Search Results for author:
Found 18 papers, 7 papers with code
You Impress Me: Dialogue Generation via Mutual Persona Perception
Despite the continuing efforts to improve the engagingness and consistency of chit-chat dialogue systems, the majority of current work simply focus on mimicking human-like responses, leaving understudied the aspects of modeling understanding between interlocutors.
Ranked #2 on
Dialogue Generation
on Persona-Chat
(using extra training data)
Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Clues
As realistic facial manipulation technologies have achieved remarkable progress, social concerns about potential malicious abuse of these technologies bring out an emerging research topic of face forgery detection.
DEA-Net: Single image dehazing based on detail-enhanced convolution and content-guided attention
In this paper, a detail-enhanced attention block (DEAB) consisting of the detail-enhanced convolution (DEConv) and the content-guided attention (CGA) is proposed to boost the feature learning for improving the dehazing performance.
Ranked #2 on
Image Dehazing
on Haze4k
CuNeRF: Cube-Based Neural Radiance Field for Zero-Shot Medical Image Arbitrary-Scale Super Resolution
Medical image arbitrary-scale super-resolution (MIASSR) has recently gained widespread attention, aiming to super sample medical volumes at arbitrary scales via a single model.
Prompt-based test-time real image dehazing: a novel pipeline
We experimentally find that given a dehazing model trained on synthetic data, by fine-tuning the statistics (i. e., mean and standard deviation) of encoding features, PTTD is able to narrow the domain gap, boosting the performance of real image dehazing.
Layered Neighborhood Expansion for Incremental Multiple Graph Matching
In this paper, we treat the graphs as graphs on a super-graph, and propose a novel breadth first search based method for expanding the neighborhood on the super-graph for a new coming graph, such that the matching with the new graph can be efficiently performed within the constructed neighborhood.
Contour Loss: Boundary-Aware Learning for Salient Object Segmentation
Yet the Contour Loss emphasizes on the local saliency.
Revamp: Enhancing Accessible Information Seeking Experience of Online Shopping for Blind or Low Vision Users
Online shopping has become a valuable modern convenience, but blind or low vision (BLV) users still face significant challenges using it, because of: 1) inadequate image descriptions and 2) the inability to filter large amounts of information using screen readers.
Semantic Segmentation on VSPW Dataset through Aggregation of Transformer Models
Semantic segmentation is an important task in computer vision, from which some important usage scenarios are derived, such as autonomous driving, scene parsing, etc.
3D-VFD: A Victim-free Detector against 3D Adversarial Point Clouds
3D deep models consuming point clouds have achieved sound application effects in computer vision.
Hard Nominal Example-aware Template Mutual Matching for Industrial Anomaly Detection
Additionally, to meet the speed-accuracy demands, we further propose \textbf{P}ixel-level \textbf{T}emplate \textbf{S}election (PTS) to streamline the original template set.
Ranked #12 on
Anomaly Detection
on MVTec LOCO AD
OSP: Boosting Distributed Model Training with 2-stage Synchronization
However, these two types of methods can result in accuracy loss due to discarded gradients and have limited enhancement on the throughput of model synchronization, respectively.
APRF: Anti-Aliasing Projection Representation Field for Inverse Problem in Imaging
However, these methods have not considered the correlation between adjacent projection views, resulting in aliasing artifacts on SV sinograms.
Accurate and lightweight dehazing via multi-receptive-field non-local network and novel contrastive regularization
Thus, a multi-receptive-field non-local network (MRFNLN) consisting of the multi-stream feature attention block (MSFAB) and cross non-local block (CNLB) is presented in this paper.
CasIL: Cognizing and Imitating Skills via a Dual Cognition-Action Architecture
Heuristically, we extend the usual notion of action to a dual Cognition (high-level)-Action (low-level) architecture by introducing intuitive human cognitive priors, and propose a novel skill IL framework through human-robot interaction, called Cognition-Action-based Skill Imitation Learning (CasIL), for the robotic agent to effectively cognize and imitate the critical skills from raw visual demonstrations.
Rethink Baseline of Integrated Gradients from the Perspective of Shapley Value
Thus, we propose a novel baseline construction method called Shapley Integrated Gradients (SIG) that searches for a set of baselines by proportional sampling to partly simulate the computation path of Shapley Value.
HITSnDIFFs: From Truth Discovery to Ability Discovery by Recovering Matrices with the Consecutive Ones Property
We call this problem "ability discovery" to emphasize the connection to and duality with the more well-studied problem of "truth discovery".
Visual Whole-Body Control for Legged Loco-Manipulation
That is, the robot can control the legs and the arm at the same time to extend its workspace.
