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Linwei Chen, Lin Gu, Ying Fu

Vision Transformers (ViTs) have significantly advanced computer vision, demonstrating strong performance across various tasks. However, the attention mechanism in ViTs makes each layer function as a low-pass filter, and the stacked-layer architecture in existing transformers suffers from frequency vanishing. This leads to the loss of critical details and textures.We propose a novel, circuit-theory-inspired strategy called Frequency-Dynamic Attention Modulation (FDAM), which can be easily plugged into ViTs. FDAM directly modulates the overall frequency response of ViTs and consists of two techniques: Attention Inversion (AttInv) and Frequency Dynamic Scaling (FreqScale).Since circuit theory uses low-pass filters as fundamental elements, we introduce AttInv, a method that generates complementary high-pass filtering by inverting the low-pass filter in the attention matrix, and dynamically combining the two. We further design FreqScale to weight different frequency components for fine-grained adjustments to the target response function.Through feature similarity analysis and effective rank evaluation, we demonstrate that our approach avoids representation collapse, leading to consistent performance improvements across various models, including SegFormer, DeiT, and MaskDINO. These improvements are evident in tasks such as semantic segmentation, object detection, and instance segmentation. Additionally, we apply our method to remote sensing detection, achieving state-of-the-art results in single-scale settings. The code is available at https://github.com/Linwei-Chen/FDAM

Yanguang Sun, Jiawei Lian, Jian Yang, Lei Luo

Large-scale foundation models provide powerful feature representations for downstream object segmentation tasks. However, when adapted to specific tasks through the full-parameter fine-tuning, the enormous parameters being updated often results in significant computational overhead, creating a bottleneck in training efficiency. Although existing methods attempt to fine-tune frozen models by directly embedding trainable prompts, these prompts lack inherent semantic priors, limiting the adaptability of large-scale models. In this paper, we propose a novel dynamic priors-based fine-tuning paradigm with fewer trainable parameters, dubbed Controllable-LPMoE, which adaptively modulates frozen foundation models by dynamically controlling local priors to enhance fine-grained perception for specific segmentation tasks. More specifically, we construct a lightweight dynamic mixed local priors extractor that captures diverse local priors from input images through heterogeneous convolutions while employing a gating network to dynamically output expert priors required for the subsequent fine-tuning. Furthermore, we design a bi-directional interaction adapter that employs cosine-aligned deformable attention and channel-oriented adaptive scale enhancement to interact and restructure between frozen and trainable features, achieving efficient fine-tuning. Extensive experiments validate the superiority of our Controllable-LPMoE approach, demonstrating excellent segmentation performance compared to 31 state-of-the-art (SOTA) methods and adaptability to multiple binary object segmentation tasks.

Zongyan Han, Mohamed El Amine Boudjoghra, Jiahua Dong, Jinhong Wang, Rao Muhammad Anwer

Unified segmentation of 3D point clouds is crucial for scene understanding, but is hindered by its sparse structure, limited annotations, and the challenge of distinguishing fine-grained object classes in complex environments. Existing methods often struggle to capture rich semantic and contextual information due to limited supervision and a lack of diverse multimodal cues, leading to suboptimal differentiation of classes and instances. To address these challenges, we propose VDG-Uni3DSeg, a novel framework that integrates pre-trained vision-language models (e.g., CLIP) and large language models (LLMs) to enhance 3D segmentation. By leveraging LLM-generated textual descriptions and reference images from the internet, our method incorporates rich multimodal cues, facilitating fine-grained class and instance separation. We further design a Semantic-Visual Contrastive Loss to align point features with multimodal queries and a Spatial Enhanced Module to model scene-wide relationships efficiently. Operating within a closed-set paradigm that utilizes multimodal knowledge generated offline, VDG-Uni3DSeg achieves state-of-the-art results in semantic, instance, and panoptic segmentation, offering a scalable and practical solution for 3D understanding.Our code is available at https://github.com/Hanzy1996/VDG-Uni3DSeg.

