Search Results for author:
Found 53 papers, 28 papers with code
Exposing the Limits of Video-Text Models through Contrast Sets
We test the robustness of recent methods on the proposed automatic contrast sets, and compare them to additionally collected human-generated counterparts, to assess their effectiveness.
Erased but Not Forgotten: How Backdoors Compromise Concept Erasure
For celebrity identity erasure, our deep attack circumvents erasure with up to 82% success, averaging 57% across all erasure methods.
V$^2$Dial: Unification of Video and Visual Dialog via Multimodal Experts
We present V$^2$Dial - a novel expert-based model specifically geared towards simultaneously handling image and video input data for multimodal conversational tasks.
DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts
The proliferation of disinformation presents a growing threat to societal trust and democracy, necessitating robust and scalable Fact-Checking systems.
InFact: A Strong Baseline for Automated Fact-Checking
The spread of disinformation poses a global threat to democratic societies, necessitating robust and scalable Automated Fact-Checking (AFC) systems.
Ranked #3 on
Fact Checking
on AVeriTeC
The Surprising Effectiveness of Multimodal Large Language Models for Video Moment Retrieval
We achieve a new state-of-the-art in moment retrieval on the widely used benchmarks Charades-STA, QVHighlights, and ActivityNet Captions.
Object-based (yet Class-agnostic) Video Domain Adaptation
Existing video-based action recognition systems typically require dense annotation and struggle in environments when there is significant distribution shift relative to the training data.
MammalNet: A Large-scale Video Benchmark for Mammal Recognition and Behavior Understanding
Monitoring animal behavior can facilitate conservation efforts by providing key insights into wildlife health, population status, and ecosystem function.
Simple Token-Level Confidence Improves Caption Correctness
The ability to judge whether a caption correctly describes an image is a critical part of vision-language understanding.
Ranked #63 on
Visual Reasoning
on Winoground
Focus! Relevant and Sufficient Context Selection for News Image Captioning
In our paper, we first demonstrate that by combining more fine-grained context that captures the key named entities (obtained via an oracle) and the global context that summarizes the news, we can dramatically improve the model's ability to generate accurate news captions.
Shape-Guided Diffusion with Inside-Outside Attention
We introduce precise object silhouette as a new form of user control in text-to-image diffusion models, which we dub Shape-Guided Diffusion.
G^3: Geolocation via Guidebook Grounding
We propose the task of Geolocation via Guidebook Grounding that uses a dataset of StreetView images from a diverse set of locations and an associated textual guidebook for GeoGuessr, a popular interactive geolocation game.
TL;DW? Summarizing Instructional Videos with Task Relevance & Cross-Modal Saliency
In this work, we focus on summarizing instructional videos, an under-explored area of video summarization.
Structured Video Tokens @ Ego4D PNR Temporal Localization Challenge 2022
First, as both images and videos contain structured information, we enrich a transformer model with a set of \emph{object tokens} that can be used across images and videos.
Point- of-no-return (PNR) temporal localization
Temporal Localization
Bringing Image Scene Structure to Video via Frame-Clip Consistency of Object Tokens
We explore a particular instantiation of scene structure, namely a \emph{Hand-Object Graph}, consisting of hands and objects with their locations as nodes, and physical relations of contact/no-contact as edges.
Reliable Visual Question Answering: Abstain Rather Than Answer Incorrectly
We first enable abstention capabilities for several VQA models, and analyze both their coverage, the portion of questions answered, and risk, the error on that portion.
K-LITE: Learning Transferable Visual Models with External Knowledge
We propose K-LITE, a simple strategy to leverage external knowledge for building transferable visual systems: In training, it enriches entities in text with WordNet and Wiktionary knowledge, leading to an efficient and scalable approach to learning image representations that uses knowledge about the visual concepts.
ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension
Training a referring expression comprehension (ReC) model for a new visual domain requires collecting referring expressions, and potentially corresponding bounding boxes, for images in the domain.
On Guiding Visual Attention with Language Specification
To do this, we ground task-relevant words or phrases with attention maps from a pretrained large-scale model.
The Abduction of Sherlock Holmes: A Dataset for Visual Abductive Reasoning
We present Sherlock, an annotated corpus of 103K images for testing machine capacity for abductive reasoning beyond literal image contents.
Watch Those Words: Video Falsification Detection Using Word-Conditioned Facial Motion
In today's era of digital misinformation, we are increasingly faced with new threats posed by video falsification techniques.
Twitter-COMMs: Detecting Climate, COVID, and Military Multimodal Misinformation
Detecting out-of-context media, such as "mis-captioned" images on Twitter, is a relevant problem, especially in domains of high public significance.
More Control for Free! Image Synthesis with Semantic Diffusion Guidance
We investigate fine-grained, continuous control of this model class, and introduce a novel unified framework for semantic diffusion guidance, which allows either language or image guidance, or both.
Object-Region Video Transformers
In this work, we present Object-Region Video Transformers (ORViT), an \emph{object-centric} approach that extends video transformer layers with a block that directly incorporates object representations.
Ranked #7 on
Action Recognition
on Diving-48
How Much Can CLIP Benefit Vision-and-Language Tasks?
Most existing Vision-and-Language (V&L) models rely on pre-trained visual encoders, using a relatively small set of manually-annotated data (as compared to web-crawled data), to perceive the visual world.
