Computer Vision

CAT: A novel dual-prompt model coordinates anatomical and textual prompts for superior multi-organ & tumor segmentation in medical imaging, overcoming limitations of single-prompt methods.

ProDet: Deepfake detection enhanced by progressively organizing blendfake and deepfake data in the latent space, improving generalization and robustness.

Simple averaging of watermarked images reveals hidden patterns, enabling watermark removal and forgery, thus highlighting the vulnerability of content-agnostic watermarking methods.

InLine attention, a novel method, bridges the performance gap between softmax and linear attention by incorporating injectivity and local modeling, achieving superior performance while maintaining lin…

Researchers discover that existing backdoor defenses leave vulnerabilities, allowing for easy re-activation of backdoors through subtle trigger manipulation.

Researchers developed a new AI-generated image detection method that overcomes the limitation of existing detectors, achieving superior cross-scene generalization by shuffling image patches and traini…

BrainBits reveals that surprisingly little brain information is needed for high-fidelity image & text reconstruction, highlighting the dominance of generative model priors over neural signal extractio…

Bi-Level Active Finetuning Framework (BiLAF) revolutionizes sample selection for efficient model finetuning. Unlike existing methods, BiLAF incorporates both global diversity and local decision bounda…

This paper introduces a novel object-centric learning (OCL) framework that enhances slot attention with a self-modulating top-down pathway, significantly improving object representation and achieving …

Boosting vision transformer adversarial attack transferability, this paper introduces Adaptive Token Tuning (ATT), improving attack success rate by 10.1% over existing methods.

Boolean Logic Deep Learning (BOLD) revolutionizes deep learning by enabling training with Boolean weights and activations, achieving state-of-the-art accuracy with drastically reduced energy consumpti…

BIRD: a novel blind image restoration method jointly optimizes degradation model parameters and the restored image, ensuring realistic outputs via fast diffusion inversion and achieving state-of-the-a…

BLAST matrix learns efficient weight structures for faster deep learning inference, achieving significant compression and performance gains on various models.

BitsFusion achieves 7.9x smaller Stable Diffusion models by quantizing UNet weights to 1.99 bits, surprisingly improving image generation quality!

bit2bit reconstructs high-quality videos from sparse, binary quanta image sensor data using self-supervised photon location prediction, significantly improving resolution and usability.

Biologically-inspired AI model integrates learning & visual guidance via a novel ‘Counter-Hebb’ learning mechanism, achieving competitive performance on multi-task learning benchmarks.

Binocular-guided 3D Gaussian splatting with self-supervision generates high-quality novel views from sparse inputs without external priors, significantly outperforming state-of-the-art methods.

BI-DiffSR, a novel binarized diffusion model, achieves high-quality image super-resolution with significantly reduced memory and computational costs, outperforming existing methods.

BiDM achieves full 1-bit quantization in diffusion models, significantly improving storage and speed without sacrificing image quality, setting a new state-of-the-art.

GPTrack: A novel unsupervised framework enhances cardiac motion tracking by using sequential Gaussian processes and bidirectional recurrence, improving accuracy and efficiency.