Tim Elsner, Paula Usinger, Julius Nehring-Wirxel, Gregor Kobsik, Victor Czech, Yanjiang He, Isaak Lim, Leif Kobbelt

In language processing, transformers benefit greatly from characters being condensed into word fragments, building outputs from a larger vocabulary of bigger pieces. This is often done with Byte Pair Encoding. In the context of images, tokenisation of visual data is usually limited to regular grids obtained from quantisation methods, not using such further abstraction of regions.Our work improves tokenisation of visual data by bringing Byte Pair Encoding from 1D to multiple dimensions, as a complementary add-on to existing compression. We achieve this through counting constellations of token pairs and replacing the most frequent token pair with a newly introduced token. Our approach only increases computation time by a factor of 2 for images, making it applicable even to large datasets like ImageNet within minutes on consumer hardware. This is a lossless preprocessing step. We further propose how networks can digest the new tokens that are no longer in a regular grid. Our evaluation shows improved training and inference performance of transformers on visual data achieved by compressing frequent constellations of tokens: The resulting sequences have more uniformly distributed information content, e.g. by condensing empty regions in an image into single tokens. As our experiments show, these condensed sequences are easier to process.

Jiong Yin, Liang Li, Jiehua Zhang, Yuhan Gao, Chenggang Yan, Xichun Sheng

Audio-visual multi-task incremental learning aims to continuously learn from multiple audio-visual tasks without the need for joint training on all tasks. The challenge of the problem is how to preserve the old task knowledge while facilitating the learning of new task with previous experiences. To address these challenges, we introduce a three-stage Progressive Homeostatic and Plastic audio-visual prompt (PHP) method. In the shallow phase, we design the task-shared modality aggregating adapter to foster cross-task and cross-modal audio-visual representation learning to enhance shared understanding between tasks. In the middle phase, we propose the task-specific modality-shared dynamic generating adapter, which constructs prompts that are tailored to individual tasks while remaining general across modalities, which balances the model's ability to retain knowledge against forgetting with its potential for versatile multi-task transferability. In the deep phase, we introduce the task-specific modality-independent prompts to further refine the understand ability by targeting individual information for each task and modality. By incorporating these three phases, PHP retains task-specific prompts while adapting shared parameters for new tasks to effectively balance knowledge sharing and specificity. Our method achieves SOTA performance in different orders of four tasks (AVE, AVVP, AVS and AVQA). Our code can be available at https://github.com/ENJOY-Yin-jiong/PHP.

Fabian Perez, Sara Rojas, Carlos Hinojosa, Hoover Rueda-Chacón, Bernard Ghanem

Neural Radiance Field (NeRF)-based segmentation methods focus on object semantics and rely solely on RGB data, lacking intrinsic material properties. This limitation restricts accurate material perception, which is crucial for robotics, augmented reality, simulation, and other applications. We introduce UnMix-NeRF, a framework that integrates spectral unmixing into NeRF, enabling joint hyperspectral novel view synthesis and unsupervised material segmentation. Our method models spectral reflectance via diffuse and specular components, where a learned dictionary of global endmembers represents pure material signatures, and per-point abundances capture their distribution. For material segmentation, we use spectral signature predictions along learned endmembers, allowing unsupervised material clustering. Additionally, UnMix-NeRF enables scene editing by modifying learned endmember dictionaries for flexible material-based appearance manipulation. Extensive experiments validate our approach, demonstrating superior spectral reconstruction and material segmentation to existing methods.

Shaobo Zhang, Yuhang Huang, Wanqing Zhao, Wei Zhao, Ziyu Guan, Jinye Peng

This paper introduces EA6D, a novel diffusion-based framework for 6D pose estimation that operates effectively in any environment. Traditional pose estimation methods struggle with the variability and complexity of real-world scenarios, often leading to overfitting on controlled datasets and poor generalization to new scenes. To address these challenges, we propose a generative pose estimation paradigm that generates environment-independent object representations for pose estimation, which are robust to environmental variations such as illumination, occlusion, and background clutter. Specifically, we propose the novel Environment Decoupling Diffusion Model (EDDM) which separates object representations from environmental factors while enabling efficient few-step sampling by leveraging input image priors instead of pure noise initialization. We validate our approach on four standard benchmarks and a self-made dataset DiverseScenes. The results demonstrate that EA6D, trained using only synthetic data, can outperform the state-of-the-art methods with both synthetic and realistic data. In particular, for fair comparisons with synthetic data, we can exceed the previous SOTA by 18.1% and 33.5% on Linemod and Linemod-Occluded datasets respectively. Project page: https://github.com/acmff22/EA6D

