GenAI4ELab/papercli-papers huggingface dataset:
Redundant Queries in DETR-Based 3D Detection Methods: Unnecessary and Prunable
Authors: Lizhen Xu; Zehao Wu; Wenzhao Qiu; Shanmin Pang; Xiuxiu Bai; Kuizhi Mei; Jianru Xue
Venue: AAAI 2026 (main)

Abstract: Query-based models are extensively used in 3D object detection tasks, with a wide range of pre-trained checkpoints readily available online. However, despite their popularity, these models often require an excessive number of object queries, far surpassing the actual number of objects to detect. The redundant queries result in unnecessary computational and memory costs. In this paper, we find that not all queries contribute equally -- a significant portion of queries have a much smaller impact compared to others. Based on this observation, we propose an embarrassingly simple approach called Gradually Pruning Queries (GPQ),  which prunes queries incrementally based on their classification scores. A key advantage of GPQ is that it requires no additional learnable parameters. It is straightforward to implement in any query-based method, as it can be seamlessly integrated as a fine-tuning step using an existing checkpoint after training. With GPQ, users can easily generate multiple models with fewer queries, starting from a checkpoint with an excessive number of queries. Experiments on various advanced 3D detectors show that GPQ effectively reduces redundant queries while maintaining performance. Using our method, model inference on desktop GPUs can be accelerated by up to 1.35x. Moreover, after deployment on edge devices, it achieves up to a 67.86% reduction in FLOPs and a 65.16% decrease in inference time.
VP-Bench: A Comprehensive Benchmark for Visual Prompting in Multimodal Large Language Models
Authors: Mingjie Xu; Jinpeng Chen; Yuzhi Zhao; Jason Chun Lok Li; Yue Qiu; Zekang Du; Mengyang Wu; Pingping Zhang; Kun Li; Hongzheng Yang; Wenao Ma; Jiaheng Wei; Qinbin Li; Kangcheng Liu; Wenqiang Lei
Venue: AAAI 2026 (main)

Abstract: Multimodal Large Language Models (MLLM) have enabled a wide range of advanced vision-language applications, including fine-grained object recognition and contextual understanding. When querying specific regions or objects in an image, human users naturally use "Visual Prompts" (VP) like bounding boxes to provide reference. However, no existing benchmark systematically evaluates the ability of MLLMs to interpret such VPs. This gap raises uncertainty about whether current MLLMs can effectively recognize VPs, an intuitive prompting method for humans, and utilize them to solve problems. To address this limitation, we introduce VP-Bench, aiming to assess MLLMs’ capability in VP perception and utilization. VP-Bench employs a two-stage evaluation framework: Stage 1 examines models’ ability to perceive VPs in natural scenes, utilizing 100K visualized prompts spanning 8 shapes and 355 attribute combinations. Stage 2 investigates the impact of VPs on downstream tasks, measuring their effectiveness in real-world problem-solving scenarios. Using VP-Bench, we evaluate 21 MLLMs, including proprietary systems (e.g., GPT-4o) and open-source models (e.g., InternVL-2.5 and Qwen2.5-VL). In addition, we conduct a comprehensive analysis of the factors influencing VP understanding, such as attribute variations and model scale. VP-Bench establishes a new reference framework for studying MLLMs’ ability to comprehend and resolve grounded referring questions.
MuSASplat: Efficient Sparse-View 3D Gaussian Splats via Lightweight Multi-Scale Adaptation
Authors: Muyu Xu; Fangneng Zhan; Xiaoqin Zhang; Ling Shao; Shijian Lu
Venue: AAAI 2026 (main)

