ICCV 2025 Papers

Vision-language pretraining (VLP) enables open-world generalization beyond predefined labels, a critical capability in surgery due to the diversity of procedures, instruments, and patient anatomies. However, applying VLP to ophthalmic surgery presents unique challenges, including limited vision-language data, intricate procedural workflows, and the need for hierarchical understanding, ranging from fine-grained surgical actions to global clinical reasoning. To address these, we introduce OphVL, a large-scale, hierarchically structured dataset containing over 375K video-text pairs, making it 15x larger than existing surgical VLP datasets. OphVL captures a diverse range of ophthalmic surgical attributes, including surgical phases, operations, actions, instruments, medications, disease causes, surgical objectives, and postoperative care recommendations. By aligning short clips with detailed narratives and full-length videos with structured titles, OphVL provides both fine-grained surgical details and high-level procedural context. Building on OphVL, we propose OphCLIP, a hierarchical retrieval-augmented VLP framework. OphCLIP leverages silent surgical videos as a knowledge base, retrieving semantically relevant content to enhance narrated procedure learning. This enables OphCLIP to integrate explicit linguistic supervision with implicit visual knowledge, improving ophthalmic workflow modeling. Evaluations across 11 benchmark datasets for surgical phase recognition and multi-instrument identification demonstrate OphCLIP's robust generalization and superior performance, establishing it as a foundation model for ophthalmic surgery.

OpenAI's CLIP models, released in early 2021, have long been the only viable choice for the research community in building multimodal foundation models. This dominance has only recently been challenged by a few alternatives like SigLIP. However, to the best of our knowledge, all these solutions are still not fully open, e.g., their training data remains proprietary and/or their training frameworks are unreleased. In this paper, we address this challenge by introducing a family of fully open vision encoders that are as competitive as, or even surpass, OpenAI's CLIP in building multimodal foundation models like LLaVA. Moreover, due to their fully open nature, we offer these vision encoders in a wide range of sizes, from as few as 5.9 million parameters to 632.1 million parameters. We further demonstrate that these variable-sized vision encoders provide significant flexibility: larger models deliver enhanced multimodal performance, while smaller models enable efficient and portable multimodal foundation models suitable for edge device deployment. The training data, code and trained models will be released soon.

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/

Remote sensing object detection has made significant progress, but most studies still focus on closed-set detection, limiting generalization across diverse datasets. Open-vocabulary object detection (OVD) provides a solution by leveraging multimodal associations between text prompts and visual features. However, existing OVD methods for remote sensing (RS) images are constrained by small-scale datasets and fail to address the unique challenges of remote sensing interpretation, include oriented object detection and the need for both high precision and real-time performance in diverse scenarios. To tackle these challenges, we propose OpenRSD, a universal open-prompt RS object detection framework. OpenRSD supports multimodal prompts and integrates multi-task detection heads to balance accuracy and real-time requirements. Additionally, we design a multi-stage training pipeline to enhance the generalization of model. Evaluated on seven public datasets, OpenRSD demonstrates superior performance in oriented and horizontal bounding box detection, with real-time inference capabilities suitable for large-scale RS image analysis. Compared to YOLO-World, OpenRSD exhibits an 8.7% higher average precision and achieves an inference speed of 20.8 FPS. Codes and models will be released.

Open-vocabulary (OV) 3D object detection is an emerging field, yet its exploration through image-based methods remains limited compared to 3D point cloud-based methods. We introduce OpenM3D, a novel open-vocabulary multi-view indoor 3D object detector trained without human annotations. In particular, OpenM3D is a single-stage detector adapting the 2D-induced voxel features from the ImGeoNet model. To support OV, it is jointly trained with a class-agnostic 3D localization loss requiring high-quality 3D pseudo boxes and a voxel-semantic alignment loss requiring diverse pre-trained CLIP features. We follow the training setting of OV-3DET where posed RGB-D images are given but no human annotations of 3D boxes or classes are available. We propose a 3D Pseudo Box Generation method using a graph embedding technique that combines 2D segments into coherent 3D structures. Our pseudo-boxes achieve higher precision and recall than other methods, including the method proposed in OV-3DET. We further sample diverse CLIP features from 2D segments associated with each coherent 3D structure to align with the corresponding voxel feature. The key to training a highly accurate single-stage detector requires both losses to be learned toward high-quality targets. At inference, OpenM3D, a highly efficient detector, requires only multi-view images for input and demonstrates superior accuracy and speed (0.3 sec. per scene) on ScanNet200 and ARKitScenes indoor benchmarks compared to existing methods. We outperform a strong two-stage method that leverages our class-agnostic detector with a ViT CLIP-based OV classifier and a baseline incorporating multi-view depth estimator on both accuracy and speed.

