3.36LADLE-MM: Limited Annotation based Detector with Learned Ensembles for Multimodal Misinformation¶

arxiv html pdf kimi

2025/12/24 05:04 GTM

With the rise of easily accessible tools for generating and manipulating multimedia content, realistic synthetic alterations to digital media have become a widespread threat, often involving manipulations across multiple modalities simultaneously. Recently, such techniques have been increasingly employed to distort narratives of important events and to spread misinformation on social media, prompting the development of misinformation detectors. In the context of misinformation conveyed through image-text pairs, several detection methods have been proposed. However, these approaches typically rely on computationally intensive architectures or require large amounts of annotated data. In this work we introduce LADLE-MM: Limited Annotation based Detector with Learned Ensembles for Multimodal Misinformation, a model-soup initialized multimodal misinformation detector designed to operate under a limited annotation setup and constrained training resources. LADLE-MM is composed of two unimodal branches and a third multimodal one that enhances image and text representations with additional multimodal embeddings extracted from BLIP, serving as fixed reference space. Despite using 60.3% fewer trainable parameters than previous state-of-the-art models, LADLE-MM achieves competitive performance on both binary and multi-label classification tasks on the DGM4 benchmark, outperforming existing methods when trained without grounding annotations. Moreover, when evaluated on the VERITE dataset, LADLE-MM outperforms current state-of-the-art approaches that utilize more complex architectures involving Large Vision-Language-Models, demonstrating the effective generalization ability in an open-set setting and strong robustness to unimodal bias.

arxiv html pdf kimi

2025/12/24 05:04 GTM

High-resolution remote sensing image semantic segmentation (HRSS) is a fundamental yet critical task in the field of Earth observation. However, it has long faced the challenges of high inter-class similarity and large intra-class variability. Existing approaches often struggle to effectively inject abstract yet strongly discriminative semantic knowledge into pixel-level feature learning, leading to blurred boundaries and class confusion in complex scenes. To address these challenges, we propose Bidirectional Co-Refinement Framework for HRSS (BiCoR-Seg). Specifically, we design a Heatmap-driven Bidirectional Information Synergy Module (HBIS), which establishes a bidirectional information flow between feature maps and class embeddings by generating class-level heatmaps. Based on HBIS, we further introduce a hierarchical supervision strategy, where the interpretable heatmaps generated by each HBIS module are directly utilized as low-resolution segmentation predictions for supervision, thereby enhancing the discriminative capacity of shallow features. In addition, to further improve the discriminability of the embedding representations, we propose a cross-layer class embedding Fisher Discriminative Loss to enforce intra-class compactness and enlarge inter-class separability. Extensive experiments on the LoveDA, Vaihingen, and Potsdam datasets demonstrate that BiCoR-Seg achieves outstanding segmentation performance while offering stronger interpretability. The released code is available at https://github.com/ShiJinghao566/BiCoR-Seg.

3.38Degradation-Aware Metric Prompting for Hyperspectral Image Restoration¶

arxiv html pdf kimi

2025/12/24 05:04 GTM

Unified hyperspectral image (HSI) restoration aims to recover various degraded HSIs using a single model, offering great practical value. However, existing methods often depend on explicit degradation priors (e.g., degradation labels) as prompts to guide restoration, which are difficult to obtain due to complex and mixed degradations in real-world scenarios. To address this challenge, we propose a Degradation-Aware Metric Prompting (DAMP) framework. Instead of relying on predefined degradation priors, we design spatial-spectral degradation metrics to continuously quantify multi-dimensional degradations, serving as Degradation Prompts (DP). These DP enable the model to capture cross-task similarities in degradation distributions and enhance shared feature learning. Furthermore, we introduce a Spatial-Spectral Adaptive Module (SSAM) that dynamically modulates spatial and spectral feature extraction through learnable parameters. By integrating SSAM as experts within a Mixture-of-Experts architecture, and using DP as the gating router, the framework enables adaptive, efficient, and robust restoration under diverse, mixed, or unseen degradations. Extensive experiments on natural and remote sensing HSI datasets show that DAMP achieves state-of-the-art performance and demonstrates exceptional generalization capability. Code is publicly available at https://github.com/MiliLab/DAMP.