Abstract: Sparse-view 3D Gaussian splatting seeks to render high-quality novel views of 3D scenes from a limited set of input images. While recent pose-free feed-forward methods leveraging pre-trained 3D priors have achieved impressive results, most of them rely on full fine-tuning of large Vision Transformer (ViT) backbones and incur substantial GPU costs.
In this work, we introduce MuSASplat, a novel framework that dramatically reduces the computational burden of training pose-free feed-forward 3D Gaussian splats models with little compromise of rendering quality. Central to our approach is a lightweight Multi-Scale Adapter that enables efficient fine-tuning of ViT-based architectures with only a small fraction of training parameters. This design avoids the prohibitive GPU overhead associated with previous full-model adaptation techniques while maintaining high fidelity in novel view synthesis, even with very sparse input views. In addition, we introduce a Feature Fusion Aggregator that integrates features across input views effectively and efficiently. Unlike widely adopted memory banks, the Feature Fusion Aggregator ensures consistent geometric integration across input views and meanwhile mitigates the memory usage, training complexity, and computational costs significantly.
Extensive experiments across diverse datasets show that MuSASplat achieves state-of-the-art rendering quality but has significantly reduced parameters and training resource requirements as compared with existing methods.
Pushing Rendering Boundaries: Hard Gaussian Splatting
Authors: Qingshan Xu; Jiequan Cui; Xuanyu Yi; Yuxuan Wang; Yuan Zhou; Yew-Soon Ong; Hanwang Zhang
Venue: AAAI 2026 (main)

Abstract: 3D Gaussian Splatting (3DGS) has demonstrated impressive Novel View Synthesis (NVS) results in a real-time rendering manner. During training, it relies heavily on the average magnitude of view-space positional gradients to grow Gaussians to reduce rendering loss. However, this average operation smooths the positional gradients from different viewpoints and rendering errors from different pixels, hindering the growth and optimization of many defective Gaussians. This leads to strong spurious artifacts in some areas. To address this problem, we propose Hard Gaussian Splatting, dubbed HGS, which considers multi-view significant positional gradients and rendering errors to grow hard Gaussians that fill the gaps of classical Gaussian Splatting on 3D scenes, thus achieving superior NVS results. In detail, we present positional gradient driven HGS, which leverages multi-view significant positional gradients to uncover hard Gaussians. Moreover, we propose rendering error guided HGS, which identifies noticeable pixel rendering errors and potentially over-large Gaussians to jointly mine hard Gaussians. By growing and optimizing these hard Gaussians, our method helps to resolve blurring and needle-like artifacts. Experiments on various datasets demonstrate that our method achieves state-of-the-art rendering quality while maintaining real-time efficiency, yielding LPIPS improvements of 5.1%, 19.7% and 6.3% on Mip-NeRF360, Tanks&Temples and Deep Blending, respectively.
NeuSpring: Neural Spring Fields for Reconstruction and Simulation of Deformable Objects from Videos
Authors: Qingshan Xu; Jiao Liu; Shangshu Yu; Yuxuan Wang; Yuan Zhou; Junbao Zhou; Jiequan Cui; Yew-Soon Ong; Hanwang Zhang
Venue: AAAI 2026 (main)

Abstract: In this paper, we aim to create physical digital twins of deformable objects under interaction. Existing methods focus more on the physical learning of current state modeling, but generalize worse to future prediction. This is because existing methods ignore the intrinsic physical properties of deformable objects, resulting in the limited physical learning in the current state modeling. To address this, we present NeuSpring, a neural spring field for the reconstruction and simulation of deformable objects from videos. Built upon spring-mass models for realistic physical simulation, our method consists of two major innovations: 1) a piecewise topology solution that efficiently models multi-region spring connection topologies using zero-order optimization, which considers the material heterogeneity of real-world objects. 2) a neural spring field that represents spring physical properties across different frames using a canonical coordinate-based neural network, which effectively leverages the spatial associativity of springs for physical learning. Experiments on real-world datasets demonstrate that our NeuSping achieves superior reconstruction and simulation performance for current state modeling and future prediction, with Chamfer distance improved by 20% and 25%, respectively.
OAD-Promoter: Enhancing Zero-Shot VQA Using Large Language Models with Object Attribute Description
Authors: Quanxing Xu; Ling Zhou; Feifei Zhang; Rubing Huang; Jinyu Tian
Venue: AAAI 2026 (main)

