Search Results for author:
Found 33 papers, 12 papers with code
Bilateral Reference for High-Resolution Dichotomous Image Segmentation
It comprises two essential components: the localization module (LM) and the reconstruction module (RM) with our proposed bilateral reference (BiRef).
Ranked #1 on
RGB Salient Object Detection
on HRSOD
(using extra training data)
Camouflaged Object Segmentation
Dichotomous Image Segmentation
+3
Semi-Supervised learning for Face Anti-Spoofing using Apex frame
Conventional feature extraction techniques in the face anti-spoofing domain either analyze the entire video sequence or focus on a specific segment to improve model performance.
Saliency-based Video Summarization for Face Anti-spoofing
Inspired by the visual saliency theory, we present a video summarization method for face anti-spoofing detection that aims to enhance the performance and efficiency of deep learning models by leveraging visual saliency.
PiTL: Cross-modal Retrieval with Weakly-supervised Vision-language Pre-training via Prompting
To further reduce the amount of supervision, we propose Prompts-in-The-Loop (PiTL) that prompts knowledge from large language models (LLMs) to describe images.
Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing
Face presentation attacks (PA), also known as spoofing attacks, pose a substantial threat to biometric systems that rely on facial recognition systems, such as access control systems, mobile payments, and identity verification systems.
XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models
The latest breakthroughs in large vision-language models, such as Bard and GPT-4, have showcased extraordinary abilities in performing a wide range of tasks.
Cross-modulated Few-shot Image Generation for Colorectal Tissue Classification
In this work, we propose a few-shot colorectal tissue image generation method for addressing the scarcity of histopathological training data for rare cancer tissues.
Video Instance Segmentation in an Open-World
Open-world formulation relaxes the close-world static-learning assumption as follows: (a) first, it distinguishes a set of known categories as well as labels an unknown object as `unknown' and then (b) it incrementally learns the class of an unknown as and when the corresponding semantic labels become available.
3D Mitochondria Instance Segmentation with Spatio-Temporal Transformers
Accurate 3D mitochondria instance segmentation in electron microscopy (EM) is a challenging problem and serves as a prerequisite to empirically analyze their distributions and morphology.
Domain Generalization via Ensemble Stacking for Face Presentation Attack Detection
The latter combines the predictions from the base models, leveraging their complementary information to better handle unseen target domains and enhance the overall performance.
Person Image Synthesis via Denoising Diffusion Model
In this work, we show how denoising diffusion models can be applied for high-fidelity person image synthesis with strong sample diversity and enhanced mode coverage of the learnt data distribution.
When to Laugh and How Hard? A Multimodal Approach to Detecting Humor and its Intensity
Our results show that the model is capable of correctly detecting whether an utterance is humorous 78% of the time and how long the audience's laughter reaction should last with a mean absolute error of 600 milliseconds.
Learning by Hallucinating: Vision-Language Pre-training with Weak Supervision
Moreover, in other V-L downstream tasks considered, our WFH models are on par with models trained with paired V-L data, revealing the utility of unpaired data.
CLIP4IDC: CLIP for Image Difference Captioning
Different from directly fine-tuning CLIP to generate sentences, we introduce an adaptation training process to adapt CLIP's visual encoder to capture and align differences in image pairs based on the textual descriptions.
DoodleFormer: Creative Sketch Drawing with Transformers
Creative sketch image generation is a challenging vision problem, where the task is to generate diverse, yet realistic creative sketches possessing the unseen composition of the visual-world objects.
Tackling the Unannotated: Scene Graph Generation with Bias-Reduced Models
Recent scene graph generation (SGG) models have shown their capability of capturing the most frequent relations among visual entities.
Deep Contextual Attention for Human-Object Interaction Detection
Our approach outperforms the state-of-the-art on all datasets.
Character-Centric Storytelling
Sequential vision-to-language or visual storytelling has recently been one of the areas of focus in computer vision and language modeling domains.
The MeMAD Submission to the WMT18 Multimodal Translation Task
Our experiments show that the effect of the visual features in our system is small.
Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification
To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification.
Ranked #12 on
Aerial Scene Classification
on AID (20% as trainset)
Saliency Revisited: Analysis of Mouse Movements versus Fixations
This paper revisits visual saliency prediction by evaluating the recent advancements in this field such as crowd-sourced mouse tracking-based databases and contextual annotations.
Towards Instance Segmentation with Object Priority: Prominent Object Detection and Recognition
The motivation behind such a problem formulation is (1) the benefits to the knowledge representation-based vision pipelines, and (2) the potential improvements in emulating bio-inspired vision systems by solving these three problems together.
Paying Attention to Descriptions Generated by Image Captioning Models
To bridge the gap between humans and machines in image understanding and describing, we need further insight into how people describe a perceived scene.
Investigating Natural Image Pleasantness Recognition using Deep Features and Eye Tracking for Loosely Controlled Human-computer Interaction
To investigate the current status in regard to affective image tagging, we (1) introduce a new eye movement dataset using an affordable eye tracker, (2) study the use of deep neural networks for pleasantness recognition, (3) investigate the gap between deep features and eye movements.
Scale Coding Bag of Deep Features for Human Attribute and Action Recognition
Most approaches to human attribute and action recognition in still images are based on image representation in which multi-scale local features are pooled across scale into a single, scale-invariant encoding.
Exploiting inter-image similarity and ensemble of extreme learners for fixation prediction using deep features
This paper presents a novel fixation prediction and saliency modeling framework based on inter-image similarities and ensemble of Extreme Learning Machines (ELM).
Frame- and Segment-Level Features and Candidate Pool Evaluation for Video Caption Generation
We present our submission to the Microsoft Video to Language Challenge of generating short captions describing videos in the challenge dataset.
Video captioning with recurrent networks based on frame- and video-level features and visual content classification
In this paper, we describe the system for generating textual descriptions of short video clips using recurrent neural networks (RNN), which we used while participating in the Large Scale Movie Description Challenge 2015 in ICCV 2015.
PinView: Implicit Feedback in Content-Based Image Retrieval
It then retrieves images with a specialized online learning algorithm that balances the tradeoff between exploring new images and exploiting the already inferred interests of the user.
S-pot - a benchmark in spotting signs within continuous signing
In this paper we present S-pot, a benchmark setting for evaluating the performance of automatic spotting of signs in continuous sign language videos.
SLMotion - An extensible sign language oriented video analysis tool
We present a software toolkit called SLMotion which provides a framework for automatic and semiautomatic analysis, feature extraction and annotation of individual sign language videos, and which can easily be adapted to batch processing of entire sign language corpora.
Comparing computer vision analysis of signed language video with motion capture recordings
We consider a non-intrusive computer-vision method for measuring the motion of a person performing natural signing in video recordings.
