AAAI 2026 论文列表

Recent advancements in weakly-supervised video anomaly detection have achieved remarkable performance by applying the multiple instance learning paradigm based on multimodal foundation models such as CLIP to highlight anomalous instances and classify categories. However, their objectives may tend to detect the most salient response segments, while neglecting to mine diverse normal patterns separated from anomalies, and are prone to category confusion due to similar appearance, leading to unsatisfactory fine-grained classification results. Therefore, we propose a novel Disentangled Semantic Alignment Network (DSANet) to explicitly separate abnormal and normal features from coarse-grained and fine-grained aspects, enhancing the distinguishability. Specifically, at the coarse-grained level, we introduce a self-guided normality modeling branch that reconstructs input video features under the guidance of learned normal prototypes, encouraging the model to exploit normality cues inherent in the video, thereby improving the temporal separation of normal patterns and anomalous events. At the fine-grained level, we present a decoupled contrastive semantic alignment mechanism, which first temporally decomposes each video into event-centric and background-centric components using frame-level anomaly scores and then applies visual-language contrastive learning to enhance class-discriminative representations. Comprehensive experiments on two standard benchmarks, namely XD-Violence and UCF-Crime, demonstrate that DSANet outperforms existing state-of-the-art methods.

Hash center-based deep hashing methods improve upon pairwise or triplet-based approaches by assigning fixed hash centers to each class as learning targets, thereby avoiding the inefficiency of local similarity optimization. However, random center initialization often disregards inter-class semantic relationships. While existing two-stage methods mitigate this by first refining hash centers with semantics and then training the hash function, they introduce additional complexity, computational overhead, and suboptimal performance due to stage-wise discrepancies. To address these limitations, we propose Center-Reassigned Hashing (CRH), an end-to-end framework that dynamically reassigns hash centers from a preset codebook while jointly optimizing the hash function. Unlike previous methods, CRH adapts hash centers to the data distribution without explicit center optimization phases, enabling seamless integration of semantic relationships into the learning process. Furthermore, a multi-head mechanism enhances the representational capacity of hash centers, capturing richer semantic structures. Extensive experiments on three benchmarks demonstrate that CRH learns semantically meaningful hash centers and outperforms state-of-the-art deep hashing methods in retrieval tasks.

Unlike discriminative approaches in autonomous driving that predict a fixed set of candidate trajectories of the ego vehicle, generative methods, such as diffusion models, learn the underlying distribution of future motion, enabling more flexible trajectory prediction. However, since these methods typically rely on denoising human-craft trajectory anchors or random noise, there remains significant room for improvement. In this paper, we propose DiffRefiner, a novel two-stage trajectory prediction framework. The first stage employs a transformer-based Proposal Decoder to generate coarse trajectory predictions by regressing from sensor inputs using predefined trajectory anchors. The second stage applies a Diffusion Refiner that iteratively denoises and refines these initial predictions. In this way, we enhance the performance of diffusion-based planning by incorporating a discriminative trajectory proposal module, which provides strong guidance for the generative refinement process. Furthermore, we design a fine-grained denoising decoder to enhance scene compliance, enabling more accurate trajectory prediction through enhanced alignment with the surrounding environment. Experimental results demonstrate that DiffRefiner achieves state-of-the-art performance, attaining 87.4 EPDMS on NAVSIM v2, and 87.1 DS along with 71.4 SR on Bench2Drive, thereby setting new records on both public benchmarks. The effectiveness of each component is validated via ablation studies as well.

Multi-view understanding, the ability to reconcile visual information across diverse viewpoints for effective navigation, manipulation, and 3D scene comprehension, is a fundamental challenge in Multi-Modal Large Language Models (MLLMs) to be used as embodied agents. While recent MLLMs have shown impressive advances in high-level reasoning and planning, they frequently fall short when confronted with multi-view geometric consistency and cross-view correspondence. To comprehensively evaluate the challenges of MLLMs in multi-view scene reasoning, we introduce All-Angles Bench, a human carefully benchmark with over 2,100 question-answer pairs from 90 diverse, real-world scenes. Our broad evaluation across 38 general-purpose and 3D spatial reasoning MLLMs reveals a substantial performance gap compared to humans. More critically, our analysis identifies two root failure modes: (1) cross-view object mismatch—the inability to establish consistent object correspondence across views; and (2) cross-view spatial misalignment—the failure to infer accurate camera poses and spatial layouts. These findings underscore a lack of multi-view awareness in current MLLMs, calling for architectural innovations beyond prompt tuning alone. We believe that our benchmark offers valuable insights toward building spatially-intelligent MLLMs.

