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Sounak Mondal, Naveen Sendhilnathan, Ting Zhang, Yue Liu, Michael Proulx, Michael Louis Iuzzolino, Chuan Qin, Tanya R. Jonker

Decoding human intent from eye gaze during a visual search task has become an increasingly important capability within augmented and virtual reality systems. However, gaze target prediction models used within such systems are constrained by the predefined target categories found within available gaze data, limiting their generalizability to novel categories and their usefulness within real-world, interactive systems. In this work, we present the Gaze-Language Alignment Model (GLAM), a vision-language model that can generalize gaze target predictions to novel categories of search targets lacking gaze annotation. To do so, GLAM uses a novel gaze encoder to encode foveal and peripheral information of a gaze scanpath. The resultant gaze embeddings are aligned with language embeddings of large language model-generated search descriptions for associated target categories using a novel contrastive learning strategy called Gaze-Language Alignment Decomposition (GLAD). When used to train GLAM in a zero-shot setup, GLAD surpassed naive contrastive learning strategies by nearly one-third in target prediction accuracy, even outperforming a fully supervised baseline. Moreover, in a fully supervised setup, GLAM outperformed previous methods in target prediction accuracy, regardless of the training strategy used.

Yilei Jiang, Wei-Hong Li, Yiyuan Zhang, Minghong Cai, Xiangyu Yue

While Diffusion Models (DM) exhibit remarkable performance across various image generative tasks, they nonetheless reflect the inherent bias presented in the training set.As DMs are now widely used in real-world applications, these biases could perpetuate a distorted worldview and hinder opportunities for minority groups. Existing methods on debiasing DMs usually requires model re-training with a human-crafted reference dataset or additional classifiers, which suffer from two major limitations: (1) collecting reference datasets causes expensive annotation cost; (2) the debiasing performance is heavily constrained by the quality of the reference dataset or the additional classifier. To address the above limitations, we propose FairGen, a plug-and-play method that learns attribute latent directions in a self-discovering manner, thus eliminating the reliance on such reference dataset. Specifically, FairGen consists of two parts: a set of attribute adapters and a distribution indicator. Each adapter in the set aims to learn an attribute latent direction, and is optimized via noise composition through a self-discovering process.Then, the distribution indicator is multiplied by the set of adapters to guide the generation process towards the prescribed distribution. Our method enables debiasing multiple attributes in DMs simultaneously, while remaining lightweight and easily integrable with other DMs, eliminating the need for re-training. Extensive experiments on debiasing gender, racial, and their intersectional biases show that our method outperforms previous SOTA by a large margin.

Yujia Tong, Yuze Wang, Jingling Yuan, Chuang Hu

Model quantization enables efficient deployment of deep neural networks on edge devices through low-bit parameter representation, yet raises critical challenges for implementing machine unlearning (MU) under data privacy regulations. Existing MU methods designed for full-precision models fail to address two fundamental limitations in quantized networks: 1) Noise amplification from label mismatch during data processing, and 2) Gradient imbalance between forgotten and retained data during training. These issues are exacerbated by quantized models' constrained parameter space and discrete optimization. We propose Q-MUL, the first dedicated unlearning framework for quantized models. Our method introduces two key innovations: 1) Similar Labels assignment replaces random labels with semantically consistent alternatives to minimize noise injection, and 2) Adaptive Gradient Reweighting dynamically aligns parameter update contributions from forgotten and retained data. Through systematic analysis of quantized model vulnerabilities, we establish theoretical foundations for these mechanisms. Extensive evaluations on benchmark datasets demonstrate Q-MUL's superiority over existing approaches.

Wenjie Zhuo, Fan Ma, Hehe Fan

We present InfiniDreamer, a novel framework for generating human motions of arbitrary length. Existing methods typically produce only short sequences, limited by the scarcity of long-range motion data. To address this, InfiniDreamer first generates short sub-motions for each textual description, then coarsely assembles them into a long sequence using randomly initialized transition segments. To refine this coarse motion, we introduce Segment Score Distillation (SSD)---an optimization-based approach that leverages a pre-trained motion diffusion model trained solely on short clips. SSD iteratively refines overlapping short segments sampled from the full sequence, progressively aligning them with the pre-trained short motion prior. This procedure ensures local fidelity within each segment and global consistency across segments. Extensive experiments demonstrate that InfiniDreamer produces coherent, diverse, and context-aware long-range motions without requiring additional long-sequence training.

