Search Results for author:
Found 54 papers, 34 papers with code
RLE: A Unified Perspective of Data Augmentation for Cross-Spectral Re-identification
From this view, we unify all data augmentation strategies for cross-spectral re-identification by mimicking such local linear transformations and categorizing them into moderate transformation and radical transformation.
AttriPrompter: Auto-Prompting with Attribute Semantics for Zero-shot Nuclei Detection via Visual-Language Pre-trained Models
Notably, this work highlights the astonishing potential of VLPMs pre-trained on natural image-text pairs for downstream tasks in the medical field as well.
PartFormer: Awakening Latent Diverse Representation from Vision Transformer for Object Re-Identification
Extracting robust feature representation is critical for object re-identification to accurately identify objects across non-overlapping cameras.
SDPT: Synchronous Dual Prompt Tuning for Fusion-based Visual-Language Pre-trained Models
Existing prompt tuning methods have not effectively addressed the modal mapping and aligning problem for tokens in different modalities, leading to poor transfer generalization.
Image Captioning via Dynamic Path Customization
This paper explores a novel dynamic network for vision and language tasks, where the inferring structure is customized on the fly for different inputs.
Zero-shot Nuclei Detection via Visual-Language Pre-trained Models
Foremost, our work demonstrates that the VLPM pre-trained on natural image-text pairs exhibits astonishing potential for downstream tasks in the medical field as well.
Cyclic Learning: Bridging Image-level Labels and Nuclei Instance Segmentation
Experiments on three datasets demonstrate the good generality of our method, which outperforms other image-level weakly supervised methods for nuclei instance segmentation, and achieves comparable performance to fully-supervised methods.
Latent Feature Relation Consistency for Adversarial Robustness
Deep neural networks have been applied in many computer vision tasks and achieved state-of-the-art performance.
CAT:Collaborative Adversarial Training
By revisiting the previous methods, we find different adversarial training methods have distinct robustness for sample instances.
Attention Disturbance and Dual-Path Constraint Network for Occluded Person Re-identification
To address this issue, we propose a novel transformer-based Attention Disturbance and Dual-Path Constraint Network (ADP) to enhance the generalization of attention networks.
Spectral Aware Softmax for Visible-Infrared Person Re-Identification
Based on the observation and analysis of SA-Softmax, we modify the SA-Softmax with the Feature Mask and Absolute-Similarity Term to alleviate the ambiguous optimization during model training.
Exploring Invariant Representation for Visible-Infrared Person Re-Identification
Cross-spectral person re-identification, which aims to associate identities to pedestrians across different spectra, faces a main challenge of the modality discrepancy.
Unsupervised Domain Adaptation on Person Re-Identification via Dual-level Asymmetric Mutual Learning
The knowledge transfer between two networks is based on an asymmetric mutual learning manner.
Towards Real-Time Panoptic Narrative Grounding by an End-to-End Grounding Network
Extensive experiments on the PNG benchmark dataset demonstrate the effectiveness and efficiency of our method.
CycleTrans: Learning Neutral yet Discriminative Features for Visible-Infrared Person Re-Identification
Visible-infrared person re-identification (VI-ReID) is a task of matching the same individuals across the visible and infrared modalities.
Dynamic Prototype Mask for Occluded Person Re-Identification
Although person re-identification has achieved an impressive improvement in recent years, the common occlusion case caused by different obstacles is still an unsettled issue in real application scenarios.
Learning Best Combination for Efficient N:M Sparsity
In this paper, we show that the N:M learning can be naturally characterized as a combinatorial problem which searches for the best combination candidate within a finite collection.
Occlusion-Resistant Instance Segmentation of Piglets in Farrowing Pens Using Center Clustering Network
Specifically, CClusnet-Inseg uses each pixel to predict object centers and trace these centers to form masks based on clustering results, which consists of a network for segmentation and center offset vector map, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, Centers-to-Mask (C2M), and Remain-Centers-to-Mask (RC2M) algorithms.