Recent diffusion-based image editing methods have made great strides in text-guided tasks but often struggle with complex, indirect instructions. Additionally, current models frequently exhibit poor identity preservation, unintended edits, or rely on manual masks. To overcome these limitations, we introduce X-Planner, a Multimodal Large Language Model (MLLM)-based planning system that bridges user intent with editing model capabilities. X-Planner uses chain-of-thought reasoning to systematically break down complex instructions into simpler sub-instructions. For each one, X-Planner automatically generates precise edit types and segmentation masks, enabling localized, identity-preserving edits without applying external tools or models during inference. To enable the training of such a planner, we also introduce a fully automated, reproducible pipeline to generate large-scale, high-quality training data. Our complete system achieves state-of-the-art results on both existing and newly proposed complex instruction-based editing benchmarks.

While conventional computer vision emphasizes pixel-level and feature-based objectives, medical image analysis of intricate biological structures necessitates explicit representation of their complex topological properties. Despite their successes, deep learning models often struggle to accurately capture the connectivity and continuity of fine, sometimes pixel-thin, yet critical structures due to their reliance on implicit learning from data. To address this challenge, we introduce Conformable Convolution, a novel convolutional layer designed to explicitly impose topological consistency. Conformable Convolution learns adaptive kernel offsets that focus on regions of high topological significance within an image. This prioritization is guided by our proposed Topological Posterior Generator (TPG) module, which leverages persistent homology. The TPG module identifies key topological features and guides the convolutional layers by applying persistent homology to feature maps transformed into cubical complexes. Unlike existing approaches that are merely aware of topology, our method explicitly constrains the learning process to ensure topological correctness. The proposed modules are architecture-agnostic, enabling them to be integrated seamlessly into various architectures. We showcase the effectiveness of our framework in the segmentation task, where preserving the interconnectedness of structures is critical. The results on three diverse datasets demonstrate that our framework effectively preserves the topology both quantitatively and qualitatively.

Continuous sign language recognition (CSLR) technology enables social communication for the hearing-impaired by converting sign language videos into text. However, due to the limited receptive fields of convolutional networks and inefficient long-range dependency modeling in temporal modules, current methods find it difficult to capture cross-regional and high-order dynamic semantics in complex gestures. To address these limitations, we propose a dynamic spatiotemporal hypergraph network named HyperSign, which optimizes feature learning through innovative graph architectures. For single-frame spatial modeling, we propose a saliency-aware spatial graph construction strategy that dynamically quantifies semantic saliency by integrating feature complexity and motion intensity information from patches. This strategy can adaptively adjust node connectivity based on the computed saliency, thereby enabling the graph structure to focus on information-dense regions such as hands and faces. For temporal dependency modeling, we abandon the conventional pairwise frame interactions and propose a temporal hypergraph construction method. This method employs a learnable clustering algorithm to aggregate semantically correlated nodes within temporal windows into hyperedges, thereby explicitly capturing high-order associations within individual gesture actions that span multiple frames. Extensive experiments on the PHOENIX14, PHOENIX14-T, and CSL-Daily datasets demonstrate that HyperSign outperforms the state-of-the-art (SOTA) approaches in CSLR without any additional annotation information, establishing a new feature learning paradigm for the CSLR task.

Establishing point-to-point correspondences across multiple 3D shapes is a fundamental problem in computer vision and graphics. In this paper, we introduce DcMatch, a novel unsupervised learning framework for non-rigid multi-shape matching. Unlike existing methods that learn a canonical embedding from a single shape, our approach leverages a shape graph attention network to capture the underlying manifold structure of the entire shape collection. This enables the construction of a more expressive and robust shared latent space, leading to more consistent shape-to-universe correspondences via a universe predictor. Simultaneously, we represent these correspondences in both the spatial and spectral domains and enforce their alignment in the shared universe space through a novel cycle consistency loss. This dual-level consistency fosters more accurate and coherent mappings. Extensive experiments on several challenging benchmarks demonstrate that our method consistently outperforms previous state-of-the-art approaches across diverse multi-shape matching scenarios.

