3301 papers
Re-Depth Anything is a test-time self-supervised framework that enhances monocular depth estimation by fusing foundation models with large-scale 2D diffusion priors to perform label-free refinement via generative re-lighting and Score Distillation Sampling, achieving state-of-the-art results without direct depth tensor optimization.
This paper introduces VOIC, a novel dual-decoder framework for monocular 3D Semantic Scene Completion that employs a Visible Region Label Extraction strategy to decouple visible-region perception from occluded-region reasoning, thereby mitigating feature dilution and achieving state-of-the-art performance on standard benchmarks.
This paper proposes "Efficient Vision Mamba," a computationally lightweight deep learning framework that combines multi-head selective state-space models with hybrid scanning to achieve state-of-the-art MRI super-resolution performance while drastically reducing parameters and computation compared to existing methods.
This paper introduces DrivingGen, the first comprehensive benchmark for generative driving world models that addresses the lack of rigorous evaluation by combining a diverse dataset with a novel suite of metrics to assess visual realism, trajectory plausibility, temporal coherence, and controllability, thereby revealing critical trade-offs in current state-of-the-art models.
This paper presents a lightweight, two-stage multitask vision-language framework that integrates a Swin Transformer encoder with sequence-to-sequence decoders to achieve state-of-the-art, explainable visual question answering for crop disease identification with near-perfect classification accuracy and strong generalization capabilities.
The paper introduces R^4, a self-improving agentic framework that enhances medical image analysis by decomposing workflows into routing, retrieval, reflection, and repair stages to iteratively refine both textual reports and spatial bounding boxes, achieving significant performance gains over single-pass VLM baselines without requiring gradient-based fine-tuning.
This paper audits the LAION-Aesthetics Predictor to reveal how its algorithmic gaze reinforces Western, male, and imperial biases by disproportionately filtering content and prioritizing specific cultural aesthetics, ultimately urging a shift toward pluralistic evaluation methods in AI development.
The paper introduces S2DiT, a novel Streaming Sandwich Diffusion Transformer that leverages efficient attention mechanisms, a budget-aware sandwich architecture, and a 2-in-1 distillation framework to achieve high-fidelity, real-time video generation on mobile devices with performance comparable to server-grade models.
This paper introduces ReViP, a novel Vision-Language-Action framework that mitigates "false completion" failures caused by proprioceptive bias through vision-proprioception rebalancing and a new benchmark suite, achieving significant performance gains over existing models.
This paper introduces ScenePilot-Bench, a large-scale benchmark built on the diverse ScenePilot-4K dataset to comprehensively evaluate and advance vision-language models in autonomous driving through multi-granularity annotations and a safety-aware, four-axis assessment framework.
This paper proposes QSTar, a novel query-guided spatial-temporal-frequency interaction method enhanced by a Query Context Reasoning block, which significantly improves Audio-Visual Question Answering performance by deeply integrating question-guided clues and audio frequency characteristics with visual perception, outperforming existing multimodal approaches on multiple benchmarks.
MeanCache is a training-free framework that accelerates Flow Matching inference by replacing instantaneous velocity caching with an average-velocity approach using cached Jacobian-vector products and a trajectory-stability scheduling strategy, achieving significant speedups (up to 4.56X) while maintaining high generation quality across models like FLUX.1 and HunyuanVideo.
PhysDrape is a hybrid neural-physical solver that combines a Physics-Informed Graph Neural Network with a differentiable two-stage force and collision projection system to achieve physically realistic garment draping with negligible interpenetration and superior fidelity compared to existing deep learning methods.
To address the challenges of parameter-efficient domain adaptation in V2X collaborative perception, the paper proposes FlowAdapt, a framework leveraging optimal transport theory and a progressive knowledge transfer mechanism to filter redundant data and preserve fine-grained semantics, achieving state-of-the-art performance with only 1% trainable parameters.
This paper proposes SToRM, a novel framework that employs a lightweight importance predictor, supervised training with pseudo-labels, and an anchor-context merging module to significantly reduce visual token redundancy in multi-modal LLMs for autonomous driving, achieving up to 30x computational savings while maintaining end-to-end performance comparable to using all tokens.
The paper introduces 3DMedAgent, a unified agent that leverages a flexible MLLM and long-term structured memory to coordinate heterogeneous tools for decomposing complex 3D CT analysis into tractable 2D-based subtasks, thereby enabling general-purpose 3D medical understanding without 3D-specific fine-tuning.
This paper demonstrates that neural networks learning equivariant operators in a latent space can effectively generalize to out-of-distribution symmetric transformations on simple datasets like rotated MNIST, while also highlighting the significant challenges involved in scaling this approach to more complex data.
OVerSeeC is a zero-shot modular framework that leverages large language models and open-vocabulary segmentation to generate executable global costmaps from satellite imagery and natural language instructions, enabling autonomous navigation to adapt to novel entities and dynamic mission constraints without requiring fixed ontologies.
This paper introduces Open-Vocabulary Domain Generalization in Semantic Segmentation (OVDG-SS), a new setting and benchmark for autonomous driving that addresses both unseen domains and categories, and proposes S2-Corr, a state-space-driven mechanism to refine text-image correlations in Vision-Language Models to achieve robust performance across diverse urban environments.
UniMatch is a novel coarse-to-fine framework that establishes dense semantic correspondences between strongly non-isometric, cross-category 3D shapes by leveraging class-agnostic segmentation, multimodal language models for part identification, and a rank-based contrastive learning scheme to overcome the limitations of prior isometry-dependent methods.