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Found 16 papers, 8 papers with code
CodeJudge-Eval: Can Large Language Models be Good Judges in Code Understanding?
Recent advancements in large language models (LLMs) have showcased impressive code generation capabilities, primarily evaluated through language-to-code benchmarks.
GLGait: A Global-Local Temporal Receptive Field Network for Gait Recognition in the Wild
Recently, some Convolution Neural Networks (ConvNets) based methods have been proposed to address the issue of gait recognition in the wild.
RealGait: Gait Recognition for Person Re-Identification
Given that our experimental results show that current gait recognition approaches designed under data collected in controlled scenarios are inappropriate for real surveillance scenarios, we propose a novel gait recognition method, called RealGait.
Lagrange Motion Analysis and View Embeddings for Improved Gait Recognition
Gait is considered the walking pattern of human body, which includes both shape and motion cues.
Will You Ever Become Popular? Learning to Predict Virality of Dance Clips
Therefore, virality prediction from dance challenges is of great commercial value and has a wide range of applications, such as smart recommendation and popularity promotion.
Few-Shot Fine-Grained Action Recognition via Bidirectional Attention and Contrastive Meta-Learning
Fine-grained action recognition is attracting increasing attention due to the emerging demand of specific action understanding in real-world applications, whereas the data of rare fine-grained categories is very limited.
Silhouette based View embeddings for Gait Recognition under Multiple Views
Gait recognition under multiple views is an important computer vision and pattern recognition task.
Cross-View Gait Recognition With Deep Universal Linear Embeddings
Furthermore, a novel framework based on convolutional variational autoencoder and deep Koopman embedding is proposed to approximate the Koopman operators, which is used as dynamical features from the linearized embedding space for cross-view gait recognition.
Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction
Event extraction is challenging due to the complex structure of event records and the semantic gap between text and event.
Ranked #3 on
Event Extraction
on ACE2005
ISCAS at SemEval-2020 Task 5: Pre-trained Transformers for Counterfactual Statement Modeling
ISCAS participated in two subtasks of SemEval 2020 Task 5: detecting counterfactual statements and detecting antecedent and consequence.
Attribute-aware Identity-hard Triplet Loss for Video-based Person Re-identification
Video-based person re-identification (Re-ID) is an important computer vision task.
A Temporal Attentive Approach for Video-Based Pedestrian Attribute Recognition
In this paper, we first tackle the problem of pedestrian attribute recognition by video-based approach.
stagNet: An Attentive Semantic RNN for Group Activity Recognition
Group activity recognition plays a fundamental role in a variety of applications, e. g. sports video analysis and intelligent surveillance.
Adversarial Binary Coding for Efficient Person Re-identification
Specifically, instead of learning explicit projections or adding fully-connected mapping layers, the proposed Adversarial Binary Coding (ABC) framework guides the extraction of binary codes implicitly and effectively.
Cross-pose Face Recognition by Canonical Correlation Analysis
We argue that one of the diffculties in this problem is the severe misalignment in face images or feature vectors with different poses.