Size Wu, Wenwei Zhang, Lumin Xu, Sheng Jin, Zhonghua Wu, Qingyi Tao, Wentao Liu, Wei Li, Chen Change Loy

Unifying visual understanding and generation within a single multimodal framework remains a significant challenge, as the two inherently heterogeneous tasks require representations at different levels of granularity. Current approaches that utilize vector quantization (VQ) or variational autoencoders (VAE) for unified visual representation prioritize intrinsic imagery features over semantics, compromising understanding performance. In this work, we take inspiration from masked image modelling (MIM) that learns rich semantics via a mask-and-reconstruct pre-training and its successful extension to masked autoregressive (MAR) image generation. A preliminary study on the MAR encoder's representation reveals exceptional linear probing accuracy and precise feature response to visual concepts, which indicates MAR's potential for visual understanding tasks beyond its original generation role. Based on these insights, we present Harmon, a unified autoregressive framework that harmonizes understanding and generation tasks with a shared MAR encoder. Through a three-stage training procedure that progressively optimizes understanding and generation capabilities, Harmon achieves state-of-the-art image generation results on the GenEval (instruction alignment) and MJHQ30K (visual quality) benchmarks while matching the performance of methods with dedicated semantic encoders (e.g., Janus) on image understanding benchmarks. Our code and models will be available at https://github.com/wusize/Harmon.

Jiasheng Guo, Xin Gao, Yuxiang Yan, Guanghao Li, Jian Pu

Low-light Object detection is crucial for many real-world applications but remains challenging due to degraded image quality. While recent studies have shown that RAW images offer superior potential over RGB images, existing approaches either use RAW-RGB images with information loss or employ complex frameworks. To address these, we propose a lightweight and self-adaptive Image Signal Processing (ISP) plugin, Dark-ISP, which directly processes Bayer RAW images in dark environments, enabling seamless end-to-end training for object detection. Our key innovations are: (1) We deconstruct conventional ISP pipelines into sequential linear (sensor calibration) and nonlinear (tone mapping) sub-modules, recasting them as differentiable components optimized through task-driven losses. Each module is equpped with content-aware adaptability and physics-informed priors, enabling automatic RAW-to-RGB conversion aligned with detection objectives. (2) By exploiting the ISP pipeline's intrinsic cascade structure, we devise a self-boosting strategy that facilitates cooperation between sub-modules. Through extensive experiments on three RAW image datasets, we demonstrate that our method outperforms state-of-the-art RGB- and RAW-based detection approaches, achieving superior results with minimal parameters in challenging low-light environments.

Xiyu Zhang, Jiayi Ma, Jianwei Guo, Wei Hu, Zhaoshuai Qi, Fei Hui, Jiaqi Yang, Yanning Zhang

Geometric constraints between feature matches are critical in 3D point cloud registration problems. Existing approaches typically model unordered matches as a consistency graph and sample consistent matches to generate hypotheses. However, explicit graph construction introduces noise, posing great challenges for handcrafted geometric constraints to render consistency. To overcome this, we propose HyperGCT, a flexible dynamic Hyper-GNN-learned geometric ConstrainT that leverages high-order consistency among 3D correspondences. To our knowledge, HyperGCT is the first method that mines robust geometric constraints from dynamic hypergraphs for 3D registration. By dynamically optimizing the hypergraph through vertex and edge feature aggregation, HyperGCT effectively captures the correlations among correspondences, leading to accurate hypothesis generation. Extensive experiments on 3DMatch, 3DLoMatch, KITTI-LC, and ETH show that HyperGCT achieves state-of-the-art performance. Furthermore, HyperGCT is robust to graph noise, demonstrating a significant advantage in terms of generalization.

