud methods. The code will be available at https://github.com/ChicalH/DCGL.
Generating naturalistic and nuanced listener motions for extended interactions remains an open problem. Existing methods often rely on low-dimensional motion codes for facial behavior generation followed by photorealistic rendering, limiting both visual fidelity and expressive richness. To address these challenges, we introduce DiTaiListener, powered by a video diffusion model with multimodal conditions. Our approach first generates short segments of listener responses conditioned on the speaker's speech and facial motions with DiTaiListener-Gen. It then refines the transitional frames via DiTaiListener-Edit for a seamless transition. Specifically, DiTaiListener-Gen adapts a Diffusion Transformer (DiT) for the task of listener head portrait generation by introducing a Causal Temporal Multimodal Adapter (CTM-Adapter) to process speakers' auditory and visual cues. CTM-Adapter integrates speakers' input in a causal manner into the video generation process to ensure temporally coherent listener responses. For long-form video generation, we introduce DiTaiListener-Edit, a transition refinement video-to-video diffusion model. The model fuses video segments into smooth and continuous videos, ensuring temporal consistency in facial expressions and image quality when merging short video segments produced by DiTaiListener-Gen. Quantitatively, DiTaiListener achieves the state-of-the-art performance on benchmark datasets in both photorealism (+73.8% in FID on RealTalk) and motion representation (+6.1% in FD metric on VICO) spaces. User studies confirm the superior performance of DiTaiListener, with the model being the clear preference in terms of feedback, diversity, and smoothness, outperforming competitors by a significant margin. See the project page for more results: https://ihp-lab.github.io/DiTaiListener/
EVOLVE: Event-Guided Deformable Feature Transfer and Dual-Memory Refinement for Low-Light Video Object Segmentation
PDF ↗Video Object Segmentation (VOS) in low-light scenarios remains highly challenging due to significant texture loss and severe noise, which often lead to unreliable image feature generation and degraded segmentation performance. To address this issue, we propose EVOLVE, a novel event-guided deformable feature transfer and dual-memory refinement framework for low-light VOS. EVOLVE addresses spatial misalignment between frames and improves object representation by utilizing event-driven cues. The event-guided deformable feature transfer (EDFT) module enhances feature alignment through event-driven deformable convolutions, where offsets derived from event features enable motion-aware spatial adjustments, leading to more precise propagation of object features in reference frames. Furthermore, the dual-memory object transformer (DMOT) iteratively refines object representations by maintaining and updating both image-based and event-based memory representations. Through its memory refinement module (MRM), DMOT selectively enhances relevant object features while suppressing background noise, resulting in stable and temporally coherent segmentation results. Extensive experiments on low-light VOS benchmarks demonstrate that EVOLVE achieves state-of-the-art segmentation performance, surpassing both event-based and image-based VOS methods in accuracy and computational efficiency.
Mitigating Geometric Degradation in Fast DownSampling via FastAdapter for Point Cloud Segmentation
PDF ↗Farthest Point Sampling (FPS) is widely used in existing point-based models because it effectively preserves structural integrity during downsampling. However, it incurs significant computational overhead, severely impacting the model's inference efficiency. Random sampling or grid sampling is considered faster downsampling methods; however, these fast downsampling methods may lead to the loss of geometric information during the downsampling process due to their overly simplistic and fixed rules, which can negatively affect model performance. To address this issue, we propose FastAdapter, which aggregates local contextual information through a small number of anchor points and facilitates interactions across spatial and layer dimensions, ultimately feeding this information back into the downsampled point cloud to mitigate the information degradation caused by fast downsampling methods. In addition to using FastAdapter to enhance model performance in methods that already employ fast downsampling, we aim to explore a more challenging yet valuable application scenario. Specifically, we focus on pre-trained models that utilize FPS, embedding FastAdapter and replacing FPS with random sampling for lightweight fine-tuning. This approach aims to significantly improve inference speed while maintaining relatively unchanged performance. Experimental results on ScanNet, S3DIS, and SemanticKITTI demonstrate that our method effectively mitigates the geometric information degradation issues caused by fast downSampling.
Visual relation detection (VRD) is the task of identifying the relationships between objects in a scene. VRD models trained solely on relation detection data struggle to generalize beyond the relations on which they are trained. While prompt tuning has been used to adapt vision-language models (VLMs) for VRD, it uses handcrafted prompts and struggles with novel or complex relations. We argue that instruction tuning offers a more effective solution by fine-tuning VLMs on diverse instructional data. We thus introduce ART, an Adaptive Relation Tuning framework that adapts VLMs for VRD through instruction tuning and strategic instance selection. By converting VRD datasets into an instruction-tuning format and employing an adaptive sampling algorithm, ART directs the VLM to focus on informative relations while maintaining generalizability. Specifically, we focus on the relation classification, where subject-object boxes are given and the model predicts the predicate between them. We tune on a held-in set and evaluate across multiple held-out datasets of varying complexity. Our approach strongly improves over its baselines and can infer unseen relation concepts, a capability absent in mainstream VRD methods. We demonstrate ART's practical value by using the predicted relations for segmenting complex scenes.