Léopold Maillard, Tom Durand, Adrien Ramanana Rahary, Maks Ovsjanikov

Existing generative approaches for guided image synthesis of multi-object scenes typically rely on 2D controls in the image or text space. As a result, these methods struggle to maintain and respect consistent three-dimensional geometric structure, underlying the scene. In this paper, we propose a novel conditioning approach, training method and adapter network that can be plugged into pretrained text-to-image diffusion models. Our approach provides a way to endow such models with 3D-awareness, while leveraging their rich prior knowledge. Our method supports camera control, conditioning on explicit 3D geometries and, for the first time, accounts for the entire context of a scene, i.e., both on and off-screen items, to synthesize plausible and semantically rich images. Despite its multi-modal nature, our model is lightweight, requires a reasonable number of data for supervised learning and shows remarkable generalization power. We also introduce methods for intuitive and consistent image editing and restyling, e.g., by positioning, rotating or resizing individual objects in a scene. Our method integrates well within various image creation workflows and enables a richer set of applications compared to previous approaches.

Haonan Wang, Qixiang Zhang, Lehan Wang, Xuanqi Huang, Xiaomeng Li

Decoding visual stimuli from neural activity is essential for understanding the human brain. While fMRI methods have successfully reconstructed static images, fMRI-tovideo reconstruction faces challenges due to the need for capturing spatiotemporal dynamics like motion and scene transitions. Recent approaches have improved semantic and perceptual alignment but struggle to integrate coarse fMRI data with detailed visual features. Inspired by the hierarchical organization of the visual system, we propose NEURONS, a novel framework that decouples learning into four correlated sub-tasks: key object segmentation, concept recognition, scene description, and blurry video reconstruction. This approach simulates the visual cortex's functional specialization, allowing the model to capture diverse video content. In the inference stage, NEURONS generates robust conditioning signals for a pre-trained textto-video diffusion model to reconstruct the videos. Extensive experiments demonstrate that NEURONS outperforms state-of-the-art baselines, achieving solid improvements in video consistency (26.6%) and semantic-level accuracy (19.1%). Notably, NEURONS shows a strong functional correlation with the visual cortex, highlighting its potential for brain-computer interfaces and clinical applications. Code and model weights are available at: https://github.com/xmed-lab/NEURONS.

Junsung Park, Jungbeom Lee, Jongyoon Song, Sangwon Yu, Dahuin Jung, Sungroh Yoon

While CLIP has significantly advanced multimodal understanding by bridging vision and language, the inability to grasp negation -- such as failing to differentiate concepts like "parking" from "no parking" -- poses substantial challenges.By analyzing the data used in the public CLIP model's pre-training, we posit this limitation stems from a lack of negation-inclusive data.To address this, we introduce data generation pipelines that employ a large language model (LLM) and a multimodal LLM to produce negation-inclusive captions.Fine-tuning CLIP with data generated from our pipelines, we develop NegationCLIP, which enhances negation awareness while preserving the generality.Moreover, to enable a comprehensive evaluation of negation understanding, we propose NegRefCOCOg--a benchmark tailored to test VLMs' ability to interpret negation across diverse expressions and positions within a sentence.Experiments on various CLIP architectures validate the effectiveness of our data generation pipelines in enhancing CLIP's ability to perceive negation accurately.Additionally, NegationCLIP's enhanced negation awareness has practical applications across various multimodal tasks, demonstrated by performance gains in text-to-image generation and referring image segmentation.

Renjie Lu, Yu Zhou, Hao Cheng, Jingke Meng, Wei-Shi Zheng

Vision and Language Navigation(VLN) requires agents to navigate 3D environments by following natural language instructions. While existing methods predominantly assume access to panoramic observations, many practical robotics are equipped with monocular RGBD cameras, creating a significant configuration disparity. In this work, we address this critical gap by developing a novel 3DGS-based framework for monocular VLN agents, focusing on the intrinsic information incompleteness challenge. Our approach incorporates two key innovations: (1) implicit partial completion module for inferring representations of missing regions in incompletely rendered panoramic feature maps, and (2) an uncertainty-aware active perception strategy that enables the agent to actively acquire visual observation when uncertain about its decision. Extensive experiments on R2R-CE and RxR-CE datasets demonstrate that our monoVLN outperforms all existing monocular methods, significantly improve 8% success rate on R2R-CE compared to previous monocular methods. We also validate our monoVLN in real-world environments, providing a practical solution for real-world VLN.