Hanshen Zhu, Zhen Zhu, Kaile Zhang, Yiming Gong, Yuliang Liu, Xiang Bai

We tackle the task of geometric image editing, where an object within an image is repositioned, reoriented, or reshaped while preserving overall scene coherence. Previous diffusion-based editing methods often attempt to handle all relevant subtasks in a single step, proving difficult when transformations become large or structurally complex. We address this by proposing a decoupled pipeline that separates object transformation, source region inpainting, and target region refinement. Both inpainting and refinement are implemented using a training-free diffusion approach, FreeFine. In experiments on our new GeoBench benchmark, which contains both 2D and 3D editing scenarios, FreeFine outperforms state-of-the-art alternatives in image fidelity, and edit precision, especially under demanding transformations. Code and benchmark are available at: https://github.com/CIawevy/FreeFine

Shenyu Lu, Zhaoying Pan, Xiaoqian Wang

Contrastive Language-Image Pre-training (CLIP) models exhibit intriguing properties, particularly in their zero-shot classification capability. However, the reliability of CLIP zero-shot classification is severely undermined by spurious correlations. Existing efforts to enhance the robustness of zero-shot CLIP models often rely on prior knowledge or annotations of spurious correlations, limiting real-world applicability due to the unavailability of such information. Alternative methods attempt to detect distribution shift at test time but require training statistics whose access is often restricted or computationally expensive. To address the challenges brought by spurious correlation under zero-shot settings, we propose a novel test-time reasoning approach. Our method, inspired by human recognition, localizes the object and refines the classification accordingly. The inherent capacity of CLIP for semantic understanding allows us to isolate the object of interest without auxiliary models. Zero-shot classification is then performed exclusively on the localized objects, effectively mitigating the influence of spurious correlation. The proposed approach is interpretable and flexible as it requires no spurious annotations or prior knowledge, making it widely applicable. The substantial improvements across multiple benchmark datasets validated the effectiveness of our approach.

Sophia Sirko-Galouchenko, Spyros Gidaris, Antonin Vobecky, Andrei Bursuc, Nicolas Thome

We introduce DIP, a novel unsupervised post-training method designed to enhance dense representations in large-scale pretrained vision encoders for in-context scene understanding. Unlike prior approaches using complex self-distillation architectures, our method trains the vision encoder using pseudo-tasks that simulate downstream in-context scenarios, inspired by meta-learning principles. To enable post-training on unlabeled data, we propose an automatic mechanism for generating in-context tasks that combines a pretrained diffusion model and the vision encoder. DI"P is simple, unsupervised, and computationally efficient, requiring under 9 hours on a single A100 GPU. By learning dense representations through pseudo in-context tasks, it achieves strong performance across a variety of downstream real-world in-context scene understanding tasks. It outperforms both the initial vision encoder and prior methods, offering a practical and effective solution for improving dense representations.

Mingze Sun, Shiwei Mao, Keyi Chen, Yurun Chen, Shunlin Lu, Jingbo Wang, Junting Dong, Ruqi Huang

Recent advancements in large-scale generative models have significantly improved the quality and diversity of 3D shape generation. However, most existing methods focus primarily on generating static 3D models, overlooking the potential dynamic nature of certain shapes, such as humanoids, animals, and insects. To address this gap, we focus on rigging, a fundamental task in animation that establishes skeletal structures and skinning for 3D models. In this paper, we introduce OmniRig, the first large-scale rigging dataset, comprising 79,499 meshes with detailed skeleton and skinning information. Unlike traditional benchmarks that rely on predefined standard poses (e.g., A-pose, T-pose), our dataset embraces diverse shape categories, styles, and poses. Leveraging this rich dataset, we propose ARMO, a novel rigging framework that utilizes an autoregressive model to predict both joint positions and connectivity relationships in a unified manner. By treating the skeletal structure as a complete graph and discretizing it into tokens, we encode the joints using an auto-encoder to obtain a latent embedding and an autoregressive model to predict the tokens. A mesh-conditioned latent diffusion model is used to predict the latent embedding for conditional skeleton generation. Our method addresses the limitations of regression-based approaches, which often suffer from error accumulation and suboptimal connectivity estimation. Through extensive experiments on the OmniRig dataset, our approach achieves state-of-the-art performance in skeleton prediction, demonstrating improved generalization across diverse object categories. The code and dataset will be made public for academic use upon acceptance.

