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首次观测 · 记录 #1CVF · CVPR 2026:cvf:1d2a7595b585f2b852eb69ec239dbf05CVF · CVPR 2026

Consistent Instance Field for Dynamic Scene Understanding

Wu, Junyi, Nguyen, Van Nguyen, Planche, Benjamin, Tao, Jiachen, Sun, Changchang, Gao, Zhongpai, Zhao, Zhenghao, Choudhuri, Anwesa 等 14 位作者

原文摘要

We introduce Consistent Instance Field, a continuous and probabilistic spatio-temporal representation for dynamic scene understanding.Unlike prior methods that rely on discrete tracking or view-dependent features, our approach disentangles visibility from persistent object identity by modeling each space-time point with an occupancy probability and a conditional instance distribution. To realize this, we introduce a novel instance-embedded representation based on deformable 3D Gaussians, which jointly encode radiance and semantic information and are learned directly from input RGB images and instance masks through differentiable rasterization.Furthermore, we introduce new mechanisms to calibrate per-Gaussian identities and resample Gaussians toward semantically active regions, ensuring consistent instance representations across space and time. Experiments on HyperNeRF and Neu3D datasets demonstrate that our method significantly outperforms state-of-the-art methods on novel-view panoptic segmentation and open-vocabulary 4D querying tasks.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:7aecaa7b4c5155a2fd6feebebf38e642CVF · CVPR 2026

Streamlined Open-Vocabulary Human-Object Interaction Detection

Sun, Chang, Liao, Dongliang, Ding, Changxing

原文摘要

Open-vocabulary human-object interaction (HOI) detection aims to localize and recognize all human-object interactions in an image, including those unseen during training. Existing approaches usually rely on the collaboration between a conventional HOI detector and a Vision-Language Model (VLM) to recognize unseen HOI categories. However, feature fusion in this paradigm is challenging due to significant gaps in cross-model representations. To address this issue, we introduce SL-HOI, a StreamLined open vocabulary HOI detection framework based solely on the powerful DINOv3 model. Our design leverages the complementary strengths of DINOv3's components: its backbone for fine-grained localization and its text-aligned vision head for open-vocabulary interaction classification. Moreover, to facilitate smooth cross-attention between the interaction queries and the vision head's output, we propose first feeding both the interaction queries and the backbone image tokens into the vision head, effectively bridging their representation gaps. All DINOv3 parameters in our approach are frozen, with only a small number of learnable parameters added, allowing a fast adaptation to the HOI detection task. Extensive experiments show that SL-HOI achieves state-of-the-art performance on both the SWiG-HOI and HICO-DET benchmarks, demonstrating the effectiveness of our streamlined model architecture. Code is available at https://github.com/MPI-Lab/SL-HOI.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:81bb038ddf1500f1511fb1671754ca07CVF · CVPR 2026

PFGNet: A Fully Convolutional Frequency-Guided Peripheral Gating Network for Efficient Spatiotemporal Predictive Learning

Cai, Xinyong, Sun, Changbin, Wang, Yong, Yang, Hongyu, Wu, Yuankai

原文摘要

Spatiotemporal predictive learning (STPL) aims to forecast future frames from past observations and is essential across a wide range of applications. Compared with recurrent or hybrid architectures, pure convolutional models offer superior efficiency and full parallelism, yet their fixed receptive fields limit their ability to adaptively capture spatially varying motion patterns. Inspired by biological center-surround organization and frequency-selective signal processing, we propose PFGNet, a fully convolutional framework that dynamically modulates receptive fields through pixel-wise frequency-guided gating. The core Peripheral Frequency Gating (PFG) block extracts localized spectral cues and adaptively fuses multi-scale large-kernel peripheral responses with learnable center suppression, effectively forming spatially adaptive band-pass filters. To maintain efficiency, all large kernels are decomposed into separable 1D convolutions (1xk followed by kx1), reducing per-channel computational cost from O(k2) to O(2k). PFGNet enables structure-aware spatiotemporal modeling without recurrence or attention. Experiments on Moving MNIST, TaxiBJ, Human3.6M, and KTH show that PFGNet delivers SOTA or near-SOTA forecasting performance with substantially fewer parameters and FLOPs. Our code is available at https://github.com/fhjdqaq/PFGNet.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:c1e1032a0923d34083baaf23972c97cdCVF · CVPR 2026

