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Found 23 papers, 11 papers with code
Benchmarks and Challenges in Pose Estimation for Egocentric Hand Interactions with Objects
We interact with the world with our hands and see it through our own (egocentric) perspective.
A Simple Baseline for Efficient Hand Mesh Reconstruction
3D hand pose estimation has found broad application in areas such as gesture recognition and human-machine interaction tasks.
Ranked #2 on
3D Hand Pose Estimation
on DexYCB
FAAC: Facial Animation Generation with Anchor Frame and Conditional Control for Superior Fidelity and Editability
Our method's efficacy has been validated on multiple representative DreamBooth and LoRA models, delivering substantial improvements over the original outcomes in terms of facial fidelity, text-to-image editability, and video motion.
Sparse Beats Dense: Rethinking Supervision in Radar-Camera Depth Completion
In this paper, we find that the challenge of using sparse supervision for training Radar-Camera depth prediction models is the Projection Transformation Collapse (PTC).
HiDiffusion: Unlocking High-Resolution Creativity and Efficiency in Low-Resolution Trained Diffusion Models
We introduce HiDiffusion, a tuning-free framework comprised of Resolution-Aware U-Net (RAU-Net) and Modified Shifted Window Multi-head Self-Attention (MSW-MSA) to enable pretrained large text-to-image diffusion models to efficiently generate high-resolution images (e. g. 1024$\times$1024) that surpass the training image resolution.
Boosting Generalization with Adaptive Style Techniques for Fingerprint Liveness Detection
We introduce a high-performance fingerprint liveness feature extraction technique that secured first place in LivDet 2023 Fingerprint Representation Challenge.
1st Place Solution of Egocentric 3D Hand Pose Estimation Challenge 2023 Technical Report:A Concise Pipeline for Egocentric Hand Pose Reconstruction
This report introduce our work on Egocentric 3D Hand Pose Estimation workshop.
Cumulative Spatial Knowledge Distillation for Vision Transformers
(2) Distilling knowledge from CNN limits the network convergence in the later training period since ViT's capability of integrating global information is suppressed by CNN's local-inductive-bias supervision.
DOT: A Distillation-Oriented Trainer
In this paper, we observe a trade-off between task and distillation losses, i. e., introducing distillation loss limits the convergence of task loss.
Joint Token Pruning and Squeezing Towards More Aggressive Compression of Vision Transformers
Experiments on various transformers demonstrate the effectiveness of our method, while analysis experiments prove our higher robustness to the errors of the token pruning policy.
Ranked #1 on
Efficient ViTs
on ImageNet-1K (with DeiT-S)
Boosting Semi-Supervised Learning by Exploiting All Unlabeled Data
ANL introduces the additional negative pseudo-label for all unlabeled data to leverage low-confidence examples.
DarkVisionNet: Low-Light Imaging via RGB-NIR Fusion with Deep Inconsistency Prior
To handle this, we propose a new RGB-NIR fusion algorithm called Dark Vision Net (DVN) with two technical novelties: Deep Structure and Deep Inconsistency Prior (DIP).
Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalization
In this paper, we analyse the generalization ability of binary classifiers for the task of deepfake detection.
SimpleDG: Simple Domain Generalization Baseline without Bells and Whistles
We present a simple domain generalization baseline, which wins second place in both the common context generalization track and the hybrid context generalization track respectively in NICO CHALLENGE 2022.
Efficient One Pass Self-distillation with Zipf's Label Smoothing
This paper proposes an efficient self-distillation method named Zipf's Label Smoothing (Zipf's LS), which uses the on-the-fly prediction of a network to generate soft supervision that conforms to Zipf distribution without using any contrastive samples or auxiliary parameters.
Explaining Deepfake Detection by Analysing Image Matching
Besides the supervision of binary labels, deepfake detection models implicitly learn artifact-relevant visual concepts through the FST-Matching (i. e. the matching fake, source, target images) in the training set.
Decoupled Knowledge Distillation
To provide a novel viewpoint to study logit distillation, we reformulate the classical KD loss into two parts, i. e., target class knowledge distillation (TCKD) and non-target class knowledge distillation (NCKD).
Discriminability-Transferability Trade-Off: An Information-Theoretic Perspective
This work simultaneously considers the discriminability and transferability properties of deep representations in the typical supervised learning task, i. e., image classification.
Dynamic MLP for Fine-Grained Image Classification by Leveraging Geographical and Temporal Information
Therefore, it is helpful to leverage additional information, e. g., the locations and dates for data shooting, which can be easily accessible but rarely exploited.
Sharp Impossibility Results for Hyper-graph Testing
In a broad Degree-Corrected Mixed-Membership (DCMM) setting, we test whether a non-uniform hypergraph has only one community or has multiple communities.
Information Theoretic Limits of Exact Recovery in Sub-hypergraph Models for Community Detection
Our bounds are tight and pertain to the community detection problems in various models such as the planted hypergraph stochastic block model, the planted densest sub-hypergraph model, and the planted multipartite hypergraph model.
Scene Text Recognition from Two-Dimensional Perspective
Inspired by speech recognition, recent state-of-the-art algorithms mostly consider scene text recognition as a sequence prediction problem.
Ranked #30 on
Scene Text Recognition
on SVT
EAST: An Efficient and Accurate Scene Text Detector
Previous approaches for scene text detection have already achieved promising performances across various benchmarks.
Ranked #3 on
Scene Text Detection
on COCO-Text
Curved Text Detection
Optical Character Recognition (OCR)
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