Abstract: Large Language Models (LLMs) have become a crucial tool in Visual Question Answering (VQA) for handling knowledge-intensive questions in few-shot or zero-shot scenarios. However, their reliance on massive training datasets often causes them to inherit language biases during the acquisition of knowledge. This limitation imposes two key constraints on existing methods: (1) LLM predictions become less reliable due to bias exploitation, and (2) despite strong knowledge reasoning capabilities, LLMs still struggle with out-of-distribution (OOD) generalization. To address these issues, we propose Object Attribute Description Promoter (OAD-Promoter), a novel approach for enhancing LLM-based VQA by mitigating language bias and improving domain-shift robustness. OAD-Promoter comprises three components: the Object-concentrated Example Generation (OEG) module, the Memory Knowledge Assistance (MKA) module, and the OAD Prompt. The OEG module generates global captions and object-concentrated samples, jointly enhancing visual information input to the LLM and mitigating bias through complementary global and regional visual cues. The MKA module assists the LLM in handling OOD samples by retrieving relevant knowledge from stored examples to support questions from unseen domains. Finally, the OAD Prompt integrates the outputs of the preceding modules to optimize LLM inference. Experiments demonstrate that OAD-Promoter significantly improves the performance of LLM-based VQA methods in few-shot or zero-shot settings, achieving new state-of-the-art results.
SCALAR: Scale-wise Controllable Visual Autoregressive Learning
Authors: Ryan Xu; Dongyang Jin; Yancheng Bai; Rui Lan; Xu Duan; Lei Sun; Xiangxiang Chu
Venue: AAAI 2026 (main)

Abstract: Controllable image synthesis, which enables fine-grained control over generated outputs, has emerged as a key focus in visual generative modeling.
However, controllable generation remains challenging for Visual Autoregressive (VAR) models due to their hierarchical, next-scale prediction style.
Existing VAR-based methods often suffer from inefficient control encoding and disruptive injection mechanisms that compromise both fidelity and efficiency. 
In this work, we present SCALAR, a controllable generation method based on VAR, incorporating a Scale-wise Conditional Decoding mechanism. 
SCALAR leverages a pretrained image encoder to extract semantic control signal encodings, which are projected into scale-specific representations and injected into the corresponding layers of the VAR backbone. 
This design provides persistent and structurally aligned guidance throughout the generation process.
Building on SCALAR, we develop SCALAR-Uni, a unified extension that aligns multiple control modalities into a shared latent space, supporting flexible multi-conditional guidance in a single model.
Extensive experiments show that SCALAR achieves superior generation quality and control precision across various tasks.
CAG-GS: Consistent Anchor Guided Gaussian Splatting for Large-scale Scene Rendering
Authors: Shijie Xu; Qiulei Dong
Venue: AAAI 2026 (main)

Abstract: Recently, 3D Gaussian Splatting for scene rendering has attracted much attention in computer vision and graphics, but generally suffers from large burdens of both computation and storage when handling large-scale scenes. Some existing works in literature employ a divide-and-conquer strategy for alleviating this issue, where an input large scene is divided into lots of local blocks, and each block is handled separately. However, such a strategy generally leads to limited performance due to the inevitable inconsistency among the 3D Gaussians from different blocks. To address this problem, we propose a Consistent Anchor Guided Gaussian Splatting for large-scale scene rendering under the divide-and-conquer strategy, called CAG-GS. In CAG-GS, a set of learnable anchors for each local block is injected with the corresponding semantic features from a pre-trained semantic segmentation model SAM2 through an explored semantic mapping module, and then these anchors are used to predict the attributes of 3D Gaussians. Moreover, we explore a coarse-to-fine training strategy for CAG-GS, where each local block is optimized independently while being guided by globally consistent semantics. Extensive experimental results on five large-scale scenes demonstrate the superiority of the proposed method over five state-of-the-art methods in most cases.
SRD: Reinforcement-Learned Semantic Perturbation for Backdoor Defense in VLMs
Authors: Shuhan Xu; Siyuan Liang; Hongling Zheng; Aishan Liu; Xinbiao Wang; Yong Luo; Fu Lin; Leszek Rutkowski; Dacheng Tao
Venue: AAAI 2026 (main)

