1863 papers
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.
This paper introduces SJD-PV, a training-free acceleration framework for autoregressive image generation that leverages phrase-level speculative verification based on token co-occurrence statistics to jointly validate multiple correlated tokens, achieving up to 30% faster decoding without compromising visual fidelity.
The paper proposes CalibFusion, a Transformer-based differentiable calibration framework that learns to implicitly refine Radar-Camera extrinsics end-to-end to overcome the challenges of textureless, cluttered water-surface environments and significantly improve fusion-based 2D object detection.
This paper investigates whether semantic noise initialization, known to improve image diffusion models, transfers to text-to-video generation, finding that while it shows a slight positive trend on temporal metrics, it does not significantly outperform standard Gaussian noise due to weak or unstable signals in the noise space.
This paper proposes an unsupervised CNN autoencoder with a novel weighted spectral angle distance loss to enable blind, automated unmixing of complex ATR-FTIR hyperspectral images from historical oil painting cross-sections, significantly improving the interpretability and scalability of material analysis compared to traditional manual methods.
AutoFigure-Edit is an end-to-end system that generates fully editable, high-quality scientific illustrations from long-form text with flexible style adaptation via reference images, leveraging long-context understanding and native SVG support to overcome limitations in editability and efficiency found in existing automated tools.
This paper proposes a hybrid few-shot learning model integrating Siamese and Prototypical Networks with Grad-CAM-based Explainable AI to achieve high-accuracy, interpretable classification of maize, rice, and wheat leaf diseases under limited data conditions.
This paper addresses the limitations of current Large Vision-Language Models in deep chart research by proposing Parallel Relative Policy Optimization (PRPO) to resolve training conflicts and constructing the MCDR-Bench evaluation framework to enable objective assessment of complex reasoning capabilities.