Towards Lightweight Transformer via Group-wise Transformation for Vision-and-Language Tasks
Despite the exciting performance, Transformer is criticized for its excessive parameters and computation cost.
Global2Local: A Joint-Hierarchical Attention for Video Captioning
Recently, automatic video captioning has attracted increasing attention, where the core challenge lies in capturing the key semantic items, like objects and actions as well as their spatial-temporal correlations from the redundant frames and semantic content.
CF-ViT: A General Coarse-to-Fine Method for Vision Transformer
Our proposed CF-ViT is motivated by two important observations in modern ViT models: (1) The coarse-grained patch splitting can locate informative regions of an input image.
Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution Networks
However, these methods suffer from severe performance degradation when quantizing the SR models to ultra-low precision (e. g., 2-bit and 3-bit) with the low-cost layer-wise quantizer.
Towards Language-guided Visual Recognition via Dynamic Convolutions
Based on the LaConv module, we further build the first fully language-driven convolution network, termed as LaConvNet, which can unify the visual recognition and multi-modal reasoning in one forward structure.
OMPQ: Orthogonal Mixed Precision Quantization
Instead of solving a problem of the original integer programming, we propose to optimize a proxy metric, the concept of network orthogonality, which is highly correlated with the loss of the integer programming but also easy to optimize with linear programming.
Fine-grained Data Distribution Alignment for Post-Training Quantization
While post-training quantization receives popularity mostly due to its evasion in accessing the original complete training dataset, its poor performance also stems from scarce images.
Toward Joint Thing-and-Stuff Mining for Weakly Supervised Panoptic Segmentation
To date, learning weakly supervised panoptic segmentation (WSPS) with only image-level labels remains unexplored.
RSTNet: Captioning With Adaptive Attention on Visual and Non-Visual Words
Then, we build a BERTbased language model to extract language context and propose Adaptive-Attention (AA) module on top of a transformer decoder to adaptively measure the contribution of visual and language cues before making decisions for word prediction.
Discover Cross-Modality Nuances for Visible-Infrared Person Re-Identification
In this paper, we propose a joint Modality and Pattern Alignment Network (MPANet) to discover cross-modality nuances in different patterns for visible-infrared person Re-ID, which introduces a modality alleviation module and a pattern alignment module to jointly extract discriminative features.
HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping
In this work, we propose a high fidelity face swapping method, called HifiFace, which can well preserve the face shape of the source face and generate photo-realistic results.
Ranked #7 on
Face Swapping
on FaceForensics++
Auxiliary learning induced graph convolutional networks
In this article, we propose a novel auxiliary learning induced graph convolutional network in a multi-task fashion.
Black-Box Dissector: Towards Erasing-based Hard-Label Model Stealing Attack
We argue this is due to the lack of rich information in the probability prediction and the overfitting caused by hard labels.
Carrying out CNN Channel Pruning in a White Box
Specifically, to model the contribution of each channel to differentiating categories, we develop a class-wise mask for each channel, implemented in a dynamic training manner w. r. t.
Distilling a Powerful Student Model via Online Knowledge Distillation
Besides, a self-distillation module is adopted to convert the feature map of deeper layers into a shallower one.
Image-to-image Translation via Hierarchical Style Disentanglement
Recently, image-to-image translation has made significant progress in achieving both multi-label (\ie, translation conditioned on different labels) and multi-style (\ie, generation with diverse styles) tasks.
Disentanglement
Multimodal Unsupervised Image-To-Image Translation
+1
Network Pruning using Adaptive Exemplar Filters
Inspired by the face recognition community, we use a message passing algorithm Affinity Propagation on the weight matrices to obtain an adaptive number of exemplars, which then act as the preserved filters.
Dual-Level Collaborative Transformer for Image Captioning
Descriptive region features extracted by object detection networks have played an important role in the recent advancements of image captioning.