This paper addresses the challenge of animal re-identification, an emerging field that shares similarities with person re-identification but presents unique complexities due to the diverse species, environments and poses. To facilitate research in this domain, we introduce OpenAnimals, a flexible and extensible codebase designed specifically for animal re-identification. We conduct a comprehensive study by revisiting several state-of-the-art person re-identification methods, including BoT, AGW, SBS, and MGN, and evaluate their effectiveness on animal re-identification benchmarks such as HyenaID, LeopardID, SeaTurtleID, and WhaleSharkID. Our findings reveal that while some techniques generalize well, many do not, underscoring the significant differences between the two tasks. To bridge this gap, we propose ARBase, a strong Base model tailored for Animal Re-identification, which incorporates insights from extensive experiments and introduces simple yet effective animal-oriented designs. Experiments demonstrate that ARBase consistently outperforms existing baselines, achieving state-of-the-art performance across various benchmarks.

We introduce Hierarchical Streaming Video Understanding, a task that combines online temporal action localization with free-form description generation. Given the scarcity of datasets with hierarchical and fine-grained temporal annotations, we demonstrate that LLMs can effectively group atomic actions into higher-level events, enriching existing datasets.We then propose OpenHOUSE (Open-ended Hierarchical Online Understanding System for Events), which extends streaming action perception beyond action classification. OpenHOUSE features a specialized streaming module that accurately detects boundaries between closely adjacent actions, nearly doubling the performance of direct extensions of existing methods.We envision the future of streaming action perception in the integration of powerful generative models, with OpenHOUSE representing a key step in that direction.

Learning skills in open-world environments is essential for developing agents capable of handling a variety of tasks by combining basic skills. Online demonstration videos are typically long but unsegmented, making them difficult to segment and label with skill identifiers. Unlike existing methods that rely on random splitting or human labeling, we have developed a self-supervised learning-based approach to segment these long videos into a series of semantic-aware and skill-consistent segments. Drawing inspiration from human cognitive event segmentation theory, we introduce Skill Boundary Detection (SBD), an annotation-free temporal video segmentation algorithm. SBD detects skill boundaries in a video by leveraging prediction errors from a pretrained unconditional action-prediction model. This approach is based on the assumption that a significant increase in prediction error indicates a shift in the skill being executed. We evaluated our method in Minecraft, a rich open-world simulator with extensive gameplay videos available online. The SBD-generated segments yielded relative performance improvements of 63.7% and 52.1% for conditioned policies on short-term atomic tasks, and 11.3% and 20.8% for their corresponding hierarchical agents on long-horizon tasks, compared to unsegmented baselines. Our method can leverage the diverse YouTube videos to train instruction-following agents. The project page is at https://craftjarvis.github.io/SkillDiscovery/.

Open Vocabulary Human-Object Interaction (HOI) detection aims to detect interactions between humans and objects while generalizing to novel interaction classes beyond the training set. Current methods often rely on Vision and Language Models (VLMs) but face challenges due to suboptimal image encoders, as image-level pre-training does not align well with the fine-grained region-level interaction detection required for HOI. Additionally, effectively encoding textual descriptions of visual appearances remains difficult, limiting the model's ability to capture detailed HOI relationships. To address these issues, we propose INteraction-aware Prompting with Concept Calibration (INP-CC), an end-to-end open-vocabulary HOI detector that integrates interaction-aware prompts and concept calibration. Specifically, we propose an interaction-aware prompt generator that dynamically generates a compact set of prompts based on the input scene, enabling selective sharing among similar interactions. This approach directs the model's attention to key interaction patterns rather than generic image-level semantics, enhancing HOI detection. Furthermore, we refine HOI concept representations through language model-guided calibration, which helps distinguish diverse HOI concepts by investigating visual similarities across categories. A negative sampling strategy is also employed to improve inter-modal similarity modeling, enabling the model to better differentiate visually similar but semantically distinct actions. Extensive experimental results demonstrate that INP-CC significantly outperforms state-of-the-art models on the SWIG-HOI and HICO-DET datasets. Code is available at https://github.com/ltttpku/INP-CC.