3.39Unified Multimodal Brain Decoding via Cross-Subject Soft-ROI Fusion¶

arxiv html pdf kimi

2025/12/24 05:04 GTM

Multimodal brain decoding aims to reconstruct semantic information that is consistent with visual stimuli from brain activity signals such as fMRI, and then generate readable natural language descriptions. However, multimodal brain decoding still faces key challenges in cross-subject generalization and interpretability. We propose a BrainROI model and achieve leading-level results in brain-captioning evaluation on the NSD dataset. Under the cross-subject setting, compared with recent state-of-the-art methods and representative baselines, metrics such as BLEU-4 and CIDEr show clear improvements. Firstly, to address the heterogeneity of functional brain topology across subjects, we design a new fMRI encoder. We use multi-atlas soft functional parcellations (soft-ROI) as a shared space. We extend the discrete ROI Concatenation strategy in MINDLLM to a voxel-wise gated fusion mechanism (Voxel-gate). We also ensure consistent ROI mapping through global label alignment, which enhances cross-subject transferability. Secondly, to overcome the limitations of manual and black-box prompting methods in stability and transparency, we introduce an interpretable prompt optimization process. In a small-sample closed loop, we use a locally deployed Qwen model to iteratively generate and select human-readable prompts. This process improves the stability of prompt design and preserves an auditable optimization trajectory. Finally, we impose parameterized decoding constraints during inference to further improve the stability and quality of the generated descriptions.

3.40IndicDLP: A Foundational Dataset for Multi-Lingual and Multi-Domain Document Layout Parsing¶

arxiv html pdf kimi

2025/12/24 05:04 GTM

Document layout analysis is essential for downstream tasks such as information retrieval, extraction, OCR, and digitization. However, existing large-scale datasets like PubLayNet and DocBank lack fine-grained region labels and multilingual diversity, making them insufficient for representing complex document layouts. In contrast, human-annotated datasets such as M6Doc and D4LA offer richer labels and greater domain diversity, but are too small to train robust models and lack adequate multilingual coverage. This gap is especially pronounced for Indic documents, which encompass diverse scripts yet remain underrepresented in current datasets, further limiting progress in this space. To address these shortcomings, we introduce IndicDLP, a large-scale foundational document layout dataset spanning 11 representative Indic languages alongside English and 12 common document domains. Additionally, we curate UED-mini, a dataset derived from DocLayNet and M6Doc, to enhance pretraining and provide a solid foundation for Indic layout models. Our experiments demonstrate that fine-tuning existing English models on IndicDLP significantly boosts performance, validating its effectiveness. Moreover, models trained on IndicDLP generalize well beyond Indic layouts, making it a valuable resource for document digitization. This work bridges gaps in scale, diversity, and annotation granularity, driving inclusive and efficient document understanding.

3.41How I Met Your Bias: Investigating Bias Amplification in Diffusion Models¶

arxiv html pdf kimi

2025/12/24 05:04 GTM

Diffusion-based generative models demonstrate state-of-the-art performance across various image synthesis tasks, yet their tendency to replicate and amplify dataset biases remains poorly understood. Although previous research has viewed bias amplification as an inherent characteristic of diffusion models, this work provides the first analysis of how sampling algorithms and their hyperparameters influence bias amplification. We empirically demonstrate that samplers for diffusion models -- commonly optimized for sample quality and speed -- have a significant and measurable effect on bias amplification. Through controlled studies with models trained on Biased MNIST, Multi-Color MNIST and BFFHQ, and with Stable Diffusion, we show that sampling hyperparameters can induce both bias reduction and amplification, even when the trained model is fixed. Source code is available at https://github.com/How-I-met-your-bias/how_i_met_your_bias.

