7299 papers
The paper introduces ULTRA, a unified framework that combines physics-driven neural retargeting with a multimodal reinforcement learning controller to enable autonomous, goal-conditioned whole-body loco-manipulation on humanoids using sparse high-level task specifications and noisy visual inputs, eliminating the need for predefined motion references.
This paper presents a two-stage learning framework for fine-grained robotic manipulation tasks like peeling, which combines force-aware imitation learning with preference-based finetuning to achieve over 90% success rates and strong zero-shot generalization by aligning robot behavior with human qualitative preferences.
This paper introduces MIBURI, a novel online causal framework that leverages body-part aware gesture codecs and LLM-based speech embeddings to generate expressive, diverse, and real-time synchronized full-body gestures and facial expressions for Embodied Conversational Agents, overcoming the rigidity and latency limitations of existing methods.
The paper introduces Utonia, a self-supervised point transformer encoder that unifies diverse 3D point cloud domains into a single consistent representation space, thereby enhancing perception capabilities and enabling emergent benefits for embodied and multimodal reasoning tasks.
This paper proposes a novel framework that replaces the classification task in one-stage object detectors with a ranking task optimized via Average-Precision (AP) loss, utilizing a new algorithm that combines perceptron learning and backpropagation to overcome the loss's non-differentiability and non-convexity, thereby significantly improving detection performance without altering network architectures.
This paper proposes a novel one-stage object detection framework that replaces the standard classification task with a ranking task optimized via a new Average-Precision (AP) loss and a specialized error-driven backpropagation algorithm, effectively addressing foreground-background class imbalance to achieve state-of-the-art performance.
The paper proposes OmniTracker, a unified object tracking framework that employs a novel "tracking-with-detection" paradigm to resolve diverse tracking tasks with a single shared architecture, thereby eliminating redundant parameters while achieving state-of-the-art performance across seven benchmark datasets.
This paper introduces Receptive-Field Attention (RFA) and the corresponding RFAConv module to overcome the limitations of existing spatial attention mechanisms in handling large convolutional kernels by effectively addressing parameter sharing, thereby significantly improving network performance with negligible computational overhead.
This paper introduces "Task-Driven Lens Design," a stable optimization framework that freezes a pretrained vision model to directly optimize lens parameters for specific computer vision tasks, resulting in novel optical systems that outperform traditional aberration-minimizing lenses with fewer elements.
The paper proposes Topology Guided MIL (TG-MIL), a method that incorporates topological inductive biases to preserve instance distribution structures within bags, significantly improving the performance and generalizability of Multiple Instance Learning in data-scarce scenarios.
This paper proposes a novel "distance indexing" approach combined with an iterative reference-based estimation strategy to disambiguate object motion in video frame interpolation, thereby overcoming the limitations of traditional time indexing to achieve superior perceptual quality and enable flexible temporal manipulation for video editing.
This paper proposes a novel latent partial causal model to address the limitations of DAGs in multimodal learning, proving that MultiModal Contrastive Learning (MMCL) learns identifiable coupled variables and demonstrating that this theoretical insight enables CLIP models to achieve disentangled representations for improved few-shot learning and domain generalization.
This paper introduces the ZACAF framework, enhanced with Transfer Learning and data augmentation techniques, to overcome the limitations of supervised deep learning models by enabling robust, automated cardiovascular quantification across diverse zebrafish imaging setups and mutant types, as demonstrated in the analysis of nrap cardiomyopathy models.
FiLo is a novel zero-shot anomaly detection method that leverages Large Language Models to generate adaptive fine-grained descriptions and employs a multi-scale cross-modal interaction module with position-enhanced prompts to achieve state-of-the-art detection and localization performance across diverse object categories.
This paper proposes USEFUL, a method that mitigates simplicity bias by strategically upsampling underrepresented data clusters identified early in training, thereby significantly improving in-distribution generalization across various architectures and datasets when combined with gradient descent or sharpness-aware minimization.
The paper presents OS-Det3D, a two-stage training framework that leverages LiDAR-derived geometric cues and a joint selection module to enable camera-based 3D object detectors to effectively discover and identify both known and unknown objects in autonomous driving scenarios.
The paper introduces PO-GUISE+, a multi-task video transformer that leverages driver pose and object interaction information to efficiently select tokens, achieving state-of-the-art distracted driving recognition accuracy on multiple datasets while significantly reducing computational costs for real-world onboard deployment.
MSSPlace is a state-of-the-art multimodal place recognition method that leverages a late fusion approach to integrate multi-camera images, LiDAR point clouds, semantic segmentation masks, and text annotations, demonstrating significant performance improvements over single-modality approaches on the Oxford RobotCar and NCLT datasets.
This paper introduces a publicly available dataset comprising 21 videos of blind and low-vision individuals navigating outdoor spaces, along with a refined taxonomy of 90 crucial objects and corresponding annotations, to address the limitations of existing computer vision models in supporting safe navigation for this demographic.
This paper proposes a two-stage method that combines a VQ-VAE for compact latent space embedding with a Structural Causal Model using a closed-form Generalized Linear Model to generate high-quality, diverse 3D brain MRI counterfactuals, effectively overcoming the limitations of existing deep learning and causal models in handling small sample sizes and high-dimensional data.