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Found 25 papers, 13 papers with code
Self-Explainable Affordance Learning with Embodied Caption
In the field of visual affordance learning, previous methods mainly used abundant images or videos that delineate human behavior patterns to identify action possibility regions for object manipulation, with a variety of applications in robotic tasks.
Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performance
The shared embeddings, which describe the absolute coordinates of multi-resolution images (namely, the template and search images), are inherited from the pre-trained backbones.
VastTrack: Vast Category Visual Object Tracking
The rich annotations of VastTrack enables development of both the vision-only and the vision-language tracking.
Image Fusion via Vision-Language Model
Therefore, we introduce a novel fusion paradigm named image Fusion via vIsion-Language Model (FILM), for the first time, utilizing explicit textual information in different source images to guide image fusion.
Orion-14B: Open-source Multilingual Large Language Models
In this study, we introduce Orion-14B, a collection of multilingual large language models with 14 billion parameters.
Language-Enhanced Session-Based Recommendation with Decoupled Contrastive Learning
Furthermore, we introduce a novel Decoupled Contrastive Learning method to enhance the effectiveness of the language representation.
Divert More Attention to Vision-Language Object Tracking
To thoroughly evidence the effectiveness of our method, we integrate the proposed framework on three tracking methods with different designs, i. e., the CNN-based SiamCAR, the Transformer-based OSTrack, and the hybrid structure TransT.
Augment and Criticize: Exploring Informative Samples for Semi-Supervised Monocular 3D Object Detection
In this paper, we improve the challenging monocular 3D object detection problem with a general semi-supervised framework.
AUNet: Learning Relations Between Action Units for Face Forgery Detection
Observing that face manipulation may alter the relation between different facial action units (AU), we propose the Action Units Relation Learning framework to improve the generality of forgery detection.
Structured Knowledge Distillation Towards Efficient and Compact Multi-View 3D Detection
Detecting 3D objects from multi-view images is a fundamental problem in 3D computer vision.
One for All: One-stage Referring Expression Comprehension with Dynamic Reasoning
However, one unsolved issue of these models is that the number of reasoning steps needs to be pre-defined and fixed before inference, ignoring the varying complexity of expressions.
Divert More Attention to Vision-Language Tracking
By revealing the potential of VL representation, we expect the community to divert more attention to VL tracking and hope to open more possibilities for future tracking beyond Transformer.
Attract me to Buy: Advertisement Copywriting Generation with Multimodal Multi-structured Information
Therefore, we present a dataset, E-MMAD (e-commercial multimodal multi-structured advertisement copywriting), which requires, and supports much more detailed information in text generation.
Learning Target-aware Representation for Visual Tracking via Informative Interactions
The proposed GIM module and InBN mechanism are general and applicable to different backbone types including CNN and Transformer for improvements, as evidenced by our extensive experiments on multiple benchmarks.
SwinTrack: A Simple and Strong Baseline for Transformer Tracking
The potential of Transformer in representation learning remains under-explored.
Ranked #9 on
Visual Object Tracking
on TrackingNet
VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows
Different from visible cameras which record intensity images frame by frame, the biologically inspired event camera produces a stream of asynchronous and sparse events with much lower latency.
Ranked #1 on
Object Tracking
on VisEvent
Learn to Match: Automatic Matching Network Design for Visual Tracking
Siamese tracking has achieved groundbreaking performance in recent years, where the essence is the efficient matching operator cross-correlation and its variants.
Discovering Distinctive "Semantics" in Super-Resolution Networks
We show that a well-trained deep SR network is naturally a good descriptor of degradation information.
One More Check: Making "Fake Background" Be Tracked Again
Eventually, it helps to reload the ``fake background'' and repair the broken tracklets.
Towards More Flexible and Accurate Object Tracking with Natural Language: Algorithms and Benchmark
We believe this benchmark will greatly boost related researches on natural language guided tracking.
Ranked #3 on
Visual Object Tracking
on TNL2K
(precision metric)
Reality Transform Adversarial Generators for Image Splicing Forgery Detection and Localization
For detecting the tampered regions, a forgery localization generator GM is proposed based on a multi-decoder-single-task strategy.
Rethinking the competition between detection and ReID in Multi-Object Tracking
However, the inherent differences and relations between detection and re-identification (ReID) are unconsciously overlooked because of treating them as two isolated tasks in the one-shot tracking paradigm.
Ranked #1 on
Multi-Object Tracking
on HiEve
(using extra training data)
Towards Accurate Pixel-wise Object Tracking by Attention Retrieval
We first build a look-up-table (LUT) with the ground-truth mask in the starting frame, and then retrieves the LUT to obtain an attention map for spatial constraints.
Ocean: Object-aware Anchor-free Tracking
In this paper, we propose a novel object-aware anchor-free network to address this issue.
Ranked #2 on
Visual Object Tracking
on VOT2019
Deeper and Wider Siamese Networks for Real-Time Visual Tracking
Siamese networks have drawn great attention in visual tracking because of their balanced accuracy and speed.
Ranked #2 on
Visual Object Tracking
on VOT2017