Deepfake detection methods are becoming increasingly crucial for identity security and have recently been employed to support legal proceedings. However, these methods often exhibit unfairness due to flawed logical reasoning, undermining the reliability of their predictions and raising concerns about their applicability in legal contexts. To mitigate this bias, existing approaches typically rely on predefined demographic attributes, such as race and gender. However, these assumptions are inherently limited, as different deepfake detectors exhibit substantial variations in fairness performance, often uncovering intricate and unforeseen bias patterns. To this end, we propose the Adversarial Open-Unfairness Discovery and Mitigation Network (AdvOU), a novel framework designed to mitigate unpredictable unfairness in deepfake detection. Our approach strengthens general deepfake detectors by equipping them with a lightweight Unfairness Regulator (UR), which dynamically identifies and mitigates bias. Furthermore, we propose an adversarial learning paradigm that alternates between the training of the Open-Unfairness Discovery (OUD) module and the Unfairness Adversarial Mitigation (UAM) module. The former intensifies unfairness within UR to reveal underlying bias patterns, while the latter leverages fairness in the detector by enforcing adversarial robustness against unfairness. Extensive experiments on widely used deepfake datasets validate the effectiveness of our approach, outperforming state-of-the-art methods in both fairness and generalization evaluations for cross-domain deepfake detection. The code is available at https://github.com/liacaaa/AdvOU.

Reasoning segmentation (RS) aims to identify and segment objects of interest based on implicit text queries. As such, RS is a catalyst for embodied AI agents, enabling them to interpret high-level commands without requiring explicit step-by-step guidance. However, current RS approaches rely heavily on the visual perception capabilities of multimodal large language models (LLMs), leading to several major limitations. First, they struggle with queries that require multiple steps of reasoning or those that involve complex spatial/temporal relationships. Second, they necessitate LLM fine-tuning, which may require frequent updates to maintain compatibility with contemporary LLMs and may increase risks of catastrophic forgetting during fine-tuning. Finally, being primarily designed for static images or offline video processing, they scale poorly to online video data. To address these limitations, we propose an agent framework that disentangles perception and reasoning for online video RS without LLM fine-tuning. Our innovation is the introduction of a just-in-time digital twin concept, where -- given an implicit query -- an LLM plans the construction of a low-level scene representation from high-level video using specialist vision models. We refer to this approach to creating a digital twin as "just-in-time" because the LLM planner will anticipate the need for specific information and only request this limited subset instead of always evaluating every specialist model. The LLM then performs reasoning on this digital twin representation to identify target objects. To evaluate our approach, we introduce a new comprehensive video reasoning segmentation benchmark comprising 200 videos with 895 implicit text queries. The benchmark spans three reasoning categories (semantic, spatial, and temporal) with three different reasoning chain complexity. Experimental results demonstrate that our method performs best across all reasoning categories, suggesting that our just-in-time digital twin can bridge the gap between high-level reasoning and low-level perception in embodied AI. Benchmark is available at https://github.com/yiqings/jitbench/.

To enable AI agents to interact seamlessly with both humans and 3D environments, they must not only perceive the 3D world accurately but also align human language with 3D spatial representations. While prior work has made significant progress by integrating language features into geometrically detailed 3D scene representations using 3D Gaussian Splatting (GS), these approaches rely on computationally intensive offline preprocessing of language features for each input image, limiting adaptability to new environments. In this work, we introduce Online Language Splatting, the first framework to achieve online, near real-time, open-vocabulary language mapping within a 3DGS-SLAM system without requiring pre-generated language features. The key challenge lies in efficiently fusing high-dimensional language features into 3D representations while balancing the computation speed, memory usage, rendering quality and open-vocabulary capability. To this end, we innovatively design: (1) a high-resolution CLIP embedding module capable of generating detailed language feature maps in 18ms per frame, (2) a two-stage online auto-encoder that compresses 768-dimensional CLIP features to 15 dimensions while preserving open-vocabulary capabilities, and (3) a color-language disentangled optimization approach to improve rendering quality. Experimental results show that our online method not only surpasses the state-of-the-art offline methods in accuracy but also achieves more than 40x efficiency boost, demonstrating the potential for dynamic and interactive AI applications.