HoliTracer: Holistic Vectorization of Geographic Objects from Large-Size Remote Sensing Imagery
PDF ↗With the increasing resolution of remote sensing imagery (RSI), large-size RSI has emerged as a vital data source for high-precision vector mapping of geographic objects. Existing methods are typically constrained to processing small image patches, which often leads to the loss of contextual information and produces fragmented vector outputs. To address these, this paper introduces HoliTracer, the first framework designed to holistically extract vectorized geographic objects from large-size RSI. In HoliTracer, we enhance segmentation of large-size RSI using the Context Attention Net (CAN), which employs a local-to-global attention mechanism to capture contextual dependencies. Furthermore, we achieve holistic vectorization through a robust pipeline that leverages the Mask Contour Reformer (MCR) to reconstruct polygons and the Polygon Sequence Tracer (PST) to trace vertices. Extensive experiments on large-size RSI datasets, including buildings, water bodies, and roads, demonstrate that HoliTracer outperforms state-of-the-art methods. Our code and data are available in https://github.com/vvangfaye/HoliTracer.
Tissue segmentation in pathology images is crucial for computer-aided diagnostics of human cancers. Traditional tissue segmentation models rely heavily on large-scale labeled datasets, where every tissue type must be annotated by experts. However, due to the complexity of tumor micro-environment, collecting annotations for all possible tissue types is challenging, which makes the traditional methods ineffective in segmenting unseen tissue types with zero training samples. With the rapid development of vision-language models (VLMs), recent studies extend their powerful zero-shot capabilities to pixel-level segmentation tasks, where the model is trained only on seen classes but can perform tissue segmentation on both seen and unseen categories in the testing phase. However, these VLM-based zero-shot segmentation models still require substantial annotation efforts on seen classes. To attach desirable segmentation performance on both seen and unseen categories with limited labeled data, we propose AcZeroTS, a novel active learning framework for zero-shot tissue segmentation in pathology images. Specifically, AcZeroTS is built on a VLM-based prototype-guided zero-shot segmentation model called ProZS. We introduce a novel active selection criterion to select the most valuable samples for annotation on seen classes, which not only considers both uncertainty and diversity of unlabeled samples, but also ensures that the generated prototypes of ProZS can effectively summarize both seen and unseen classes during inference. We evaluate our method on two pathology datasets (TNBC and HPBC) as well as a natural dataset (Pascal VOC 2012), and the experimental results demonstrate the superiority of our method in comparison with the existing studies.
Can a model distinguish between the sound of a spoon hitting a hardwood floor versus a carpeted one? Everyday object interactions produce sounds unique to the objects involved. We introduce the sounding object detection task to evaluate a model's ability to link these sounds to the objects directly involved. Inspired by human perception, our multimodal object-aware framework learns from in-the-wild egocentric videos. To encourage an object-centric approach, we first develop an automatic pipeline to compute segmentation masks of the objects involved to guide the model's focus during training towards the most informative regions of the interaction. A slot attention visual encoder is used to further enforce an object prior. We demonstrate state of the art performance on our new task along with existing multimodal action understanding tasks.
Continual Multiple Instance Learning with Enhanced Localization for Histopathological Whole Slide Image Analysis
PDF ↗Multiple instance learning (MIL) significantly reduced annotation costs via bag-level weak labels for large-scale images, such as histopathological whole slide images (WSIs). However, its adaptability to continual tasks with minimal forgetting has been rarely explored, especially on instance classification for localization. Weakly incremental learning for semantic segmentation has been studied for continual localization, but it focused on natural images, leveraging global relationships among hundreds of small patches (e.g., 16 x 16) using pre-trained models. This approach seems infeasible for MIL localization due to enormous amounts (up to 100,000) of large patches (e.g., 256 x 256) and no available global relationships such as cancer cells. To address these challenges, we propose Continual Multiple Instance Learning with Enhanced Localization (CoMEL), an MIL framework for both localization and adaptability with minimal forgetting. CoMEL consists of (1) Grouped Double Attention Transformer (GDAT) for efficient instance encoding, (2) Bag Prototypes-based Pseudo-Labeling (BPPL) for reliable instance pseudo-labeling, and (3) Orthogonal Weighted Low-Rank Adaptation (OWLoRA) to mitigate forgetting in both bag and instance classification. Extensive experiments on three public WSI datasets demonstrate superior performance of CoMEL, outperforming the prior arts by up to 11.00% in bag-level accuracy and up to 23.4% in localization accuracy under the continual MIL setup.