Search Results for author:
Found 12 papers, 3 papers with code
LDTR: Transformer-based Lane Detection with Anchor-chain Representation
Despite recent advances in lane detection methods, scenarios with limited- or no-visual-clue of lanes due to factors such as lighting conditions and occlusion remain challenging and crucial for automated driving.
Pre-Training on Large-Scale Generated Docking Conformations with HelixDock to Unlock the Potential of Protein-ligand Structure Prediction Models
In this work, we show that by pre-training a geometry-aware SE(3)-Equivariant neural network on a large-scale docking conformation generated by traditional physics-based docking tools and then fine-tuning with a limited set of experimentally validated receptor-ligand complexes, we can achieve outstanding performance.
GRASS: Unified Generation Model for Speech-to-Semantic Tasks
This paper explores the instruction fine-tuning technique for speech-to-semantic tasks by introducing a unified end-to-end (E2E) framework that generates target text conditioned on a task-related prompt for audio data.
CANet: Curved Guide Line Network with Adaptive Decoder for Lane Detection
A lane instance is first responded by the heat-map on the U-shaped curved guide line at global semantic level, thus the corresponding features of each lane are aggregated at the response point.
Ranked #1 on
Lane Detection
on CurveLanes
(Recall metric)
Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form Solution
Inductive one-bit matrix completion is motivated by modern applications such as recommender systems, where new users would appear at test stage with the ratings consisting of only ones and no zeros.
S4OD: Semi-Supervised learning for Single-Stage Object Detection
In this paper, we design a dynamic self-adaptive threshold (DSAT) strategy in classification branch, which can automatically select pseudo labels to achieve an optimal trade-off between quality and quantity.
Spatio-temporal Contrastive Domain Adaptation for Action Recognition
Unsupervised domain adaptation (UDA) for human action recognition is a practical and challenging problem.
An End-to-End Visual-Audio Attention Network for Emotion Recognition in User-Generated Videos
Emotion recognition in user-generated videos plays an important role in human-centered computing.
Ranked #4 on
Video Emotion Recognition
on Ekman6
Multi-source Distilling Domain Adaptation
Deep neural networks suffer from performance decay when there is domain shift between the labeled source domain and unlabeled target domain, which motivates the research on domain adaptation (DA).
Domain Adaptation
Multi-Source Unsupervised Domain Adaptation
Multi-source Domain Adaptation for Semantic Segmentation
In this paper, we propose to investigate multi-source domain adaptation for semantic segmentation.
Ranked #2 on
Domain Adaptation
on GTA5+Synscapes to Cityscapes
ROAM: Recurrently Optimizing Tracking Model
In this paper, we design a tracking model consisting of response generation and bounding box regression, where the first component produces a heat map to indicate the presence of the object at different positions and the second part regresses the relative bounding box shifts to anchors mounted on sliding-window locations.
POI Semantic Model with a Deep Convolutional Structure
An accurate similarity calculation is challenging since the mismatch between a query and a retrieval text may exist in the case of a mistyped query or an alias inquiry.