Can Multimodal Large Language Models (MLLMs) discern confused objects that are visually present but audio-absent? To study this, we introduce a new benchmark, AV-ConfuseBench, which simulates an “Audio-Visual Confusion” scene by modifying the corresponding sound of an object in the video, e.g., mute the sounding object and ask MLLMs “Is there a/an {muted-object} sound”. Experimental results reveal that MLLMs, such as Qwen2.5-Omni and Gemini 2.5, struggle to discriminate non-existent audio due to visually dominated reasoning. Motivated by this observation, we introduce RL-CoMM, a Reinforcement Learning-based Collaborative Multi-MLLM that is built upon the Qwen2.5-Omni foundation. RL-CoMM includes two stages: 1) To alleviate visually dominated ambiguities, we introduce an external model, a Large Audio Language Model (LALM), as the reference model to generate audio-only reasoning. Then, we design a Step-wise Reasoning Reward function that enables MLLMs to self-improve audio-visual reasoning with the audio-only reference. 2) To ensure an accurate answer prediction, we introduce Answer-centered Confidence Optimization to reduce the uncertainty of potential heterogeneous reasoning differences. Extensive experiments on audio-visual question answering and audio-visual hallucination show that RL-CoMM improves accuracy by 10~30% over the baseline model with limited training data.

Open-world object detection (OWOD) aims to detect known and unknown objects in dynamic environments. However, only known classes are labeled during training, making it challenging for detectors to recognize unknown objects during inference. Existing methods typically rely on supervision from known categories, leading models to overconfidently misclassify visually similar unknowns as known, and dissimilar ones as background. This known-class prior bias limits the model’s ability to detect unknown objects. In this paper, we propose a novel method, OW-DAR, which enhances foreground-background separability through collaborative fine-grained and coarse-grained modeling. At the fine-grained level, we propose Fine-grained Masked Reconstruction (FMR), which randomly masks regions of the feature map to guide the reconstruction toward semantic structures, rather than memorizing low-level patterns. At the coarse-grained level, we propose Adaptive Region-based Error Aggregation (AREA), which operates on object proposals to aggregate reconstruction errors. This enables the model to attend to semantically ambiguous foreground-background boundaries while suppressing the influence of local outliers during optimization. Finally, we leverage robust reconstruction errors to perform unsupervised foreground-background modeling, enabling probabilistic estimation for potential unknown objects. We validate the effectiveness of OW-DAR on standard OWOD benchmark. Experimental results demonstrate that OW-DAR consistently outperforms existing state-of-the-art methods, achieving a +18.8 improvement in unknown object recall (U-Recall).

Referring Image Segmentation (RIS), which aims to segment specific objects based on natural language descriptions, plays an essential role in vision-language understanding. Despite its progress in remote sensing applications, RIS under Low-Altitude Drone (LAD) scenarios remains underexplored, as existing datasets and methods are typically designed for high-altitude and static-view imagery. They struggled to handle the unique characteristics of LAD views, such as diverse viewpoints and high object density. In this paper, we propose RIS-LAD, the first fine-grained RIS benchmark tailored for LAD scenarios, featuring 13,871 meticulously annotated image-text-mask triplets collected from real-world drone footage with emphasis on small, densely cluttered objects and multi-view perspectives. Additionally, we propose the Semantic-Aware Adaptive Reasoning Network, which decomposes and adaptively routes semantic information to different network stages rather than uniformly injecting all linguistic features. Specifically, the Category-Dominated Linguistic Enhancement aligns visual features with object categories during early encoding, while the Adaptive Reasoning Fusion Module dynamically selects semantic cues across scales to enhance reasoning in complex scenes. Extensive experiments reveal that RIS-LAD presents substantial challenges to state-of-the-art RIS algorithms, and also demonstrate the effectiveness of our proposed model in addressing these challenges.

With the advancement of vision-language models, image captioning has made significant progress, leading to the generation of more accurate and detailed descriptions. Current image captioning primarily focuses on describing the apparent visual characteristics, which are easily observed by most humans, but less helpful in real-world scenarios. When users seek a deeper understanding of visual content, they may be concerned with fine-grained categories, function properties, and other background knowledge, rather than merely appearances. Additionally, as users' interests vary, there is a growing demand for customizable content generation. To address these challenges, we propose the task of image narrative generation, which aims to produce knowledge-rich natural language responses for input images, customized to the user preference. Furthermore, we propose T^4, an image narrative generation model progressing through cascade steps: Tailor, reTrieve, Think, and Tell. Specifically, it takes the image and various types of prompts as input, and first refines or predicts potentially interesting queries that are tailored to the user expertise level. Subsequently, the model enriches contextual knowledge through retrieval-augmentation and employs chain-of-thoughts to decompose the generation process step by step, thereby telling an accurate and logically coherent image narrative. In addition, we construct the ImgNarr-23K dataset to support task training and evaluation. Experimental results demonstrate that the proposed approach generates image narratives that better satisfy user requirements, and achieves state-of-the-art performance in knowledge-based VQA tasks without additional finetuning. T^4 presents a promising solution for customized content generation in specialized domains.