Yuxi Xiao, Jianyuan Wang, Nan Xue, Nikita Karaev, Yuri Makarov, Bingyi Kang, Xing Zhu, Hujun Bao, Yujun Shen, Xiaowei Zhou

We present SpatialTrackerV2, a feed-forward 3D point tracking method for monocular videos. Going beyond modular pipelines built on off-the-shelf components for 3D tracking, our approach unifies the intrinsic connections between point tracking, monocular depth, and camera pose estimation into a high-performing and feedforward 3D point tracker. It decomposes world-space 3D motion into scene geometry, camera ego-motion, and pixel-wise object motion, with a fully differentiable and end-to-end architecture, allowing scalable training across a wide range of datasets, including synthetic sequences, posed RGB-D videos, and unlabeled in-the-wild footage. By learning geometry and motion jointly from such heterogeneous data, SpatialTrackerV2 outperforms existing 3D tracking methods by 30%, and matches the accuracy of leading dynamic 3D reconstruction approaches while running 50x faster.

Aysan Aghazadeh, Adriana Kovashka

We address the task of advertisement image generation and introduce three evaluation metrics to assess Creativity, prompt Alignment, and Persuasiveness (CAP) in generated advertisement images. Despite recent advancements in Text-to-Image (T2I) methods and their performance in generating high-quality images for explicit descriptions, evaluating these models remains challenging. Existing evaluation methods focus largely on assessing alignment with explicit, detailed descriptions, but evaluating alignment with visually implicit prompts remains an open problem. Additionally, creativity and persuasiveness are essential qualities that enhance the effectiveness of advertisement images, yet are seldom measured. To address this, we propose three novel metrics for evaluating the creativity, alignment, and persuasiveness of generated images. We show that current T2I models struggle with creativity, persuasiveness, and alignment when the input text is implicit messages. We further introduce a simple yet effective approach to enhance T2I models' capabilities in producing images that are better aligned, more creative, and more persuasive.

Ruiyang Ha, Songyi Jiang, Bin Li, Bikang Pan, Yihang Zhu, Junjie Zhang, Xiatian Zhu, Shaogang Gong, Jingya Wang

Conventional person re-identification (ReID) research is often limited to single-modality sensor data from static cameras, which fails to address the complexities of real-world scenarios where multi-modal signals are increasingly prevalent. For instance, consider an urban ReID system integrating stationary RGB cameras, nighttime infrared sensors, and UAVs equipped with dynamic tracking capabilities. Such systems face significant challenges due to variations in camera perspectives, lighting conditions, and sensor modalities, hindering effective person ReID.To address these challenges, we introduce the MP-ReID benchmark, a novel dataset designed specifically for multi-modality and multi-platform ReID. This benchmark uniquely compiles data from 1,930 identities across diverse modalities, including RGB, infrared, and thermal imaging, captured by both UAVs and ground-based cameras in indoor and outdoor environments.Building on this benchmark, we introduce Uni-Prompt ReID, a framework with specific-designed prompts, tailored for cross-modality and cross-platform scenarios. Our method consistently outperforms state-of-the-art approaches, establishing a robust foundation for future research in complex and dynamic ReID environments. Additionally, our dataset will be made publicly available to support further advancements.

Tian-Xing Xu, Xiangjun Gao, Wenbo Hu, Xiaoyu Li, Song-Hai Zhang, Ying Shan

Despite remarkable advancements in video depth estimation, existing methods fall short in geometric fidelity due to their affine-invariant predictions, restricting their applicability in reconstruction and other metrically grounded downstream tasks. We propose a novel point map Variational Autoencoder (VAE) for encoding and decoding unbounded point maps. Notably, its latent space is agnostic to video latent distributions of video diffusion models, allowing us to leverage generation priors to model the distribution of point map sequences conditioned on the input videos. Thus, we can recover high-fidelity point map sequences with temporal coherence from open-world videos, facilitating accurate 3D/4D reconstruction, camera parameter estimation, and other depth-based applications. Extensive evaluations on diverse datasets demonstrate that our method achieves state-of-the-art 3D accuracy, temporal consistency, and generalization capability.

Wenjie Chang, Hanzhi Chang, Yueyi Zhang, Wenfei Yang, Tianzhu Zhang

Indirect Time-of-Flight (iToF) cameras are popular for 3D perception because they are cost-effective and easy to deploy. They emit modulated infrared signals to illuminate the scene and process the received signals to generate amplitude and phase images. The depth is calculated from the phase using the modulation frequency. However, the obtained depth often suffers from noise caused by multi-path interference, low signal-to-noise ratio (SNR), and depth wrapping. Building on recent advancements in neural scene representations, which have shown great potential in 3D modeling from multi-view RGB images, we propose leveraging this approach to reconstruct 3D representations from noisy iToF data. Our method utilizes the multi-view consistency of amplitude and phase maps, fusing information from all input views to generate an accurate scene representation. Considering the impact of infrared illumination, we propose a new rendering scheme for amplitude maps based on signed distance function (SDF) and introduce a neural lighting function to model the appearance variations caused by active illumination. We also incorporate a phase-guided sampling strategy and a wrapping-aware phase-to-depth loss to utilize raw phase information and mitigate depth wrapping. Additionally, we add a noise-weight loss to prevent excessive smoothing information across noisy multi-view measurements. Experiments conducted on synthetic and real-world datasets demonstrate that the proposed method outperforms state-of-the-art techniques.

