Search Results for author:
Found 17 papers, 6 papers with code
Misalignment-Robust Frequency Distribution Loss for Image Transformation
This paper aims to address a common challenge in deep learning-based image transformation methods, such as image enhancement and super-resolution, which heavily rely on precisely aligned paired datasets with pixel-level alignments.
ColNeRF: Collaboration for Generalizable Sparse Input Neural Radiance Field
Through this, we construct a novel collaborative module that aligns information from various views and meanwhile imposes self-supervised constraints to ensure multi-view consistency in both geometry and appearance.
Glow in the Dark: Low-Light Image Enhancement with External Memory
Benefiting from the learned memory, more complex distributions of reference images in the entire dataset can be “remembered” to facilitate the adjustment of the testing samples more adaptively.
Ranked #2 on
Low-Light Image Enhancement
on LOL-v2
Multi-modal Large Language Model Enhanced Pseudo 3D Perception Framework for Visual Commonsense Reasoning
On the other hand, BLIP-2 as an MLLM is employed to process images and texts, and the referring expressions in texts involving specific visual objects are modified with linguistic object labels to serve as comprehensible MLLM inputs.
Just Noticeable Difference Modeling for Face Recognition System
In particular, in order to maximum redundancy removal without impairment of robust identity information, we apply the encoder with multiple feature extraction and attention-based feature decomposition modules to progressively decompose face features into two uncorrelated components, i. e., identity and residual features, via self-supervised learning.
High Dynamic Range Image Quality Assessment Based on Frequency Disparity
In this paper, a novel and effective image quality assessment (IQA) algorithm based on frequency disparity for high dynamic range (HDR) images is proposed, termed as local-global frequency feature-based model (LGFM).
Coherent Visual Storytelling via Parallel Top-Down Visual and Topic Attention
In this work, a coherent visual storytelling (CoVS) framework is designed to address the above-mentioned problems.
Ranked #4 on
Visual Storytelling
on VIST
Cycle-Interactive Generative Adversarial Network for Robust Unsupervised Low-Light Enhancement
Getting rid of the fundamental limitations in fitting to the paired training data, recent unsupervised low-light enhancement methods excel in adjusting illumination and contrast of images.
Taking an Emotional Look at Video Paragraph Captioning
In this work, a comprehensive study is conducted on video paragraph captioning, with the goal to generate paragraph-level descriptions for a given video.
Two-stream Hierarchical Similarity Reasoning for Image-text Matching
Second, previous approaches only consider learning single-stream similarity alignment (i. e., image-to-text level or text-to-image level), which is inadequate to fully use similarity information for image-text matching.
Knowledge-enriched Attention Network with Group-wise Semantic for Visual Storytelling
Third, a unified one-stage story generation model with encoder-decoder structure is proposed to simultaneously train and infer the knowledge-enriched attention network, group-wise semantic module and multi-modal story generation decoder in an end-to-end fashion.
Generalized Visual Quality Assessment of GAN-Generated Face Images
However, little work has been dedicated to automatic quality assessment of such GAN-generated face images (GFIs), even less have been devoted to generalized and robust quality assessment of GFIs generated with unseen GAN model.
Visual Storytelling with Hierarchical BERT Semantic Guidance
As there is no ground-truth topic information, a pre-trained BERT model based on visual contents and annotated stories is utilized to mine topics.
Ranked #2 on
Visual Storytelling
on VIST
CSformer: Bridging Convolution and Transformer for Compressive Sensing
The proposed approach is an end-to-end compressive image sensing method, composed of adaptive sampling and recovery.
Categorizing Concepts With Basic Level for Vision-to-Language
Vision-to-language tasks require a unified semantic understanding of visual content.
Real-time Action Recognition with Enhanced Motion Vector CNNs
The deep two-stream architecture exhibited excellent performance on video based action recognition.
Ranked #74 on
Action Recognition
on UCF101
A new network-based algorithm for human activity recognition in video
Based on this network, we further model people in the scene as packages while human activities can be modeled as the process of package transmission in the network.