Abstract: Visual language models (VLMs) have made significant progress in image captioning tasks, yet recent studies have found they are vulnerable to backdoor attacks. Attackers can inject undetectable perturbations into the data during inference, triggering abnormal behavior and generating malicious captions. These attacks are particularly challenging to detect and defend against due to the stealthiness and cross-modal propagation of the trigger signals. In this paper, we identify two key vulnerabilities by analyzing existing attack patterns: (1) the model exhibits abnormal attention concentration on certain regions of the input image, and (2) backdoor attacks often induce semantic drift and sentence incoherence. Based on these insights, we propose Semantic Reward Defense (SRD), a reinforcement learning framework that mitigates backdoor behavior without requiring any prior knowledge of trigger patterns. SRD learns to apply discrete perturbations to sensitive contextual regions of image inputs via a deep Q-network policy, aiming to confuse attention and disrupt the activation of malicious paths. To guide policy optimization, we design a reward signal named semantic fidelity score, which jointly assesses the semantic consistency and linguistic fluency of the generated captions, encouraging the agent to achieve a robust yet faithful output. SRD offers a trigger-agnostic, policy-interpretable defense paradigm that effectively mitigates local (TrojVLM) and global (Shadowcast) backdoor attacks, reducing ASR to 3.6% and 5.6% respectively, with less than 15% average CIDEr drop on the clean inputs.
CNM-UNet: Continuous Ordinary Differential Equations for Medical Image Segmentation
Authors: Tianqi Xu; Yashi Zhu; Quansong He; Yue Cao; Kaishen Wang; Zhang Yi; Tao He
Venue: AAAI 2026 (main)

Abstract: Integrating Ordinary Differential Equations (ODEs) with U-shaped neural networks has emerged as a novel direction in medical image segmentation. Current networks predominantly employ discretization methods incorporating ODEs. However, these methods face inherent trade-offs between model compactness, computational accuracy, and efficiency. Continuous ODE solutions were rarely studied because they face three limitations: high computational costs, long training time, and poor generalization ability. To address these limitations, we propose an innovative Continuous Neural Memory ODE UNet (CNM-UNet), which replaces all hierarchical decoder layers in vanilla UNet with a single Continuous Neural Memory ODEs Block (CNM-Block) decoder, significantly reducing computation costs and improving training efficiency. CNM-UNet leverages ODEs' dynamic properties to establish continuous temporal feature extraction. For alleviating the generalization problem, a DUal SElf-updated (DUSE) strategy based on test-time adaptation principles is introduced to enhance cross-domain generalization. Experimental results demonstrate CNM-UNet's comprehensive advantages in computational capacity, convergence speed, and cross-domain adaptability, offering new insights for practical deployment of continuous ODE methodologies for medical image segmentation.
ICLR: Inter-Chrominance and Luminance Interaction for Natural Color Restoration in Low-Light Image Enhancement
Authors: Xin Xu; Hao Liu; Wei Liu; Wei Wang; Jiayi Wu; Kui Jiang
Venue: AAAI 2026 (main)

Abstract: Low-Light Image Enhancement (LLIE) task aims at improving contrast while restoring details and textures for images captured in low-light conditions. HVI color space has made significant progress in this task by enabling precise decoupling of chrominance and luminance. However, for the interaction of chrominance and luminance branches, substantial distributional differences between the two branches prevalent in natural images limit complementary feature extraction, and luminance errors are propagated to chrominance channels through the nonlinear parameter. Furthermore, for interaction between different chrominance branches, images with large homogeneous-color regions usually exhibit weak correlation between chrominance branches due to concentrated distributions. Traditional pixel-wise losses exploit strong inter-branch correlations for co-optimization, causing gradient conflicts in weakly correlated regions. Therefore, we propose an Inter-Chrominance and Luminance Interaction (ICLR) framework including a Dual-stream Interaction Enhancement Module (DIEM) and a Covariance Correction Loss (CCL). The DIEM improves the extraction of complementary information from two dimensions, fusion and enhancement, respectively. The CCL utilizes luminance residual statistics to penalize chrominance errors and balances gradient conflicts by constraining chrominance branches covariance. Experimental results on multiple datasets show that the proposed ICLR framework outperforms state-of-the-art methods.
FedARKS: Federated Aggregation via Robust and Discriminative Knowledge Selection and Integration for Person Re-identification
Authors: Xin Xu; Binchang Ma; Zhixi Yu; Wei Liu
Venue: AAAI 2026 (main)

