Search Results for author:
Found 16 papers, 9 papers with code
Skews in the Phenomenon Space Hinder Generalization in Text-to-Image Generation
We hypothesize that the underlying phenomenological coverage has not been proportionally scaled up, leading to a skew of the presented phenomenon which harms generalization.
End-to-end Knowledge Retrieval with Multi-modal Queries
We investigate knowledge retrieval with multi-modal queries, i. e. queries containing information split across image and text inputs, a challenging task that differs from previous work on cross-modal retrieval.
Mining Unseen Classes via Regional Objectness: A Simple Baseline for Incremental Segmentation
Our MicroSeg is based on the assumption that background regions with strong objectness possibly belong to those concepts in the historical or future stages.
Class-Incremental Semantic Segmentation
Continual Learning
+1
Tragedy Plus Time: Capturing Unintended Human Activities from Weakly-labeled Videos
In videos that contain actions performed unintentionally, agents do not achieve their desired goals.
Injecting Semantic Concepts into End-to-End Image Captioning
In this paper, we are concerned with a better-performing detector-free image captioning model, and propose a pure vision transformer-based image captioning model, dubbed as ViTCAP, in which grid representations are used without extracting the regional features.
Compressing Visual-linguistic Model via Knowledge Distillation
In this paper, we study knowledge distillation (KD) to effectively compress a transformer-based large VL model into a small VL model.
SEED: Self-supervised Distillation For Visual Representation
This paper is concerned with self-supervised learning for small models.
Weak Supervision and Referring Attention for Temporal-Textual Association Learning
The referring attention is our designed mechanism acting as a scoring function for grounding the given queries over frames temporally.
HRDNet: High-resolution Detection Network for Small Objects
By extracting various features from high to low resolutions, the MD-IPN is able to improve the performance of small object detection as well as maintaining the performance of middle and large objects.
ViTAA: Visual-Textual Attributes Alignment in Person Search by Natural Language
Person search by natural language aims at retrieving a specific person in a large-scale image pool that matches the given textual descriptions.
Ranked #18 on
Text based Person Retrieval
on CUHK-PEDES
Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning
In videos that involve active agents such as humans, the agent's actions can bring about myriad changes in the scene.
Blocksworld Revisited: Learning and Reasoning to Generate Event-Sequences from Image Pairs
The process of identifying changes or transformations in a scene along with the ability of reasoning about their causes and effects, is a key aspect of intelligence.
Modularized Textual Grounding for Counterfactual Resilience
Computer Vision applications often require a textual grounding module with precision, interpretability, and resilience to counterfactual inputs/queries.
Weakly Supervised Attention Learning for Textual Phrases Grounding
Grounding textual phrases in visual content is a meaningful yet challenging problem with various potential applications such as image-text inference or text-driven multimedia interaction.
Range Loss for Deep Face Recognition With Long-Tailed Training Data
Unlike these work, this paper investigated how long-tailed data impact the training of face CNNs and develop a novel loss function, called range loss, to effectively utilize the tailed data in training process.
Range Loss for Deep Face Recognition with Long-tail
Convolutional neural networks have achieved great improvement on face recognition in recent years because of its extraordinary ability in learning discriminative features of people with different identities.