MDCS-MoAME: Multi-directional Composite Scanning with Mixture of Attention and Mamba Experts for Cancer Survival Prediction

Qu, Linjie, Xiao, Jin, Liu, Xiangrong, Sun, Changming, Cui, Hui, Fang, Yuqi, Su, Ran, Jin, Qiangguo 等 9 位作者

原文摘要

Multi-modal learning approaches that integrate pathological images with genomic profiles have significantly enhanced the accuracy of survival prediction tasks. However, previous methods often struggle to effectively process long-range gigapixel whole slide images (WSIs) and sparse genomic profiles due to the limitations of conventional scanning strategies to serialize data and the complex and heterogeneous nature of the modalities. Inspired by recent advancements in Mamba and mixture of experts (MoE), we propose a novel multi-directional composite scanning strategy with mixture of attention and Mamba experts (MDCS-MoAME) for cancer survival prediction. Specifically, we introduce a multi-directional composite scanning (MDCS) strategy to both WSIs and genomic profiles, and use the Mamba encoder to process intra-modal representations at the region, patch, and gene level, ensuring sufficient utilization of the intrinsic information within each modality. To further capture heterogeneous inter-modal representations, we introduce mixture of attention and Mamba experts (MoAME), which dynamically selects tailored experts to model complex inter-modal correlations, flexibly focusing on the interactions between modalities. Finally, we introduce alignment constraints to recalibrate inter-modal interactions and reduce intra- and inter-modal representation redundancy, enhancing its discriminative power for comprehensive survival analysis. Experimental results on five publicly available datasets demonstrate that our method outperforms existing approaches, achieving state-of-the-art performance.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:d026313481cd620f9f3081239a109d05CVF · CVPR 2026

D2Cache: Second-Order Delta Caching for Higher Video Diffusion Acceleration

Liu, Enhuai, Wang, Yunke, Sun, Changming, Xu, Chang

原文摘要

Video diffusion models achieve impressive visual fidelity but remain computationally prohibitive for real-time or interactive generation due to their sequential denoising process. Recent caching methods accelerate inference by reusing outputs across timesteps, typically estimating each new output from the first-order residual, which is the difference between adjacent model predictions.To mitigate the accumulated error in caching methods, we propose D2Cache, a training-free method that leverages the smoothness of second-order residual delta, which is temporal differences between consecutive first-order residuals, to predict future timesteps more accurately. We theoretically show that this second-order correction improves prediction accuracy and effectively suppresses cumulative errors. Moreover, D2Cache adaptively scales second-order deltas using error estimates derived from timestep embeddings, maintaining accuracy across varying cache intervals.Empirically, D2Cache outperforms the state-of-the-art TeaCache across four video diffusion models (Latte, Open-Sora, LTX-video, and Wan2.1) at comparable acceleration rates, showing even larger gains under higher acceleration settings.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:f1b429c3acb857e115ed969dcc9dd1f4CVF · CVPR 2026

Depth Hypothesis Guided Iterative Refinement for Event-Image Monocular Depth Estimation

Liu, Daikun, Wang, Teng, Sun, Changyin

原文摘要

Event cameras hold excellent dynamic properties, showing great potential for monocular depth estimation (MDE). However, existing methods mainly improve performance by optimizing contextual features, but still struggle with the ill-posed and nonlinear nature of direct full-depth regression. In this paper, we propose HypoDepth, the first event-image monocular depth iterative refinement framework. By introducing a discrete Depth Hypothesis Volume (DHV), we transform the depth regression problem into a constrained depth search task. Specifically, we construct a 3D cost volume between the DHV features and contextual features and perform a multi-scale correlation search to guide stable residual optimization. This lightweight cost volume enables efficient global-to-local refinement across multi-resolution. Our method outperforms existing approaches on DSEC and MVSEC with state-of-the-art results and strong zero-shot generalization. Meanwhile, our tiny model achieves an excellent balance between accuracy and efficiency, enabling real-time performance on resource-limited devices.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