10079 papers
This paper introduces CompBench, a large-scale benchmark featuring fine-grained instructions and an MLLM-human collaborative framework to rigorously evaluate and expose the limitations of current models in complex, instruction-guided image editing tasks.
The paper introduces SPKLIP, the first architecture designed to align sparse spike video streams with natural language through hierarchical feature extraction and contrastive learning, achieving state-of-the-art few-shot performance and enhanced energy efficiency for neuromorphic deployment.
This paper introduces Foresight Diffusion (ForeDiff), a framework that enhances sampling consistency in predictive diffusion models by decoupling condition understanding from target denoising through a separate deterministic predictive stream, thereby improving both accuracy and consistency in robot video and scientific spatiotemporal forecasting tasks.
This paper proposes a novel hybrid deep-learning framework that combines an adaptive Discrete Cosine Transform preprocessing module with ViT-B16 and ResNet50 backbones, along with a Bayesian linear classifier, to achieve state-of-the-art rare animal image classification performance under extreme data scarcity by effectively fusing learned frequency-domain cues with multi-scale spatial representations.
This paper introduces SynPO, a novel preference optimization framework that synergizes descriptiveness and preference learning to enhance fine-grained video captioning by constructing cost-effective preference pairs and eliminating the reference model, thereby outperforming DPO variants with improved training efficiency and preserved language capabilities.
ReSpace is a generative framework that leverages autoregressive next-token prediction and supervised fine-tuning with preference alignment to enable text-driven 3D indoor scene synthesis and editing with explicit room boundaries and superior spatial reasoning, outperforming state-of-the-art methods in object manipulation and human-perceived quality.
This paper introduces MicroG-4M, the first benchmark dataset comprising over 4,700 clips from real space missions and simulations to address the critical gap in video understanding for microgravity environments by supporting action recognition, video captioning, and visual question answering tasks.
This paper proposes an explainability-driven framework that utilizes Grad-CAM to iteratively refine and prune a CNN architecture for brain tumor classification, achieving high accuracy and strong generalization while ensuring model transparency and clinical trustworthiness.
This paper introduces SymmFlow, a unified Symmetrical Flow Matching framework that jointly learns forward and reverse transformations to achieve state-of-the-art performance in image generation, segmentation, and classification within a single model while enabling efficient one-step inference.
This paper introduces HVLFormer, a semi-supervised image segmentation framework that leverages hierarchical, domain-aware textual object queries and cross-view consistency regularization to effectively align visual and textual representations from Vision Language Models, achieving state-of-the-art performance with less than 1% labeled data.