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Found 20 papers, 10 papers with code
See, Say, and Segment: Teaching LMMs to Overcome False Premises
Current open-source Large Multimodal Models (LMMs) excel at tasks such as open-vocabulary language grounding and segmentation but can suffer under false premises when queries imply the existence of something that is not actually present in the image.
Self-correcting LLM-controlled Diffusion Models
Steered by an LLM controller, SLD turns text-to-image generation into an iterative closed-loop process, ensuring correctness in the resulting image.
WLST: Weak Labels Guided Self-training for Weakly-supervised Domain Adaptation on 3D Object Detection
In the field of domain adaptation (DA) on 3D object detection, most of the work is dedicated to unsupervised domain adaptation (UDA).
MuRAL: Multi-Scale Region-based Active Learning for Object Detection
MuRAL identifies informative regions of various scales to reduce annotation costs for well-learned objects and improve training performance.
Free-form 3D Scene Inpainting with Dual-stream GAN
Thus, we present a novel task named free-form 3D scene inpainting.
Learning Fine-Grained Visual Understanding for Video Question Answering via Decoupling Spatial-Temporal Modeling
While recent large-scale video-language pre-training made great progress in video question answering, the design of spatial modeling of video-language models is less fine-grained than that of image-language models; existing practices of temporal modeling also suffer from weak and noisy alignment between modalities.
CrossDTR: Cross-view and Depth-guided Transformers for 3D Object Detection
To achieve accurate 3D object detection at a low cost for autonomous driving, many multi-camera methods have been proposed and solved the occlusion problem of monocular approaches.
Fair Robust Active Learning by Joint Inconsistency
Fairness and robustness play vital roles in trustworthy machine learning.
MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer
Moreover, different from conventional pixel-wise positional encodings, we introduce a novel depth positional encoding (DPE) to inject depth positional hints into transformers.
D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic Segmentation
In the field of domain adaptation, a trade-off exists between the model performance and the number of target domain annotations.
Anomaly-Aware Semantic Segmentation by Leveraging Synthetic-Unknown Data
We design a new Masked Gradient Update (MGU) module to generate auxiliary data along the boundary of in-distribution data points.
ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation
Despite the success of deep learning on supervised point cloud semantic segmentation, obtaining large-scale point-by-point manual annotations is still a significant challenge.
S3: Learnable Sparse Signal Superdensity for Guided Depth Estimation
Dense depth estimation plays a key role in multiple applications such as robotics, 3D reconstruction, and augmented reality.
$S^3$: Learnable Sparse Signal Superdensity for Guided Depth Estimation
Dense depth estimation plays a key role in multiple applications such as robotics, 3D reconstruction, and augmented reality.
AGAIN-VC: A One-shot Voice Conversion using Activation Guidance and Adaptive Instance Normalization
With a proper activation as an information bottleneck on content embeddings, the trade-off between the synthesis quality and the speaker similarity of the converted speech is improved drastically.
Audio and Speech Processing Sound
Input-independent Attention Weights Are Expressive Enough: A Study of Attention in Self-supervised Audio Transformers
In this paper, we seek solutions for reducing the computation complexity of transformer-based models for speech representation learning.
Audio ALBERT: A Lite BERT for Self-supervised Learning of Audio Representation
We use the representations with two downstream tasks, speaker identification, and phoneme classification.
Expanding Sparse Guidance for Stereo Matching
The performance of image based stereo estimation suffers from lighting variations, repetitive patterns and homogeneous appearance.
BERT's output layer recognizes all hidden layers? Some Intriguing Phenomena and a simple way to boost BERT
Although Bidirectional Encoder Representations from Transformers (BERT) have achieved tremendous success in many natural language processing (NLP) tasks, it remains a black box.
Indoor Depth Completion with Boundary Consistency and Self-Attention
We utilize self-attention mechanism, previously used in image inpainting fields, to extract more useful information in each layer of convolution so that the complete depth map is enhanced.
Ranked #2 on
Depth Completion
on Matterport3D
