Search Results for author:
Found 45 papers, 33 papers with code
WIDER Face and Pedestrian Challenge 2018: Methods and Results
This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian.
GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond
In this paper, we take advantage of this finding to create a simplified network based on a query-independent formulation, which maintains the accuracy of NLNet but with significantly less computation.
Ranked #25 on
Object Detection
on COCO-O
Design and Interpretation of Universal Adversarial Patches in Face Detection
We consider universal adversarial patches for faces -- small visual elements whose addition to a face image reliably destroys the performance of face detectors.
Point-Set Anchors for Object Detection, Instance Segmentation and Pose Estimation
A recent approach for object detection and human pose estimation is to regress bounding boxes or human keypoints from a central point on the object or person.
Restoring Negative Information in Few-Shot Object Detection
In this paper, we restore the negative information in few-shot object detection by introducing a new negative- and positive-representative based metric learning framework and a new inference scheme with negative and positive representatives.
RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder
The proposed module is named \emph{bridging visual representations} (BVR).
Ranked #53 on
Object Detection
on COCO test-dev
Global Context Networks
The Non-Local Network (NLNet) presents a pioneering approach for capturing long-range dependencies within an image, via aggregating query-specific global context to each query position.
Ranked #40 on
Instance Segmentation
on COCO test-dev
High-Fidelity and Arbitrary Face Editing
However, we observe that the generator tends to find a tricky way to hide information from the original image to satisfy the constraint of cycle consistency, making it impossible to maintain the rich details (e. g., wrinkles and moles) of non-editing areas.
Aligning Pretraining for Detection via Object-Level Contrastive Learning
Image-level contrastive representation learning has proven to be highly effective as a generic model for transfer learning.
End-to-End Semi-Supervised Object Detection with Soft Teacher
This paper presents an end-to-end semi-supervised object detection approach, in contrast to previous more complex multi-stage methods.
Ranked #6 on
Semi-Supervised Object Detection
on COCO 100% labeled data
(using extra training data)
Dual Path Learning for Domain Adaptation of Semantic Segmentation
In this paper, based on the observation that domain adaptation frameworks performed in the source and target domain are almost complementary in terms of image translation and SSL, we propose a novel dual path learning (DPL) framework to alleviate visual inconsistency.
ADNet: Leveraging Error-Bias Towards Normal Direction in Face Alignment
On the other hand, AAM is an attention module which can get anisotropic attention mask focusing on the region of point and its local edge connected by adjacent points, it has a stronger response in tangent than in normal, which means relaxed constraints in the tangent.
Ranked #5 on
Face Alignment
on 300W
Self-supervised Discovery of Human Actons from Long Kinematic Videos
However, methods for understanding short semantic actions cannot be directly translated to long kinematic sequences such as dancing, where it becomes challenging even to semantically label the human movements.
Particle Based Stochastic Policy Optimization
Stochastic polic have been widely applied for their good property in exploration and uncertainty quantification.
Ranked #1 on
MuJoCo Games
on Ant-v3
Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning
The confidence bank is leveraged as an indicator to tilt training towards under-performing categories, instantiated in three strategies: 1) adaptive Copy-Paste and CutMix data augmentation approaches which give more chance for under-performing categories to be copied or cut; 2) an adaptive data sampling approach to encourage pixels from under-performing category to be sampled; 3) a simple yet effective re-weighting method to alleviate the training noise raised by pseudo-labeling.
Bootstrap Your Object Detector via Mixed Training
We introduce MixTraining, a new training paradigm for object detection that can improve the performance of existing detectors for free.
Towards Tokenized Human Dynamics Representation
For human action understanding, a popular research direction is to analyze short video clips with unambiguous semantic content, such as jumping and drinking.
Cross-Model Pseudo-Labeling for Semi-Supervised Action Recognition
Typically in recent work, the pseudo-labels are obtained by training a model on the labeled data, and then using confident predictions from the model to teach itself.
A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language Model
However, semantic segmentation and the CLIP model perform on different visual granularity, that semantic segmentation processes on pixels while CLIP performs on images.
A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translation
Concretely, we pretrain the sign-to-gloss visual network on the general domain of human actions and the within-domain of a sign-to-gloss dataset, and pretrain the gloss-to-text translation network on the general domain of a multilingual corpus and the within-domain of a gloss-to-text corpus.
Ranked #2 on
Sign Language Translation
on CSL-Daily
Frame-wise Action Representations for Long Videos via Sequence Contrastive Learning
In this paper, we introduce a novel contrastive action representation learning (CARL) framework to learn frame-wise action representations, especially for long videos, in a self-supervised manner.
Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model
In this paper, we introduce a novel method, detection prompt (DetPro), to learn continuous prompt representations for open-vocabulary object detection based on the pre-trained vision-language model.
Unsupervised Prompt Learning for Vision-Language Models
In this paper, we explore a different scenario, in which the labels of the target datasets are unprovided, and we present an unsupervised prompt learning (UPL) approach to avoid prompt engineering while simultaneously improving transfer performance of CLIP-like vision-language models.
