GenAI4ELab/papercli-papers huggingface dataset:
Temporal Representation Enhancement (TRE): Learning to Forget Dominant Patterns for Enhanced Temporal Spiking Features
Authors: Wei Liu; Li Yang; Yufei Wang; Han Xiao; Boyu Cai; Weiming Hu
Venue: CVPR 2026 (main)

Abstract: Spiking Neural Networks (SNNs) naturally process visual inputs across multiple timesteps, offering rich temporal dynamics and energy-efficient computation. However, the temporally invariant supervision commonly used in training tends to reinforce the same dominant response patterns across timesteps, leading to redundant representations and limiting temporal discriminability. To overcome this limitation, we introduce Temporal Representation Enhancement(TRE), a novel learning-to-forget paradigm that encourages more diverse and complementary temporal representations. TRE identifies high-contribution semantic patterns through class-specific contribution estimation and temporal accumulation, and selectively suppresses them using a dynamic modulation strategy. By redirecting the model's attention toward alternative yet informative semantic cues, TRE promotes the learning of complementary features across timesteps. This approach not only strengthens the temporal discriminative capacity of SNNs but also enables more effective multi-timestep learning by leveraging richer semantic information. Extensive experiments on both static image datasets and dynamic neuromorphic datasets demonstrate that TRE consistently improves classification accuracy and feature diversity across different SNN backbones.
Dynamic Token Reweighting for Robust Vision-Language Models
Authors: Tanqiu Jiang; Jiacheng Liang; Rongyi Zhu; Jiawei Zhou; Fenglong Ma; Ting Wang
Venue: CVPR 2026 (main)

Abstract: Large vision-language models (VLMs) are highly vulnerable to multimodal jailbreak attacks that exploit visual-textual interactions to bypass safety guardrails. In this paper, we present DTR, a novel inference-time defense that mitigates multimodal jailbreak attacks through optimizing the model's key-value (KV) caches. Rather than relying on curated safety-specific data or costly image-to-text conversion, we introduce a new formulation of the safety-relevant distributional shift induced by the visual modality. This formulation enables DTR to dynamically adjust visual token weights, minimizing the impact of adversarial visual inputs while preserving the model's general capabilities and inference efficiency. Extensive evaluation across diverse VLMs and attack benchmarks demonstrates that DTR outperforms existing defenses in both attack robustness and benign-task performance, marking the first successful application of KV cache optimization for safety enhancement in multimodal foundation models. The code for replicating DTR is available at: https://github.com/TanqiuJiang/DTR.
Paper2Figure: A Multi-Agent Collaborative System for Figure Generation Towards Academic Research Paper
Authors: Siwei Han; Haonian Ji; Siyang Xin; Juanquan Shi; Shi Qiu; Xinyu Ye; Peng Xia; Jiaqi Liu; Zhaorun Chen; Yiyang Zhou; Linjie Li; Lijuan Wang; Huaxiu Yao
Venue: CVPR 2026 (main)

Abstract: Automatically generating clear and accurate figures for research papers remains challenging, as it requires semantic understanding, precise structure, and visual aesthetics. Existing approaches struggle to balance fidelity and quality: large language model (LLM) code-based methods (e.g., SVG, Mermaid) are structured but inflexible, while image-generation models (e.g., GPT-Image-1, Nano Banana) produce hard-to-edit and often inaccurate figures. We present Paper2Figure, a dual multi-agent system with an interactive web platform for paper-to-figure generation. Generation Agents convert text into our designed FigScript language, encoding figure semantics, styles and layout. The web system renders the FigScript into an initial image, which Refinement Agents iteratively analyze to locate issues and revise the FigScript for improved logic, alignment, aesthetics and text accuracy. Crucially, users can further refine results through an intuitive web interface, ensuring full control over the final output. To evaluate Paper2Figure, we introduce Paper2Figure Bench, a benchmark comprising 100 academic figures with paired descriptions. Experiments demonstrate that Paper2Figure markedly improves accuracy by 12%, beauty by 13.5%, and completeness by 17.0% over state-of-the-art baselines in fully automatic generation without human adjustment. By combining automated generation with interactive edit, Paper2Figure bridges the gap between AI assistance and researcher control, offering a practical solution for high-quality academic figure creation.
OctoT2I: A Self-Evolving Agentic Text-to-Image Router
Authors: Xu Jiang; Bin Chen; Gehui Li; Yule Duan; Ronggang Wang; Jian Zhang
Venue: CVPR 2026 (main)

