2775 papers
This paper evaluates DINOv3 as a frozen backbone for blueberry robotic harvesting tasks, finding that while it excels in segmentation through stable patch-level representations, its detection performance is limited by scale variation and spatial aggregation challenges, suggesting it functions best as a semantic backbone requiring downstream spatial modeling tailored to fruit structures.
This paper introduces GramCol and a motion-feature selection algorithm to generate Interpretable Motion-Attentive Maps (IMAPs) that effectively localize both motion and non-motion concepts in Video Diffusion Transformers without requiring gradient calculations or parameter updates.
This paper introduces CGL, a continual GUI learning framework that mitigates catastrophic forgetting by dynamically balancing Supervised Fine-Tuning and Reinforcement Learning through an entropy-guided proportion adjustment mechanism and a specialized gradient surgery strategy, validated by a new AndroidControl-CL benchmark.
This paper introduces LDP-Slicing, a lightweight, training-free framework that overcomes the utility limitations of applying Local Differential Privacy to high-dimensional images by decomposing pixel values into binary bit-planes and integrating perceptual obfuscation and optimized budget allocation to achieve rigorous privacy with high downstream task performance.
This paper introduces DeepScope, a deep learning-based system that analyzes microscopic images of unincubated water samples to detect fecal contamination in seconds with 93% accuracy and a cost of $0.44 per test, significantly outperforming traditional incubation methods in speed and affordability.
The paper introduces OptiRoulette, a stochastic meta-optimizer that dynamically selects update rules from a pool during training, demonstrating significantly faster convergence and higher test accuracy across multiple image-classification benchmarks compared to a standard AdamW baseline.
This paper proposes a unified linear representation for diffusion models and flow matching to theoretically demonstrate that the often weak correlation between noisy data and predicted targets in existing methods may adversely impact the learning process.
RECAP is a bio-inspired image classification method that couples untrained reservoir dynamics with a self-organizing Hebbian prototype readout to achieve robust, backpropagation-free learning capable of generalizing to corrupted inputs without prior exposure.
This paper reveals that pruning-based unlearning in diffusion models is inherently insecure because the locations of pruned weights act as side-channel signals that enable a novel, data-free, and training-free attack to fully revive erased concepts, prompting a call for safer pruning mechanisms that conceal these locations.
This paper introduces ObjChangeVR, a novel framework and corresponding dataset designed to enhance object state change reasoning in virtual reality by addressing the challenges of detecting background changes without direct interaction through viewpoint-aware retrieval and cross-view reasoning.
This paper proposes a margin-consistent deep subtyping framework for invasive lung adenocarcinoma that integrates attention-weighted aggregation, contrastive regularization, and a novel Perturbation Fidelity scoring mechanism to achieve robust, high-accuracy classification across multiple architectures and demonstrate cross-institutional generalizability on whole-slide images.
PaLMR is a novel framework that enhances the faithfulness of multimodal large language models by aligning both the reasoning process and outcomes through a perception-aligned data layer and a hierarchical reward fusion scheme, thereby significantly reducing visual hallucinations while achieving state-of-the-art performance on key benchmarks.
This paper introduces FCBNet, a parameter-efficient convolutional model featuring a frozen ConvNeXt backbone and a Feature Correction Block that achieves superior weed segmentation accuracy (over 85% mIoU) and computational efficiency across RGB and multispectral aerial imagery compared to existing state-of-the-art models.
The paper introduces GameVerse, a comprehensive benchmark featuring a novel reflect-and-retry paradigm and a hierarchical taxonomy across 15 games, demonstrating that Vision-Language Models can effectively improve their gameplay policies through video-based reflection by combining failure trajectories with expert tutorials.
The paper introduces ASMIL, a unified framework that addresses unstable attention dynamics, overfitting, and over-concentrated attention in attention-based multiple instance learning for whole slide imaging by employing an anchor model with a normalized sigmoid function and token random dropping, resulting in significant performance improvements over state-of-the-art methods.
The paper introduces EnsAug, a novel training paradigm that improves human motion analysis by replacing the conventional single-model approach with an ensemble of specialists, each trained on data augmented by a distinct geometric transformation, thereby achieving state-of-the-art performance while respecting kinematic constraints.
The paper introduces HyperTokens, a transformer-based token generator augmented with meta-inspired regularizers and causal auxiliary supervision to enable efficient, low-forgetting continual Video-Language Understanding by dynamically producing task-specific prompts without increasing memory costs.
The paper proposes Graph-of-Mark (GoM), a novel pixel-level visual prompting technique that overlays scene graphs onto images to capture object relationships, thereby significantly enhancing the spatial reasoning and zero-shot performance of multimodal language models.
This paper introduces a system-level inference optimization for Diffusion Transformer-based video generation that employs a sequence-parallel Causal-RoPE mechanism and operator fusion to overcome memory and latency bottlenecks, achieving near real-time speeds and sub-second first-frame latency on an eight-GPU cluster.
This paper reveals that Chain-of-Thought prompting often underperforms direct answering in medical visual question answering due to a "medical perception bottleneck," and proposes training-free grounding interventions to restore visual accuracy and improve model reasoning.