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Found 16 papers, 5 papers with code
Dual Refinement Underwater Object Detection Network
Due to the complex underwater environment, underwater imaging often encounters some problems such as blur, scale variation, color shift, and texture distortion.
EasyTrack: Efficient and Compact One-stream 3D Point Clouds Tracker
Most of 3D single object trackers (SOT) in point clouds follow the two-stream multi-stage 3D Siamese or motion tracking paradigms, which process the template and search area point clouds with two parallel branches, built on supervised point cloud backbones.
Exploring Self- and Cross-Triplet Correlations for Human-Object Interaction Detection
In this work, we propose to explore Self- and Cross-Triplet Correlations (SCTC) for HOI detection.
Human-Object Interaction Detection
Knowledge Distillation
+2
Robust 3D Tracking with Quality-Aware Shape Completion
Considering that, we propose to construct a synthetic target representation composed of dense and complete point clouds depicting the target shape precisely by shape completion for robust 3D tracking.
SA$^2$VP: Spatially Aligned-and-Adapted Visual Prompt
Typical methods for visual prompt tuning follow the sequential modeling paradigm stemming from NLP, which represents an input image as a flattened sequence of token embeddings and then learns a set of unordered parameterized tokens prefixed to the sequence representation as the visual prompts for task adaptation of large vision models.
D$^2$ST-Adapter: Disentangled-and-Deformable Spatio-Temporal Adapter for Few-shot Action Recognition
In particular, we devise the anisotropic Deformable Spatio-Temporal Attention module as the core component of D$^2$ST-Adapter, which can be tailored with anisotropic sampling densities along spatial and temporal domains to learn spatial and temporal features specifically in corresponding pathways, allowing our D$^2$ST-Adapter to encode features in a global view in 3D spatio-temporal space while maintaining a lightweight design.
A Shadow Imaging Bilinear Model and Three-branch Residual Network for Shadow Removal
Thus, our network ensures the fidelity of nonshadow areas and restores the light intensity of shadow areas through three-branch collaboration.
Activating the Discriminability of Novel Classes for Few-shot Segmentation
To circumvent these two challenges, we propose to activate the discriminability of novel classes explicitly in both the feature encoding stage and the prediction stage for segmentation.
Few-Shot Object Detection by Knowledge Distillation Using Bag-of-Visual-Words Representations
In this work we design a novel knowledge distillation framework to guide the learning of the object detector and thereby restrain the overfitting in both the pre-training stage on base classes and fine-tuning stage on novel classes.
Multi-Faceted Distillation of Base-Novel Commonality for Few-shot Object Detection
Most of existing methods for few-shot object detection follow the fine-tuning paradigm, which potentially assumes that the class-agnostic generalizable knowledge can be learned and transferred implicitly from base classes with abundant samples to novel classes with limited samples via such a two-stage training strategy.
Tracking-by-Counting: Using Network Flows on Crowd Density Maps for Tracking Multiple Targets
State-of-the-art multi-object tracking~(MOT) methods follow the tracking-by-detection paradigm, where object trajectories are obtained by associating per-frame outputs of object detectors.
Depth and Image Restoration From Light Field in a Scattering Medium
First, we present a new single image restoration algorithm which removes backscatter and attenuation from images better than existing methods, and apply it to each view in the light field.
DeshadowNet: A Multi-Context Embedding Deep Network for Shadow Removal
Two levels of features are derived from the global network and transferred to two parallel networks.
Video Desnowing and Deraining Based on Matrix Decomposition
The existing snow/rain removal methods often fail for heavy snow/rain and dynamic scene.
RGBD Salient Object Detection via Deep Fusion
Numerous efforts have been made to design different low level saliency cues for the RGBD saliency detection, such as color or depth contrast features, background and color compactness priors.
Ranked #25 on
RGB-D Salient Object Detection
on NJU2K
Pixel-wise Orthogonal Decomposition for Color Illumination Invariant and Shadow-free Image
In this paper, we propose a novel, effective and fast method to obtain a color illumination invariant and shadow-free image from a single outdoor image.
