Search Results for author:
Found 15 papers, 8 papers with code
Aligning Large Language Models for Enhancing Psychiatric Interviews through Symptom Delineation and Summarization
Recent advancements in Large Language Models (LLMs) have accelerated their usage in various domains.
Improving Visual Prompt Tuning for Self-supervised Vision Transformers
Visual Prompt Tuning (VPT) is an effective tuning method for adapting pretrained Vision Transformers (ViTs) to downstream tasks.
Ranked #2 on
Visual Prompt Tuning
on VTAB-1k(Natural<7>)
Probabilistic Concept Bottleneck Models
To provide a reliable interpretation against this ambiguity, we propose Probabilistic Concept Bottleneck Models (ProbCBM).
Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization
This manipulation is realized in an anti-adversarial manner, so that the original image is perturbed along pixel gradients in directions opposite to those used in an adversarial attack.
Bridging the Gap between Classification and Localization for Weakly Supervised Object Localization
Weakly supervised object localization aims to find a target object region in a given image with only weak supervision, such as image-level labels.
Weakly Supervised Semantic Segmentation using Out-of-Distribution Data
However, training on class labels only, classifiers suffer from the spurious correlation between foreground and background cues (e. g. train and rail), fundamentally bounding the performance of WSSS.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Robust End-to-End Focal Liver Lesion Detection using Unregistered Multiphase Computed Tomography Images
The computer-aided diagnosis of focal liver lesions (FLLs) can help improve workflow and enable correct diagnoses; FLL detection is the first step in such a computer-aided diagnosis.
Automatic Liver And Tumor Segmentation
Computed Tomography (CT)
+4
Variational Perturbations for Visual Feature Attribution
Explaining a complex black-box system in a post-hoc manner is important to understand its predictions.
XProtoNet: Diagnosis in Chest Radiography with Global and Local Explanations
Automated diagnosis using deep neural networks in chest radiography can help radiologists detect life-threatening diseases.
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation
Weakly supervised semantic segmentation produces a pixel-level localization from a classifier, but it is likely to restrict its focus to a small discriminative region of the target object.
Variational saliency maps for explaining model's behavior
Saliency maps have been widely used to explain the behavior of an image classifier.
Interpretation of NLP models through input marginalization
To demystify the "black box" property of deep neural networks for natural language processing (NLP), several methods have been proposed to interpret their predictions by measuring the change in prediction probability after erasing each token of an input.
Information-Theoretic Visual Explanation for Black-Box Classifiers
IG map provides a class-independent answer to "How informative is each pixel?
Frame-to-Frame Aggregation of Active Regions in Web Videos for Weakly Supervised Semantic Segmentation
We propose a method of using videos automatically harvested from the web to identify a larger region of the target object by using temporal information, which is not present in the static image.
FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference
The main obstacle to weakly supervised semantic image segmentation is the difficulty of obtaining pixel-level information from coarse image-level annotations.