Ranked #4 on
Vision and Language Navigation
on RxR
(using extra training data)
CLIP-It! Language-Guided Video Summarization
A generic video summary is an abridged version of a video that conveys the whole story and features the most important scenes.
Ranked #2 on
Supervised Video Summarization
on TvSum
DETReg: Unsupervised Pretraining with Region Priors for Object Detection
Recent self-supervised pretraining methods for object detection largely focus on pretraining the backbone of the object detector, neglecting key parts of detection architecture.
Ranked #1 on
Few-Shot Object Detection
on COCO 2017
NewsCLIPpings: Automatic Generation of Out-of-Context Multimodal Media
Online misinformation is a prevalent societal issue, with adversaries relying on tools ranging from cheap fakes to sophisticated deep fakes.
Identity-Aware Multi-Sentence Video Description
This auxiliary task allows us to propose a two-stage approach to Identity-Aware Video Description.
Compositional Video Synthesis with Action Graphs
Our generative model for this task (AG2Vid) disentangles motion and appearance features, and by incorporating a scheduling mechanism for actions facilitates a timely and coordinated video generation.
Are You Looking? Grounding to Multiple Modalities in Vision-and-Language Navigation
The actual grounding can connect language to the environment through multiple modalities, e. g. "stop at the door" might ground into visual objects, while "turn right" might rely only on the geometric structure of a route.
Language-Conditioned Graph Networks for Relational Reasoning
E. g., conditioning on the "on" relationship to the plate, the object "mug" gathers messages from the object "plate" to update its representation to "mug on the plate", which can be easily consumed by a simple classifier for answer prediction.
Ranked #3 on
Referring Expression Comprehension
on CLEVR-Ref+
Robust Change Captioning
We present a novel Dual Dynamic Attention Model (DUDA) to perform robust Change Captioning.
Adversarial Inference for Multi-Sentence Video Description
Among the main issues are the fluency and coherence of the generated descriptions, and their relevance to the video.
Object Hallucination in Image Captioning
Despite continuously improving performance, contemporary image captioning models are prone to "hallucinating" objects that are not actually in a scene.
Textual Explanations for Self-Driving Vehicles
Finally, we explore a version of our model that generates rationalizations, and compare with introspective explanations on the same video segments.
Women also Snowboard: Overcoming Bias in Captioning Models (Extended Abstract)
Most machine learning methods are known to capture and exploit biases of the training data.
Speaker-Follower Models for Vision-and-Language Navigation
We use this speaker model to (1) synthesize new instructions for data augmentation and to (2) implement pragmatic reasoning, which evaluates how well candidate action sequences explain an instruction.
Women also Snowboard: Overcoming Bias in Captioning Models
We introduce a new Equalizer model that ensures equal gender probability when gender evidence is occluded in a scene and confident predictions when gender evidence is present.
Video Object Segmentation with Language Referring Expressions
We show that our language-supervised approach performs on par with the methods which have access to a pixel-level mask of the target object on DAVIS'16 and is competitive to methods using scribbles on the challenging DAVIS'17 dataset.
Ranked #1 on
Video Object Segmentation
on DAVIS 2017
(mIoU metric)
Multimodal Explanations: Justifying Decisions and Pointing to the Evidence
We propose a multimodal approach to explanation, and argue that the two modalities provide complementary explanatory strengths.
Attentive Explanations: Justifying Decisions and Pointing to the Evidence (Extended Abstract)
We also introduce a multimodal methodology for generating visual and textual explanations simultaneously.
Gradient-free Policy Architecture Search and Adaptation
We develop a method for policy architecture search and adaptation via gradient-free optimization which can learn to perform autonomous driving tasks.
Fooling Vision and Language Models Despite Localization and Attention Mechanism
Our work sheds new light on understanding adversarial attacks on vision systems which have a language component and shows that attention, bounding box localization, and compositional internal structures are vulnerable to adversarial attacks.
Generating Descriptions with Grounded and Co-Referenced People
At training time, we first learn how to localize characters by relating their visual appearance to mentions in the descriptions via a semi-supervised approach.
A dataset and exploration of models for understanding video data through fill-in-the-blank question-answering
In addition to presenting statistics and a description of the dataset, we perform a detailed analysis of 5 different models' predictions, and compare these with human performance.
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
Approaches to multimodal pooling include element-wise product or sum, as well as concatenation of the visual and textual representations.
Movie Description
In addition we also collected and aligned movie scripts used in prior work and compare the two sources of descriptions.
Grounding of Textual Phrases in Images by Reconstruction
We propose a novel approach which learns grounding by reconstructing a given phrase using an attention mechanism, which can be either latent or optimized directly.
Ranked #13 on
Phrase Grounding
on Flickr30k Entities Test
The Long-Short Story of Movie Description
Generating descriptions for videos has many applications including assisting blind people and human-robot interaction.
Recognizing Fine-Grained and Composite Activities using Hand-Centric Features and Script Data
To attack the second challenge, recognizing composite activities, we leverage the fact that these activities are compositional and that the essential components of the activities can be obtained from textual descriptions or scripts.
A Dataset for Movie Description
In this work we propose a novel dataset which contains transcribed DVS, which is temporally aligned to full length HD movies.