2025/12/24 05:04 GTM

As Earth’s climate changes, it is impacting disasters and extreme weather events across the planet. Record-breaking heat waves, drenching rainfalls, extreme wildfires, and widespread flooding during hurricanes are all becoming more frequent and more intense. Rapid and efficient response to disaster events is essential for climate resilience and sustainability. A key challenge in disaster response is to accurately and quickly identify disaster locations to support decision-making and resources allocation. In this paper, we propose a Probabilistic Cross-view Geolocalization approach, called ProbGLC, exploring new pathways towards generative location awareness for rapid disaster response. Herein, we combine probabilistic and deterministic geolocalization models into a unified framework to simultaneously enhance model explainability (via uncertainty quantification) and achieve state-of-the-art geolocalization performance. Designed for rapid diaster response, the ProbGLC is able to address cross-view geolocalization across multiple disaster events as well as to offer unique features of probabilistic distribution and localizability score. To evaluate the ProbGLC, we conduct extensive experiments on two cross-view disaster datasets (i.e., MultiIAN and SAGAINDisaster), consisting diverse cross-view imagery pairs of multiple disaster types (e.g., hurricanes, wildfires, floods, to tornadoes). Preliminary results confirms the superior geolocalization accuracy (i.e., 0.86 in Acc@1km and 0.97 in Acc@25km) and model explainability (i.e., via probabilistic distributions and localizability scores) of the proposed ProbGLC approach, highlighting the great potential of leveraging generative cross-view approach to facilitate location awareness for better and faster disaster response. The data and code is publicly available at https://github.com/bobleegogogo/ProbGLC

arxiv html pdf kimi

2025/12/24 05:04 GTM

Real-world image captions often lack contextual depth, omitting crucial details such as event background, temporal cues, outcomes, and named entities that are not visually discernible. This gap limits the effectiveness of image understanding in domains like journalism, education, and digital archives, where richer, more informative descriptions are essential. To address this, we propose a multimodal pipeline that augments visual input with external textual knowledge. Our system retrieves semantically similar images using BEIT-3 (Flickr30k-384 and COCO-384) and SigLIP So-384, reranks them using ORB and SIFT for geometric alignment, and extracts contextual information from related articles via semantic search. A fine-tuned Qwen3 model with QLoRA then integrates this context with base captions generated by Instruct BLIP (Vicuna-7B) to produce event-enriched, context-aware descriptions. Evaluated on the OpenEvents v1 dataset, our approach generates significantly more informative captions compared to traditional methods, showing strong potential for real-world applications requiring deeper visual-textual understanding

3.62FlashLips: 100-FPS Mask-Free Latent Lip-Sync using Reconstruction Instead of Diffusion or GANs¶

2025/12/24 05:04 GTM

Recognizing whether outputs from large language models (LLMs) contain faithfulness hallucination is crucial for real-world applications, e.g., retrieval-augmented generation and summarization. In this paper, we introduce FaithLens, a cost-efficient and effective faithfulness hallucination detection model that can jointly provide binary predictions and corresponding explanations to improve trustworthiness. To achieve this, we first synthesize training data with explanations via advanced LLMs and apply a well-defined data filtering strategy to ensure label correctness, explanation quality, and data diversity. Subsequently, we fine-tune the model on these well-curated training data as a cold start and further optimize it with rule-based reinforcement learning, using rewards for both prediction correctness and explanation quality. Results on 12 diverse tasks show that the 8B-parameter FaithLens outperforms advanced models such as GPT-4.1 and o3. Also, FaithLens can produce high-quality explanations, delivering a distinctive balance of trustworthiness, efficiency, and effectiveness.

