Search Results for author:
Found 28 papers, 16 papers with code
Invariant Grounding for Video Question Answering
At its core is understanding the alignments between visual scenes in video and linguistic semantics in question to yield the answer.
PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language Models
We show that PEVL enables state-of-the-art performance of detector-free VLP models on position-sensitive tasks such as referring expression comprehension and phrase grounding, and also improves the performance on position-insensitive tasks with grounded inputs.
Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection
The laborious and time-consuming manual annotation has become a real bottleneck in various practical scenarios.
3D Magic Mirror: Clothing Reconstruction from a Single Image via a Causal Perspective
In particular, to solve the inherent ambiguity among four implicit variables, i. e., camera position, shape, texture, and illumination, we study existing works and introduce an explainable structural causal map (SCM) to build our model.
Fine-Grained Scene Graph Generation with Data Transfer
Scene graph generation (SGG) aims to extract (subject, predicate, object) triplets in images.
Video Question Answering: Datasets, Algorithms and Challenges
Video Question Answering (VideoQA) aims to answer natural language questions according to the given videos.
Content-Variant Reference Image Quality Assessment via Knowledge Distillation
The comparisons of distribution differences between HQ and LQ images can help our model better assess the image quality.
Exploring Denoised Cross-Video Contrast for Weakly-Supervised Temporal Action Localization
Inspired by recent success in unsupervised contrastive representation learning, we propose a novel denoised cross-video contrastive algorithm, aiming to enhance the feature discrimination ability of video snippets for accurate temporal action localization in the weakly-supervised setting.
Generating Diverse and Natural 3D Human Motions From Text
Automated generation of 3D human motions from text is a challenging problem.
Video as Conditional Graph Hierarchy for Multi-Granular Question Answering
To align with the multi-granular essence of linguistic concepts in language queries, we propose to model video as a conditional graph hierarchy which weaves together visual facts of different granularity in a level-wise manner, with the guidance of corresponding textual cues.
Rethinking the Two-Stage Framework for Grounded Situation Recognition
Since each verb is associated with a specific set of semantic roles, all existing GSR methods resort to a two-stage framework: predicting the verb in the first stage and detecting the semantic roles in the second stage.
Joint Semantic Mining for Weakly Supervised RGB-D Salient Object Detection
As a by-product, a CapS dataset is constructed by augmenting existing benchmark training set with additional image tags and captions.
Meeting Summarization with Pre-training and Clustering Methods
Lastly, we compare the performance of our baseline models with BART, a state-of-the-art language model that is effective for summarization.
PP-PicoDet: A Better Real-Time Object Detector on Mobile Devices
We investigate the applicability of the anchor-free strategy on lightweight object detection models.
Ranked #1 on
Object Detection
on MSCOCO
Decoupling Strategy and Surface Realization for Task-oriented Dialogues
The core is to construct a latent content space for strategy optimization and disentangle the surface style from it.
Advancing biological super-resolution microscopy through deep learning: a brief review
We conclude with an outlook on how deep learning could shape the future of this new generation of light microscopy technology.
Calibrated RGB-D Salient Object Detection
Complex backgrounds and similar appearances between objects and their surroundings are generally recognized as challenging scenarios in Salient Object Detection (SOD).
Learning Calibrated Medical Image Segmentation via Multi-Rater Agreement Modeling
To our knowledge, our work is the first in producing calibrated predictions under different expertise levels for medical image segmentation.
Deconfounded Video Moment Retrieval with Causal Intervention
To fill the research gap, we propose a causality-inspired VMR framework that builds structural causal model to capture the true effect of query and video content on the prediction.
Deep Learning for Weakly-Supervised Object Detection and Object Localization: A Survey
With the success of deep neural networks in object detection, both WSOD and WSOL have received unprecedented attention.
Conditional Hyper-Network for Blind Super-Resolution with Multiple Degradations
We extract degradation prior at task-level with the proposed ConditionNet, which will be used to adapt the parameters of the basic SR network (BaseNet).
Boundary Proposal Network for Two-Stage Natural Language Video Localization
State-of-the-art NLVL methods are almost in one-stage fashion, which can be typically grouped into two categories: 1) anchor-based approach: it first pre-defines a series of video segment candidates (e. g., by sliding window), and then does classification for each candidate; 2) anchor-free approach: it directly predicts the probabilities for each video frame as a boundary or intermediate frame inside the positive segment.
Dynamic Context-Sensitive Filtering Network for Video Salient Object Detection
Our bidirectional dynamic fusion strategy encourages the interaction of spatial and temporal information in a dynamic manner.
Ranked #13 on
Video Polyp Segmentation
on SUN-SEG-Easy
ChemistryQA: A Complex Question Answering Dataset from Chemistry
We notice that some real-world QA tasks are more complex, which cannot be solved by end-to-end neural networks or translated to any kind of formal representations.
Table2Charts: Recommending Charts by Learning Shared Table Representations
It is common for people to create different types of charts to explore a multi-dimensional dataset (table).
Accurate RGB-D Salient Object Detection via Collaborative Learning
The explicitly extracted edge information goes together with saliency to give more emphasis to the salient regions and object boundaries.
Ranked #18 on
RGB-D Salient Object Detection
on NJU2K
An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety
In particular, by analysing speech recordings from these patients, we construct audio-only-based models to automatically categorise the health state of patients from four aspects, including the severity of illness, sleep quality, fatigue, and anxiety.
Context-Aware Deep Spatio-Temporal Network for Hand Pose Estimation from Depth Images
As a fundamental and challenging problem in computer vision, hand pose estimation aims to estimate the hand joint locations from depth images.
