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Found 20 papers, 16 papers with code
FreCaS: Efficient Higher-Resolution Image Generation via Frequency-aware Cascaded Sampling
While image generation with diffusion models has achieved a great success, generating images of higher resolution than the training size remains a challenging task due to the high computational cost.
Dense Multimodal Alignment for Open-Vocabulary 3D Scene Understanding
Recent vision-language pre-training models have exhibited remarkable generalization ability in zero-shot recognition tasks.
SyncNoise: Geometrically Consistent Noise Prediction for Text-based 3D Scene Editing
Meanwhile, the 2D diffusion models also exhibit substantial potentials for 3D editing tasks.
Source Prompt Disentangled Inversion for Boosting Image Editability with Diffusion Models
Text-driven diffusion models have significantly advanced the image editing performance by using text prompts as inputs.
ScatterFormer: Efficient Voxel Transformer with Scattered Linear Attention
In this paper, we introduce ScatterFormer, which to the best of our knowledge, is the first to directly apply attention to voxels across different windows as a single sequence.
TMP: Temporal Motion Propagation for Online Video Super-Resolution
Online video super-resolution (online-VSR) highly relies on an effective alignment module to aggregate temporal information, while the strict latency requirement makes accurate and efficient alignment very challenging.
One-to-Few Label Assignment for End-to-End Dense Detection
The positive and negative weights of these soft anchors are dynamically adjusted during training so that they can contribute more to ``representation learning'' in the early training stage, and contribute more to ``duplicated prediction removal'' in the later stage.
MSF: Motion-guided Sequential Fusion for Efficient 3D Object Detection from Point Cloud Sequences
Current top-performing multi-frame detectors mostly follow a Detect-and-Fuse framework, which extracts features from each frame of the sequence and fuses them to detect the objects in the current frame.
SIM: Semantic-aware Instance Mask Generation for Box-Supervised Instance Segmentation
Weakly supervised instance segmentation using only bounding box annotations has recently attracted much research attention.
DynaMask: Dynamic Mask Selection for Instance Segmentation
The representative instance segmentation methods mostly segment different object instances with a mask of the fixed resolution, e. g., 28*28 grid.
Adversarial Style Augmentation for Domain Generalization
By updating the model against the adversarial statistics perturbation during training, we allow the model to explore the worst-case domain and hence improve its generalization performance.
FPR: False Positive Rectification for Weakly Supervised Semantic Segmentation
Without introducing any external supervision and human priors, the proposed FPR effectively suppresses wrong activations from the background objects.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Point-DAE: Denoising Autoencoders for Self-supervised Point Cloud Learning
We also validate the effectiveness of affine transformation corruption with the Transformer backbones, where we decompose the reconstruction of the complete point cloud into the reconstructions of detailed local patches and rough global shape, alleviating the position leakage problem in the reconstruction.
Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference Modeling
Instead of directly feeding consecutive frames into a VSR model, we propose to compute the temporal difference between frames and divide those pixels into two subsets according to the level of difference.
Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds
VoxSeT is built upon a voxel-based set attention (VSA) module, which reduces the self-attention in each voxel by two cross-attentions and models features in a hidden space induced by a group of latent codes.
Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentation
One popular solution to this challenging task is self-training, which selects high-scoring predictions on target samples as pseudo labels for training.
Ranked #9 on
Image-to-Image Translation
on SYNTHIA-to-Cityscapes
A Dual Weighting Label Assignment Scheme for Object Detection
Existing LA methods mostly focus on the design of pos weighting function, while the neg weight is directly derived from the pos weight.
Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization
In this work, we, for the first time to our best knowledge, propose to perform Exact Feature Distribution Matching (EFDM) by exactly matching the empirical Cumulative Distribution Functions (eCDFs) of image features, which could be implemented by applying the Exact Histogram Matching (EHM) in the image feature space.
Ranked #7 on
Style Transfer
on StyleBench
T-SVDNet: Exploring High-Order Prototypical Correlations for Multi-Source Domain Adaptation
Most existing domain adaptation methods focus on adaptation from only one source domain, however, in practice there are a number of relevant sources that could be leveraged to help improve performance on target domain.
Ranked #2 on
Unsupervised Domain Adaptation
on PACS
Reciprocal Multi-Layer Subspace Learning for Multi-View Clustering
Multi-view clustering is a long-standing important research topic, however, remains challenging when handling high-dimensional data and simultaneously exploring the consistency and complementarity of different views.