Abstract: The explosive growth of Text-to-Image (T2I) models, from large-scale versions to lightweight, real-time ones, now faces diminishing marginal returns from single-model scaling. Agentic T2I methods emerged to alleviate this bottleneck by using multiple models. However, existing agentic T2I methods suffer from three key challenges: reliance on expensive handcrafted priors or human annotations, rigid single-path decision mechanisms, and a neglect of inference efficiency. To address these challenges, we introduce OctoT2I, a novel agentic framework that reformulates the T2I task as a joint optimization of generation quality and inference efficiency. OctoT2I implements a stateful, multi-round routing strategy that adaptively selects the most suitable tool based on its knowledge and memory. This strategy is enabled by a knowledge base built from scratch by our novel Self-Evolving Mechanism. This mechanism, which requires no human supervision, first autonomously defines foundational Conceptual Dimensions (e.g., style, color, count) and then intelligently explores their combinations via an iterative "Propose--Solve--Evaluate--Learn" (PSEL) loop. The PSEL loop efficiently discovers each tool's capability frontier, driving continuous improvement without external guidance. Extensive experiments demonstrate that OctoT2I achieves competitive performance (0.96) on GenEval while delivering a 90.3% inference speedup and a 56.6% energy-efficiency gain over the leading baseline (Flow-GRPO), striking an exceptional balance between performance and efficiency. Code and models will be made available.
Chorus: Multi-Teacher Pretraining for Holistic 3D Gaussian Scene Encoding
Authors: Yue Li; Qi Ma; Runyi Yang; Mengjiao Ma; Bin Ren; Nikola Popovic; Nicu Sebe; Theo Gevers; Luc Van Gool; Danda Pani Paudel; Martin R. Oswald
Venue: CVPR 2026 (main)

Abstract: While 3DGS has emerged as a high-fidelity scene representation, encoding rich, general-purpose features directly from its primitives remains under-explored. We address this gap by introducing Chorus, a multi-teacher pretraining framework that learns a holistic feed-forward 3D Gaussian Splatting (3DGS) scene encoder by distilling complementary signals from 2D foundation models.  Chorus employs a shared 3D encoder and teacher-specific projectors to learn from language-aligned, generalist, and object-aware teachers, encouraging a shared embedding space that captures signals from high-level semantics to fine-grained structure. We evaluate Chorus on a wide range of tasks: open-vocabulary semantic and instance segmentation, linear and decoder probing, data-efficient supervision, as well as LLM-based Q&A. Besides 3DGS, we also test Chorus on several benchmarks that only support point clouds by pretraining a variant using only Gaussians' centers, colors, estimated normals. Interestingly, this encoder shows strong transfer and outperforms the point clouds baseline while using 39.9xfewer training scenes. Finally, we propose a render-and-distill adaptation that facilitates out-of-domain finetuning. Our code and model is released at this codebase.
FlowFM: Advancing Dark Optical Flow Estimation with Flow Matching
Authors: Fengyuan Zuo; Haiyan Jin; Yuanlin Zhang; Zhaolin Xiao; Bin Wang; Yuerong Mu
Venue: CVPR 2026 (main)

Abstract: Dark optical flow estimation (DOFE) faces critical challenges: discriminative models are less robust to noise and struggle with weakened motion patterns, while diffusion models suffer from discontinuous flow fields and low efficiency. Flow matching (FM), though efficient, remains underexplored for conditional generation in DOFE. In this paper, we propose FlowFM, the first flow matching model tailored to DOFE tasks. Instead of conventional vector field regression, FlowFM proposes estimating the global transformation path constrained by the ground truth optical flow. It generates noisy flow by mixing Gaussian noise with ground truth, then performs a one-step denoising process conditioned on the initial flow field, cost volume, and contextual features for optimal accuracy and efficiency. FlowFM incorporates an implicit Fourier denoising decoder (IFDD) for reliable motion understanding. By leveraging the Fourier transform, IFDD uses amplitude to characterize motion intensity and phase to encode target spatial relationships within flow fields, then directly enhances amplitude to restore dark-caused motion information loss. Experiments show that FlowFM significantly outperforms state-of-the-art methods on the FCDN and VBOF benchmarks, setting a new performance record for DOFE.
Residual Diffusion Bridge Model for Image Restoration
Authors: Hebaixu Wang; Jing Zhang; Haoyang Chen; Haonan Guo; Di Wang; Jiayi Ma; Bo Du
Venue: CVPR 2026 (main)