Eunseo Koh, Seunghoo Hong, Tae-Young Kim, Simon S. Woo, Jae-Pil Heo

Text-to-Image (T2I) diffusion models have made significant progress in generating diverse high-quality images from textual prompts. However, these models still face challenges in suppressing content that is strongly entangled with specific words. For example, when generating an image of "Charlie Chaplin", a "mustache" consistently appears even if explicitly instructed not to include it, as the concept of "mustache" is strongly entangled with "Charlie Chaplin". To address this issue, we propose a novel approach to directly suppress such entangled content within the text embedding space of diffusion models. Our method introduces a delta vector that modifies the text embedding to weaken the influence of undesired content in the generated image, and we further demonstrate that this delta vector can be easily obtained through a zero-shot approach. Furthermore, we propose a Selective Suppression with Delta Vector (SSDV) method to adapt delta vector into the cross-attention mechanism, enabling more effective suppression of unwanted content in regions where it would otherwise be generated. Additionally, we enabled more precise suppression in personalized T2I models by optimizing delta vector, which previous baselines were unable to achieve. Extensive experimental results demonstrate that our approach significantly outperforms existing methods, both in terms of quantitative and qualitative metrics.

Sen Wang, Shao Zeng, Tianjun Gu, Zhizhong Zhang, Ruixin Zhang, Shouhong Ding, Jingyun Zhang, Jun Wang, Xin Tan, Yuan Xie 等

Low-level enhancement and high-level visual understanding in low-light vision have traditionally been treated separately. Low-light enhancement improves image quality for downstream tasks, but existing methods rely on physical or geometric priors, limiting generalization. Evaluation mainly focuses on visual quality rather than downstream performance. Low-light visual understanding, constrained by scarce labeled data, primarily uses task-specific domain adaptation, which lacks scalability. To address these challenges, we build a generalized bridge between low-light enhancement and low-light understanding, which we term Generalized Enhancement For Understanding (GEFU). This paradigm improves both generalization and scalability. To address the diverse causes of low-light degradation, we leverage pretrained generative diffusion models to optimize images, achieving zero-shot generalization performance. Building on this, we propose Semantically Consistent Unsupervised Fine-tuning (SCUF). Specifically, to overcome text prompt limitations, we introduce an illumination-aware image prompt to explicitly guide image generation and propose a cycle-attention adapter to maximize its semantic potential. To mitigate semantic degradation in unsupervised training, we propose caption and reflectance consistency to learn high-level semantics and image-level spatial semantics. Extensive experiments demonstrate that our proposed method outperforms current state-of-the-art methods in traditional image quality and GEFU tasks including classification, detection, and semantic segmentation.

Chao Zhou, Tianyi Wei, Nenghai Yu

Recent advancements in unified image generation models, such as OmniGen, have enabled the handling of diverse image generation and editing tasks within a single framework, accepting multimodal, interleaved texts and images in free form. This unified architecture eliminates the need for text encoders, greatly reducing model complexity and standardizing various image generation and editing tasks, making it more user-friendly. However, we found that it suffers from text instruction neglect, especially when the text instruction contains multiple sub-instructions. To explore this issue, we performed a perturbation analysis on the input to identify critical steps and layers. By examining the cross-attention maps of these key steps, we observed significant conflicts between neglected sub-instructions and the activations of the input image. In response, we propose Self-Adaptive Attention Scaling (SaaS), a method that leverages the consistency of cross-attention between adjacent timesteps to dynamically scale the attention activation for each sub-instruction. Our SaaS enhances instruction-following fidelity without requiring additional training or test-time optimization. Experimental results on instruction-based image editing and visual conditional image generation validate the effectiveness of our SaaS, showing superior instruction-following fidelity over existing methods. The code is available at https://github.com/zhouchao-ops/SaaS.

