6581 papers
This paper introduces a robustness-aware framework that jointly selects tools and plans contact-rich manipulation trajectories by leveraging an energy-based metric to optimize for disturbance resilience in robotic tool-use tasks.
This paper proposes a structured methodology that integrates the Robot Operating System (ROS) with Model-Based Systems Engineering (MBSE) through a specialized SysML metamodel called MeROS and an adapted V-model, aiming to enhance the semantic coherence, structural traceability, and reliable coordination of complex heterogeneous robotic systems.
VisualPrompter is a novel, training-free framework that enhances text-to-image synthesis by employing a self-reflection module to identify missing concepts and a fine-grained optimization mechanism to refine prompts, thereby achieving state-of-the-art semantic alignment between user descriptions and generated images.
This paper introduces Token Bottleneck (ToBo), a self-supervised learning framework that compresses dynamic scenes into a compact token to predict future states with minimal hints, thereby enabling robust sequential scene understanding and robotic manipulation across both simulated and real-world environments.
This paper introduces NarrLV, the first comprehensive benchmark for evaluating long video generation models by defining Temporal Narrative Atoms to quantify narrative richness and employing an MLLM-based framework to assess narrative expression, revealing current model capabilities and limitations.
This paper introduces TomatoMAP, a comprehensive dataset of 64,464 multi-angle, multi-pose tomato images with detailed annotations for seven regions of interest and 50 growth stages, which was validated to demonstrate that a cascading deep learning framework achieves fine-grained phenotyping accuracy and speed comparable to human experts.
The paper proposes Gaussian Set Surface Reconstruction (GSSR), a method that optimizes individual Gaussian placement and alignment to achieve accurate 3D geometry reconstruction and uniform surface distribution, thereby overcoming the geometric limitations of existing 3D Gaussian Splatting variants while preserving high-quality rendering.
TrinityDNA is a novel, bio-inspired foundational model that integrates structural feature capture, symmetry handling, multi-scale attention, and evolutionary training to efficiently model long DNA sequences, significantly advancing gene function prediction and regulatory discovery while introducing a new long-sequence CDS annotation benchmark.
This paper introduces an interactive, no-code framework with a graphical interface and graph-based representation to democratize scenario generation for autonomous driving validation in CARLA, enabling non-technical users to efficiently create, manage, and execute diverse test scenarios without programming expertise.
This paper introduces a novel SCC-constrained bilevel model to demonstrate that synchronous generators at advantageous grid locations can exploit the localized nature of short-circuit current services to exercise market power, significantly inflating revenues and consumer costs, thereby highlighting the urgent need for specific market design mitigations.
DianJin-OCR-R1 is a reasoning-enhanced vision-language model that improves OCR accuracy by interleaving its own recognition with expert tool outputs, guiding the model to iteratively re-examine images and integrate evidence to reduce hallucinations and enhance fine-grained perception.
This paper introduces PyloChain, a hierarchical sharded blockchain that balances performance and availability by utilizing speculative execution across local chains and a DAG-based main chain to efficiently handle global transactions while ensuring data availability and fault tolerance.
This paper systematically evaluates the potential of carbon-aware execution strategies for scientific workflows, demonstrating that leveraging their inherent flexibility through temporal shifting and dynamic resource scaling can reduce carbon emissions by over 80% and 67%, respectively.
This paper introduces -Motif, a GPU-accelerated subgraph isomorphism algorithm that reformulates graph matching as scalable database operations on motifs, achieving speedups of up to 595 over traditional methods like VF2 while enabling efficient quantum circuit compilation through familiar relational abstractions.
This paper proposes SSL-SLR, a self-supervised learning framework for sign language recognition that addresses the limitations of standard contrastive methods by introducing free-negative pairs and a novel data augmentation technique to better handle video redundancy and shared movements, thereby achieving significant accuracy improvements across various evaluation settings.
This paper introduces RED, a robust event-guided motion deblurring network that employs a robustness-oriented perturbation strategy and a modality-specific disentanglement mechanism to effectively reconstruct sharp images from fragmented event data caused by real-world sensor under-reporting.
This paper presents a multimodal framework that integrates natural language understanding of pilot radio calls with trajectory data to significantly improve the accuracy of aircraft goal prediction in non-towered airspace compared to motion-only baselines.
The paper presents GLIDE, a cooperative search-and-rescue framework that pairs two specialized UAVs with a UGV to enable rapid, safe navigation in unknown environments through real-time victim detection, terrain scouting, and guided long-horizon planning.
This paper presents a vision-based framework for legged robots that enables robust decision-driven semantic exploration by integrating confidence-calibrated perception, controlled-growth topological memory, and utility-driven subgoal selection to overcome the limitations of conventional geometry-centric navigation in open-world environments.
DeCLIP is a replay-free, parameter-efficient framework for Multi-Label Class-Incremental Learning that decouples CLIP representations through class-specific prompting and Adaptive Similarity Tempering to effectively mitigate catastrophic forgetting and reduce false-positive rates without violating CLIP's single image-text alignment paradigm.