Driven by advances in GANs and diffusion models, deepfake content has reached an unprecedented level of photorealism, causing detectors to deteriorate once they leave their training domain. Most prior studies adopt CLIP as the backbone of an image-level binary classifier, yet overlook CLIP’s core strength: text-to-image semantic alignment. Moreover, captions generated by CLIP-CAP lack sufficient high-level semantics to distinguish between authentic and manipulated faces. Deepfake generators often fail to maintain semantic coherence, resulting in contradictions that traditional visual models cannot capture. Existing approaches also intermingle all samples during training and thus lack a systematic, difficulty-aware curriculum. To bridge these gaps, we introduce Semantic- and Frequency-Enhanced (SAFE) deepfake detection, a two-component framework: 1) Semantic-enhanced multimodal alignment. Authenticity cues are injected into CLIP-CAP captions, and low-rank LoRA fine-tuning is applied to CLIP’s visual branch, yielding dual supervision for text–image alignment and forgery discrimination. 2) Dual-score curriculum learning. Fourier Correlation Variance (FCV) measures local spectral consistency and, combined with the loss value, is transformed into a difficulty score that ranks training samples from easy to hard, reducing training time by 23.3% and enhancing generalization. SAFE attains state-of-the-art performance on several cross-dataset and cross-manipulation benchmarks. Ablation studies confirm that semantic enhancement, LoRA fine-tuning, and dual-score curriculum are complementary, jointly delivering substantial gains in open-set generalization.

Autonomous vehicles must navigate safely in complex driving environments. Imitating a single expert trajectory, as in regression-based approaches, usually does not explicitly assess the safety of the predicted trajectory. Selection-based methods address this by generating and scoring multiple trajectory candidates and predicting the safety score for each. However, they face optimization challenges in precisely selecting the best option from thousands of candidates and distinguishing subtle but safety-critical differences, especially in rare and challenging scenarios. We propose DriveSuprim to overcome these challenges and advance the selection-based paradigm through a coarse-to-fine paradigm for progressive candidate filtering, a rotation-based augmentation method to improve robustness in out-of-distribution scenarios, and a self-distillation framework to stabilize training. DriveSuprim achieves state-of-the-art performance, reaching 93.5% PDMS in NAVSIM v1 and 87.1% EPDMS in NAVSIM v2 without extra data, with 83.02 Driving Score and 60.00 Success Rate on Bench2Drive, demonstrating superior planning capabilities in various driving scenarios.

Generating high-fidelity full-body human interactions with dynamic objects and static scenes remains a critical challenge in computer graphics and animation. Existing methods for human-object interaction often neglect scene context, leading to implausible penetrations, while human-scene interaction approaches struggle to coordinate fine-grained manipulations with long-range navigation. To address these limitations, we propose HOSIG, a novel framework for synthesizing full-body interactions through hierarchical scene perception. Our method decouples the task into three key components: 1) a scene-aware grasp pose generator that ensures collision-free whole-body postures with precise hand-object contact by integrating local geometry constraints, 2) a heuristic navigation algorithm that autonomously plans obstacle-avoiding paths in complex indoor environments via compressed 2D floor maps and dual-component spatial reasoning, and 3) a scene-guided motion diffusion model that generates trajectory-controlled, full-body motions with finger-level accuracy by incorporating spatial anchors and dual-space gradient-based guidance. Extensive experiments on the TRUMANS dataset demonstrate superior performance over state-of-the-art methods. Notably, our framework supports unlimited motion length through autoregressive generation and requires minimal manual intervention. This work bridges the critical gap between scene-aware navigation and dexterous object manipulation, advancing the frontier of embodied interaction synthesis.

