6584 papers
This paper proposes RL-Co, a reinforcement learning-based sim-real co-training framework that combines supervised fine-tuning on mixed real and simulated data with interactive simulation fine-tuning anchored by real-world data, achieving significant improvements in real-world success rates, generalization, and data efficiency for Vision-Language-Action models.
DAV-GSWT is a data-efficient framework that combines diffusion priors with active view sampling to synthesize high-fidelity Gaussian Splatting Wang Tiles from minimal input observations, enabling the generation of expansive, photorealistic landscapes without relying on densely sampled exemplar reconstructions.
This paper proposes "Operational Agency," a legal framework utilizing an "Operational Agency Graph" to trace and apportion human culpability in autonomous AI systems by evaluating their goal-directedness, foresight, and safety architecture, thereby ensuring accountability without granting AI legal personhood.
The paper proposes RobSelf, a robust self-supervised model that jointly optimizes a misalignment-aware feature translator and a content-aware reference filter to achieve state-of-the-art cross-modal super-resolution on real-world misaligned data with significantly improved efficiency.
UrbanAlign proposes a novel post-hoc calibration framework that aligns frozen vision-language models with human preferences for urban scene assessment by mining interpretable dimensions, extracting robust concept scores via an Observer-Debater-Judge chain, and calibrating them through locally-weighted ridge regression, achieving state-of-the-art accuracy without any model retraining.
This paper demonstrates that affordance reasoning in Vision Foundation Models can be achieved in a zero-shot, training-free manner by fusing DINO's inherent geometric part structures with Flux's verb-conditioned interaction priors, thereby establishing geometric and interaction perception as the fundamental, composable building blocks of affordance understanding.
StoryTailor is a zero-shot pipeline that generates temporally coherent, action-rich multi-subject visual narratives on a single RTX 4090 by synergizing Gaussian-Centered Attention, Action-Boost Singular Value Reweighting, and a Selective Forgetting Cache to simultaneously ensure action faithfulness, subject identity fidelity, and cross-frame background continuity.
The paper introduces UniVBench, a comprehensive benchmark featuring 200 high-quality, human-created multi-shot videos and a unified agentic evaluation system (UniV-Eval) to holistically assess video foundation models across understanding, generation, editing, and reconstruction tasks, addressing the limitations of existing fragmented and task-specific evaluations.
This paper introduces a geometry-aware Graph Neural Network autodecoder that leverages protein-structure priors and ellipsoidal support lifting to achieve higher accuracy in heterogeneous single-particle cryo-EM reconstruction compared to traditional MLP-based methods.
The paper introduces DPCache, a training-free acceleration framework for diffusion models that formulates sampling as a global path planning problem and utilizes dynamic programming on a path-aware cost tensor to select optimal key timesteps, thereby achieving significant speedups with minimal quality loss and even surpassing full-step baselines in certain metrics.
This paper proposes a unified, query-dependent learning-to-rank model that effectively merges heterogeneous retrieval channels for large-scale e-commerce search by jointly optimizing business KPIs and capturing short-term user intent, resulting in a 2.85% conversion lift and deployment on Target.com while meeting strict latency constraints.
This paper introduces Synthetic Visual Genome 2 (SVG2), a large-scale automated panoptic video scene graph dataset with over 636K videos, and presents TRaSER, a novel model that leverages trajectory-aligned token mechanisms to significantly outperform existing baselines in scene graph generation and downstream video question answering tasks.
This paper presents a novel framework that combines a calibrated 3D simulation environment, a robust TD3-based control policy with domain randomization, and a mapping layer to successfully enable miniature blimp robots to achieve and maintain inverted poses in real-world settings.
This paper proposes a novel adaptive dynamic dehazing framework that utilizes a closed-loop optimization mechanism combining task performance feedback and text-based instruction guidance to enable real-time, training-free adaptation of dehazing outputs for diverse downstream vision tasks.
This paper introduces PanScale, a large-scale cross-scale pansharpening dataset and benchmark, alongside ScaleFormer, a novel transformer-based architecture that achieves superior generalization across varying image resolutions by reframing scale adaptation as sequence length generalization through tokenization and rotary positional encoding.
This paper proposes a provenance-aware framework for tracking OCR correction lineage in digital humanities pipelines, demonstrating that recording edit details at the span level significantly improves the reproducibility and interpretability of downstream NLP tasks by revealing how textual transformations impact scholarly analysis.
Mobile-VTON is a high-fidelity, privacy-preserving framework that enables fully offline virtual try-on on commodity mobile devices by utilizing a modular TGT architecture with feature-guided adversarial distillation and trajectory-consistency training to match server-based performance without requiring cloud computing.
The paper introduces ROSER, a lightweight few-shot retrieval framework that extracts reusable, task-centric segments from unlabeled robotic logs using only 3-5 reference examples, thereby overcoming data scarcity by enabling scalable, high-accuracy utilization of large-scale continuous interaction datasets without task-specific training.
FastLightGen is a novel algorithm that simultaneously compresses model parameters and reduces inference steps through an optimized teacher-student distillation framework, achieving state-of-the-art efficiency and visual quality in video generation with significantly fewer resources.
This paper introduces VSearcher, a reinforcement learning-based multimodal search agent that transforms static models into capable long-horizon web browsers through an iterative data synthesis pipeline and an SFT-then-RL training strategy, achieving superior performance on the proposed MM-SearchExam benchmark.