Search Results for author:
Found 31 papers, 21 papers with code
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
Face detection and alignment in unconstrained environment are challenging due to various poses, illuminations and occlusions.
Ranked #27 on
Face Detection
on WIDER Face (Easy)
A Discriminative Feature Learning Approach for Deep Face Recognition
In most of the available CNNs, the softmax loss function is used as the supervision signal to train the deep model.
Detecting Faces Using Inside Cascaded Contextual CNN
Deep Convolutional Neural Networks (CNNs) achieve substantial improvements in face detection in the wild.
Super-Identity Convolutional Neural Network for Face Hallucination
Face hallucination is a generative task to super-resolve the facial image with low resolution while human perception of face heavily relies on identity information.
Bootstrap Model Ensemble and Rank Loss for Engagement Intensity Regression
Based on our winner solution last year, we mainly explore head features and body features with a bootstrap strategy and two novel loss functions in this paper.
FarSee-Net: Real-Time Semantic Segmentation by Efficient Multi-scale Context Aggregation and Feature Space Super-resolution
One challenge of semantic segmentation is to deal with the object scale variations and leverage the context.
Neural Routing by Memory
However, they use the same procedure sequence for all inputs, regardless of the intermediate features. This paper proffers a simple yet effective idea of constructing parallel procedures and assigning similar intermediate features to the same specialized procedures in a divide-and-conquer fashion.
DiffRate : Differentiable Compression Rate for Efficient Vision Transformers
Token compression aims to speed up large-scale vision transformers (e. g. ViTs) by pruning (dropping) or merging tokens.
Ranked #4 on
Efficient ViTs
on ImageNet-1K (with DeiT-S)
LVLM-eHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Models
Large Vision-Language Models (LVLMs) have recently played a dominant role in multimodal vision-language learning.
Align, Adapt and Inject: Sound-guided Unified Image Generation
Then, we propose the audio adapter to adapt audio representation into an audio token enriched with specific semantics, which can be injected into a frozen T2I model flexibly.
Meta-Transformer: A Unified Framework for Multimodal Learning
Multimodal learning aims to build models that can process and relate information from multiple modalities.
Tiny LVLM-eHub: Early Multimodal Experiments with Bard
Secondly, it conducts an in-depth analysis of LVLMs' predictions using the ChatGPT Ensemble Evaluation (CEE), which leads to a robust and accurate evaluation and exhibits improved alignment with human evaluation compared to the word matching approach.
Foundation Model is Efficient Multimodal Multitask Model Selector
This paper investigates an under-explored but important problem: given a collection of pre-trained neural networks, predicting their performance on each multi-modal task without fine-tuning them, such as image recognition, referring, captioning, visual question answering, and text question answering.
OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models
LWC modulates the extreme values of weights by optimizing the clipping threshold.
ImageBind-LLM: Multi-modality Instruction Tuning
During training, we adopt a learnable bind network to align the embedding space between LLaMA and ImageBind's image encoder.
Open-Vocabulary Animal Keypoint Detection with Semantic-feature Matching
Current image-based keypoint detection methods for animal (including human) bodies and faces are generally divided into full-supervised and few-shot class-agnostic approaches.
Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching
The ultimate goal of Dataset Distillation is to synthesize a small synthetic dataset such that a model trained on this synthetic set will perform equally well as a model trained on the full, real dataset.
Towards Unified and Effective Domain Generalization
We propose $\textbf{UniDG}$, a novel and $\textbf{Uni}$fied framework for $\textbf{D}$omain $\textbf{G}$eneralization that is capable of significantly enhancing the out-of-distribution generalization performance of foundation models regardless of their architectures.
Ranked #1 on
Domain Generalization
on TerraIncognita
DREAM+: Efficient Dataset Distillation by Bidirectional Representative Matching
Dataset distillation plays a crucial role in creating compact datasets with similar training performance compared with original large-scale ones.
MLLMs-Augmented Visual-Language Representation Learning
Visual-language pre-training has achieved remarkable success in many multi-modal tasks, largely attributed to the availability of large-scale image-text datasets.
OneLLM: One Framework to Align All Modalities with Language
In detail, we first train an image projection module to connect a vision encoder with LLM.
Ranked #73 on
Visual Question Answering
on MM-Vet
TagCLIP: A Local-to-Global Framework to Enhance Open-Vocabulary Multi-Label Classification of CLIP Without Training
As a result, we dissect the preservation of patch-wise spatial information in CLIP and proposed a local-to-global framework to obtain image tags.
ChartAssisstant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning
Charts play a vital role in data visualization, understanding data patterns, and informed decision-making.
Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient Matching
Training on large-scale graphs has achieved remarkable results in graph representation learning, but its cost and storage have raised growing concerns.
SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models
We propose SPHINX-X, an extensive Multimodality Large Language Model (MLLM) series developed upon SPHINX.
Ranked #4 on
Video Question Answering
on MVBench
BESA: Pruning Large Language Models with Blockwise Parameter-Efficient Sparsity Allocation
Large language models (LLMs) have demonstrated outstanding performance in various tasks, such as text summarization, text question-answering, and etc.
RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation
To bridge this ``ideal-to-real'' gap, this paper presents \textbf{RobotScript}, a platform for 1) a deployable robot manipulation pipeline powered by code generation; and 2) a code generation benchmark for robot manipulation tasks in free-form natural language.
Towards Implicit Prompt For Text-To-Image Models
We call for increased attention to the potential and risks of implicit prompts in the T2I community and further investigation into the capabilities and impacts of implicit prompts, advocating for a balanced approach that harnesses their benefits while mitigating their risks.
AVIBench: Towards Evaluating the Robustness of Large Vision-Language Model on Adversarial Visual-Instructions
To bridge this gap, we introduce AVIBench, a framework designed to analyze the robustness of LVLMs when facing various adversarial visual-instructions (AVIs), including four types of image-based AVIs, ten types of text-based AVIs, and nine types of content bias AVIs (such as gender, violence, cultural, and racial biases, among others).
DiffAgent: Fast and Accurate Text-to-Image API Selection with Large Language Model
Text-to-image (T2I) generative models have attracted significant attention and found extensive applications within and beyond academic research.
Adapting LLaMA Decoder to Vision Transformer
We first "LLaMAfy" a standard ViT step-by-step to align with LLaMA's architecture, and find that directly applying a casual mask to the self-attention brings an attention collapse issue, resulting in the failure to the network training.
