Research

IN THIS , WE ADDRESS THIS CHALLENGE, AND PROPOSE GPTQ, A NEW ONE-SHOT WEIGHT QUANTIZATION METHOD BASED ON APPROXIMATE SECOND-ORDER INFORMATION, THAT IS BOTH HIGHLY-ACCURATE AND HIGHLY-EFFICIENT.

OBJECTIVE AND SUBJECTIVE EVALUATIONS SHOW THAT TEXTIT{PHONEME HALLUCINATOR} OUTPERFORMS EXISTING VC METHODS FOR BOTH INTELLIGIBILITY AND SPEAKER SIMILARITY.

FOR 3D OBJECT DETECTION, WE INSTANTIATE THIS METHOD AS FOCALFORMER3D, A SIMPLE YET EFFECTIVE DETECTOR THAT EXCELS AT EXCAVATING DIFFICULT OBJECTS AND IMPROVING PREDICTION RECALL.

INSPIRED BY THE PROMPT LEARNING IN NATURAL LANGUAGE PROCESSING (NLP), WHICH HAS PRESENTED SIGNIFICANT EFFECTIVENESS IN LEVERAGING PRIOR KNOWLEDGE FOR VARIOUS NLP TASKS, WE STUDY THE PROMPTING TOPIC FOR GRAPHS WITH THE MOTIVATION OF FILLING THE GAP BETWEEN PRE-TRAINED MODELS AND VARIOUS GRAPH TASKS.

WE PRESENT THE ALL-SEEING (AS) PROJECT: A LARGE-SCALE DATA AND MODEL FOR RECOGNIZING AND UNDERSTANDING EVERYTHING IN THE OPEN WORLD.

WE PRESENT NNVISR - AN OPEN-SOURCE FILTER PLUGIN FOR THE VAPOURSYNTH VIDEO PROCESSING FRAMEWORK, WHICH FACILITATES THE APPLICATION OF NEURAL NETWORKS FOR VARIOUS KINDS OF VIDEO ENHANCING TASKS, INCLUDING DENOISING, SUPER RESOLUTION, INTERPOLATION, AND SPATIO-TEMPORAL SUPER-RESOLUTION.

THERE HAS BEEN AN INCREASING RESEARCH INTEREST IN DEVELOPING SPECIALIZED DIALOGUE SYSTEMS THAT CAN OFFER MENTAL HEALTH SUPPORT.

DIFFERENT FROM THE PREVIOUS SELF-KNOWLEDGE DISTILLATION, THIS STAGE FINETUNES THE STUDENT'S HEAD WITH ONLY 20% TRAINING TIME AS A PLUG-AND-PLAY TRAINING STRATEGY.

WE, FOR THE FIRST TIME, PROPOSE AN IMAGE RETRIEVAL PARADIGM LEVERAGING GLOBAL FEATURE ONLY TO ENABLE ACCURATE AND LIGHTWEIGHT IMAGE RETRIEVAL FOR BOTH COARSE RETRIEVAL AND RERANKING, THUS THE NAME - SUPERGLOBAL.

THIS EVOLUTION REQUIRES A COMBINATION OF BOTH TRADITIONAL METHODS (SUCH AS TERM-BASED SPARSE RETRIEVAL METHODS WITH RAPID RESPONSE) AND MODERN NEURAL ARCHITECTURES (SUCH AS LANGUAGE MODELS WITH POWERFUL LANGUAGE UNDERSTANDING CAPACITY).

JOINTLY PROCESSING INFORMATION FROM MULTIPLE SENSORS IS CRUCIAL TO ACHIEVING ACCURATE AND ROBUST PERCEPTION FOR RELIABLE AUTONOMOUS DRIVING SYSTEMS.

LARGE LANGUAGE MODELS (LLMS) USUALLY SUFFER FROM KNOWLEDGE CUTOFF OR FALLACY ISSUES, WHICH MEANS THEY ARE UNAWARE OF UNSEEN EVENTS OR GENERATE TEXT WITH INCORRECT FACTS OWING TO THE OUTDATED/NOISY DATA.

WE INTRODUCE LIGHTGLUE, A DEEP NEURAL NETWORK THAT LEARNS TO MATCH LOCAL FEATURES ACROSS IMAGES.

IN THIS WORK, WE PROPOSE A NEW SEGMENTATION TASK -- REASONING SEGMENTATION.

SPECIFICALLY, OUR APPROACH FINDS A SUFFIX THAT, WHEN ATTACHED TO A WIDE RANGE OF QUERIES FOR AN LLM TO PRODUCE OBJECTIONABLE CONTENT, AIMS TO MAXIMIZE THE PROBABILITY THAT THE MODEL PRODUCES AN AFFIRMATIVE RESPONSE (RATHER THAN REFUSING TO ANSWER).

AS LARGE LANGUAGE MODELS IMPROVE, THERE IS INCREASING INTEREST IN TECHNIQUES THAT LEVERAGE THESE MODELS' CAPABILITIES TO REFINE THEIR OWN OUTPUTS.

TO THIS END, WE PROPOSE A BANK OF 3D-AWARE HIERARCHICAL FEATURES, INCLUDING GLOBAL, POINT-LEVEL, AND PIXEL-ALIGNED FEATURES, TO FACILITATE INFORMATIVE ENCODING.

BASED ON THIS OBSERVATION, WE PROPOSE A DATA SELECTOR BASED ON INSTAG TO SELECT 6K DIVERSE AND COMPLEX SAMPLES FROM OPEN-SOURCE DATASETS AND FINE-TUNE MODELS ON INSTAG-SELECTED DATA.

OUR APPROACH, NAMELY DIFFUSION-BASED CONDITIONAL INPAINTING FOR VIRTUAL TRY-ON (DCI-VTON), EFFECTIVELY UTILIZES THE POWER OF THE DIFFUSION MODEL, AND THE INCORPORATION OF THE WARPING MODULE HELPS TO PRODUCE HIGH-QUALITY AND REALISTIC VIRTUAL TRY-ON RESULTS.

RECENT ADVANCES IN LARGE LANGUAGE MODELS (LLMS) HAVE ACHIEVED REMARKABLE BREAKTHROUGHS IN UNDERSTANDING AND RESPONDING TO USER INTENTS.