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Found 16 papers, 4 papers with code
Contextual Associated Triplet Queries for Panoptic Scene Graph Generation
The Panoptic Scene Graph generation (PSG) task aims to extract the triplets composed of subject, object, and relation based on panoptic segmentation.
Transformation vs Tradition: Artificial General Intelligence (AGI) for Arts and Humanities
Recent advances in artificial general intelligence (AGI), particularly large language models and creative image generation systems have demonstrated impressive capabilities on diverse tasks spanning the arts and humanities.
ATM: Action Temporality Modeling for Video Question Answering
Despite significant progress in video question answering (VideoQA), existing methods fall short of questions that require causal/temporal reasoning across frames.
Ranked #27 on
Video Question Answering
on NExT-QA
Defending Adversarial Patches via Joint Region Localizing and Inpainting
In this paper, we analyse the properties of adversarial patches, and find that: on the one hand, adversarial patches will lead to the appearance or contextual inconsistency in the target objects; on the other hand, the patch region will show abnormal changes on the high-level feature maps of the objects extracted by a backbone network.
Focusing on what to decode and what to train: Efficient Training with HOI Split Decoders and Specific Target Guided DeNoising
However, the current methods redirect the detection target of the object decoder, and the box target is not explicitly separated from the query embeddings, which leads to long and hard training.
Ranked #3 on
Human-Object Interaction Detection
on HICO-DET
GateHUB: Gated History Unit with Background Suppression for Online Action Detection
We present GateHUB, Gated History Unit with Background Suppression, that comprises a novel position-guided gated cross-attention mechanism to enhance or suppress parts of the history as per how informative they are for current frame prediction.
Ranked #1 on
Online Action Detection
on TVSeries
Transformer-based Cross-Modal Recipe Embeddings with Large Batch Training
In this paper, we present a cross-modal recipe retrieval framework, Transformer-based Network for Large Batch Training (TNLBT), which is inspired by ACME~(Adversarial Cross-Modal Embedding) and H-T~(Hierarchical Transformer).
QAHOI: Query-Based Anchors for Human-Object Interaction Detection
Human-object interaction (HOI) detection as a downstream of object detection tasks requires localizing pairs of humans and objects and extracting the semantic relationships between humans and objects from an image.
Ranked #9 on
Human-Object Interaction Detection
on HICO-DET
Explainable Video Entailment With Grounded Visual Evidence
Video entailment aims at determining if a hypothesis textual statement is entailed or contradicted by a premise video.
Group Activity Prediction with Sequential Relational Anticipation Model
Our model explicitly anticipates both activity features and positions by two graph auto-encoders, aiming to learn a discriminative group representation for group activity prediction.
ContourRend: A Segmentation Method for Improving Contours by Rendering
A good object segmentation should contain clear contours and complete regions.
Learning Syntactic and Dynamic Selective Encoding for Document Summarization
Our approach has the following contributions: first, we incorporate syntactic information such as constituency parsing trees into the encoding sequence to learn both the semantic and syntactic information from the document, resulting in more accurate summary; second, we propose a dynamic gate network to select the salient information based on the context of the decoder state, which is essential to document summarization.
Adversarial Multi-Binary Neural Network for Multi-class Classification
Multi-class text classification is one of the key problems in machine learning and natural language processing.
Selective Attention Encoders by Syntactic Graph Convolutional Networks for Document Summarization
Abstractive text summarization is a challenging task, and one need to design a mechanism to effectively extract salient information from the source text and then generate a summary.
DELTA: A DEep learning based Language Technology plAtform
In this paper we present DELTA, a deep learning based language technology platform.
Ranked #3 on
Named Entity Recognition
on CoNLL 2003 (English)