Letian Zhang, Quan Cui, Bingchen Zhao, Cheng Yang

The success of multi-modal large language models (MLLMs) has been largely attributed to the large-scale training data. However, the training data of many MLLMs is unavailable due to privacy concerns. The expensive and labor-intensive process of collecting multi-modal data further exacerbates the problem. Is it possible to synthesize multi-modal training data automatically without compromising diversity and quality? In this paper, we propose a new method, Oasis, to synthesize high-quality multi-modal data with only images. Oasis breaks through traditional methods by prompting only images to the MLLMs, thus extending the data diversity by a large margin. Our method features a delicate quality control method which ensures the data quality. We collected over 500k data and conducted incremental experiments on LLaVA-NeXT. Extensive experiments demonstrate that our method can significantly improve the performance of MLLMs. The image-based synthesis also allows us to focus on the specific-domain ability of MLLMs. Code and data will be publicly available.

Chengxu Liu, Lu Qi, Jinshan Pan, Xueming Qian, Ming-Hsuan Yang

Since acquiring large amounts of realistic blurry-sharp image pairs is difficult and expensive, learning blind image deblurring from unpaired data is a more practical and promising solution. Unfortunately, most existing approaches only use adversarial learning to bridge the gap from blurry domains to sharp domains, ignoring the complex and unpredictable nature of real-world blurry patterns. In this paper, we propose a novel diffusion model (DM)-based framework, dubbed TP-Diff, for image deblurring by learning spatially varying texture prior from unpaired sharp data. In particular, TP-Diff performs DM to generate the prior knowledge used to recover the texture of blurry images. To implement it, we propose a Texture Prior Encoder (TPE) that introduces a memory mechanism to encode the texture prior and thereby provide supervision for the DM training. To fully exploit the generated texture priors, we further present the Texture Transfer Transformer layer (TTformer), in which a novel Filter-Modulated Multi-head Self-Attention (FM-MSA) efficiently removes spatially varying blurring through adaptive filtering. In addition, a wavelet-based adversarial loss is used to preserve high-frequency texture details. Extensive evaluations demonstrate that TP-Diff provides a promising unsupervised deblurring solution and outperforms SOTA methods in six widely-used benchmarks.

Anand Kumar, Jiteng Mu, Nuno Vasconcelos

Text-to-image (T2I) models have recently gained widespread adoption. This has spurred concerns about safeguarding intellectual property rights and an increasing demand for mechanisms that prevent the generation of specific artistic styles. Existing methods for style extraction typically necessitate the collection of custom datasets and the training of specialized models. This, however, is resource-intensive, time-consuming, and often impractical for real-time applications. We present a novel, training-free framework to solve the style attribution problem, using the features produced by a diffusion model alone, without any external modules or retraining. This is denoted as Introspective Style attribution(IntroStyle) and is shown to have superior performance to state-of-the-art models for style attribution. We also introduce a synthetic dataset of Artistic Style Split (ArtSplit) to isolate artistic style and evaluate fine-grained style attribution performance. Our experimental results on WikiArt and DomainNet datasets show that \ours is robust to the dynamic nature of artistic styles, outperforming existing methods by a wide margin.

Jingyi Yang, Xun Lin, Zitong Yu, Liepiao Zhang, Xin Liu, Hui Li, Xiaochen Yuan, Xiaochun Cao