Generic Event Boundary Detection (GEBD) aims to interpret long-form videos through the lens of human perception. However, current GEBD methods require processing complete video frames to make predictions, unlike humans processing data online and in real-time. To bridge this gap, we introduce a new task, Online Generic Event Boundary Detection (On-GEBD), aiming to detect boundaries of generic events immediately in streaming videos. This task faces unique challenges of identifying subtle, taxonomy-free event changes in real-time, without the access to future frames. To tackle these challenges, we propose a novel On-GEBD framework, ESTimator, inspired by Event Segmentation Theory (EST) which explains how humans segment ongoing activity into events by leveraging the discrepancies between predicted and actual information. Our framework consists of two key components: the Consistent Event Anticipator (CEA), and the Online Boundary Discriminator (OBD). Specifically, the CEA generates a prediction of the future frame reflecting current event dynamics based solely on prior frames. Then, the OBD measures the prediction error and adaptively adjusts the threshold using statistical tests on past errors to capture diverse, subtle event transitions. Experimental results demonstrate that ESTimator outperforms all baselines adapted from recent online video understanding models and achieves performance comparable to prior offline-GEBD methods on the Kinetics-GEBD and TAPOS datasets.

Dense point tracking is a challenging task requiring the continuous tracking of every point in the initial frame throughout a substantial portion of a video, even in the presence of occlusions. Traditional methods use optical flow models to directly estimate long-range motion, but they often suffer from appearance drifting without considering temporal consistency. Recent point tracking algorithms usually depend on sliding windows for indirect information propagation from the first frame to the current one, which is slow and less effective for long-range tracking. To account for temporal consistency and enable efficient information propagation, we present a lightweight and fast model with Streaming memory for dense POint Tracking and online video processing. The SPOT framework features three core components: a customized memory reading module for feature enhancement, a sensory memory for short-term motion dynamics modeling, and a visibility-guided splatting module for accurate information propagation. This combination enables SPOT to perform dense point tracking with state-of-the-art accuracy on the CVO benchmark, as well as comparable or superior performance to offline models on sparse tracking benchmarks such as TAP-Vid and RoboTAP. Notably, SPOT with 10x smaller parameter numbers operates at least 2x faster than previous state-of-the-art models while maintaining the best performance on CVO. We will release the models and codes at: https://dqiaole.github.io/SPOT/.

Existing solutions for creating high-fidelity digital head avatars encounter various obstacles. Traditional rendering tools offer realistic results, while heavily requiring expert skills. Neural rendering methods are more efficient but often compromise between the generated fidelity and flexibility. We present OneGT that adheres to the frameworks of the rendering tools, while restructuring individual stages of the rendering pipeline through neural networks. OneGT maintains high systemic interpretability, inheriting the superior performances of neural rendering approaches. Specifically, OneGT contains a skeleton-anchoring stage and a texture-rendering stage, in which well-designed Transformers learn the geometric transformations and the proposed reference-perceptible DiT renders the textures respectively. Our framework learns geometric consistency from the innovatively introduced synthetic data, thus achieving superior performance while requiring only 10%-30% of the real-world data typically used by competitive methods. Experimental results demonstrate that OneGT achieves high fidelity in producing portrait avatars, meanwhile maintaining the flexibility of editing.

Specular highlights, though valuable for human perception, are often undesirable in computer vision and graphics tasks as they can obscure surface details and affect analysis. Existing methods rely on multi-stage pipelines or multi-label datasets, making training difficult. In this study, we propose a one-step diffusion-based model for specular highlight removal, leveraging a pre-trained diffusion-based image generation model with an adaptation mechanism to enhance efficiency and adaptability. To further improve the adaptation process, we introduce ProbLoRA, a novel modification of Low-Rank Adaptation (LoRA), designed to adapt the diffusion model for highlight removal effectively. Our approach surpasses existing methods, achieving state-of-the-art performance in both quantitative metrics and visual quality. Extensive experiments on synthetic and real-world datasets demonstrate the effectiveness of our method, highlighting its robustness and generalization capabilities.

Edge computing in person re-identification (ReID) is crucial for reducing the load on central cloud servers and ensuring user privacy. Conventional compression methods for obtaining compact models require computations for each individual student model. When multiple models of varying sizes are needed to accommodate different resource conditions, this leads to repetitive and cumbersome computations. To address this challenge, we propose a novel knowledge inheritance approach named OSKT (One-Shot Knowledge Transfer), which consolidates the knowledge of the teacher model into an intermediate carrier called a weight chain. When a downstream scenario demands a model that meets specific resource constraints, this weight chain can be expanded to the target model size without additional computation. OSKT significantly outperforms state-of-the-art compression methods, with the added advantage of one-time knowledge transfer that eliminates the need for frequent computations for each target model.