Abstract: Diffusion bridge models establish probabilistic paths between arbitrary paired distributions and exhibit great potential for universal image restoration. Most existing methods merely treat them as simple variants of stochastic interpolants, lacking a unified analytical perspective. Besides, they indiscriminately reconstruct images through global noise injection and removal, inevitably distorting undegraded regions due to imperfect reconstruction. To address these challenges, we propose the  R esidual  D iffusion  B ridge  M odel (RDBM). Specifically, we theoretically reformulate the stochastic differential equations of generalized diffusion bridge and derive the analytical formulas of its forward and reverse processes. Crucially, we leverage the residuals from given distributions to modulate the noise injection and removal, enabling adaptive restoration of degraded regions while preserving intact others. Additionally, we unravel the fundamental mathematical essence of existing bridge models, all of which are special cases of RDBM and empirically demonstrate the optimality of our proposed models. Extensive experiments are conducted to demonstrate the state-of-the-art performance of our method both qualitatively and quantitatively across diverse image restoration tasks. Code is publicly available at https://github.com/MiliLab/RDBM.
Rewis3d: Reconstruction Improves Weakly-Supervised Semantic Segmentation
Authors: Jonas Ernst; Wolfgang Boettcher; Lukas Hoyer; Jan Eric Lenssen; Bernt Schiele
Venue: CVPR 2026 (main)

Abstract: We present Rewis3d, a framework that leverages recent advances in feed-forward 3D reconstruction to significantly improve weakly supervised semantic segmentation on 2D images. Obtaining dense, pixel-level annotations remains a costly bottleneck for training segmentation models. Alleviating this issue, sparse annotations offer an efficient weakly-supervised alternative. However, they still incur a performance gap. To address this, we introduce a novel approach that leverages 3D scene reconstruction as an auxiliary supervisory signal. Our key insight is that 3D geometric structure recovered from 2D videos provides strong cues that can propagate sparse annotations across entire scenes. Specifically, a dual student-teacher architecture enforces semantic consistency between 2D images and reconstructed 3D point clouds, using state-of-the-art feed-forward reconstruction to generate reliable geometric supervision. Extensive experiments demonstrate that Rewis3d achieves state-of-the-art performance in sparse supervision, outperforming existing approaches by 2-7% without requiring additional labels or inference overhead.  Project page: https://rewis3d-mpi.github.io/Rewis3d/
Keep it SymPL: Symbolic Projective Layout for Allocentric Spatial Reasoning in Vision-Language Models
Authors: Jaeyun Jang; Seunghui Shin; Taeho Park; Hyoseok Hwang
Venue: CVPR 2026 (main)

Abstract: Perspective-aware spatial reasoning involves understanding spatial relationships from specific viewpoints--either egocentric (observer-centered) or allocentric (object-centered).While vision-language models (VLMs) perform well in egocentric settings, their performance deteriorates when reasoning from allocentric viewpoints, where spatial relations must be inferred from the perspective of objects within the scene.In this study, we address this underexplored challenge by introducing Symbolic Projective Layout (SymPL), a framework that reformulates allocentric reasoning into symbolic-layout forms that VLMs inherently handle well.By leveraging four key factors--projection, abstraction, bipartition, and localization--SymPL converts allocentric questions into structured symbolic-layout representations.Extensive experiments demonstrate that this reformulation substantially improves performance in both allocentric and egocentric tasks, enhances robustness under visual illusions and multi-view scenarios, and that each component contributes critically to these gains.These results show that SymPL provides an effective and principled approach for addressing complex perspective-aware spatial reasoning.
QueryMe: Query-Driven Open-Vocabulary 3D Object Affordances Grounding from Multimodal Evidence
Authors: Weiyu Zhao; Ru Li; Jiaqi Liu; Sizhe Zhao; Qinglin Liu; Shengping Zhang
Venue: CVPR 2026 (main)

