Search Results for author:
Found 18 papers, 8 papers with code
Learning discriminative trajectorylet detector sets for accurate skeleton-based action recognition
The introduction of low-cost RGB-D sensors has promoted the research in skeleton-based human action recognition.
Less is more: zero-shot learning from online textual documents with noise suppression
Classifying a visual concept merely from its associated online textual source, such as a Wikipedia article, is an attractive research topic in zero-shot learning because it alleviates the burden of manually collecting semantic attributes.
Structured Learning of Tree Potentials in CRF for Image Segmentation
In this fashion, we easily achieve nonlinear learning of potential functions on both unary and pairwise terms in CRFs.
Visually Aligned Word Embeddings for Improving Zero-shot Learning
To overcome this visual-semantic discrepancy, this work proposes an objective function to re-align the distributed word embeddings with visual information by learning a neural network to map it into a new representation called visually aligned word embedding (VAWE).
Contrastive Learning for Compact Single Image Dehazing
In this paper, we propose a novel contrastive regularization (CR) built upon contrastive learning to exploit both the information of hazy images and clear images as negative and positive samples, respectively.
Ranked #5 on
Image Dehazing
on RS-Haze
Novelty Detection via Contrastive Learning with Negative Data Augmentation
Moreover, our model is more stable for training in a non-adversarial manner, compared to other adversarial based novelty detection methods.
Head and Body: Unified Detector and Graph Network for Person Search in Media
It is by far the largest dataset for person search in media.
HybridCR: Weakly-Supervised 3D Point Cloud Semantic Segmentation via Hybrid Contrastive Regularization
To address the huge labeling cost in large-scale point cloud semantic segmentation, we propose a novel hybrid contrastive regularization (HybridCR) framework in weakly-supervised setting, which obtains competitive performance compared to its fully-supervised counterpart.
Scene Consistency Representation Learning for Video Scene Segmentation
Additionally, we suggest a more fair and reasonable benchmark to evaluate the performance of Video Scene Segmentation methods.
Open-Vocabulary Multi-Label Classification via Multi-Modal Knowledge Transfer
Specifically, our method exploits multi-modal knowledge of image-text pairs based on a vision and language pre-training (VLP) model.
Ranked #1 on
Multi-label zero-shot learning
on Open Images V4
Exploiting Feature Diversity for Make-up Temporal Video Grounding
This technical report presents the 3rd winning solution for MTVG, a new task introduced in the 4-th Person in Context (PIC) Challenge at ACM MM 2022.
See Finer, See More: Implicit Modality Alignment for Text-based Person Retrieval
To explore the fine-grained alignment, we further propose two implicit semantic alignment paradigms: multi-level alignment (MLA) and bidirectional mask modeling (BMM).
VLMAE: Vision-Language Masked Autoencoder
Image and language modeling is of crucial importance for vision-language pre-training (VLP), which aims to learn multi-modal representations from large-scale paired image-text data.
NewsNet: A Novel Dataset for Hierarchical Temporal Segmentation
Temporal video segmentation is the get-to-go automatic video analysis, which decomposes a long-form video into smaller components for the following-up understanding tasks.
Collaborative Noisy Label Cleaner: Learning Scene-aware Trailers for Multi-modal Highlight Detection in Movies
Based on existing efforts, this work has two observations: (1) For different annotators, labeling highlight has uncertainty, which leads to inaccurate and time-consuming annotations.
D3G: Exploring Gaussian Prior for Temporal Sentence Grounding with Glance Annotation
Under this setup, we propose a Dynamic Gaussian prior based Grounding framework with Glance annotation (D3G), which consists of a Semantic Alignment Group Contrastive Learning module (SA-GCL) and a Dynamic Gaussian prior Adjustment module (DGA).
Ranked #10 on
Temporal Sentence Grounding
on Charades-STA
Coarse-to-Fine: Learning Compact Discriminative Representation for Single-Stage Image Retrieval
Image retrieval targets to find images from a database that are visually similar to the query image.
Unified and Dynamic Graph for Temporal Character Grouping in Long Videos
In this paper, we present a unified and dynamic graph (UniDG) framework for temporal character grouping.
