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Found 22 papers, 14 papers with code
GhostRNN: Reducing State Redundancy in RNN with Cheap Operations
Recurrent neural network (RNNs) that are capable of modeling long-distance dependencies are widely used in various speech tasks, eg., keyword spotting (KWS) and speech enhancement (SE).
Vista3D: Unravel the 3D Darkside of a Single Image
We embark on the age-old quest: unveiling the hidden dimensions of objects from mere glimpses of their visible parts.
Isomorphic Pruning for Vision Models
For instance, we improve the accuracy of DeiT-Tiny from 74. 52% to 77. 50% by pruning an off-the-shelf DeiT-Base model.
Learning-to-Cache: Accelerating Diffusion Transformer via Layer Caching
In this study, we make an interesting and somehow surprising observation: the computation of a large proportion of layers in the diffusion transformer, through introducing a caching mechanism, can be readily removed even without updating the model parameters.
MotionMix: Weakly-Supervised Diffusion for Controllable Motion Generation
Specifically, we separate the denoising objectives of a diffusion model into two stages: obtaining conditional rough motion approximations in the initial $T-T^*$ steps by learning the noisy annotated motions, followed by the unconditional refinement of these preliminary motions during the last $T^*$ steps using unannotated motions.
Semantic Segmentation in Multiple Adverse Weather Conditions with Domain Knowledge Retention
They struggle to acquire new knowledge while also retaining previously learned knowledge. To address these problems, we propose a semantic segmentation method for multiple adverse weather conditions that incorporates adaptive knowledge acquisition, pseudolabel blending, and weather composition replay.
HEAP: Unsupervised Object Discovery and Localization with Contrastive Grouping
Further, to ensure the distinguishability among various regions, we introduce a region-level contrastive clustering loss to pull closer similar regions across images.
DreamDrone: Text-to-Image Diffusion Models are Zero-shot Perpetual View Generators
We introduce DreamDrone, a novel zero-shot and training-free pipeline for generating unbounded flythrough scenes from textual prompts.
Learning Unorthogonalized Matrices for Rotation Estimation
Estimating 3D rotations is a common procedure for 3D computer vision.
Ranked #16 on
3D Human Pose Estimation
on 3DPW
Priority-Centric Human Motion Generation in Discrete Latent Space
We also present a motion discrete diffusion model that employs an innovative noise schedule, determined by the significance of each motion token within the entire motion sequence.
PARTNER: Level up the Polar Representation for LiDAR 3D Object Detection
Recently, polar-based representation has shown promising properties in perceptual tasks.
Lens-to-lens bokeh effect transformation. NTIRE 2023 challenge report
We present the new Bokeh Effect Transformation Dataset (BETD), and review the proposed solutions for this novel task at the NTIRE 2023 Bokeh Effect Transformation Challenge.
Overcoming the Trade-off Between Accuracy and Plausibility in 3D Hand Shape Reconstruction
However, the reconstructed meshes are prone to artifacts and do not appear as plausible hand shapes.
Enhancing Video Super-Resolution via Implicit Resampling-based Alignment
We show that bilinear interpolation inherently attenuates high-frequency information while an MLP-based coordinate network can approximate more frequencies.
Ranked #2 on
Video Super-Resolution
on REDS4- 4x upscaling
TM2D: Bimodality Driven 3D Dance Generation via Music-Text Integration
We propose a novel task for generating 3D dance movements that simultaneously incorporate both text and music modalities.
Ranked #3 on
Motion Synthesis
on AIST++
DepGraph: Towards Any Structural Pruning
Structural pruning enables model acceleration by removing structurally-grouped parameters from neural networks.
Bias-Compensated Integral Regression for Human Pose Estimation
Experimental results on both the human body and hand benchmarks show that BCIR is faster to train and more accurate than the original integral regression, making it competitive with state-of-the-art detection methods.
Improving Deep Regression with Ordinal Entropy
In computer vision, it is often observed that formulating regression problems as a classification task often yields better performance.
Ranked #19 on
Crowd Counting
on ShanghaiTech B
Object Detection in Foggy Scenes by Embedding Depth and Reconstruction into Domain Adaptation
In our DA framework, we retain the depth and background information during the domain feature alignment.
ONCE-3DLanes: Building Monocular 3D Lane Detection
We present ONCE-3DLanes, a real-world autonomous driving dataset with lane layout annotation in 3D space.
PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervision
Existing self-supervised 3D human pose estimation schemes have largely relied on weak supervisions like consistency loss to guide the learning, which, inevitably, leads to inferior results in real-world scenarios with unseen poses.
Ranked #42 on
3D Human Pose Estimation
on MPI-INF-3DHP
Point2Seq: Detecting 3D Objects as Sequences
We further propose a lightweight scene-to-sequence decoder that can auto-regressively generate words conditioned on features from a 3D scene as well as cues from the preceding words.