arxiv html pdf kimi

2025/12/24 05:04 GTM

Large Language Models are increasingly used in conversational systems such as digital personal assistants, shaping how people interact with technology through language. While their responses often sound fluent and natural, they can also carry subtle tone biases such as sounding overly polite, cheerful, or cautious even when neutrality is expected. These tendencies can influence how users perceive trust, empathy, and fairness in dialogue. In this study, we explore tone bias as a hidden behavioral trait of large language models. The novelty of this research lies in the integration of controllable large language model based dialogue synthesis with tone classification models, enabling robust and ethical emotion recognition in personal assistant interactions. We created two synthetic dialogue datasets, one generated from neutral prompts and another explicitly guided to produce positive or negative tones. Surprisingly, even the neutral set showed consistent tonal skew, suggesting that bias may stem from the model’s underlying conversational style. Using weak supervision through a pretrained DistilBERT model, we labeled tones and trained several classifiers to detect these patterns. Ensemble models achieved macro F1 scores up to 0.92, showing that tone bias is systematic, measurable, and relevant to designing fair and trustworthy conversational AI.

arxiv html pdf kimi

2025/12/24 05:04 GTM

3.131ActionFlow: A Pipelined Action Acceleration for Vision Language Models on Edge¶

arxiv html pdf kimi

2025/12/24 05:04 GTM

Vision-Language-Action (VLA) models have emerged as a unified paradigm for robotic perception and control, enabling emergent generalization and long-horizon task execution. However, their deployment in dynamic, real-world environments is severely hin dered by high inference latency. While smooth robotic interaction requires control frequencies of 20 to 30 Hz, current VLA models typi cally operate at only 3-5 Hz on edge devices due to the memory bound nature of autoregressive decoding. Existing optimizations often require extensive retraining or compromise model accuracy. To bridge this gap, we introduce ActionFlow, a system-level inference framework tailored for resource-constrained edge plat forms. At the core of ActionFlow is a Cross-Request Pipelin ing strategy, a novel scheduler that redefines VLA inference as a macro-pipeline of micro-requests. The strategy intelligently batches memory-bound Decode phases with compute-bound Prefill phases across continuous time steps to maximize hardware utilization. Furthermore, to support this scheduling, we propose a Cross Request State Packed Forward operator and a Unified KV Ring Buffer, which fuse fragmented memory operations into efficient dense computations. Experimental results demonstrate that ActionFlow achieves a 2.55x improvement in FPS on the OpenVLA-7B model without retraining, enabling real-time dy namic manipulation on edge hardware. Our work is available at https://anonymous.4open.science/r/ActionFlow-1D47.

3.132Finite-Time Control Based on Differential Flatness for Wheeled Mobile Robots with Experimental Validation¶

arxiv html pdf kimi

2025/12/24 05:04 GTM

A robust tracking control strategy is designed to empower wheeled mobile robots (WMRs) to track predetermined routes while operating in diverse fields and encountering disturbances like strong winds or uneven path conditions, which affect tracking performance. Ensuring the applicability of this tracking method in real-world scenarios is essential. To accomplish this, the WMR model is initially transformed into a linear canonical form by leveraging the differential flatness of its kinematic model, facilitating controller design. Subsequently, a novel integral nonlinear hyperplane-based sliding mode control (INH-SMC) technique is proposed for WMR under disturbances. The stability of the technique is analyzed and verified. Finally, its practical viability is demonstrated through a comparative real-world indoor experiment on a TurtleBot3 WMR subjected to disturbances, confirming the feasibility and efficacy of the proposed approach.

arxiv html pdf kimi

2025/12/24 05:04 GTM

Due to the limitations of a single autonomous vehicle, Cellular Vehicle-to-Everything (C-V2X) technology opens a new window for achieving fully autonomous driving through sensor information sharing. However, real-world datasets supporting vehicle-infrastructure cooperative navigation in complex urban environments remain rare. To address this gap, we present UrbanV2X, a comprehensive multisensory dataset collected from vehicles and roadside infrastructure in the Hong Kong C-V2X testbed, designed to support research on smart mobility applications in dense urban areas. Our onboard platform provides synchronized data from multiple industrial cameras, LiDARs, 4D radar, ultra-wideband (UWB), IMU, and high-precision GNSS-RTK/INS navigation systems. Meanwhile, our roadside infrastructure provides LiDAR, GNSS, and UWB measurements. The entire vehicle-infrastructure platform is synchronized using the Precision Time Protocol (PTP), with sensor calibration data provided. We also benchmark various navigation algorithms to evaluate the collected cooperative data. The dataset is publicly available at https://polyu-taslab.github.io/UrbanV2X/.