Abstract: The application of federated domain generalization in person re-identification (FedDG-ReID) aims to enhance the model's generalization ability in unseen domains while protecting client data privacy. However, existing mainstream methods typically rely on global feature representations and simple averaging operations for model aggregation, leading to two limitations in domain generalization: (1) Using only global features makes it difficult to capture subtle, domain-invariant local details (such as accessories or textures); (2) Uniform parameter averaging treats all clients as equivalent, ignoring their differences in robust feature extraction capabilities, thereby diluting the contributions of high-quality clients. To address these issues, we propose a novel federated learning framework—Federated Aggregation via Robust and Discriminative Knowledge Selection and Integration (FedARKS)—comprising two mechanisms: RK (Robust Knowledge) and KS (Knowledge Selection). In our design, each client employs a dual-branch network of RK: the Global Feature Processing Branch serves as the primary component, extracting overall representations for model aggregation and server-side updates; while the Body Part Processing Branch acts as an auxiliary component, focusing on extracting domain-invariant local details to supplement and guide the local training process during global feature learning. Additionally, our KS mechanism adaptively assigns corresponding aggregation weights to clients based on their ability to extract domain-invariant knowledge, enabling the server to better integrate cross-domain invariant knowledge extracted by clients. Extensive experiments validate that FedARKS achieves state-of-the-art generalization results on the FedDG-ReID benchmark, demonstrating that learning subtle body part features can effectively assist and reinforce global representations, thereby enabling robust cross-domain person ReID capabilities.
STMI: Segmentation-Guided Token Modulation with Cross-Modal Hypergraph Interaction for Multi-Modal Object Re-Identification
Authors: Xingguo Xu; Zhanyu Liu; Weixiang Zhou; Yuansheng Gao; Junjie Cao; Yuhao Wang; Jixiang Luo; Dell Zhang
Venue: AAAI 2026 (main)

Abstract: Multi-modal object Re-Identification (ReID) aims to exploit complementary information from different modalities to retrieve specific objects. However, existing methods often rely on hard token filtering or simple fusion strategies, which can lead to the loss of discriminative cues and increased background interference. To address these challenges, we propose STMI, a novel multi-modal learning framework consisting of three key components: (1) Segmentation-Guided Feature Modulation (SFM) module leverages SAM-generated masks to enhance foreground representations and suppress background noise through learnable attention modulation; (2) Semantic Token Reallocation (STR) module employs learnable query tokens and an adaptive reallocation mechanism to extract compact and informative representations without discarding any tokens; (3) Cross-Modal Hypergraph Interaction (CHI) module constructs a unified hypergraph across modalities to capture high-order semantic relationships. Extensive experiments on public benchmarks (i.e., RGBNT201, RGBNT100, and MSVR310) demonstrate the effectiveness and robustness of our proposed STMI framework in multi-modal ReID scenarios.
Dream-IF: Dynamic Relative EnhAnceMent for Image Fusion
Authors: Xingxin Xu; Bing Cao; Dongdong Li; Qinghua Hu; Pengfei Zhu
Venue: AAAI 2026 (main)

Abstract: Image fusion aims to integrate comprehensive information from images acquired through multiple sources. However, images captured by diverse sensors often encounter various degradations that can negatively affect fusion quality. Traditional fusion methods generally treat image enhancement and fusion as separate processes, overlooking the inherent correlation between them; notably, the dominant regions in one modality of a fused image often indicate areas where the other modality might benefit from enhancement. Inspired by this observation, we introduce the concept of dominant regions for image enhancement and present a Dynamic Relative EnhAnceMent framework for Image Fusion (Dream-IF). This framework quantifies the relative dominance of each modality across different layers and leverages this information to facilitate reciprocal cross-modal enhancement. By integrating the relative dominance derived from image fusion, our approach supports not only image restoration but also a broader range of image enhancement applications. Furthermore, we employ prompt-based encoding to capture degradation-specific details, which dynamically steer the restoration process and promote coordinated enhancement in both multi-modal image fusion and image enhancement scenarios. Extensive experimental results demonstrate that Dream-IF consistently outperforms its counterparts.