With the availability of diverse sensor modalities (i.e., RGB, Depth, Infrared) and the success of multi-modal learning, multi-modal face anti-spoofing (FAS) has emerged as a prominent research focus. The intuition behind it is that leveraging multiple modalities can uncover more intrinsic spoofing traces. However, this approach presents more risk of misalignment. We identify two main types of misalignment: (1) Intra-domain modality misalignment, where the importance of each modality varies across different attacks. For instance, certain modalities (e.g., Depth) may be non-defensive against specific attacks (e.g., 3D mask), indicating that each modality has unique strengths and weaknesses in countering particular attacks. Consequently, simple fusion strategies may fall short. (2) Inter-domain modality misalignment, where the introduction of additional modalities exacerbates domain shifts, potentially overshadowing the benefits of complementary fusion. To tackle (1), we propose an alignment module between modalities based on mutual information, which adaptively enhances favorable modalities while suppressing unfavorable ones. To address (2), we employ a dual alignment optimization method that aligns both sub-domain hyperplanes and modality angle margins, thereby mitigating domain gaps. Our method, dubbed Dual Alignment of Domain and Modality (DADM), achieves state-of-the-art performance in extensive experiments across four challenging protocols, demonstrating its robustness in multi-modal domain generalization scenarios. Our code is available at \href https://github.com/yjyddq/DADM here .

Jun Xiang, Yudong Guo, Leipeng Hu, Boyang Guo, Yancheng Yuan, Juyong Zhang

Building realistic and animatable avatars still requires minutes of multi-view or monocular self-rotating videos, and most methods lack precise control over gestures and expressions. To push this boundary, we address the challenge of constructing a whole-body talking avatar from a single image. We propose a novel pipeline that tackles two critical issues: 1) complex dynamic modeling and 2) generalization to novel gestures and expressions. To achieve seamless generalization, we leverage recent pose-guided image-to-video diffusion models to generate imperfect video frames as pseudo-labels. To overcome the dynamic modeling challenge posed by inconsistent and noisy pseudo-frames, we introduce a tightly coupled 3DGS-mesh hybrid avatar representation and apply several key regularizations to mitigate inconsistencies caused by imperfect labels. Extensive experiments on diverse subjects demonstrate that our method enables the creation of a photorealistic, precisely animatable, and expressive whole-body talking avatar from just a single image.

Siyuan Yan, Ming Hu, Yiwen Jiang, Xieji Li, Hao Fei, Philipp Tschandl, Harald Kittler, Zongyuan Ge

The emergence of vision-language models has transformed medical AI, enabling unprecedented advances in diagnostic capability and clinical applications. However, progress in dermatology has lagged behind other medical domains due to the lack of standard image-text pairs. Existing dermatological datasets are limited in both scale and depth, offering only single-label annotations across a narrow range of diseases instead of rich textual descriptions, and lacking the crucial clinical context needed for real-world applications. To address these limitations, we present Derm1M, the first large-scale vision-language dataset for dermatology, comprising 1,029,761 image-text pairs. Built from diverse educational resources and structured around a standard ontology collaboratively developed by experts, Derm1M provides comprehensive coverage for over 390 skin conditions across four hierarchical levels and 130 clinical concepts with rich contextual information such as medical history, symptoms, and skin tone. To demonstrate Derm1M's potential in advancing both AI research and clinical application, we pretrained a series of CLIP-like models, collectively called DermLIP, on this dataset. The DermLIP family significantly outperforms state-of-the-art foundation models on eight diverse datasets across multiple tasks, including zero-shot skin disease classification, clinical and artifacts concept identification, few-shot/full-shot learning, and cross-modal retrieval. Our dataset and code are available at https://github.com/SiyuanYan1/Derm1M.

Zhengxuan Wei, Jiajin Tang, Sibei Yang

Existing Moment Retrieval methods face three critical bottlenecks: (1) data scarcity forces models into shallow keyword-feature associations; (2) boundary ambiguity in transition regions between adjacent events; (3) insufficient discrimination of fine-grained semantics (e.g., distinguishing "kicking" vs. "throwing" a ball). In this paper, we propose a zero-external-dependency Augmented Moment Retrieval framework, AMR, designed to overcome local optima caused by insufficient data annotations and the lack of robust boundary and semantic discrimination capabilities. AMR is built upon two key insights: (1) it resolves ambiguous boundary information and semantic confusion in existing annotations without additional data (avoiding costly manual labeling), and (2) it preserves boundary and semantic discriminative capabilities enhanced by training while generalizing to real-world scenarios, significantly improving performance. Furthermore, we propose a two-stage training framework with cold-start and distillation adaptation. The cold-start stage employs curriculum learning on augmented data to build foundational boundary/semantic awareness. The distillation stage introduces dual query sets: Original Queries maintain DETR-based localization using frozen Base Queries from the cold-start model, while Active Queries dynamically adapt to real-data distributions. A cross-stage distillation loss enforces consistency between Original and Base Queries, preventing knowledge forgetting while enabling real-world generalization. Experiments on multiple benchmarks show that AMR achieves improved performance over prior state-of-the-art approaches.