Aha! Adaptive History-Driven Attack for Decision-Based Black-Box Models
A common practice is to start from a large perturbation and then iteratively reduce it with a deterministic direction and a random one while keeping it adversarial.
Parallel Detection-and-Segmentation Learning for Weakly Supervised Instance Segmentation
Weakly supervised instance segmentation (WSIS) with only image-level labels has recently drawn much attention.
Improving Image Captioning by Leveraging Intra- and Inter-layer Global Representation in Transformer Network
The latter contains a Global Adaptive Controller that can adaptively fuse the global information into the decoder to guide the caption generation.
UWSOD: Toward Fully-Supervised-Level Capacity Weakly Supervised Object Detection
In this paper, we propose a unified WSOD framework, termed UWSOD, to develop a high-capacity general detection model with only image-level labels, which is self-contained and does not require external modules or additional supervision.
Towards Scalable Distributed Training of Deep Learning on Public Cloud Clusters
Distributed training techniques have been widely deployed in large-scale deep neural networks (DNNs) training on dense-GPU clusters.
Rotated Binary Neural Network
In this paper, for the first time, we explore the influence of angular bias on the quantization error and then introduce a Rotated Binary Neural Network (RBNN), which considers the angle alignment between the full-precision weight vector and its binarized version.
Channel Pruning via Automatic Structure Search
In this paper, we propose a new channel pruning method based on artificial bee colony algorithm (ABC), dubbed as ABCPruner, which aims to efficiently find optimal pruned structure, i. e., channel number in each layer, rather than selecting "important" channels as previous works did.
Variational Structured Semantic Inference for Diverse Image Captioning
To model these two inherent diversities in image captioning, we propose a Variational Structured Semantic Inferring model (termed VSSI-cap) executed in a novel structured encoder-inferer-decoder schema.
Semi-Supervised Adversarial Monocular Depth Estimation
In this paper, we address the problem of monocular depth estimation when only a limited number of training image-depth pairs are available.
Interpretable Neural Network Decoupling
More specifically, we introduce a novel architecture controlling module in each layer to encode the network architecture by a vector.
DDPNAS: Efficient Neural Architecture Search via Dynamic Distribution Pruning
With the proposed efficient network generation method, we directly obtain the optimal neural architectures on given constraints, which is practical for on-device models across diverse search spaces and constraints.
Towards Optimal Discrete Online Hashing with Balanced Similarity
In this paper, we propose a novel supervised online hashing method, termed Balanced Similarity for Online Discrete Hashing (BSODH), to solve the above problems in a unified framework.
Exploiting Kernel Sparsity and Entropy for Interpretable CNN Compression
The relationship between the input feature maps and 2D kernels is revealed in a theoretical framework, based on which a kernel sparsity and entropy (KSE) indicator is proposed to quantitate the feature map importance in a feature-agnostic manner to guide model compression.
Temporal Action Detection by Joint Identification-Verification
Temporal action detection aims at not only recognizing action category but also detecting start time and end time for each action instance in an untrimmed video.
GroupCap: Group-Based Image Captioning With Structured Relevance and Diversity Constraints
In offline optimization, we adopt an end-to-end formulation, which jointly trains the visual tree parser, the structured relevance and diversity constraints, as well as the LSTM based captioning model.
Cross-Modality Binary Code Learning via Fusion Similarity Hashing
In this paper, we propose a hashing scheme, termed Fusion Similarity Hashing (FSH), which explicitly embeds the graph-based fusion similarity across modalities into a common Hamming space.
Fast and Accurate Neural Word Segmentation for Chinese
Neural models with minimal feature engineering have achieved competitive performance against traditional methods for the task of Chinese word segmentation.
Ordinal Constrained Binary Code Learning for Nearest Neighbor Search
By given a large-scale training data set, it is very expensive to embed such ranking tuples in binary code learning.