Underwater Image Enhancement (UIE) focuses on improving visual quality from various underwater scenes. Existing methods simplistically treat various degradations as homogeneous, disregarding their intrinsic connections and causing models to blindly learn, resulting in conflicting optimization goals and visual distortions. To address above limitations, we propose a Conditional Prompt Learning via Degradation Perception (CPLDP) model, which employs conditional prompt as degradation perception priors and guides underwater image enhancement. Specifically, we show that the natural language prompts not only promote distinguishing different degraded images, but also aid in exploring more details with semantic information. Therefore, our method generates five key degradation prompts (green/blue/green-blue color casts, uneven illumination and haze) with conditional prompt learning. Subsequently, considering the intrinsic relationships among different degradations, we employ degradation perceptions as priors and fine-tune the learning strategy to enhance underwater images. During training, an adaptive loss function with multi-degradations is designed, allowing it to effectively handle the task conflicts among multiple underwater degradations. Additionally, we conduct a human visual-based underwater dataset with various degradation types by subjective statistics. Extensive experiments on both full-reference and non-reference datasets demonstrate that our CPLDP can achieve better visual results and outperforms state-of-the-art UIE methods across various degradation scenarios.

Remote sensing imagery presents vast, inherently unstructured spatial data, necessitating sophisticated reasoning to interpret complex user intents and contextual relationships beyond simple recognition tasks. In this paper, we aim to construct an Earth observation workflow to handle complex queries by reasoning about spatial context and user intent. As a reasoning workflow, it should autonomously explore and construct its own inference paths, rather than being confined to predefined ground‑truth sequences. Ideally, its architecture ought to be unified yet generalized, possessing capabilities to perform diverse reasoning tasks through one model without requiring additional fine-tuning. Existing remote sensing approaches rely on supervised fine-tuning paradigms and task‑specific heads, limiting both autonomous reasoning and unified generalization. To this end, we propose RemoteReasoner, a unified workflow for geospatial reasoning. The design of RemoteReasoner integrates a multi-modal large language model (MLLM) for interpreting user instructions and localizing targets, together with task transformation strategies that enable multi-granularity tasks, including object-, region-, and pixel-level. In contrast to existing methods, our framework is trained with reinforcement learning (RL) to endow the MLLM sufficient reasoning autonomy. At the inference stage, our transformation strategies enable diverse task output formats without requiring task-specific decoders or further fine-tuning. Experiments demonstrated that RemoteReasoner achieves state-of-the-art performance across multi-granularity reasoning tasks. Furthermore, it retains the MLLM's inherent generalization capability, demonstrating robust performance on unseen tasks and categories.

To identify objects beyond predefined categories, open-vocabulary aerial object detection (OVAD) leverages the zero-shot capabilities of visual-language models (VLMs) to generalize from base to novel categories. Existing approaches typically utilize self-learning mechanisms with weak text supervision to generate region-level pseudo-labels to align detectors with VLMs semantic spaces. However, text dependence induces semantic bias, restricting open-vocabulary expansion to text-specified concepts. We propose VK-Det, a visual knowledge-guided open-vocabulary object detection framework without extra supervision. First, we discover and leverage vision encoder's inherent informative region perception to attain fine-grained localization and adaptive distillation. Second, we introduce a novel prototype-aware pseudo-labeling strategy. It models inter-class decision boundaries through feature clustering and maps detection regions to latent categories via prototype matching. This enhances attention to novel objects while compensating for missing supervision. Extensive experiments show state-of-the-art performance, achieving 30.1 mAPᴺ on DIOR and 23.3 mAPᴺ on DOTA, outperforming even extra supervised methods.

Source-Free Object Detection (SFOD) aims to adapt a source-pretrained object detector to a target domain without access to source data. However, existing SFOD methods predominantly rely on internal knowledge from the source model, which limits their capacity to generalize across domains and often results in biased pseudo-labels, thereby hindering both transferability and discriminability. In contrast, Vision Foundation Models (VFMs), pretrained on massive and diverse data, exhibit strong perception capabilities and broad generalization, yet their potential remains largely untapped in the SFOD setting. In this paper, we propose a novel SFOD framework that leverages VFMs as external knowledge sources to jointly enhance feature alignment and label quality. Specifically, we design three VFM-based modules: (1) Patch-weighted Global Feature Alignment (PGFA) distills global features from VFMs using patch-similarity–based weighting to enhance global feature transferability; (2) Prototype-based Instance Feature Alignment (PIFA) performs instance-level contrastive learning guided by momentum-updated VFM prototypes; and (3) Dual-source Enhanced Pseudo-label Fusion (DEPF) fuses predictions from detection VFMs and teacher models via an entropy-aware strategy to yield more reliable supervision. Extensive experiments on six benchmarks demonstrate that our method achieves state-of-the-art SFOD performance, validating the effectiveness of integrating VFMs to simultaneously improve transferability and discriminability.