3.134Asynchronous Fast-Slow Vision-Language-Action Policies for Whole-Body Robotic Manipulation¶

arxiv html pdf kimi

2025/12/24 05:04 GTM

Most Vision-Language-Action (VLA) systems integrate a Vision-Language Model (VLM) for semantic reasoning with an action expert generating continuous action signals, yet both typically run at a single unified frequency. As a result, policy performance is constrained by the low inference speed of large VLMs. This mandatory synchronous execution severely limits control stability and real-time performance in whole-body robotic manipulation, which involves more joints, larger motion spaces, and dynamically changing views. We introduce a truly asynchronous Fast-Slow VLA framework (DuoCore-FS), organizing the system into a fast pathway for high-frequency action generation and a slow pathway for rich VLM reasoning. The system is characterized by two key features. First, a latent representation buffer bridges the slow and fast systems. It stores instruction semantics and action-reasoning representation aligned with the scene-instruction context, providing high-level guidance to the fast pathway. Second, a whole-body action tokenizer provides a compact, unified representation of whole-body actions. Importantly, the VLM and action expert are still jointly trained end-to-end, preserving unified policy learning while enabling asynchronous execution. DuoCore-FS supports a 3B-parameter VLM while achieving 30 Hz whole-body action-chunk generation, approximately three times as fast as prior VLA models with comparable model sizes. Real-world whole-body manipulation experiments demonstrate improved task success rates and significantly enhanced responsiveness compared to synchronous Fast-Slow VLA baselines. The implementation of DuoCore-FS, including training, inference, and deployment, is provided to commercial users by Astribot as part of the Astribot robotic platform.

3.135LoLA: Long Horizon Latent Action Learning for General Robot Manipulation¶

arxiv html pdf kimi

2025/12/24 05:04 GTM

The capability of performing long-horizon, language-guided robotic manipulation tasks critically relies on leveraging historical information and generating coherent action sequences. However, such capabilities are often overlooked by existing Vision-Language-Action (VLA) models. To solve this challenge, we propose LoLA (Long Horizon Latent Action Learning), a framework designed for robot manipulation that integrates long-term multi-view observations and robot proprioception to enable multi-step reasoning and action generation. We first employ Vision-Language Models to encode rich contextual features from historical sequences and multi-view observations. We further introduces a key module, State-Aware Latent Re-representation, which transforms visual inputs and language commands into actionable robot motion space. Unlike existing VLA approaches that merely concatenate robot proprioception (e.g., joint angles) with VL embeddings, this module leverages such robot states to explicitly ground VL representations in physical scale through a learnable “embodiment-anchored” latent space. We trained LoLA on diverse robotic pre-training datasets and conducted extensive evaluations on simulation benchmarks (SIMPLER and LIBERO), as well as two real-world tasks on Franka and Bi-Manual Aloha robots. Results show that LoLA significantly outperforms prior state-of-the-art methods (e.g., pi0), particularly in long-horizon manipulation tasks.

2feed¶

2.1NeurIPS 2025 | 从“唯Key论”到非对称解耦：利用KV差异重塑长文本推理¶

2.2视频衍生数据集来了！港科大×美团开源OpenSubject，专攻复杂场景生成与编辑¶

3paper¶

3.1SemanticGen: Video Generation in Semantic Space¶

3.2LongVideoAgent: Multi-Agent Reasoning with Long Videos¶

3.3SpatialTree: How Spatial Abilities Branch Out in MLLMs¶

3.4Active Intelligence in Video Avatars via Closed-loop World Modeling¶

3.5FedPOD: the deployable units of training for federated learning¶

3.6Repurposing Video Diffusion Transformers for Robust Point Tracking¶

3.7Cube Bench: A Benchmark for Spatial Visual Reasoning in MLLMs¶

3.8LEAD: Minimizing Learner-Expert Asymmetry in End-to-End Driving¶

3.9FlashVLM: Text-Guided Visual Token Selection for Large Multimodal Models¶

3.10Learning to Reason in 4D: Dynamic Spatial Understanding for Vision Language Models¶