Abstract: Open-vocabulary 3D object affordance grounding aims to identify functional regions of objects given arbitrary semantic descriptions. However, existing methods often rely on fixed training categories and geometric priors, lacking geometric invariance and analogical reasoning capabilities. Since there exists a significant domain gap when transferring affordance knowledge learned from 2D images to 3D point clouds, existing methods struggle to generalize well to objects with diverse shapes or unseen categories, and fail to perform effective category reasoning.To address these challenges, we propose **QueryMe**, a **Query**-driven framework that learns from **M**ultimodal **e**vidence spaces to achieve open-vocabulary 3D affordance grounding.The proposed approach is to project human-object interaction images into 3D space, employ an Adaptive Spatial Attention module to focus on key interaction regions, and introduce a multimodal query structure to retrieve available geometrically consistent functional parts within the point cloud, effectively fusing visual, linguistic, and geometric cues.Leveraging attention-based query mechanisms, our method adaptively localizes affordance regions and performs analogy reasoning through geometric similarity, thereby exhibiting strong generalization to unseen scenes and objects. Experimental results demonstrate that QueryMe consistently outperforms state-of-the-art approaches, with the AUC improving by 4.19% compared to previous work for unseen affordance grounding tasks.
Portable Active Learning for Object Detection
Authors: Rashi Sharma; Justin Timothy C. Bersamin; Karthikk Subramanian
Venue: CVPR 2026 (main)

Abstract: Annotating bounding boxes is costly and limits the scalability of object detection. This challenge is compounded by the need to preserve high accuracy while minimizing manual effort in real-world applications. Prior active learning methods often depend on model features or modify detector internals and training schedules, increasing integration overhead. Moreover, they rarely jointly exploit the benefits of image-level signals, class-imbalance cues, and instance-level uncertainty for comprehensive selection. We present Portable Active Learning (PAL), a detector-agnostic, easily portable framework that operates solely on inference outputs. PAL combines class-wise instance uncertainty with image-level diversity to guide data selection. At each round, PAL trains lightweight class-specific logistic classifiers to distinguish true from false positives, producing entropy-based uncertainty scores for proposals. Candidate images are then refined using global image entropy, class diversity, and image similarity, yielding batches that are both informative and diverse. PAL requires no changes to model internals or training pipelines, ensuring broad compatibility across detectors. Extensive experiments on COCO, PASCAL VOC, and BDD100K demonstrate that PAL consistently improves label efficiency and detection accuracy compared to existing active learning baselines, making it a practical solution for scalable and cost-effective deployment of object detection in real-world settings.
Dynamic Label Noise Suppression with Optimal Teacher Pool for Facial Expression Recognition
Authors: Yuzhuang Yang; Xiaolin Tian; Qigong Sun
Venue: CVPR 2026 (main)

Abstract: Due to the inherent ambiguity of facial expressions and subjectivity in dataset labeling, learning with noisy labels remains a critical challenge in facial expression recognition (FER). The supervisory mechanism of teacher-student network offers a promising approach for noisy-labeled FER. However, this approach is prone to noise accumulation and gradual coupling between the teacher and student parameters during training. We propose an Optimal Teacher Pool-driven dynamic label Noise Suppression framework for facial expression recognition (OTP-NS). Specifically, we construct an optimal teacher pool architecture that dynamically maintains multiple best teacher models while fusing their predictions, thereby mitigating noise accumulation and coupling of teacher-student parameters via update mechanisms. Furthermore, we develop two sample-level noise suppression parts: (1) Similarity-Aware Label Smoothing (SALS), diverging from the static smoothing strength in traditional label smoothing, automatically modulates the smoothing strength for teacher model based on prediction-label similarity, achieving fine-grained noise suppression. (2) Confidence-Weighted Logits (CWL), adaptively adjusting the classification loss of student model based on sample-to-centroid confidence metrics, alleviates the detrimental effects of noisy samples on model training. Extensive experiments on multiple benchmark datasets demonstrate that our method outperforms state-of-the-art approaches across various noise levels, validating the effectiveness of our proposed framework in learning robust representations from noisy data.
LASAR: Towards Spatio-temporal Reasoning with Latent Cognitive Map
Authors: Jinzhou Tang; Sidi Liu; Waikit Xiu; Weixing Chen; Keze Wang
Venue: CVPR 2026 (main)

