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Found 35 papers, 22 papers with code
Asymmetric Decision-Making in Online Knowledge Distillation:Unifying Consensus and Divergence
Building on these findings, we propose Asymmetric Decision-Making (ADM) to enhance feature consensus learning for student models while continuously promoting feature diversity in teacher models.
LEDiT: Your Length-Extrapolatable Diffusion Transformer without Positional Encoding
Diffusion transformers(DiTs) struggle to generate images at resolutions higher than their training resolutions.
Optimizing Knowledge Distillation in Transformers: Enabling Multi-Head Attention without Alignment Barriers
Knowledge distillation (KD) in transformers often faces challenges due to misalignment in the number of attention heads between teacher and student models.
Improving Video Generation with Human Feedback
Video generation has achieved significant advances through rectified flow techniques, but issues like unsmooth motion and misalignment between videos and prompts persist.
1st Place Solution of Multiview Egocentric Hand Tracking Challenge ECCV2024
Multi-view egocentric hand tracking is a challenging task and plays a critical role in VR interaction.
Cascade Prompt Learning for Vision-Language Model Adaptation
Prompt learning has surfaced as an effective approach to enhance the performance of Vision-Language Models (VLMs) like CLIP when applied to downstream tasks.
Revisiting Prompt Pretraining of Vision-Language Models
To address the above issues, in this paper, we propose a general framework termed Revisiting Prompt Pretraining (RPP), which targets at improving the fitting and generalization ability from two aspects: prompt structure and prompt supervision.
MegActor-$Σ$: Unlocking Flexible Mixed-Modal Control in Portrait Animation with Diffusion Transformer
To address this issue, we introduce MegActor-$\Sigma$: a mixed-modal conditional diffusion transformer (DiT), which can flexibly inject audio and visual modality control signals into portrait animation.
Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory
This work introduces a novel and efficient Bayesian federated learning algorithm, namely, the Federated Averaging stochastic Hamiltonian Monte Carlo (FA-HMC), for parameter estimation and uncertainty quantification.
MegActor: Harness the Power of Raw Video for Vivid Portrait Animation
Despite raw driving videos contain richer information on facial expressions than intermediate representations such as landmarks in the field of portrait animation, they are seldom the subject of research.
Towards RGB-NIR Cross-modality Image Registration and Beyond
This paper focuses on the area of RGB(visible)-NIR(near-infrared) cross-modality image registration, which is crucial for many downstream vision tasks to fully leverage the complementary information present in visible and infrared images.
MCSDNet: Mesoscale Convective System Detection Network via Multi-scale Spatiotemporal Information
Different from the previous models, MCSDNet targets on multi-frames detection and leverages multi-scale spatiotemporal information for the detection of MCS regions in remote sensing imagery(RSI).
Benchmarks and Challenges in Pose Estimation for Egocentric Hand Interactions with Objects
A holistic 3Dunderstanding of such interactions from egocentric views is important for tasks in robotics, AR/VR, action recognition and motion generation.
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 #3 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
To this end, we revisit the task of radar-camera depth completion and present a new method with sparse LiDAR supervision to outperform previous dense LiDAR supervision methods in both accuracy and speed.
HiDiffusion: Unlocking Higher-Resolution Creativity and Efficiency in Pretrained Diffusion Models
Extensive experiments demonstrate that our approach can address object duplication and heavy computation issues, achieving state-of-the-art performance on higher-resolution image synthesis tasks.
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.
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.
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.
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 #32 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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