Jun Yin, Pengyu Zeng, Licheng Shen, Miao Zhang, Jing Zhong, Yuxing Han, Shuai Lu

Image-based 3D Genetation has made significant progress in typical scenarios, achieving high fidelity in capturing intricate textures. However, in the Architecture, Engineering, and Construction (AEC) design stages, existing technologies still face considerable challenges, particularly in handling specific window-to-wall ratios, ensuring window detail consistency, and enabling interactive editing. To address this research gap and encourage greater community attention on this practical architectural design problem, we propose a new task: Editable and Consistent Single-View 3D Genetation of Buildings with Specific Window-to-Wall Ratios. To accomplish this: 1) We introduce the ArchiSet dataset, the first public, real-world architectural design dataset, including 13,728 3D building forms in the format of point clouds, voxels, meshes, and window-to-wall ratio information, providing comprehensive support for 3D architectural design research. The dataset also contains over 1,482,624 images in three types--sketches, color block diagrams, and renderings--accompanied by paired window masks for detailed evaluation. 2) We evaluated state-of-the-art single-view 3D Genetation algorithms on ArchiSet, identifying several limitations, such as the loss of volumetric detail, incomplete window details, and limited editability. 3) We introduce BuildingMesh, a diffusion model specifically designed for generating and editing 3D architectural forms from a single image with customizable window-to-wall ratios, suitable for dynamic architectural design workflows. We propose an regularized method to ensure window consistency. Our framework also includes an interactive module for easy further editing, enhancing platform efficiency and accuracy in professional architectural design workflows. Experimental results demonstrate that BuildingMesh achieves high-quality 3D generation with improved design flexibility.

Zeyu Xi, Haoying Sun, Yaofei Wu, Junchi Yan, Haoran Zhang, Lifang Wu, Liang Wang, Changwen Chen

Existing sports video captioning methods often focus on the action yet overlook player identities, limiting their applicability. Although some methods integrate extra information to generate identity-aware descriptions, the player identities are sometimes incorrect because the extra information is independent of the video content. This paper proposes a player-centric multimodal prompt generation network for identity-aware sports video captioning (LLM-IAVC), which focuses on recognizing player identities from a visual perspective. Specifically, an identity-related information extraction module (IRIEM) is designed to extract player-related multimodal embeddings. IRIEM includes a player identification network (PIN) for extracting visual features and player names, and a bidirectional semantic interaction module (BSIM) to link player features with video content for mutual enhancement. Additionally, a visual context learning module (VCLM) is designed to capture the key video context information. Finally, by integrating the outputs of the above modules as the multimodal prompt for the large language model (LLM), it facilitates the generation of descriptions with player identities. To support this work, we construct a new benchmark called NBA-Identity, a large identity-aware basketball video captioning dataset with 9,726 videos covering 9 major event types. The experimental results on NBA-Identity and VC-NBA-2022 demonstrate that our proposed model achieves advanced performance. Code and dataset are publicly available at https://github.com/Zeyu1226-mt/LLM-IAVC.

Zhile Chen, Hui Ji

High Dynamic Range (HDR) imaging with modulo cameras involves solving a challenging inverse problem, where degradation occurs due to the modulo operation applied to the target HDR image. Existing methods operate directly in the image domain, overlooking the underlying properties of the modulo operation. Motivated by Itoh's continuity condition in optics, we reformulate modulo HDR reconstruction in image gradient domain, leveraging the inherent properties of modulo-wrapped gradients to simplify the problem. Furthermore, to address possible ambiguities on large image gradients, we introduce an auxiliary variable with a learnable sparsity prior in an optimization formulation to absorb the related residuals. This is implemented within an unfolding network, where sparsity is enforced through a spiking neuron-based module. Experiments show that our method outperforms existing approaches while being among the lightest models of existing works.