Abstract: A fundamental challenge in embodied AI is verifying if agents build internal models of spatial structure or merely learn to mimic task-specific expert trajectories. This is critical as foundational approaches rooted in action-centric tasks (e.g., VLN) and reasoning-centric tasks (e.g., EQA) often share a common limitation: they lack a learning signal that forces them to encode fine-grained spatial relationships (like topology or distance) over long-range, fragmented experiences. To address this, we first propose LASAR, an architecture featuring a dual-memory system designed to maintain both episodic experiences and a semantic cognitive map. We then introduce Spatio-temporal Contextual Representation Learning (ST-CRL), a contrastive objective designed to train this architecture. ST-CRL leverages spatio-temporal cues from cognitive queries generated through annotated spatio-temporal context in simulation to build sample pairs, thereby forming the internal cognitive map from the agent's experiences. Experiments demonstrate that our method achieves 2%-3.5% gains in both zero-shot generalization on standard VLN-CE and VSI-Bench benchmarks. We also demonstrate that our proposed cognitive map has high self-consistency.
DREAM: Document Recognition with Explicit Adaptive Memory
Authors: Tianqi Zhao; Di Wu; Liangrui Peng; Yifan Huang; Kemeng Zhao; Shuo Li; Zhiyu Li; Yizhu Wang; Borui Jiang; Yuyang Li
Venue: CVPR 2026 (main)

Abstract: Large multimodal models (LMMs) have shown promising performance for various document recognition tasks. However, LMMs adopt implicit modeling, and the parameters lack interpretability. Inspired by recent advances in human memory and learning research, we propose an explicit multiscale prototype memory that augments document recognition models, explicitly modeling recurrent layout and stylistic patterns across different spatial resolutions. A Memory Retrieval Mechanism enables local regions to sparsely attend to a few prototypes (e.g., image borders, tilted text); the retrieved compositional factors are concatenated with visual features and passed to the decoder, providing explicit region-wise structural context. Prototype memory consolidation updates and stabilizes prototypes via attention-weighted exponential moving average (EMA) strategy, while sparsity and anti-collapse regularization promote selective activation. We further adopt hierarchical memory for multi-resolution encoding. The proposed DREAM module is a plug-and-play component, allowing seamless integration into various encoder-decoder architectures. We validate on two tasks including document recognition on public datasets and the self-built DreamDoc dataset, and handwriting recognition on the SCUT-HCCDoc and SCUT-EPT datasets. Experimental results show that the proposed method is effective.
YOSE: You Only Select Essential Tokens for Efficient DiT-based Video Object Removal
Authors: Chenyang Wu; Lina Lei; Fan Li; Chunle Guo; Dehong Kong; Xinran Qin; Zhixin Wang; Mingming Cheng; Chongyi Li
Venue: CVPR 2026 (main)

Abstract: Recent advances in Diffusion Transformer (DiT)-based video generation technologies have shown impressive results for video object removal. However, these methods still suffer from substantial inference latency. For instance, although MiniMax Remover achieves state-of-the-art visual quality, it operates at only around 10FPS, primarily due to dense computations over the entire spatiotemporal token space, even when only a small masked region actually requires processing. In this paper, we present YOSE, You Only Select Essential Tokens, an efficient fine-tuning framework. YOSE introduces two key components: Batch Variable-length Indexing (BVI) and Diffusion Process Simulator (DiffSim) Module. BVI is a differentiable dynamic indexing operator that adaptively selects essential tokens based on mask information, enabling variable-length token processing across samples. DiffSim provides a diffusion process approximation mechanism for unmasked tokens, which simulates the influence of unmasked regions within DiT self-attention to maintain semantic consistency for masked tokens. With these designs, YOSE achieves mask-aware acceleration, where the inference time scales approximately linearly with the masked regions, in contrast to full-token diffusion methods whose computation remains constant regardless of the mask size. Extensive experiments demonstrate that YOSE achieves up to 2.5X speedup in 70% of cases while maintaining visual quality comparable to the baseline. Code is available at: https://github.com/Wucy0519/YOSE-CVPR26.