3.11Multi-Grained Text-Guided Image Fusion for Multi-Exposure and Multi-Focus Scenarios¶

3.12AlignPose: Generalizable 6D Pose Estimation via Multi-view Feature-metric Alignment¶

3.13SirenPose: Dynamic Scene Reconstruction via Geometric Supervision¶

3.14Bridging Modalities and Transferring Knowledge: Enhanced Multimodal Understanding and Recognition¶

3.15Multi-temporal Adaptive Red-Green-Blue and Long-Wave Infrared Fusion for You Only Look Once-Based Landmine Detection from Unmanned Aerial Systems¶

3.16UTDesign: A Unified Framework for Stylized Text Editing and Generation in Graphic Design Images¶

3.17Snapshot 3D image projection using a diffractive decoder¶

3.18Beyond Motion Pattern: An Empirical Study of Physical Forces for Human Motion Understanding¶

3.19Dual-Encoder Transformer-Based Multimodal Learning for Ischemic Stroke Lesion Segmentation Using Diffusion MRI¶

3.20High Dimensional Data Decomposition for Anomaly Detection of Textured Images¶

3.21Skin Lesion Classification Using a Soft Voting Ensemble of Convolutional Neural Networks¶

3.22Simplifying Multi-Task Architectures Through Task-Specific Normalization¶

3.23Chain-of-Anomaly Thoughts with Large Vision-Language Models¶

3.24DETACH : Decomposed Spatio-Temporal Alignment for Exocentric Video and Ambient Sensors with Staged Learning¶

3.25Generative Digital Twins: Vision-Language Simulation Models for Executable Industrial Systems¶

3.26SmartSplat: Feature-Smart Gaussians for Scalable Compression of Ultra-High-Resolution Images¶

3.27Linking Faces and Voices Across Languages: Insights from the FAME 2026 Challenge¶

3.28CLIP Based Region-Aware Feature Fusion for Automated BBPS Scoring in Colonoscopy Images¶

3.29CRAFT: Continuous Reasoning and Agentic Feedback Tuning for Multimodal Text-to-Image Generation¶

3.30Field-Space Attention for Structure-Preserving Earth System Transformers¶

3.31The devil is in the details: Enhancing Video Virtual Try-On via Keyframe-Driven Details Injection¶

3.32KnowVal: A Knowledge-Augmented and Value-Guided Autonomous Driving System¶

3.33TAVID: Text-Driven Audio-Visual Interactive Dialogue Generation¶

3.34UbiQVision: Quantifying Uncertainty in XAI for Image Recognition¶

3.35D3{D}^{3}D3{ETOR}: D{D}Debate-Enhanced Pseudo Labeling and Frequency-Aware Progressive D{D}Debiasing for Weakly-Supervised Camouflaged Object D{D}Detect...¶

3.36LADLE-MM: Limited Annotation based Detector with Learned Ensembles for Multimodal Misinformation¶

3.37BiCoR-Seg: Bidirectional Co-Refinement Framework for High-Resolution Remote Sensing Image Segmentation¶

3.38Degradation-Aware Metric Prompting for Hyperspectral Image Restoration¶

3.39Unified Multimodal Brain Decoding via Cross-Subject Soft-ROI Fusion¶

3.40IndicDLP: A Foundational Dataset for Multi-Lingual and Multi-Domain Document Layout Parsing¶

3.41How I Met Your Bias: Investigating Bias Amplification in Diffusion Models¶

3.42LiteFusion: Taming 3D Object Detectors from Vision-Based to Multi-Modal with Minimal Adaptation¶

3.43JDPNet: A Network Based on Joint Degradation Processing for Underwater Image Enhancement¶

3.44Generative Latent Coding for Ultra-Low Bitrate Image Compression¶

3.45Towards Natural Language-Based Document Image Retrieval: New Dataset and Benchmark¶

