1378 papers
The MEGC 2026 challenge introduces two new tasks, Micro-Expression Video Question Answering (ME-VQA) and Micro-Expression Long-Video Question Answering (ME-LVQA), to advance the analysis of facial micro-expressions by leveraging the multimodal reasoning capabilities of large vision-language models on both short and long-duration video sequences.
This paper introduces a Compact Bidirectional Transformer model for image captioning that tightly couples left-to-right and right-to-left flows to leverage bidirectional context in parallel, achieving state-of-the-art results on the MSCOCO benchmark through sentence-level ensembling and an extended two-flow self-critical training strategy.
This paper introduces Visual Query Segmentation (VQS), a new paradigm for pixel-level object localization in untrimmed videos, supported by the large-scale VQS-4K benchmark and the high-performing VQ-SAM method that extends SAM 2.
This paper introduces a systematic framework for evaluating black-box patch attacks on three vision-language model-based autonomous driving architectures in CARLA simulation, revealing severe, sustained vulnerabilities and distinct failure patterns that highlight the inadequacy of current designs against physical adversarial threats.
This paper proposes a model-based image compression technique for tetherless underwater remotely operated vehicles that leverages novel view synthesis priors and gradient descent optimization to achieve superior compression ratios and image quality, particularly in scenarios involving new objects within the scene.
HECTOR is a novel video generation pipeline that enables fine-grained compositional control by supporting hybrid reference conditioning from static images and dynamic videos, while allowing users to explicitly specify the trajectories, locations, scales, and speeds of individual objects to synthesize coherent, high-fidelity videos.
This paper introduces PanoAffordanceNet, a novel framework and the first high-quality dataset (360-AGD) designed to enable holistic affordance grounding in 360-degree indoor environments by addressing challenges like geometric distortion and semantic dispersion through distortion-aware calibration and multi-level constraints.
The paper introduces -Occ, a robust 3D semantic occupancy prediction framework that leverages a Multi-view Masked Reconstruction module and a Feature Memory Module to maintain geometric and semantic coherence under incomplete multi-camera inputs, significantly outperforming existing methods in scenarios with missing views.
This paper proposes Multi-Kernel Gated Adapters (MKGA) and their residual variant (ResMKGA) to address asymmetric feature degradation under cross-center domain shifts in thyroid ultrasound by leveraging the complementary strengths of CNNs and ViTs to enhance both segmentation robustness and malignancy diagnostic accuracy.
This paper proposes a cost-effective, computer vision-based smart parking system that utilizes YOLOv8 for vehicle detection and inverse perspective mapping to merge multi-camera views into a simulated 3D environment for guiding users to vacant spots.
This paper introduces X-GS, an extensible open framework that unifies 3D Gaussian Splatting with downstream multimodal models through a real-time, semantically enriched pipeline capable of processing unposed video streams for tasks like object detection and zero-shot captioning.
This paper introduces the first exemplar-free continual learning benchmark for Audio-Visual Segmentation (AVS) and proposes the ATLAS baseline, which utilizes audio-guided pre-fusion conditioning and Low-Rank Anchoring to effectively mitigate catastrophic forgetting in dynamic, evolving environments.
The paper introduces VisionCreator-R1, a native visual-generation agent enhanced with explicit reflection mechanisms and trained via a Reflection-Plan Co-Optimization (RPCO) methodology that addresses credit assignment challenges to outperform state-of-the-art models on both single and multi-image generation benchmarks.
This paper presents a representation-native framework for explainable 3D Gaussian Splatting watermarking that utilizes a Trio-Experts module and a Safety and Budget Aware Gate to optimize carrier selection and visual compensation, achieving superior robustness and fidelity while providing auditable insights into the watermarking process.
Granulon is a novel multimodal large language model that leverages a DINOv3-based visual encoder enhanced with a text-conditioned granularity controller and adaptive token aggregation to dynamically unify pixel-level perception with coarse-grained semantics, significantly improving accuracy and reducing hallucinations compared to existing approaches.
CycleULM is a novel, label-free deep learning framework that leverages CycleGAN to bridge the simulation-to-reality gap in ultrasound localisation microscopy, significantly enhancing microbubble localisation accuracy, image resolution, and processing speed for real-time clinical application without requiring paired ground truth data.
This study demonstrates that the longitudinal progression of radiologic pleuroparenchymal fibroelastosis (PPFE), quantified via automated analysis of low-dose CT scans, independently predicts increased mortality and adverse respiratory outcomes in large lung cancer screening cohorts.
This paper systematically diagnoses the performance gap between text and image inputs in multimodal LLMs, revealing that visual text primarily amplifies reading errors rather than reasoning failures, and proposes a self-distillation method that effectively bridges this gap by training models on their own text-based reasoning traces paired with image inputs.
ADHint is a novel reinforcement learning framework that enhances reasoning capabilities and generalization by integrating sample difficulty priors to adaptively schedule hint ratios and employing consistency-based gradient modulation with rollout difficulty posteriors to stabilize learning and prevent destructive imitation.
This paper addresses the challenge of LiDAR-based 3D semantic segmentation under noisy labels and domain shifts by introducing the DGLSS-NL task, establishing a new benchmark, and proposing DuNe, a dual-view framework that achieves state-of-the-art robustness across multiple datasets.