Boosting Zero-shot Learning via Contrastive Optimization of Attribute Representations
In this paper, we propose a new framework to boost ZSL by explicitly learning attribute prototypes beyond images and contrastively optimizing them with attribute-level features within images.
Conditional DETR V2: Efficient Detection Transformer with Box Queries
Inspired by Conditional DETR, an improved DETR with fast training convergence, that presented box queries (originally called spatial queries) for internal decoder layers, we reformulate the object query into the format of the box query that is a composition of the embeddings of the reference point and the transformation of the box with respect to the reference point.
AniFaceGAN: Animatable 3D-Aware Face Image Generation for Video Avatars
To achieve meaningful control over facial expressions via deformation, we propose a 3D-level imitative learning scheme between the generator and a parametric 3D face model during adversarial training of the 3D-aware GAN.
Two-Stream Network for Sign Language Recognition and Translation
RGB videos, however, are raw signals with substantial visual redundancy, leading the encoder to overlook the key information for sign language understanding.
Attentive Mask CLIP
To address this issue, we propose an attentive token removal approach for CLIP training, which retains tokens with a high semantic correlation to the text description.
Iterative Proposal Refinement for Weakly-Supervised Video Grounding
Weakly-Supervised Video Grounding (WSVG) aims to localize events of interest in untrimmed videos with only video-level annotations.
TinyMIM: An Empirical Study of Distilling MIM Pre-trained Models
Our TinyMIM model of tiny size achieves 79. 6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget.
Side Adapter Network for Open-Vocabulary Semantic Segmentation
A side network is attached to a frozen CLIP model with two branches: one for predicting mask proposals, and the other for predicting attention bias which is applied in the CLIP model to recognize the class of masks.
DeepMIM: Deep Supervision for Masked Image Modeling
Deep supervision, which involves extra supervisions to the intermediate features of a neural network, was widely used in image classification in the early deep learning era since it significantly reduces the training difficulty and eases the optimization like avoiding gradient vanish over the vanilla training.
Two-shot Video Object Segmentation
The underlying idea is to generate pseudo labels for unlabeled frames during training and to optimize the model on the combination of labeled and pseudo-labeled data.
Natural Language-Assisted Sign Language Recognition
Sign languages are visual languages which convey information by signers' handshape, facial expression, body movement, and so forth.
Ranked #1 on
Sign Language Recognition
on WLASL-2000
CiCo: Domain-Aware Sign Language Retrieval via Cross-Lingual Contrastive Learning
Our framework, termed as domain-aware sign language retrieval via Cross-lingual Contrastive learning or CiCo for short, outperforms the pioneering method by large margins on various datasets, e. g., +22. 4 T2V and +28. 0 V2T R@1 improvements on How2Sign dataset, and +13. 7 T2V and +17. 1 V2T R@1 improvements on PHOENIX-2014T dataset.
Ranked #1 on
Sign Language Retrieval
on CSL-Daily
Improving Continuous Sign Language Recognition with Cross-Lingual Signs
Experimentally, our approach achieves state-of-the-art performance on two widely-used CSLR datasets: Phoenix-2014 and Phoenix-2014T.
AniPortraitGAN: Animatable 3D Portrait Generation from 2D Image Collections
For the new task, we base our method on the generative radiance manifold representation and equip it with learnable facial and head-shoulder deformations.
Exploring Non-additive Randomness on ViT against Query-Based Black-Box Attacks
In this work, we first taxonomize the stochastic defense strategies against QBBA.
RAIN: Your Language Models Can Align Themselves without Finetuning
We discover that by integrating self-evaluation and rewind mechanisms, unaligned LLMs can directly produce responses consistent with human preferences via self-boosting.
A Simple Baseline for Spoken Language to Sign Language Translation with 3D Avatars
The objective of this paper is to develop a functional system for translating spoken languages into sign languages, referred to as Spoken2Sign translation.
Towards Online Sign Language Recognition and Translation
Our approach comprises three phases: 1) developing a sign language dictionary encompassing all glosses present in a target sign language dataset; 2) training an isolated sign language recognition model on augmented signs using both conventional classification loss and our novel saliency loss; 3) employing a sliding window approach on the input sign sequence and feeding each sign clip to the well-optimized model for online recognition.
EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty
In this paper, we reconsider speculative sampling and derive two key observations.
AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls
We also revisit the evaluation protocol introduced by previous works and identify a limitation in this protocol that leads to an artificially high pass rate.
Rethinking Generative Large Language Model Evaluation for Semantic Comprehension
Through a comprehensive evaluation of 24 models across 11 benchmarks, we highlight several potential drawbacks of MCQA, for instance, the inconsistency between the MCQA evaluation and the generation of open-ended responses in practical scenarios.
Beyond Text: Frozen Large Language Models in Visual Signal Comprehension
To achieve this, we present the Vision-to-Language Tokenizer, abbreviated as V2T Tokenizer, which transforms an image into a ``foreign language'' with the combined aid of an encoder-decoder, the LLM vocabulary, and a CLIP model.