3.46AMoE: Agglomerative Mixture-of-Experts Vision Foundation Model¶

3.47CoDi -- an exemplar-conditioned diffusion model for low-shot counting¶

3.48Enhancing annotations for 5D apple pose estimation through 3D Gaussian Splatting (3DGS)¶

3.49Retrieval-augmented Prompt Learning for Pre-trained Foundation Models¶

3.50Dreamcrafter: Immersive Editing of 3D Radiance Fields Through Flexible, Generative Inputs and Outputs¶

3.51milliMamba: Specular-Aware Human Pose Estimation via Dual mmWave Radar with Multi-Frame Mamba Fusion¶

3.52HEART-VIT: Hessian-Guided Efficient Dynamic Attention and Token Pruning in Vision Transformer¶

3.53DDAVS: Disentangled Audio Semantics and Delayed Bidirectional Alignment for Audio-Visual Segmentation¶

3.54Multi Modal Attention Networks with Uncertainty Quantification for Automated Concrete Bridge Deck Delamination Detection¶

3.55UMAMI: Unifying Masked Autoregressive Models and Deterministic Rendering for View Synthesis¶

3.56LiDARDraft: Generating LiDAR Point Cloud from Versatile Inputs¶

3.57Effect of Activation Function and Model Optimizer on the Performance of Human Activity Recognition System Using Various Deep Learning Models¶

3.58Item Region-based Style Classification Network (IRSN): A Fashion Style Classifier Based on Domain Knowledge of Fashion Experts¶

3.59Progressive Learned Image Compression for Machine Perception¶

3.60Towards Generative Location Awareness for Disaster Response: A Probabilistic Cross-view Geolocalization Approach¶

3.61Beyond Vision: Contextually Enriched Image Captioning with Multi-Modal Retrieva¶

3.62FlashLips: 100-FPS Mask-Free Latent Lip-Sync using Reconstruction Instead of Diffusion or GANs¶

3.63VALLR-Pin: Dual-Decoding Visual Speech Recognition for Mandarin with Pinyin-Guided LLM Refinement¶

3.64H2\text{H}^2H2em: Learning Hierarchical Hyperbolic Embeddings for Compositional Zero-Shot Learning¶

3.65MAPI-GNN: Multi-Activation Plane Interaction Graph Neural Network for Multimodal Medical Diagnosis¶

3.66A Contextual Analysis of Driver-Facing and Dual-View Video Inputs for Distraction Detection in Naturalistic Driving Environments¶

3.67SegEarth-R2: Towards Comprehensive Language-guided Segmentation for Remote Sensing Images¶

3.68PaveSync: A Unified and Comprehensive Dataset for Pavement Distress Analysis and Classification¶

3.69Few-Shot-Based Modular Image-to-Video Adapter for Diffusion Models¶

3.70A Dual-Branch Local-Global Framework for Cross-Resolution Land Cover Mapping¶

3.71A Novel CNN Gradient Boosting Ensemble for Guava Disease Detection¶

3.72WSD-MIL: Window Scale Decay Multiple Instance Learning for Whole Slide Image Classification¶

3.73HistoWAS: A Pathomics Framework for Large-Scale Feature-Wide Association Studies of Tissue Topology and Patient Outcomes¶

3.74How Much 3D Do Video Foundation Models Encode?¶

3.75SE360: Semantic Edit in 360° Panoramas via Hierarchical Data Construction¶

3.35 ${D}^{3}$ {ETOR}: ${D}$ ebate-Enhanced Pseudo Labeling and Frequency-Aware Progressive ${D}$ ebiasing for Weakly-Supervised Camouflaged Object ${D}$ etect...¶

3.64 $\text{H}^2$ em: Learning Hierarchical Hyperbolic Embeddings for Compositional Zero-Shot Learning¶

3.111M $^3$ KG-RAG: Multi-hop Multimodal Knowledge Graph-enhanced Retrieval-Augmented Generation¶