4952 papers
To address model collapse and knowledge loss in long-term test-time adaptation, this paper proposes an Adaptive and Selective Reset (ASR) framework that dynamically determines optimal reset timing and scope while employing an importance-aware regularizer to recover essential knowledge and an on-the-fly adjustment scheme to enhance adaptability.
The paper introduces STAR, a novel autoregressive pretraining method that utilizes image separators to quadruple the input sequence length of Vision Mamba, achieving a competitive 83.5% accuracy on ImageNet-1k by effectively leveraging long-range dependencies.
This paper proposes a lightweight underwater object detection framework based on YOLOv10 that integrates a Multi-Stage Adaptive Enhancement module, a Dual-Pooling Sequential Attention mechanism, and a Focal Generalized IoU loss to significantly improve accuracy and robustness on benchmark datasets while maintaining a compact model size suitable for resource-constrained environments.
This paper addresses the significant storage overhead of soft labels in dataset distillation by introducing a vector-quantized autoencoder (VQAE) that achieves 30–40x additional compression on benchmarks like ImageNet-1K while preserving over 90% of the original model performance.
This paper proposes Structure-aware Prompt Adaptation (SPA), a plug-and-play method that leverages the consistent local structures of semantically related concepts in the embedding space to effectively generalize from seen to unseen attributes and objects in Open-Vocabulary Compositional Zero-Shot Learning.
This paper identifies "Lazy Attention Localization" as a key bottleneck in multimodal cold-start training, where models fail to increase visual attention, and proposes the Attention-Guided Visual Anchoring and Reflection (AVAR) framework to effectively reshape attention distributions, achieving a 7.0% performance gain on multimodal reasoning benchmarks.
This paper introduces FoundPS, a universal pansharpening foundation model that overcomes the limitations of existing satellite-specific methods by employing a modality-interleaved transformer, latent diffusion bridge, and pixel-to-latent interaction mechanisms to achieve robust, generalizable fusion across diverse sensors and scenes, supported by a new comprehensive benchmark called PSBench.
This paper proposes CWP-Net, a novel all-in-one image restoration framework that utilizes causal deconfounding and wavelet-disentangled prompts to eliminate spurious correlations and biased degradation estimation, thereby achieving superior performance over state-of-the-art methods.
DeepScan is a training-free framework that enhances visually grounded reasoning in Large Vision-Language Models by employing a bottom-up approach of Hierarchical Scanning, Refocusing, and Evidence-Enhanced Reasoning to effectively mitigate distractive contexts and achieve state-of-the-art performance across diverse architectures and scales.
This paper proposes a feedback reinforcement framework for infrared and visible image fusion that leverages a newly introduced large-scale human feedback dataset and a trained reward model to optimize fusion networks via Group Relative Policy Optimization, thereby significantly aligning fusion outcomes with human visual preferences.
Yolo-Key-6D is a novel single-stage, end-to-end framework that achieves real-time monocular 6D pose estimation with competitive accuracy by integrating a keypoint-based auxiliary head for enhanced 3D geometry understanding and utilizing a continuous 9D rotation representation for stable training.
The paper introduces UniSync, a unified lip synchronization framework that combines mask-free pose-anchored training with mask-based blending inference to achieve high-fidelity, generalizable results across diverse real-world scenarios, including stylized avatars and challenging lighting conditions, while also proposing the RealWorld-LipSync benchmark for evaluation.
This paper introduces a novel convolution-inspired network that effectively classifies cuneiform tablet metadata by integrating local and global information from high-resolution point clouds, outperforming the state-of-the-art Point-BERT model while addressing challenges posed by limited annotated datasets.
This paper proposes a unified evaluation framework with new metrics (DS-F1 and DS-AURC) and an improved method (SURE+) to jointly assess and enhance classifier reliability by integrating out-of-distribution detection and in-distribution failure prediction, demonstrating that double scoring functions significantly outperform traditional single scoring approaches.
This paper proposes a Modular Asynchronous Tracking Architecture (MATA) that integrates a transformer-based tracker with an Extended Kalman Filter and ego-motion compensation to address UAV tracking challenges, while introducing a hardware-independent evaluation protocol and a new Normalized Time to Failure (NT2F) metric to better quantify robustness and real-time performance on embedded systems.
This paper introduces FGAesthetics, a large-scale fine-grained image aesthetic assessment database with pairwise comparison annotations, and proposes FGAesQ, a novel framework that leverages relative ranks through specialized tokenization and alignment techniques to achieve superior discriminative performance in both fine-grained and coarse-grained aesthetic evaluation scenarios.
This paper proposes an N-gram Injection (NGI) method that dynamically adapts Transformer-based handwritten text recognition models to target language distributions at inference time by injecting external n-gram language models, thereby significantly reducing performance gaps caused by language shifts without requiring additional training on target data.
The paper introduces DISC, a fully GPU-accelerated framework that utilizes a novel single-pass, distance-weighted mechanism to extract dense semantic context from CLIP embeddings, enabling efficient, real-time open-set semantic mapping that significantly outperforms existing state-of-the-art methods in accuracy and scalability.
This paper presents CMDR-IAD, a lightweight unsupervised framework that achieves state-of-the-art industrial anomaly detection by combining bidirectional 2D-3D cross-modal mapping with dual-branch reconstruction to robustly handle noisy, weak-texture, or missing modalities without relying on memory banks.
This study demonstrates that a DenseNet121-based deep learning model effectively detects hyper- and hypointense intensity artifacts on high b-value (1500 s/mm²) breast diffusion-weighted MRI slices, achieving high AUROC scores and providing promising results for automated quality assessment.