Search Results for author:
Found 27 papers, 13 papers with code
Mind the Discriminability: Asymmetric Adversarial Domain Adaptation
Adversarial domain adaptation has made tremendous success by learning domain-invariant feature representations.
T-Rex2: Towards Generic Object Detection via Text-Visual Prompt Synergy
Recognizing the complementary strengths and weaknesses of both text and visual prompts, we introduce T-Rex2 that synergizes both prompts within a single model through contrastive learning.
TAPTR: Tracking Any Point with Transformers as Detection
Based on the observation that point tracking bears a great resemblance to object detection and tracking, we borrow designs from DETR-like algorithms to address the task of TAP.
Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks
We introduce Grounded SAM, which uses Grounding DINO as an open-set object detector to combine with the segment anything model (SAM).
T-Rex: Counting by Visual Prompting
Guided by the visual feedback from T-Rex, users can also interactively refine the counting results by prompting on missing or falsely-detected objects.
DFA3D: 3D Deformable Attention For 2D-to-3D Feature Lifting
Existing feature lifting approaches, such as Lift-Splat-based and 2D attention-based, either use estimated depth to get pseudo LiDAR features and then splat them to a 3D space, which is a one-pass operation without feature refinement, or ignore depth and lift features by 2D attention mechanisms, which achieve finer semantics while suffering from a depth ambiguity problem.
detrex: Benchmarking Detection Transformers
To address this issue, we develop a unified, highly modular, and lightweight codebase called detrex, which supports a majority of the mainstream DETR-based instance recognition algorithms, covering various fundamental tasks, including object detection, segmentation, and pose estimation.
A Strong and Reproducible Object Detector with Only Public Datasets
This work presents Focal-Stable-DINO, a strong and reproducible object detection model which achieves 64. 6 AP on COCO val2017 and 64. 8 AP on COCO test-dev using only 700M parameters without any test time augmentation.
Ranked #5 on
Object Detection
on COCO minival
(using extra training data)
Detection Transformer with Stable Matching
We point out that the unstable matching in DETR is caused by a multi-optimization path problem, which is highlighted by the one-to-one matching design in DETR.
Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
To effectively fuse language and vision modalities, we conceptually divide a closed-set detector into three phases and propose a tight fusion solution, which includes a feature enhancer, a language-guided query selection, and a cross-modality decoder for cross-modality fusion.
Ranked #1 on
Zero-Shot Object Detection
on MSCOCO
Tencent-MVSE: A Large-Scale Benchmark Dataset for Multi-Modal Video Similarity Evaluation
In this paper, we propose the Tencent-MVSE dataset, which is the first benchmark dataset for the multi-modal video similarity evaluation task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Contrastive Learning of Global and Local Video Representations
In this work, we propose to learn video representations that generalize to both the tasks which require global semantic information (e. g., classification) and the tasks that require local fine-grained spatio-temporal information (e. g., localization).
CLIP4Caption ++: Multi-CLIP for Video Caption
We make the following improvements on the proposed CLIP4Caption++: We employ an advanced encoder-decoder model architecture X-Transformer as our main framework and make the following improvements: 1) we utilize three strong pre-trained CLIP models to extract the text-related appearance visual features.
Multi-modal Representation Learning for Video Advertisement Content Structuring
Video advertisement content structuring aims to segment a given video advertisement and label each segment on various dimensions, such as presentation form, scene, and style.
Reference-based Defect Detection Network
To solve the partial visual confusion issue, we propose to leverage the carried context information of context reference, which is the concentric bigger box of each region proposal, to perform more accurate region classification and regression.
Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning
As region-based visual features usually represent parts of an image, it is challenging for existing vision-language models to fully understand the semantics from paired natural languages.
Ranked #5 on
Visual Entailment
on SNLI-VE val
Contrastive Learning of Global-Local Video Representations
In this work, we propose to learn video representations that generalize to both the tasks which require global semantic information (e. g., classification) and the tasks that require local fine-grained spatio-temporal information (e. g., localization).
Contrastive Self-Supervised Learning of Global-Local Audio-Visual Representations
Contrastive self-supervised learning has delivered impressive results in many audio-visual recognition tasks.
Suppressing Mislabeled Data via Grouping and Self-Attention
Specifically, this plug-and-play AFM first leverages a \textit{group-to-attend} module to construct groups and assign attention weights for group-wise samples, and then uses a \textit{mixup} module with the attention weights to interpolate massive noisy-suppressed samples.
Active Contrastive Learning of Audio-Visual Video Representations
Contrastive learning has been shown to produce generalizable representations of audio and visual data by maximizing the lower bound on the mutual information (MI) between different views of an instance.
Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers
We aim to build a more accurate and thorough connection between image pixels and language semantics directly from image and sentence pairs instead of using region-based image features as the most recent vision and language tasks.
Learning Rich Image Region Representation for Visual Question Answering
We propose to boost VQA by leveraging more powerful feature extractors by improving the representation ability of both visual and text features and the ensemble of models.
SMP Challenge: An Overview of Social Media Prediction Challenge 2019
In the SMP Challenge at ACM Multimedia 2019, we introduce a novel prediction task Temporal Popularity Prediction, which focuses on predicting future interaction or attractiveness (in terms of clicks, views or likes etc.)
WSOD2: Learning Bottom-Up and Top-Down Objectness Distillation for Weakly-Supervised Object Detection
We study on weakly-supervised object detection (WSOD) which plays a vital role in relieving human involvement from object-level annotations.
WSOD2: Learning Bottom-up and Top-down Objectness Distillation forWeakly-supervised Object Detection
We study on weakly-supervised object detection (WSOD)which plays a vital role in relieving human involvement fromobject-level annotations.
WSOD^2: Learning Bottom-up and Top-down Objectness Distillation for Weakly-supervised Object Detection
We study on weakly-supervised object detection (WSOD) which plays a vital role in relieving human involvement from object-level annotations.
Activitynet 2019 Task 3: Exploring Contexts for Dense Captioning Events in Videos
The overall system achieves the state-of-the-art performance on the dense-captioning events in video task with 9. 91 METEOR score on the challenge testing set.
