5358 papers
This paper proposes MC-INR, a novel framework that leverages meta-learning, dynamic error-based re-clustering, and a branched architecture to efficiently encode complex multivariate scientific simulation data on unstructured grids, overcoming the inflexibility and single-variable limitations of existing Implicit Neural Representation methods.
CoBELa is a decoder-free, energy-based framework that enables transparent and compositional generative control by conditioning a frozen pretrained generator entirely through additive per-concept energy functions, achieving high image quality and concept accuracy without requiring model retraining.
InstructVLA introduces a novel Vision-Language-Action Instruction Tuning paradigm that successfully bridges flexible multimodal reasoning and precise manipulation by jointly optimizing embodied reasoning and action generation, thereby achieving state-of-the-art performance in both simulated and real-world robotic tasks without sacrificing pre-trained capabilities.
DMTrack introduces a novel dual-adapter architecture featuring a spatio-temporal modality adapter and a progressive modality complementary adapter to achieve state-of-the-art spatio-temporal multimodal tracking performance with only 0.93M trainable parameters.
This paper proposes a novel zero-shot 3D CT super-resolution framework that combines diffusion-based 2D projection priors with a signed 3D Gaussian splatting method featuring Negative Alpha Blending to reconstruct high-resolution volumes from single low-resolution inputs without requiring paired training data.
MMTok is a novel method that enhances the inference efficiency of Vision-Language Models by formulating vision token selection as a maximum coverage problem, leveraging complementary multimodal information from both vision and text to prune redundant tokens while preserving high performance.
This paper introduces ConEQsA, a novel agentic framework and benchmark designed to address the challenges of concurrent and asynchronous embodied question answering by leveraging shared memory and urgency-aware scheduling to outperform traditional sequential approaches in realistic multi-question scenarios.
This paper systematically evaluates Vision-Language Models' capabilities in autonomous driving lane topology awareness through a new BEV-based diagnostic framework, revealing that while performance correlates with model size and reasoning depth, current models—including frontier closed-source systems—still struggle with fundamental spatial reasoning tasks essential for safe navigation.
The paper introduces SiNGER, a novel knowledge distillation framework that utilizes singular nullspace-guided energy reallocation via a LoRA-based adapter to suppress high-norm artifacts in Vision Transformer teachers while preserving informative signals, thereby enabling student models to achieve state-of-the-art performance and clearer representations.
The paper introduces Earth-Agent, a novel agentic framework that unifies RGB and spectral Earth observation data within an MCP-based tool ecosystem to enable complex, multi-step quantitative reasoning, accompanied by the Earth-Bench benchmark for comprehensive evaluation of such capabilities.
PROFusion achieves robust and accurate real-time dense 3D reconstruction under unstable camera motions by combining a learning-based camera pose regression network for reliable initialization with an optimization-based refinement algorithm to align depth images with scene geometry.
Proxy-GS introduces a fast, sub-millisecond proxy system to provide occlusion awareness that simultaneously accelerates rendering through efficient culling and improves training quality by guiding densification toward visible surfaces, thereby outperforming existing methods in both speed and fidelity for large-scale, occluded scenes.
EchoGen introduces the first feed-forward subject-driven generation framework built on Visual Auto-Regressive (VAR) models, utilizing a novel dual-path injection strategy to achieve high-fidelity, zero-shot subject generation with significantly faster inference speeds than existing diffusion-based methods.
TTT3R is a training-free method that enhances the length generalization of 3D reconstruction models by framing them as online learning problems and deriving a closed-form learning rate based on alignment confidence, achieving significant improvements in pose estimation while maintaining high efficiency.
BindWeave is a unified framework that leverages an MLLM-DiT architecture to perform deep cross-modal reasoning for grounding complex prompt semantics, thereby enabling high-fidelity, subject-consistent video generation across diverse single and multi-subject scenarios.
The paper introduces ArbInterp, a novel generative video frame interpolation framework that overcomes the limitations of fixed-length synthesis by enabling flexible, high-fidelity interpolation at arbitrary timestamps and durations through a Timestamp-aware Rotary Position Embedding and an appearance-motion decoupled segment-wise generation strategy.
The D2E framework demonstrates that scaling vision-action pretraining on large-scale, standardized desktop gaming data enables a 1B-parameter model to achieve state-of-the-art performance in real-world embodied AI tasks, effectively bridging the gap between digital interactions and physical robot manipulation and navigation.
Human3R is a unified, feed-forward framework that enables real-time, single-pass online 4D reconstruction of multiple humans, dense 3D scenes, and camera trajectories from monocular videos, eliminating the need for multi-stage pipelines and heavy pre-processing dependencies.
MIRAGE is an efficient runtime scheduling framework for multi-vector image retrieval that employs a novel hierarchical decomposition paradigm with automatic parameter configuration to significantly enhance retrieval accuracy while reducing computational costs by up to 3.5 times compared to existing systems.
This paper reveals that the generalization of reinforcement learning-based image quality assessment models stems from their conversion of visual data into compact text representations, leading to the proposal of RALI, a lightweight algorithm that directly aligns images with these representations to achieve comparable performance with significantly reduced computational costs.