Search Results for author:
Found 9 papers, 5 papers with code
Bridging the Gap between Audio and Text using Parallel-attention for User-defined Keyword Spotting
This paper proposes a novel user-defined keyword spotting framework that accurately detects audio keywords based on text enrollment.
Robust Multimodal 3D Object Detection via Modality-Agnostic Decoding and Proximity-based Modality Ensemble
Additionally, existing fusion methods overlook the detrimental impact of sensor noise induced by environmental changes, on detection performance.
Ranked #15 on
3D Object Detection
on nuScenes
Groupwise Query Specialization and Quality-Aware Multi-Assignment for Transformer-based Visual Relationship Detection
Groupwise Query Specialization trains a specialized query by dividing queries and relations into disjoint groups and directing a query in a specific query group solely toward relations in the corresponding relation group.
Ranked #1 on
Scene Graph Generation
on Visual Genome
Open-vocabulary Video Question Answering: A New Benchmark for Evaluating the Generalizability of Video Question Answering Models
We hence propose a new benchmark, Open-vocabulary Video Question Answering (OVQA), to measure the generalizability of VideoQA models by considering rare and unseen answers.
Ranked #8 on
Visual Question Answering (VQA)
on MSRVTT-QA
That's What I Said: Fully-Controllable Talking Face Generation
The goal of this paper is to synthesise talking faces with controllable facial motions.
Joint unsupervised and supervised learning for context-aware language identification
Language identification (LID) recognizes the language of a spoken utterance automatically.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Metric Learning for User-defined Keyword Spotting
In particular, we make the following contributions: (1) we construct a large-scale keyword dataset with an existing speech corpus and propose a filtering method to remove data that degrade model training; (2) we propose a metric learning-based two-stage training strategy, and demonstrate that the proposed method improves the performance on the user-defined keyword spotting task by enriching their representations; (3) to facilitate the fair comparison in the user-defined KWS field, we propose unified evaluation protocol and metrics.
Consistency Learning via Decoding Path Augmentation for Transformers in Human Object Interaction Detection
Motivated by various inference paths for HOI detection, we propose cross-path consistency learning (CPC), which is a novel end-to-end learning strategy to improve HOI detection for transformers by leveraging augmented decoding paths.
Ranked #1 on
Human-Object Interaction Detection
on V-COCO
(MAP metric)
Deformable Graph Convolutional Networks
To address the two common problems of graph convolution, in this paper, we propose Deformable Graph Convolutional Networks (Deformable GCNs) that adaptively perform convolution in multiple latent spaces and capture short/long-range dependencies between nodes.
Ranked #3 on
Node Classification on Non-Homophilic (Heterophilic) Graphs
on Cornell (48%/32%/20% fixed splits)
Node Classification on Non-Homophilic (Heterophilic) Graphs
Representation Learning
