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This paper introduces a novel optimization approach for stain separation in digital histopathological images. Our stain separation cost function incorporates a smooth total variation regularization and is minimized by using a projected gradient algorithm. To enhance computational efficiency and enable supervised learning of the hyperparameters, we further unroll our algorithm into a neural network. The unrolled architecture is not only more efficient for solving the stain separation problem, but also allows to design a highly interpretable and flexible method.

Recent advancements in artificial intelligence algorithms for medical imaging show significant potential in automating the detection of lung infections from chest radiograph scans. However, current approaches often focus solely on either 2-D or 3-D scans, failing to leverage the combined advantages of both modalities. Moreover, conventional slice-based methods place a manual burden on radiologists for slice selection.

In recent years, Multi-Camera Multiple Object Tracking (MCMT) has gained significant attention as a crucial computer vision application. Research focuses on data association and track detection. However, accurately selecting datasets from raw vision data remains challenging due to real-world complexities like object types, varying speeds, and unknown directions. To address these problems, this paper proposes the Object Tracking Model (OTM) to capture the feature of target area with the Camera Monitoring Network (CMN) based on Graph Convolutional Network (GCN).

We propose Gumbel-NeRF, a mixture-of-expert (MoE) neural radiance fields (NeRF) model with a hindsight expert selection mechanism for synthesizing novel views of unseen objects. Previous studies have shown that the MoE structure provides high-quality representations of a given large-scale scene consisting of many objects. However, we observe that such a MoE NeRF model often produces low-quality representations in the vicinity of experts’ boundaries when applied to the task of novel view synthesis of an unseen object from one/few-shot input.

Previous works on object detection have achieved high accuracy in closed-set scenarios, but their performance in open-world scenarios is not satisfactory. One of the challenging open-world problems is corner case detection in autonomous driving. Existing detectors struggle with these cases, relying heavily on visual appearance and exhibiting poor generalization ability. In this paper, we propose a solution by reducing the discrepancy between known and unknown classes and introduce a multimodal-enhanced objectness notion learner.

The introduction of diverse text-to-image generation models has sparked significant interest across various sectors. While these models provide the groundbreaking capability to convert textual descriptions into visual data, their widespread usage has ignited concerns over misusing realistic synthesized images. Despite the pressing need, research on detecting such synthetic images remains limited. This paper aims to bridge this gap by evaluating the ability of several existing detectors to detect synthesized images produced by text-to-image generation models.

It is presentation slides for the ICIP 2024 paper, where we addressed the challenge of regularization in lensless single-shot phase retrieval (PR) by noise suppression.

Deep learners tend to perform well when trained under the closed set assumption but struggle when deployed under open set conditions. This motivates the field of Open Set Recognition in which we seek to give deep learners the ability to recognize whether a data sample belongs to the known classes trained on or comes from the surrounding infinite world. Existing open set recognition methods typically rely upon a single function for the dual task of distinguishing between knowns and unknowns as well as making known class distinction.

Emergency response missions depend on the fast relay of visual information, a task to which unmanned aerial vehicles are well adapted. However, the effective use of unmanned aerial vehicles is often compromised by bandwidth limitations that impede fast data transmission, thereby delaying the quick decision-making necessary in emergency situations. To address these challenges, this paper presents a streamlined hybrid annotation framework that utilizes the JPEG 2000 compression algorithm to facilitate object detection under limited bandwidth.

In this work, we propose a semi-supervised deep feature generation network that accounts for local similarities. It is based on the deep dictionary learning (DDL) framework. The formulation accounts for two unique aspects of hyperspectral classification. First, the fact that the total number of pixels / samples to be labeled is constant; this allows for a semi-supervised formulation allowing only a few pixels / samples to be labeled as training data. Second, the samples / pixels are spatially correlated; this leads to a graph regularization formulation.