91 citations · 174 across the 10 of their papers we have counts for
17 papers
PLSQLBench: Benchmarking LLM Systems for Executable Procedural Database Programming
Marianne Menglin Liu, Leonid Boytsov, Daniel W. Peterson +13
We present PLSQLBench, to our knowledge the first benchmark for evaluating whether LLMs can write executable PL/SQL programs, with correctness measured through execution-based test…
SOMA-SQL: Resolving Multi-Source Ambiguity in NL-to-SQL via Synthetic Log and Execution Probing
Sai Ashish Somayajula, Marianne Menglin Liu, Chuan Lei +9
Natural language interfaces to databases aim to translate user questions into executable SQL, yet remain brittle in real-world settings where questions are underspecified and schem…
DocTr: Document Transformer for Structured Information Extraction in Documents
Haofu Liao, Aruni RoyChowdhury, Weijian Li +6
We present a new formulation for structured information extraction (SIE) from visually rich documents. It aims to address the limitations of existing IOB tagging or graph-based for…
Object-Aware Cropping for Self-Supervised Learning
Shlok Mishra, Anshul Shah, Ankan Bansal +5
A core component of the recent success of self-supervised learning is cropping data augmentation, which selects sub-regions of an image to be used as positive views in the self-sup…
Learning Visual Representations for Transfer Learning by Suppressing Texture
Shlok Mishra, Anshul Shah, Ankan Bansal +5
Recent literature has shown that features obtained from supervised training of CNNs may over-emphasize texture rather than encoding high-level information. In self-supervised learn…
Pose And Joint-Aware Action Recognition
Anshul Shah, Shlok Mishra, Ankan Bansal +3
Recent progress on action recognition has mainly focused on RGB and optical flow features. In this paper, we approach the problem of joint-based action recognition. Unlike other mo…
Visual Question Answering on Image Sets
Ankan Bansal, Yuting Zhang, Rama Chellappa
We introduce the task of Image-Set Visual Question Answering (ISVQA), which generalizes the commonly studied single-image VQA problem to multi-image settings. Taking a natural lang…
Spatial Priming for Detecting Human-Object Interactions
Ankan Bansal, Sai Saketh Rambhatla, Abhinav Shrivastava +1
The relative spatial layout of a human and an object is an important cue for determining how they interact. However, until now, spatial layout has been used just as side-informatio…
How are attributes expressed in face DCNNs?
Prithviraj Dhar, Ankan Bansal, Carlos D. Castillo +3
As deep networks become increasingly accurate at recognizing faces, it is vital to understand how these networks process faces. While these networks are solely trained to recognize…
Detecting Human-Object Interactions via Functional Generalization
Ankan Bansal, Sai Saketh Rambhatla, Abhinav Shrivastava +1
We present an approach for detecting human-object interactions (HOIs) in images, based on the idea that humans interact with functionally similar objects in a similar manner. The p…
A Fast and Accurate System for Face Detection, Identification, and Verification
Rajeev Ranjan, Ankan Bansal, Jingxiao Zheng +7
The availability of large annotated datasets and affordable computation power have led to impressive improvements in the performance of CNNs on various object detection and recogni…
Zero-Shot Object Detection
Ankan Bansal, Karan Sikka, Gaurav Sharma +2
We introduce and tackle the problem of zero-shot object detection (ZSD), which aims to detect object classes which are not observed during training. We work with a challenging set…
Crystal Loss and Quality Pooling for Unconstrained Face Verification and Recognition
Rajeev Ranjan, Ankan Bansal, Hongyu Xu +4
In recent years, the performance of face verification and recognition systems based on deep convolutional neural networks (DCNNs) has significantly improved. A typical pipeline for…
UPSET and ANGRI : Breaking High Performance Image Classifiers
Sayantan Sarkar, Ankan Bansal, Upal Mahbub +1
In this paper, targeted fooling of high performance image classifiers is achieved by developing two novel attack methods. The first method generates universal perturbations for tar…
The Do's and Don'ts for CNN-based Face Verification
Ankan Bansal, Carlos Castillo, Rajeev Ranjan +1
While the research community appears to have developed a consensus on the methods of acquiring annotated data, design and training of CNNs, many questions still remain to be answer…
UMDFaces: An Annotated Face Dataset for Training Deep Networks
Ankan Bansal, Anirudh Nanduri, Carlos Castillo +2
Recent progress in face detection (including keypoint detection), and recognition is mainly being driven by (i) deeper convolutional neural network architectures, and (ii) larger d…
People Counting in High Density Crowds from Still Images
Ankan Bansal, K. S. Venkatesh
We present a method of estimating the number of people in high density crowds from still images. The method estimates counts by fusing information from multiple sources. Most of th…