Search Results for author:
Found 26 papers, 7 papers with code
Spatial Autoregressive Coding for Graph Neural Recommendation
To adequately leverage neighbor proximity and high-order information, we design a novel spatial autoregressive paradigm.
K-LITE: Learning Transferable Visual Models with External Knowledge
In this paper, we propose K-LITE (Knowledge-augmented Language-Image Training and Evaluation), a simple strategy to leverage external knowledge to build transferable visual systems: In training, it enriches entities in natural language with WordNet and Wiktionary knowledge, leading to an efficient and scalable approach to learning image representations that can understand both visual concepts and their knowledge; In evaluation, the natural language is also augmented with external knowledge and then used to reference learned visual concepts (or describe new ones) to enable zero-shot and few-shot transfer of the pre-trained models.
Enhancing Pseudo Label Quality for Semi-Supervised Domain-Generalized Medical Image Segmentation
With these augmentations as perturbations, we feed the input to a confidence-aware cross pseudo supervision network to measure the variance of pseudo labels and regularize the network to learn with more confident pseudo labels.
Injecting Semantic Concepts into End-to-End Image Captioning
In this paper, we are concerned with a better-performing detector-free image captioning model, and propose a pure vision transformer-based image captioning model, dubbed as ViTCAP, in which grid representations are used without extracting the regional features.
Scaling Up Vision-Language Pre-training for Image Captioning
In this paper, we present LEMON, a LargE-scale iMage captiONer, and provide the first empirical study on the scaling behavior of VLP for image captioning.
Ranked #1 on
Image Captioning
on nocaps-val-overall
Crossing the Format Boundary of Text and Boxes: Towards Unified Vision-Language Modeling
In this paper, we propose UNICORN, a vision-language (VL) model that unifies text generation and bounding box prediction into a single architecture.
UFO: A UniFied TransfOrmer for Vision-Language Representation Learning
In this paper, we propose a single UniFied transfOrmer (UFO), which is capable of processing either unimodal inputs (e. g., image or language) or multimodal inputs (e. g., the concatenation of the image and the question), for vision-language (VL) representation learning.
Strategic Inventories in a Supply Chain with Downstream Cournot Duopoly
The inventories carried in a supply chain as a strategic tool to influence the competing firms are considered to be strategic inventories (SI).
An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA
To address this challenge, we propose PICa, a simple yet effective method that Prompts GPT3 via the use of Image Captions, for knowledge-based VQA.
Single-Stage Instance Shadow Detection with Bidirectional Relation Learning
Instance shadow detection aims to find shadow instances paired with the objects that cast the shadows.
Ranked #1 on
Instance Shadow Detection
on SOBA
Compressing Visual-linguistic Model via Knowledge Distillation
In this paper, we study knowledge distillation (KD) to effectively compress a transformer-based large VL model into a small VL model.
Global Guidance Network for Breast Lesion Segmentation in Ultrasound Images
The BD modules learn additional breast lesion boundary map to enhance the boundary quality of a segmentation result refinement.
Deep Texture-Aware Features for Camouflaged Object Detection
Camouflaged object detection is a challenging task that aims to identify objects having similar texture to the surroundings.
Incorporating Vision Bias into Click Models for Image-oriented Search Engine
Most typical click models assume that the probability of a document to be examined by users only depends on position, such as PBM and UBM.
VinVL: Revisiting Visual Representations in Vision-Language Models
In our experiments we feed the visual features generated by the new object detection model into a Transformer-based VL fusion model \oscar \cite{li2020oscar}, and utilize an improved approach \short\ to pre-train the VL model and fine-tune it on a wide range of downstream VL tasks.
Ranked #5 on
Visual Question Answering
on VQA v2 test-std
MiniVLM: A Smaller and Faster Vision-Language Model
We design a Two-stage Efficient feature Extractor (TEE), inspired by the one-stage EfficientDet network, to significantly reduce the time cost of visual feature extraction by $95\%$, compared to a baseline model.
VIVO: Visual Vocabulary Pre-Training for Novel Object Captioning
It is highly desirable yet challenging to generate image captions that can describe novel objects which are unseen in caption-labeled training data, a capability that is evaluated in the novel object captioning challenge (nocaps).
Ranked #1 on
Image Captioning
on nocaps-XD out-of-domain
Revisiting Shadow Detection: A New Benchmark Dataset for Complex World
Shadow detection in general photos is a nontrivial problem, due to the complexity of the real world.
Probabilistic Multilayer Regularization Network for Unsupervised 3D Brain Image Registration
This paper presents a novel framework for unsupervised 3D brain image registration by capturing the feature-level transformation relationships between the unaligned image and reference image.
Depth-Attentional Features for Single-Image Rain Removal
Rain is a common weather phenomenon, where object visibility varies with depth from the camera and objects faraway are visually blocked more by fog than by rain streaks.
Ranked #4 on
Single Image Deraining
on RainCityscapes
Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Data
This paper presents a new method for shadow removal using unpaired data, enabling us to avoid tedious annotations and obtain more diverse training samples.
SAC-Net: Spatial Attenuation Context for Salient Object Detection
This paper presents a new deep neural network design for salient object detection by maximizing the integration of local and global image context within, around, and beyond the salient objects.
Direction-aware Spatial Context Features for Shadow Detection and Removal
This paper presents a novel deep neural network design for shadow detection and removal by analyzing the spatial image context in a direction-aware manner.
Ranked #3 on
Shadow Removal
on ISTD
SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection
Based on these findings, we present a scale-insensitive convolutional neural network (SINet) for fast detecting vehicles with a large variance of scales.
Direction-aware Spatial Context Features for Shadow Detection
To achieve this, we first formulate the direction-aware attention mechanism in a spatial recurrent neural network (RNN) by introducing attention weights when aggregating spatial context features in the RNN.
Ranked #2 on
RGB Salient Object Detection
on SBU
(Bandit) Convex Optimization with Biased Noisy Gradient Oracles
Algorithms for bandit convex optimization and online learning often rely on constructing noisy gradient estimates, which are then used in appropriately adjusted first-order algorithms, replacing actual gradients.
